With a steady stream of investments pouring in, Singapore is solidifying its position as the financial technology (fintech) hub of Southeast Asia. The government has also been actively promoting its #fintech industry, offering a range of incentives to attract startups and global companies alike. As a result, Singapore has emerged as the go-to destination for fintech firms looking to expand in the region. With a supportive regulatory environment, top-notch infrastructure, and a pool of skilled talent, Singapore is well-positioned to maintain its leadership position in #ASEAN’s fintech landscape. In this article, we will be looking at how Singapore is ideal for Fintech firms to thrive.
In the previous article, we talked about Indonesia’s booming Fintech industry, in this article, we will be focusing on Southeast Asia’s Fintech Hub, Singapore. Though both are part of the Association of Southeast Asian Nations (ASEAN), Singapore and Indonesia have very different macro environments and pose different opportunities for Fintech investments.
Firstly, Singapore has a much smaller population of only 5.4 million compared with the 273.8 million in Indonesia. Secondly, Singapore also has a relatively more mature financial system, especially in terms of financial regulation. As such, when compared to Indonesia, Singapore has a smaller population of underbanked and unbanked. Despite the seemingly smaller market size which may be deemed as unattractive for Fintech firms, Singapore has successfully anchored its position as Asean’s Fintech hub and is now home to 40% of the region’s Fintech firms. Even during the pandemic, Singapore continues to bag nearly half of ASEAN’s Fintech funding, making her the top fintech investment destination.
There may be several reasons why Singapore has risen to become Asean’s Fintech Hub. We outline 4 below.
In FinTech as in many other parts of the financial services ecosystem, Singapore is recognised as the “springboard” for Fintech firms to access other ASEAN markets, rendering moot her constraint of having a small market size. Often, Fintech firms looking to expand into the ASEAN market will choose to leverage the country’s pro-business policies and set up their regional headquarters in Singapore. One such example is Futu Holdings Ltd., a Hong Kong-based Fintech firm focusing on investment offerings for retail investors. When asked about the rationale behind the move, Leaf Hua Li, CEO of Futu Holdings Ltd., said that “expansion into Singapore is a strategic decision to serve as a bridge into the rest of Southeast Asia.” Executives’ confidence in Singapore further reinforces its status as an attractive regional investment hub despite facing strong competition from Hong Kong, our Asian counterpart, which has also been dubbed one of the top financial hubs globally.
Despite the fact that Singapore’s population is very small, it is actually very diverse. Specifically, the country is home to one of the largest expatriate and migrant communities in Southeast Asia. For some Fintech firms, this characteristic makes the country an ideal destination as these communities are the target users of their home country’s Fintech offerings. One prominent community in Singapore is the community of Chinese nationals, which supposedly constitutes 18% of the country’s foreign-born population. This has contributed towards an influx of Chinese Fintech firms to serve Chinese citizens and Chinese firms in Singapore. The above phenomena can be observed from the presence of remittance fintech firms such as Panda Remit. Backed by Lightspeed, Panda Remit aims to provide safe, convenient, low-cost money transfer service to overseas Chinese.
Frequent visitor’s to Singapore’s Chinatown may have noticed the numerous Panda Remit advertisements in the MRT station and around People’s Park Complex (see Figure 1). As its name suggests, Chinatown is where many Chinese nationals and Chinese vendors congregate, and naturally, Chinese Fintech firms will focus on places commonly frequented by Chinese nationals for their marketing efforts. Once again, this shows that the diverse Singapore population brings opportunities for overseas Fintech firms who want to expand and serve their citizens abroad.
Figure 1: Chinese Fintech Panda Remit’s billboard around People’s Park Complex
Figure 2: Top 10 Wealthtech Companies in Southeast Asia (Source: Fintech News Singapore)
Singapore’s dominance as the region’s Fintech hub is also evident in the surge of applications for digital banking licences by Fintech firms. When the Monetary Authority of Singapore opened the call for applications, it received 21 submissions, including from major corporations such as Greenland Financial Holdings and Ant Financial Group. This strong interest is a testament to the market confidence in Singapore being an ideal breeding ground for digital banking.
Singapore’s advanced Fintech regulatory framework makes Singapore a good place for technology test-bedding. In a bid to establish its position as ASEAN’s Fintech hub, the Singapore government has been very supportive of the Fintech industry by launching several initiatives ahead of its ASEAN peers. Notably, in 2016, Singapore is the first country to have established a regulatory sandbox for Fintech startups. The regulatory sandbox provides a safe space for Fintech firms to test the feasibility of the product using real money and clients, without worrying about the complete set of regulatory guidelines during the sandbox period. Over the years, we have seen some notable Fintech companies making use of the Regulatory Sandbox. One example is DigiFT Tech, which has been admitted into the sandbox since June 2022, enabling it to be the first regulated decentralised security token trading platform.
Singapore’s business-friendly policies are also attractive to Fintech firms.Even when facing an economic downturn during the COVID-19 pandemic, the government launched a S$6 million MAS-SFA-AMTD Fintech Solidarity Grant to support Singapore-based Fintech firms in maintaining their operations by overcoming short-term financing gaps caused by the pandemic. In terms of network, the Singapore FinTech Association has also grown to be one of the largest FinTech associations in the world and the annual Singapore Fintech Festival has seen record turnouts, making it the world’s largest and most impactful Fintech event.
With the above mentioned, it is evident that Singapore has a vibrant entrepreneurial ecosystem, which makes her a desirable destination for a Fintech hub.Singapore’s advantageous population composition and business-friendly climate, compared to other ASEAN countries, will continue to position it as a leader among Fintech hubs and take advantage of the growing demand for Fintech offerings in the region.
Even if you live under a rock, you have probably heard of the abbreviation SVB by now. The collapse of Silicon Valley Bank has been the talk of the town in recent days — with regulators, financial markets and consumers all watching with eyes wide open to see what ramifications it might have globally. In this article, we explain how the bank came to collapse and what unpack some of the impacts it might have on the Fintech industry in Asia in particular.
What does the collapse of Silicon Valley Bank mean for Fintech?
It started with a disclosure after markets had closed on Wednesday, March 8, that it had racked up $1.8 billion dollars in losses, after offloading US Treasury and government-backed bonds that were sensitive to interest rates and had thus depreciated in value.
Before Silicon Valley Bank could open on Friday, March 10, its assets had been seized by financial regulators, making it the largest bank to fail since Washington Mutual had collapsed at the height of the financial crisis in 2008.
As share prices of the bank tanked following the announcement that it had sustained great losses, venture capital backed start-ups — the bank’s main clientele — began pulling their deposits. The news of the deposit run spurred a further decrease in share prices, which prompted even more depositors to pull their money. The loss of confidence among shareholders and depositors fed into each other and sparked a bank run at a bank that was amongst the largest 20 banks in the US.
As the dust settles over the end of Silicon Valley Bank, what does its collapse mean for the Fintech industry?
A new era of Fintech
The collapse of Silicon Valley Bank was pretty much the outcome of a single trend — an increase in interest rates.
The way a traditional bank works is this: it takes in deposits from customers, paying them interest on their deposits to get them to keep their money at the bank.
It then takes the deposit and does a few things, chief among them lending the money to both individuals and organisations in the form of car loans, mortgages and credit lines. Banks can also invest some proportion of the deposits in instruments like treasury bills.
The spread it makes between the interest it pays depositors and the returns it earns on loans and other investments is the bank’s profit.
Source: reddit.com
However, Silicon Valley Bank was not like the other banks.
Its business was focused on serving the needs of start-ups in Silicon Valley, which proved to be a lucrative gig while it lasted.
In an era of low-interest rates and a booming stock market through the 2010s, pension funds and hedge funds poured their money into Venture Capital funds in the hopes that the VCs would in turn invest their money in the next Amazon or the next Uber, giving them a huge payout and allowing them to beat the markets.
The VCs, slosh with cash, threw their money at tech startups that had lofty plans to change the world over the long term but little to show in the way of profits. Who needed a profitable business model or revenue streams when one could always raise another round from VCs?
VCs themselves were happy to provide more funding in subsequent rounds at a higher valuation, for an increase in a portfolio company’s valuation means returns on their investment, which allowed them to show investors that their money had grown and enabled them to raise even more money from them.
Startups, hence, prioritised growth at all costs — driven by the notion that if they grew more organically at a slower rate, their competitors or copy-cat firms would capture the market and that once they had grown large enough, economies of scale would kick in and give them a road to profitability.
The fiasco at companies like WeWork, for instance, was very much a product of this environment and mindset.
Source: The Economic Times
When VCs and start-ups were booming, Silicon Valley Bank was the banker of choice for founders and their venture capital backers. As startups raised more money from investors, they deposited them in the bank and allowed the amount of deposits in the bank to balloon.
Startups, however, did not need much in loans from the bank when they were getting funding from VCs. Besides, loaning money to businesses that earn no profits and often have little in collateral is a risky bet that most banks would hesitate to make. Hence, Silicon Valley Bank loaned out a lower percentage of the deposits it held than most banks.
Yet, simply holding money does not pay out any money. Hence, Silicon Valley Bank invested a large amount of the deposits in long-dated securities like agency bonds and Treasury Bonds at a proportion much larger than other banks.
While it worked out well for the bank, the party came to an end when interest rates went up.
Investors became more conservative and less willing to put their money in VCs. VCs invested less money in start-ups and thus, start-ups deposited less money in Silicon Valley bank. As the inflow of deposits was slowing down, the long-term bonds that the bank had bet heavily on also depreciated in value and were worth less, with the double whammy of a slowdown in deposits and a decrease in asset values leading to the collapse of the bank.
As an analyst told Bloomberg, the collapse of Silicon Valley bank was the result of “Fed tightening extinguish(ing) froth from those parts of the economy with the most excess.”
With Jerome Powell having indicated that higher and faster interest rate hikes are necessary to temper inflation just prior to the Silicon Valley Bank saga, the collapse of Silicon Valley Bank may thus be a signal that this is the beginning of a new era for start-ups — one where higher rates make it harder to obtain funding.
While it remains to be seen if interest rates will be hiked again given recent events, if elevated interest rates persist, the days of start-ups burning through cash in an undisciplined manner to grow at all costs might be over.
Lofty valuations might become more grounded and founders might be pushed to be more fiscally responsible and more mercenary in the ways they deploy or use the capital they raise.
A correction to the years of excess in the start-up scene, however, is not inherently a bad thing.
Fintech’s dependence on traditional finance
Among the companies exposed to Silicon Valley Bank’s implosion was the major cryptocurrency player Circle Internet Financial.
After financial regulators announced that depositors in SVB will have access to all their money, the stablecoin, the second-largest after tether, came close to regaining its peg again.
DAI, the largest decentralised stablecoin, also lost its peg during the same period.
The woes that crypto companies faced was the result of turmoil not just at Silicon Valley Bank, but also the shuttering of Signature Bank by regulators after SVB went into FDIC receivership and Silvergate Bank announcing, even before depositors started pulling money from Silicon Valley Bank, that it was going to wind down voluntarily. All three banks had serviced clients in the crypto space.
As the columnist at crypto website Coindesk George Kaloudis writes, “Crypto has a banking problem, but banking doesn’t have a crypto problem”.
In other words, despite the occasional pundit who has tried to tie the banking crisis to the crypto winter only to be rebutted extensively, the collapse of cryptocurrencies and the companies in the crypto ecosystem has, by and large, only had a limited impact on banks and the broader financial system. The trouble at a few select banks, however, has had a disproportionate impact on cryptocurrencies.
Even as cryptocurrency firms have often pitted the crypto realm against traditional finance, selling the crypto world as a more transparent and equitable alternative to the world of traditional banking built upon fiat currencies, recent events reveal just how reliant much of the crypto industry is on the services and products offered by traditional financial institutions.
Far from building a separate world, they have built a world atop the financial infrastructure provided by the very institutions they wanted to make irrelevant.
Over the longer term, this also raises serious questions about the value proposition of cryptocurrencies.
Widespread disillusionment with the mainstream financial system was among the reasons why Bitcoin as a product, based on the idea of de-centralisation, took-off after the 2008 financial crisis, finding a following among many who saw crypto as an alternative realm in which bankers who got bailed out with taxpayer dollars did not keep paying themselves huge bonuses while the average joe got laid off.
The premise that centralisation and government intervention is a problem that needs to be fixed with cryptocurrencies, however, has been undermined by what happened with Silicon Valley Bank. The swift action by the authorities to make depositors whole is demonstration of the fact that perhaps, there is value in having a centralised financial system where a single authority, backed by the legitimacy vested in it by the powers of the state, can step in and calm things down or fix things when things go awry, something De-Fi protocols will not be able to do.
Impact on the asian market
While start-ups in Singapore have not been entirely immune from what happened across the world in Silicon Valley, on the whole, it has had a limited impact on both the start-up scene and the economy at large for now.
As a corporate lawyer who specialises in Southeast Asian tech told the financial daily The Business Times: “Southeast Asia is quite far removed from the action, but there are still a number of parties with banking relationships with SVB, so we’re not totally immune.”
The Business Times also reported that companies and investors in Southeast Asia were rushing to assure stakeholders they either had zero or limited exposure to SVB.
Amidst this, Singapore’s central bank put out a statement saying that Singaporean banks were well-capitalised, had healthy liquidity positions and had insignificant exposure to the failed banks in the United States, adding that any potential feedback on Singapore startups was also limited.
Analysts also noted that Asian banks held a low proportion of their assets in investments compared to SVB, and also maintained more-than-ample liquidity coverage ratios of 120-250%, making a scenario like that at SVB unlikely even as the effect of interest rate increases continues to kick in.
The more persistent inflation is and the higher interest rates have to be increased to clamp down on inflation, the less likely a “soft-landing” becomes, increasing the likelihood of tipping into a technical recession. The more worrying possibility of a deep recession, however, is now emerging as a possibility as financial markets have been rattled by recent events and many fear that this is the start of a potential banking crisis that might threaten the global economy.
A recession will no doubt have great impacts not only on both workers and companies, but is also likely to further decrease the funding available to fintech startups while also potentially hurting their business prospects.
The run on the banks is also likely to lead to banks, particularly small and medium-sized ones facing heightened scrutiny, to increase lending standards — decreasing the availability of credit in the short to medium term. The tightening of lending, the grease to the economic machine, is likely to have an adverse effect on individuals, businesses and the economy as a whole, slowing down growth and also making a recession more likely. The fact that these smaller banks are more likely to serve smaller businesses and consumers bodes badly for those groups.
Is more regulation the answer?
A 2018 law passed in the US raised the threshold at which banks are considered systematically risky to 250 billion from 50 billion. Systematically risky banks are subject to stricter oversight from the government.
SVB had more than 200 billion in assets at the end of 2022, falling just under 250 billion and hence being spared more stringent stress tests and stricter scrutiny from regulators. The demise of the bank, of course, has brought into question the wisdom of the 2018 move.
In the age of social media and the internet, panic and contagion spreads much faster in the markets, making and breaking institutions within the blink of an eye. In this new world we inhabit, a lot more banks and financial institutions are, if not too big to fail, unlikely to go gently into the good night. Hence, in the interest of stability in not just the financial markets but in the economy as a whole, regulators should consider subjecting more banks and institutions to stricter scrutiny and more stringent regulatory requirements.
In conclusion, the collapse of Silicon Valley Bank has had a far-reaching impact on the fintech industry and the global economy. In the US, it has led to a lack of trust in the banking system and has sparked fears of a looming recession. At the same time, it has demonstrated the degree to which the fintech industry is reliant on traditional finance, and has caused some to reassess the value proposition of cryptocurrencies. In South-East Asia, start-ups and investors are being asked to provide assurances to stakeholders regarding their limited exposure to the failed bank. As we look to the future, it remains to be seen just how severe the long-term repercussions of the collapse of Silicon Valley Bank will be and how these will shape the future of the fintech industry.
In recent years, #China has been developing at an unprecedented speed. As the world’s 2nd largest economy with a GDP growth averaging 9% from 1989 to 2022, experts have forecasted that China’s GDP will overtake United States’ in 2035. Thanks to its growing economy, China’s foreign investment has grown ten-fold over the last decade and China is currently the 3rd largest source of #overseas #investment. Amdist the changing geopolitical landscape, China has been actively investing in Southeast Asia as companies actively seek for lucrative investment opportunities abroad. In this article, the author will be walking readers through the current state of Chinese investment in ASEAN’s Fintech industry, particularly in #Indonesia.
The world’s largest Fintech ecosystem is here to stay
In 2022, amidst the looming COVID-19 pandemic crisis which contributed to undesirable economic growth, China unveiled its Fintech Development Plan for 2022-2025. The Development Plan outlines 8 main tasks, with an emphasis on strengthening prudential regulation and governance of Fintech, which is not surprising against the backdrop of a harsh regulatory crackdown which has been ongoing for a few years. Despite that, we would argue that China’s position as the world’s largest fintech ecosystem remains unassailable in the near future. For one, her Fintech sector is extremely competitive. An all-time favourite case study would be Alipay, now the world’s most widely used mobile payment platform, with the total number of users standing at 1.3 billion. Furthermore, Chinese Fintech firms have been actively seeking opportunities abroad to expand their reach. Globally, China is now the world’s leading Fintech investor since 2018. And their top investment destination? Southeast Asia.
Southeast Asia: The darling of Chinese Fintech investments
There are several reasons why Chinese Fintech firms like to invest in Southeast Asia (SEA)’s Fintech Sector. Firstly, Chinese Fintech firms took a toll during China’s regulatory crackdown, which incentivised them to seek investments and partnerships abroad.Southeast Asia’s fast-growing Fintech sector. For example, back in 2020, the Chinese government halted Ant Financial’s record-breaking and long anticipated mega IPO, citing regulatory concerns. Since then, Ant Financial has been seeking opportunities in Southeast Asia and has been an active investor for the region’s Fintech startups. Secondly, China’s fintech sector is seeing stiff competition, particularly in the fields of digital payment and wealth management, which pushes companies to expand overseas, according to Zenon Kapro, the director of Kapronasia consultancy. With the aforementioned in mind, it is also natural that Chinese companies are inclined to venture into Southeast Asia when choosing their investment destinations. In 2013,In a bid to promote regional development and connectivity, China launched the Belt Road Initiative (BRI) of whichASEAN took up the greatest proportion of total BRI investments compared to other regions, suggesting strong ties between China and ASENA. In recent years, amid the COVID-19 pandemic and increased geopolitical tensions, China has been focusing increasingly on the friendlier ASEAN market andChina is now ASEAN’s largest trading partner and leading investor. Apart from that, Southeast Asia’s demographics feed into the demand for Fintech products. In general, Southeast Asia’s population, which is still expanding, consists a significant portion of young and tech-savvy individuals. Logically speaking, since much of these Fintech-related services are deployed on mobile phones, Southeast Asia is definitely one of the best places for Fintech firms to thrive. This can be reflected through investors’ growing appetite for SEA’s Fintech firms – in just the first nine months of 2022, SEA’s Fintech firms received a combined funding $4.3 billion, higher than the combined sum from 2018 to 2020.
Before we deep dive into the specific reasons why Indoensia’s Fintech sector is attractive to Chinese investors, it is important to recognise that Chinese investors favour Indonesia for investments across many sectors. For many years, China has strong bilateral ties with Indonesia which can be exemplified by the fact that China is Indonesia’s largest trading partner and second largest foreign investor. Specifically, Chinese investors’ great interest towards Indonesia’s nickel smelter industry for strategic reasons has also been widely reported. Owing to the$14 billion poured into Indonesia’s nickel sector for the past 10 years by Chinese investors, Indonesia has now become the world’s largest producer of nickel and nickel-related products.Being a member of the Association of Southeast Asian Nations (ASEAN), Indonesia has also recently signed the ASEAN Comprehensive Investment Agreement (ACIA), which further opens up the Indonesian market to foreign investments.
Indonesia: Southeast Asia’s Booming Fintech Market
Indonesia is one of the most populous countries in Southeast Asia, with the fourth largest population in the world. Indonesia is also one of the fastest-growing economies in the region, and its population is characterised by a high mobile internet penetration rate and a growing middle class with greater disposable income to adopt Fintech products. Additionally, Indonesia has a strong and vibrant Fintech sector, with the world’s third largest unbanked and underbanked population, constituting a growing demand for Fintech services.With the above in mind, Indonesia and China share many similarities, which means that it will be easier for Chinese investors and firms to serve the market using their existing knowledge and expertise.
Promising Fintech Sectors in Indonesia
Funding wise, Indonesia has received a significant amount of fintech investment compared to other SEA countries and these investments cover almost every fintech sector. However, with respect to Chinese investments, Peer-to-Peer (P2P) Lending and Payments are more popular among Chinese Fintech firms. Driven by regulatory crackdown in their home country, Chinese P2P firms flocked to Indonesia as they seek to expand. In short, P2P Lending platforms act as a central marketplace that matches borrowers’ borrowing needs to the financing capital provided by lenders, bypassing traditional financial institution. The concept of P2P Lending first emerged in China when PPDAI was introduced and since then, unregulated P2P lenders proliferated for a long period of time. At its peak, China had more than 6000 P2P lenders. Due to the lax regulations, the industry struggled with fraud. For example, in 2016, the major P2P lender Ezubao turned out to be a Ponzi scheme, leaving behind frustrated users who lost money. In a bid to protect users’ interests, in 2019, the Chinese authority demanded P2P lenders to exit the industry and become small loan providers within 2 years on the condition that they are able to meet a capital requirement of at least 50 million yuan (approx SGD $9.75M). Unable to meet the stringent requirement, many P2P lenders shut down their operations in China and there was an outflow of them to Indonesia. It is known that they constituted more than half of Indonesia’s P2P lenders. While Chinese P2P firms have helped to drive financial inclusion, their entry also resulted in a rise of illegal and unethical businesses when they first entered the Indonesian market. For example, certain P2P lenders have unlawful and aggressive debt collection processes, such as calling the friends and families of borrowers. Having observed the downfall of China’s P2P lending industry due to lax regulations at the initial development phases of the industry, Indonesia’s financial services authorities Otoritas Jasa Keuangan (OJK) decided to take active steps to regulate the industry early. In 2022, in a bid to protect users’ interests, OJK increased the minimum capital requirement for lenders from 1 billion rupiah to 25 billion rupiah. In addition, P2P lenders need to maintain at least 12.5 billion rupiah of equity throughout their operations.
Despite stricter regulations, Indonesia’s P2P lending industry still sees rapid growth in terms of loan disbursements. In just 5 years, their local P2P loan disbursement increased from 3 trillion rupiah (approx. SGD $262.5M) in 2015 to 155 trillion rupiah (approx. SGD $13.6B) in 2020. One notable Indonesia Fintech firm would be Akulaku, which offers a wide range of lending and payments-related services. It was founded by Chinese entrepreneur William Li. As Chinese entrepreneurs invest and start their own fintech firms in Indonesia, not only do they bring along their skills and expertise, but they also leverage their own network of investors to help boost the funding capabilities of their own startups. To illustrate this, let us take a look at Akulaku’s funding rounds to date.
Table 1: Akulaku’s funding rounds (as of 2022)
(Source: Crunchbase)
As can be seen, out of 8 rounds with lead investors disclosed, Chinese investors (Ant Group and Qiming Venture Partners) emerged as the lead investors for 2 rounds.
Apart from P2P Lending, Chinese investors also have a preference for Indonesia’s Payments sector and have a significant presence in some of Indonesia’s largest unicorns. For example, DANA, one of Indonesia’s biggest mobile wallet platforms, is currently backed by Ant Financial. Gopay (the payment branch of Gojek), which is Indonesia’s most widely-used e-wallet, also received significant funding from Tencent and JD.com (Table 2). Fundings from Chinese investors has helped Indonesia’s fintech firms to expand across the country.
In 2017, Tencent led its funding round of $1.2 billion
In 2018, Tencent and JD.com participated in their funding round which raised $1.2 billion
Tokopedia
(part of GoTo)
Offers GoPayLater Cicil, a Buy Now Pay Later solution to selected Tokopedia users
In 2017, Alibab led a $1.1 billion investment
Lazada
Offers HelloPay
In 2016, Alibaba acquired Lazada
In 2018, Alibaba increased their stake in the company
DANA
Offers an Indonesian digital wallet with over 30 million users
Formed by Ant Financial in collaboration with Indonesia conglomerate Emtek in 2017
Lazada, which was acquired by China’s Alibaba, led DANA’s Secondary Market round to raise a total of $304.5 million in 2022
Table 2: Examples of unicorns that provides payment services in Indonesia which received Chinese funding (Source: Suleiman, Ajisatria. “Chinese Investments in Indonesia’s Fintech Sector: Their Interaction with Indonesia’s Evolving Regulatory Governance.” Center for Indonesian Policy Studies, 2019., Crunchbase)
As can be seen, Chinese investors and firms have partaken in Indonesia’s fintech ecosystem in several ways. Firstly, Chinese entrepreneurs have started new fintech firms (e.g., Akulaku), contributing towards the variety of offerings available to consumers. Secondly, Chinese home-grown fintech firms have started fintech subsidiaries in Indonesia. Some examples are Mi Credit by Xiaomi, JD Finance fromJD.ID and OneConnect Indonesia by Ping An Group. Lastly, large and established Chinese fintech firms and Chinese Venture Capital firms have actively participated in funding rounds of Indonesia’s fintech startups. It is also notable that the key players actively pursuing investments in Indonesia are usually large and established tech firms from China, such as Alibaba, Tencent, and JD.com.
Implications for Businesses
Now that we have a better understanding of the influx of Chinese investments into Indonesia’s Fintech sector, let us look at some of the managerial implications for the industry.
Firstly, it is important for firms in the Indonesian Fintech sector to be aware of the competition from Chinese firms. While Chinese investments may be beneficial for the sector in terms of providing capital for growth, the competitive landscape may be further intensified as Chinese firms bring their expertise and resources to the table. Companies need to be aware of this competition and devise strategies to differentiate themselves from their Chinese counterparts.
Secondly, companies in the Indonesian Fintech sector should be mindful of the regulatory landscape. While the influx of Chinese investments has been beneficial for the sector, it has also brought its fair share of risks due to the lack of regulations in the Chinese P2P lending industry. In response, Indonesia’s Otoritas Jasa Keuangan (OJK) has imposed stricter regulations for the sector, such as increased minimum capital requirements for lenders. Companies should be aware of these regulations and ensure that they remain compliant with them.
Thirdly, companies should be aware of the potential opportunities that may arise from Chinese investments. Chinese firms, such as Ant Financial and Tencent, are well-known for their expertise in the Fintech space and can offer valuable insights and resources to Indonesian firms. Companies should consider partnering or collaborating with these firms in order to gain access to their resources and expertise.
In summary, the influx of Chinese investments in Indonesia’s Fintech sector presents both opportunities and challenges. Companies in the sector should be aware of the competition and regulatory landscape, as well as the potential opportunities that may arise from partnering with Chinese firms. By doing so, they will be better equipped to take advantage of the opportunities and mitigate the risks.
The launch of ChatGPT has taken the world by storm, reaching a hundred million users a mere two months after its launch. In the process, it has kick-started a conversation on the future of generative AI and the impacts it will have on us humans, while also setting off an arms race among both big tech companies determined not to be left behind and investors trying to get in on what they see as the next wave of the A.I boom.
For all the flaws of ChatGPT, the product has, in a short span, become a household name and vaulted OpenAI onto the Silicon Valley leaderboard of movers and shakers.
As companies, and even governments, find ways to incorporate ChatGPT into their existing workflow processes and teachers scramble to detect and prevent student use of ChatGPT, are there any broader lessons that Fintech companies can take away from the launch of AI chatbots?
Not only are there many new startups in the Fintech space, one ripe with many new ideas and innovations, many companies in the space are also, specifically, trying to incorporate ChatGPT and generative AI into their products. For these companies, the launch of ChatGPT and other chatbots might be a useful case study that illuminates the decisions companies need to make in the product development process, allowing them to draw lessons that are also applicable to the Fintech industry.
In reporting by The New York Times on the origins of the product and the key decisions made at OpenAI — the AI laboratory behind ChatGPT — a number of revelations were made. Chief among them was not just the decision made by executives to dust off and release a product based on an older A.I model even when engineers had been working on the finishing touches on a newer and better A.I model, but also the fact that the updated chatbot based on GPT-3 instead of GPT-4 was ready a mere 13 days after employees were instructed to make it happen.
Among the key investors in OpenAI is Microsoft, which saw an opportunity to incorporate ChatGPT into its search engine Bing — often overlooked and overshadowed in favour of Google, whose search engine was out of Bing’s league.
Yet, speaking to The New York Times, Kevin Scott, Microsoft’s Chief Technology Officer characterised these slip-ups by Bing chat as part of the learning process before the product was widely released to the public, adding: “There are things that would be impossible to discover in the lab.”
Reporting by The Wall Street Journal on Google, on the other hand, explains how the company, long seen as a leader in the AI domain, had developed a rather powerful chatbot years before ChatGPT was launched to the public but chose to sit on its AI capabilities due to reasons ranging from possible harms to their reputation to the belief that big tech firms like them had to be more thoughtful about the products they launch.
Within the decisions that OpenAI, Microsoft and Google made with regard to their product and their decision to launch, or not launch, lies a deeper question that engineers and executives at both start-ups and established businesses across a variety of industries, including Fintech firms, grapple with: when is the product good enough to be revealed to the public?
Lean and Mean vs Conservative and Reliable
There are broadly two ways in which tech startups, particularly those based in Silicon Valley, approach the question of when to release a product to others outside a company.
In the software development sector, proponents of the Lean Startup Methodology push for companies to release a minimum viable product (MVP), which as its name suggests, needs to be just good enough. A product just good enough to attract a few customers, whose use of the product allows designers and engineers to test the product and see if the feedback they receive validates their own preconceived assumptions and notions. The rationale is simple. It is very easy for teams, particularly those slosh with funding either from VCs or other profit-making divisions in their own company, to fall into a herd or mob mentality and slog away at a product for years or months, burning both cash and resources in that process, without even answering a very basic question: is there a need for my product?
Hitting the markets as soon as possible and putting products in the hands of users, even if every bug in the product has yet to be fixed and every kink has yet to be sorted, allows the product to receive user feedback much more quickly. The company can then, based on the feedback, decide whether it wants to stay the course and work on developing a better version of the same product based on comments from actual users, or instead decide that it might need to pivot to another area without burning more time, money and resources in pursuit of a product users do not need.
One key benefit of such an approach is that it minimises risks for the company and its investors. A product idea quickly abandoned before effort and dollars is poured into it ideally leaves a company with a core technology that can be implemented in other ways, as well as some resources left to then make the pivot.
Another benefit is the ability to test a product. As Kevin Scott pointed out, it’s hard for engineers or designers to truly test the most fundamental parameters, assumptions or flaws of a product — especially when working on the product for some time is likely to leave them with certain blindspots. Users with no affinity to the product and no attachment to any particular feature of the design who are using the product outside the lab controlled context, however, are more likely to give better feedback that can improve the product greatly.
That said, adopting a lean startup methodology is not a silver bullet that will guarantee that a company will be successful over the long run — especially in landing on a product that solves a problem for a group of users through pivots. Research, for instance, suggests that prior market knowledge about an industry plays a huge role in the successful implementation of the lean startup methodology, allowing the company to better interpret and understand the feedback they are receiving from users while also learning from the successes and failures of other companies in the space, therefore enabling them to make the necessary pivots in a more strategic and well thought-out manner rather than engaging in an “unplanned and unguided process of trial and error learning”.
Moreover, releasing a half-baked product comes with serious potential for reputational damage for the company involved, as well as possible real world damage to users from products without sufficient controls.
When it comes to Fintech products in particular, mis-steps can have serious ripple effects in people’s lives. Money, savings and retirement funds can be lost, financial regulations may inadvertently be broken or otherwise not adhered to, and even broader markers can at times be affected by any mistakes. Hence, releasing a buggy system might just not be an option.
Moreover, with AI products, the underlying models driving the product often involve highly complex algorithms working on huge amounts of data that are “black boxes” that make decisions based on calculations that might be difficult to interpret or reverse-engineer, even for the very engineers that built them. This “unpredictability” inherent in AI products makes both the likelihood and risk of unintended consequences much higher in them.
Thus, start-ups might no longer have access to large amounts of capital that would have allowed them to keep tinkering with prototypes of their products behind the scenes, and might therefore need to launch a product sooner than they would like. Moreover, investors are likely to have a milder risk appetite amidst a more challenging macroeconomic environment and thus, are more likely to prefer companies that are profitable and have a steady growth rate as opposed to companies betting it all on a wild idea or those prioritising growth over profitability.
Against this context, adopting a lean start-up methodology is not a terrible idea for Fintech companies as this will allow them to squeeze the most value out of the limited capital they have, while also possibly making them more attractive to investors by signalling that they are a company that can get much done with little money.
That said, fintech companies will need to weigh the pros and cons of every added feature or design; particularly how important or crucial the feature might be to a user, or on the flipside how dangerous or risky might its absence be to them, vs how much time, money and resources it might take to add the feature. Companies also need to communicate to users that the product is in its early stage and that therefore, they should be wary in using it, while also having contingency plans in place to deal with the worst-case scenarios that might arise.
The jurisdictions that the countries operate in should also inform the minimum thresholds for these companies in the product development process. Fintech companies operating within the EU, for instance, are better off treading cautiously to ensure that the company does not run afoul of any laws to prevent any regulatory action from being launched against the company — a death kneel to companies in their early stages, compared to a company in Singapore for whom time to market and return on investment for investors is likely to be a bigger factor even as regulatory concerns cannot be ignored.
Ultimately, calibrating the fine balance between the risks and the benefits of having a workable product in the hands of users might be the difference between the next unicorn and a tech idea that was going to revolutionize or disrupt the world but did not quite go anywhere.
Individual investors banded together on the subreddit thread r/wallstreetbets and egged each other on to buy shares in the company GameStop. Why? To “squeeze” a number of institutional investors who had shorted the stock, or bet on its price decreasing, by pushing its price higher with their purchase of the stock and hopefully making a dime while they were at it.
As they brought the shares of the stock “to the moon” with their “diamond hands” — at its height in late January 2021 the price of a share was nearly 500 dollars, in comparison to $17.25 at the start of January 2021 and its current trading price of around $20 — the world and more importantly, financial regulators sat up and took notice.
As the price of shares in GameStop and other “meme stocks” like AMC, an American movie theatre chain, saw much volatility, regulators grew concerned. The house financial services committee in the House of Representatives called for a hearing and the attorney generals of Texas and New York announced that they would look into the matter.
The Securities and Exchanges Commission (SEC), the primary regulator of the stock market in the US, also announced that it was reviewing the incident and months later, in October 2021, released a report about the saga.
Among the factors that contributed to what happened with meme stocks, the SEC said, was the turning of securities trading into a game by trading platforms like Robinhood. This, they said, was due to the perverse interest of trading platforms to encourage trading activity by users to increase revenue for themselves — through the controversial practice of Payment for Order Flows.
Thus, it is no surprise that the practice of payment for order flows might soon face disruption from the SEC.
Almost two years after everyday joes disrupted the financial markets through the power of social media, in December 2022 the commission voted in favour of advancing significant changes to the American stock market rules that, while falling short of banning payment for order flows, will impose additional requirements on the practice if implemented.
The SEC is currently seeking feedback from the public about the proposed changes before the agency decides whether to enact them.
Confused about what’s going on? Fret not. This article will explain what exactly the practice of payment for order flow is, why it is controversial and look into how the proposed regulation might potentially affect trading platforms and retail investors.
Payment for What?
To understand the practice of payment for order flows, we first need to understand the mechanics of how the click of a button on our desktops, or increasingly on our phones, results in the buying or selling of shares.
While it might not seem or feel this way, we are not directly buying or selling a share from a stock exchange or the securities markets when we trade on a brokerage platform like Fidelity or Ameritrade.
Instead, when you push a button, or if you are old-fashioned and call your broker to place an order, the broker receives the order from you and is now tasked with executing it on your behalf.
To execute your order to buy or sell a security, one option the broker has is to channel the order to a stock exchange like the New York Stock Exchange (NYSE) or closer to home, the SGX. If directed to a stock exchange, the trade is executed in line with the procedures of the exchange. The NYSE, for instance, is an auction market where bids and asks for the entire market is posted for the entire market to see and the players in the market can then “compete” with each other to competitively fill the order — thereby giving the investor the best price on the trade.
The other option brokers have is to send the order to a market maker. The market maker is essentially a firm, or occasionally an individual, that maintains an inventory of particular shares and from that inventory, fills the orders that brokers send to them, making a profit from the spread — the divergence in price between the bid and ask.
So for instance, if the current bid and ask price for fictional stock A listed on the NYSE is $1.00 and $1.05 — a broker can either direct an order they receive to the NYSE and allow market participants to bid on the order, or alternatively, they can send the order to a market maker who might buy the share for $1.01, sell the same share to another broker for $1.04, and pocket the difference of 3 cents.
For a market maker, having a large trading volume is important for two reasons. Firstly, having more trades to execute simply means more opportunities for them to “capture the spread” and therefore earn more profits for themself. But secondly, liquidity — the ease at which a broker can buy and sell a security with them — is also a key consideration for market makers. The more trades brokers send to them, the more “liquid” they are.
For these two reasons, market makers have an incentive to have a large volume of orders from as many brokers as possible sent to them. But how does a market maker get a broker to send them the orders they receive? By paying them to do so, of course.
Thus, market makers pay brokers to send their orders over to them, instead of other market makers or stock exchanges. Or, in other words, payment for order flow!
The Good, the Bad and the Ugly
One argument made for clamping down or outright banning the practice of payment for order flows is that there is a conflict of interest on the part of the broker. While you ideally want your broker to do what he/she can to get you the best possible price on your trade, the fact that the broker receives money from market makers to channel your order to them means that the broker has less of an incentive to get you the best price on a trade.
Another argument that has become more salient lately is the way payment for order flow pushes trading platforms and brokers to encourage more trading activity from retail traders, even if it may be against their interest. While a conventional broker receives a commission or a fee from retail investors to execute their trades for them, while perhaps also receiving more money from market makers to send them order flows, there has been a new crop of trading platforms like Robin Hood that charge zero commission from their customers to execute their trades for them. Instead, they rely more heavily on payment for order flows, even when they do have other sources of revenue.
When competitors offer zero or low commissions, other firms may feel like they also have to slash their commissions and rely more heavily on payment for order flows to attract customers.
Because of the fact that they make more money when they funnel more orders to market makers, these firms have an incentive to get investors to trade more actively on their platforms. They do so by “gamifying” the act of trading securities by, for instance, displaying confetti animation when a user makes their first trade — not unlike what is displayed to players who unlock a new level in a computer game.
This is problematic firstly because even if traders are paying no commissions on their trades, they are still buying and selling securities with their real money, money that they can potentially lose. The gamifying of the act of trading securities might obscure this reality. Secondly, the conventional wisdom is that a good strategy for retail investors to generate wealth is to buy and hold securities with solid fundamentals over the long term, not by getting in and out of stocks rapidly — which firms like Robinhood have an incentive to encourage.
On the flip side, there are two arguments often made in defence of the practice of payment for order flows.
The first argument is that retail investors actually do benefit from market makers filling their orders instead of stock exchanges. Market makers do often offer better prices for trades than exchanges and thus, individuals benefit from their orders being routed to market makers.
The issue with this claim is that this is an argument for the existence of market makers and not one that defends the specific practice of payment for order flows. One might even argue that in a world without payment for order flows, market makers are forced to compete with one another for orders from a broker solely on the basis of fundamentals like the price of trades and execution quality.
The second argument is that when brokers rely on payment for order flows, they are able to offer commission-free trading or at least lower commissions to retail investors. This is the main line of attack being pushed by firms like Robinhood in light of the proposed SEC regulations.
“If you think about the ramifications of these proposals, you’re essentially shutting the door and saying we liked it better when it was the old boys’ club,” Robinhood Chief Brokerage Officer Steve Quirk said in an interview to The Wall Street Journal this month in response to the proposed regulations.
However, as payment for order flow practices have come under both heightened scrutiny and greater criticism, companies like Robinhood have already tried to diversify their revenue streams. Some analysts also dismiss the view that the proposal will revive commissions as self-serving and alarmist, arguing that those seeking to charge for traders will face competition from firms that can offer zero commission without a heavy reliance on payment for order flows.
A Fine Line
On one hand, insufficient oversight puts retail investors, who often have little power in the markets and are at the short end of an information asymmetry, at risk of being taken advantage of or losing much of their hard-earned money.
Yet, on the other hand, excessive regulation risks securities tradings once again made inaccessible to the average Joe or the plain Jane.
The challenge for regulators will be to strike a fine balance between these two competing interests — to ensure that retail investors are able to continue to access the markets without too much difficulty while also being sufficiently protected when they do so.
While these explainers were published under the unassuming byline “CNET Money Staff”, the articles were not written by staff members, or any human beings at all.
The tech website Futurism revealed last month that should one click on “CNET Money Staff”, there was a drop-down description that read: “This article was generated using automation technology and thoroughly edited and fact-checked by an editor on our editorial staff.”
Futurism added in its article that there had been no official announcement from CNET at that time disclosing its use of AI despite more than 70 such articles being seemingly AI-generated at that time.
The use of AI in journalism is not new. The news agency Associated Press started using AI to generate short articles about business earning reports as early as 2014, eventually expanding its use and making use of AI for sports reporting as well. Bloomberg News reporters use the system Cyborg — which can sift out the most pertinent facts and figures from a financial report very quickly the moment it is released, with the New York Times reporting in 2019 that roughly a third of the content published by Bloomberg was produced with the use of some form of automated technology.
The use of technology is particularly crucial for companies like Bloomberg and Reuters that are in the business of financial journalism, where outlets race to publish crucial information that informs the trading decisions of many readers ahead of their competitors without compromising the accuracy of their journalism.
Technology has also been used in newsrooms for purposes other than to generate stories. The Financial Times, for instance, developed a bot that tracks the number of women quoted in their news stories, in response to findings that women featured far less than men in articles — identified as a potential reason for lagging female readership.
However, news articles by AP, for instance, are not exactly “written” by AI. Instead, they require reporters and editors to craft several versions of a story upfront for the various possible outcomes of an event. The AI software then creates an article by inputting data, once made available, into the pre-written story templates. CNET, on the other hand, is utilising AI to write and put together not routine stories about a company’s earnings but whole explainers breaking down complex financial ideas to consumers, with editors only making edits and amends to an article mostly written/ generated by AI.
Futurism’s story about the use of AI to write stories at CNET came just weeks after ChatGPT has taken the world by storm and teachers, regulators and technology executives are grappling with the far-reaching consequences that generative AI chatbots will have on the future of work and education.
What followed the story was outrage from critics who were worried about the use of AI decreasing the quality of journalism while potentially eliminating work — especially for entry-level writers, a post from a CNET editor confirming their “experiment” with AI, as well as much scrutiny of the nearly 75 pieces generated by AI.
With heightened scrutiny came a flurry of correction notices, as Futurism called out CNET for making a number of “dumb errors” in their AI-generated pieces. The stories, for instance, suggested that one will earn $10,300 a year after depositing a mere $10,000 dollars into a savings account that pays 3 percent interest compounding annually (instead of $300); asserted that the interest for Certificate of Deposits (CDs) only compounds once when in reality, they can compound monthly and even daily depending on the specific product; and misrepresented the way interest rate payments are made on a car loan. Beyond factual errors, the stories were also said to be rife with plagiarism, at times plagiarising from CNET itself or its sister websites. All this was despite supposedly thorough fact-checking and editing by editors.
However, such alarmist coverage of the incident risks obscuring and burying a more nuanced conversation we need to have about the use of generative AI in financial journalism and finance content creation.
Firstly, we need to understand what generative AI can and cannot do.
Generative AI cannot convince a whistle-blower to leak documents, build relationships with sources, land scoops or go out to the field and ask questions. Even when it puts together a story based on a prompt, what an AI chatbot does is “assemble” a story by churning through vast repositories for information available to it. But the fact that it does not “understand” questions or prompts the way humans do does not necessarily suggest that generative AI software has hit a ceiling in terms of its capabilities. As the AI program is fed more and more prompts and processes more and more material, the quality of its responses is only likely to get better.
But secondly and very crucially, it will be a mistake to attribute all the missteps at CNET solely, or even largely, to the deficiencies of the technology. Subsequent hard-hitting reporting by the publication The Verge has revealed that even within CNET, few people other than a small team knew much about the use of AI. Staff who were aware of the use of AI also told The Verge that workflows were unclear and that they themselves were often unable to ascertain which stories were written by colleagues and which ones weren’t.
The use of AI to generate content also needs to be understood as part of CNET’s parent company, Red Ventures’, efforts to generate more cash by churning out more search engine optimised content with advertising nestled within the article. When more people read their articles and click on advertising links, Red Ventures generates more revenue. The use of AI instead of humans to write such articles is thus an effort to increase the profit from having more articles to read.
The use of AI technology by particular companies or organisations for predatory purposes, or without much thought given to how to incorporate them in a transparent, responsible manner, is not an indictment of the technology and its potential.
AI has enormous promise in the Fintech and financial journalism space. Fintech startups can potentially use generative AI to help them ideate and even write drafts of marketing material or content for their websites. AI can also help journalists compile information, or help them plough large troughs of data and flag up areas that they should look into. It can also do more mundane tasks like write up press releases and work on more straightforward stories, while journalists can focus on more enterprise reporting and investigative work.
What will be dangerous is say, the use of AI by a news organisation giving them significant advantages over their competitors in terms of speed or revenue, thereby kick-starting a race to the bottom where other organisations feel the need to also recklessly adopt the technology or risk being left behind without being able to consider the ethical and practical questions the usage of technology raises.
Therefore, it is crucial for us to think of the norms that should surround the use of AI and generative AI in fintech and business journalism. Prominent disclosures about the use of AI to generate content, and not obscuring its use, is one important one. The procedures by which AI-generated content is edited by humans is another area where standards and governance policies will be important. Especially as it concerns financial journalism, consumers and businesses are potentially making enormously significant decisions based on the information available on topics such as interest rates and loans and thus, accuracy is vital.
There are many reservations about the use of AI by dominant institutions and companies – namely, the lack of transparency and the potential for discrimination. People are worried that the algorithms may not always be understandable, and that the accuracy of results must be upheld, along with the transparency of the algorithmic process for legal explainability. Recent studies and developments show how credit prediction algorithms tend to reflect the bias in the data, raising the risks of discrimination against minority groups. There have been recent efforts to push for safe and responsible AI, including the development of “constitutional AI” models that aim to reduce human bias, increase transparency, and improve fairness and equality.
The integration of AI into everyday institutions, products, and services have been increasingly and inevitably prevalent over the last few years. Recognising AI’s potential as early as 2019, The Money Authority of Singapore (MAS) launched the National Artificial Intelligence Programme in Finance. Under the programme, financial institutions (FIs) are able to use AI technology in financial risk assessments more productively with the intention to create commercial opportunities for businesses and employment for citizens.
The breakneck speed at which AI is being made available to institutions, companies, and citizens have caused anxiety around its potential shortcomings when it comes to ethics and fairness. Tech giants and government bodies giving technology the power to play a major role in decision making posits an urgent concern: ensuring that the integrity and responsibility of such powerful groups are not compromised. In late 2022, DBS launched an AI-driven initiative to bolster the application process for working capital loans. With the financial livelihoods of small businesses at stake, is it really safe to be employing such novel technology in its important decision-making?
What are some of the reservations people currently have around AI being used by dominant institutions and companies?
One of the major contentions surrounding AI’s role in the finance industry is the lack of transparency. There appears to be a lack of public support for these AI models. Their algorithms are also sometimes known as ‘black boxes’; the way they operate is not always understandable to users. If these algorithms are being given the power to participate in important decision making, it is legally and ethically imperative that all interested parties are privy to how the AI reaches its conclusions. In corporate risk assessments, an AI model’s complex process may produce good results but may lack visibility and interpretability. The accuracy of results need to be upheld; transparency of the algorithmic process is also needed for legal explainability.
Viral news of harmful AI or algorithmic bias has also sparked heated discussions. AI operates through machine learning (ML) – a process similar to human learning, that looks for patterns in the data with the intention that it can continue to learn and improve automatically. Though the technology is automated, it is still based on human input. In a Tweet that has garnered nearly 9,000 likes, a user posts a screenshot of a seemingly racist and sexist output from ChatGPT. The AI was asked to produce a Python function that would check if someone would be a good scientist based on race and gender. The AI responded code that discriminatorily favored the conditions “white” and “male”.
Examining the use of AI by banks, a 2022 study showed how credit prediction algorithms tended to reflect the bias in the data. Specifically, it acknowledges the potential discrimination against minority groups within the classes of race, gender, or sexual orientation. These risks should be attended to with caution by AI developers, especially if such technologies are to be commonly implemented in a country as multicultural and diverse as Singapore. A local study examines a model similar to that of most international banks. Such models examine consumer data, excluding “protected” variables such as age or gender and using “proxy” variables like education type in an attempt at inclusion and fairness. The results showed that these efforts still failed to eliminate discrimination. So why are companies and institutions still integrating these algorithms so rapidly when it appears, at least for now, that AI technology is still unable to escape potential human bias and discrimination?
The push for safe and responsible AI has recently shown promise. Anthropic, the receiver of Google’s $300 million investment, has pointed to its research on “constitutional AI”. This study posits a model that has been shown to produce less harmful outputs. It seeks to mitigate human bias by using AI feedback in the AI’s reinforcement learning, minimizing the need for labeling by humans, a process that may introduce bias. It also addresses the prior issue of transparency, using “chain-of-thought reasoning” through which the AI cannot produce evasive responses like “I’m sorry, I cannot answer that”. However, though Anthropic’s novel model seems to produce more transparent and less harmful results, helpfulness or accuracy seems to be compromised.
Despite its limitations, if constitutional AI can truly reduce the biases and discrimination that plague current AI models, it may just revolutionize the accessibility and inclusivity of the technology’s implementation in sectors where careful decision-making is key, such as the finance sector. AI has been and continues to be integral to the endeavor towards financial inclusion. Singapore-based ADVANCE.AI has partnered with Visa in an effort to improve credit accessibility across Southeast Asia. The integration of this technology affords credit companies the ability to reach the underbanked by way of alternative consumer data. In dealing with such an underprivileged group, constitutional AI and its promise of improved fairness and equality would be helpful in boosting these efforts.
There is still much work to be done in developing artificial intelligence for fair and ethical use in our financial systems. Singapore remains a frontrunner in the push for reliable and safe AI implementation. Launched in 2022 by the Infocomm Media Development Authority (IMDA), A.I. Verify is a government initiative that allows participating companies to use technical and process checks as a means of ensuring transparency and responsibility in their use of AI. The programme is still in its pilot stage but hopes to improve trust with stakeholders in the industry and contribute to international standards of development.
Progress seems to be made, but a careful balance between transparency, accuracy, and harmlessness has yet to be achieved. Success in mitigating the harmfulness of AI algorithms seems increasingly promising for the efforts towards fairness and financial inclusion. However, biases in algorithms are ultimately a reflection of the data it is based on. For now, it appears the ML cliché still rings true: “garbage in, garbage out”.
According to #PwC, 31% of Singapore’s Fintech firm provides payment services, making payments the largest subsector in Fintech.
What are some of the most commonly used #payment methods in Singapore? What is an E-wallet? How do “Buy Now, Pay Later” services work? If you are interested to find out about the answers to these questions, check out the following article as our student writer sheds light on recent developments in Singapore’s #Fintech scene and what to expect in 2023. #BNPL #ewallet #PayNow
Payments Industry: The Way Forward in 2023
2022 has been an exciting year for Singapore’s Fintech industry as we witness many new developments across various subsectors, such as the launch of virtual-only banks and heightened interest in Buy Now Pay Later (BNPL) services. Across the ASEAN region, the average size in Fintech deals increased from US$23 million in 2021 to US$26.5 million in 2022. According to KPMG, Singapore’s global market share has also doubled from 3.1% of global deal value for Fintech in FY 2021 to 6.4% in Q2 2022. As of June 2022, Singapore is home to 1007 operating Fintech firms, which constitutes 67% of the total number across the region. Such trends further anchor Singapore’s leadership position in Southeast Asia’s Fintech scene. As suggested by PwC’s 2022 Fintech State of Play report, up to 31% of Fintech firms in Singapore provide payment-related services, making Payments the largest subsector in Fintech. Let’s review the state of payments in Singapore in 2022 and the way forward for the payment market and what consumers like you and I might expect in 2023 and beyond.
Bye Bye Cash, Hello Cashless Payment!
In 2022, we observed a surge in the usage of digital payments, which can be attributed to e-commerce growth and the adoption of cashless payment technologies at physical stores. Among the many digital payment technologies, PayNow and e-wallets are becoming increasingly popular. Launched in 2017, PayNow is a P2P fund transfer service which allows payments to be paid to Corporates via UEN and to Individuals via mobile number, NRIC or Virtual Proxy Address, saving the hassle of providing elusive bank account details during the transfer. Over the years, PayNow has seen a surge in total number of registrations and has more than 5 million users till date.This fund transfer service has also been extended to corporate users and currently, the number of corporate users has already surpassed 240,000. PayNow allows users to make payments to entities by simply scanning a PayNow QR code, which is integrated with the Singapore Quick Response (SGQR) code. Given its high adoption rate and the convenience it brings, PayNow is unarguably integral to every Singaporean in their daily lives.
Apart from PayNow, the use of e-wallets is also becoming more prevalent in Singapore. According to Bloomberg, the market for e-wallets is expected to grow by 311% between 2020 and 2025 in Southeast Asia. Despite having Southeast Asia’s highest card penetration rate at 1.73 debit cards and 1.61 credit cards per capita, Singaporeans have been rather receptive to new payments technology over the years. In fact, the number of e-wallet users in Singapore has surpassed 4 million. An e-wallet is an alternative to mobile payment whereby users can make contactless payments using their mobile phones instead of using a physical card. In addition, e-wallets requires users to load funds into the them using credit or debit cards before transactions. Certain e-wallets, such as Singtel’s Dash Wallet, also allow users to top up their e-wallet through physical avenues – e.g. at 7-Eleven outlets and Singtel Shops. When one makes any transactions, the e-wallet will process the transaction directly and deduct from the amount of fund that was loaded into the e-wallet.
Locally, the top e-wallet providers are GrabPay, DBS PayLah! and Singtel Dash. Often, e-wallet providers can be categorised into a few categories as these e-wallets are embedded in various apps (See Table 1). Such embedment allows providers to integrate or even launch their own payment method to go hand in hand with their product offerings.
One reason for the popularity of e-wallets and why consumers may choose an e-wallet solution over credit cards is the attractive rewards and perks given, in addition to access to integrated features of the app. For example, for each GrabPay transaction made, users can earn GrabRewards points which can then be used to claim discount vouchers for ride-hailing and food delivery services offered on the same app. The region’s leading e-commerce platform, Shopee, also launched ShopeePay back in 2019 to provide greater convenience to users when checking out online. Secondly, certain e-wallets allow for multi-currency payment which may help to reduce costs when doing online shopping or paying while travelling overseas due to attractive exchange rates; some even absorb foreign transaction fees (which can be hefty for credit and debit card transactions). Frequent travellers may have heard of YouTrip which offers a multi-currency e-wallet that allows users to pay in more than 150 currencies when shopping online or overseas. Apart from online purchases, e-wallets have also expanded their offerings to allow payments at brick-and-mortar stores.
Furthermore, the adoption of e-wallets has made it easier for foreigners to spend in Singapore, benefitting merchants. As the local e-wallet market matures, the Singapore government has also been actively pursuing partnerships to facilitate cross-border e-wallet payments. You may have noticed Alipay+ advertisements have started to spring up at locations like Changi Airport. Alipay+ is a payment solution which allows users from China to pay using their local e-wallets when spending overseas. Currently, Alipay+ allows Singapore merchants to accept 6 different e-wallets (see Table 2), of which half was made available last year. As we examine these foreign e-wallets, it is not difficult to realise that the providers are from Singapore’s top tourist regions by arrival – ASEAN, South Korea and China. Thus, as the COVID-19 pandemic subsides, we can expect greater foreign spending with the recovery of the tourism industry, and merchants who accept e-wallet payments will benefit from more convenient spending by visitors.
*New additions in 2022 Table 2: Foreign E-wallets accepted under Alipay+ in Singapore
Due to its pros, e-wallets are predicted to surpass credit cards as the most common e-commerce payment methods with a share of 27% by 2024. However, as the use of e-wallets is becoming more prevalent, we ought to recognise that there are still limits and they cannot completely replace the use of credit or debit cards. For one, under Singapore’s Payment Services Act, consumers can only hold a maximum of $5000 in each e-wallet at any given time. Such caps may make the transfer of funds a hassle for consumers who frequently transact through e-wallets. Good news is, back in November 2022, the Singapore government initiated a public consultation and has started to review the possibility of raising the fund limit from $5000 to $20,000. We can foresee that in 2023, with greater adoption rate of e-wallets, there may be amendments prudently made to existing regulations to accommodate consumers’ needs.
Buy Now Pay Later: Stricter regulations moving forward
Buy Now Pay Later (BNPL) is a flexible form of short-term financing which allows customers to pay for goods over time through several interest-free instalments. Locally, Atome, ShopBack Pay Later and Grab PayLater are some of the most popular BNPL providers. Traditionally, banks have also been offering similar 0% interest instalment plans for credit cards and BNPL providers differentiate themselves by absorbing processing fees and providing attractive discounts. Their business model works by charging their partner merchants transactions fees for revenue generation.
Thanks to increasing e-commerce purchases, BNPL has started to gain traction in 2022 since its entry in 2017, particularly among the millennials. It is forecasted that the adoption of BNPL will see a CAGR of 19% from 2022-2028 and its Gross Merchandise Value in Singapore will increase from US$804.9 million in 2021 to surpass US$3158.3 million by 2023. Notably, Hoolah, one of Singapore’s largest BNPL service provider, experienced a rise in transactions of more than 1500% during the COVID-19 pandemic.
According to Milleu Insight research, 30% of Singaporeans aged 25 to 40 have utilised a BNPL service. There are reasons as to why BNPL is gaining traction among millennials. For one, BNPL allows millennials who are not eligible for a credit card to be able to afford big-ticket items. It could also be the case that millennials are more tech-savvy and are more accustomed to online payments. In order to increase the adoption rate, BNPL providers have also been launching campaigns and offering discounts and perks to users. For example, Atome’s app has several online shopping vouchers which allows customers to save some money when making purchases.
However, as BNPL gains traction, concerns such as BNPL services encouraging impulse purchases and whether such services will lead to over-indebtedness have surfaced. While BNPL services allow customers to pay in instalments, customers may be subjected to late payment fees ranging from $5 to $90 depending on the number of instalments missed and the type of item purchased. In 2021, the Monetary Authority of Singapore commented that while BNPL provides great convenience for consumers, consumers “may be at risk of spending more than what they have budgeted for”.
In response to these concerns, the Singapore Fintech Association partnered with industry players such as Grab Financial Group, Shopback and Atome to form the Buy Now Pay Later (BNPL) Working Group to launch a Buy Now Pay Later Code of Conduct, which includes guidelines on fees (including late fees and other charges), interest rates, and marketing practices to not mislead consumers. To prevent consumers accumulating debt, BNPL providers will ban customers from making further BNPL purchases once a payment is overdue. In addition, customers will not be able to accumulate more than $2,000 in outstanding payments unless they pass an additional assessment to review their credit worthiness.
The code of conduct also mentions that a credit information sharing bureau will be set up in late-2023 with the help of Experian, a leading global information services company. The bureau will include users’ credit information (such as BNPL balances, missed payments) shared by all accredited BNPL players in Singapore for creditworthiness checks to be carried out. With that, we can anticipate more regulations and mechanisms in place to protect consumers against overspending when using BNPL services in 2023 and to ensure that BNPL offerings provide consumers with convenience and value while minimising their credit risks.
Real-time payment systems: Transcending borders as the way forward
Back in 2021, Singapore created history by launching the linkage of Singapore’s PayNow and Thailand’s PromptPay real-time retail payment system – such linkage is the first of its kind globally. Subsequently, the Singapore government announced plans to link PayNow with DuitNow, Malaysia’s top real-time payments platform which has more than 8 million users.
For those who frequent Malaysia, this may be a good piece of news! It is known that the first phase of the linkage has been rolled out in the 4th quarter of 2022. In the future, consumers will be able to perform a variety of tasks such as making real-time fund transfers between Singapore and Malaysia with just a mobile number. In addition, customers will be able to make payments in retail stores by simply scanning the NETS or DuitNow QR codes displayed at merchants’ storefronts. This initiative is expected to bring about greater convenience to residents of both Singapore and Malaysia by simplifying the remittance process and enhancing in-store payment experiences. Moving forward, Singaporeans may be able to use PayNow when conducting transactions in Malaysia. The integration of India’s Unified Payments Interface with PayNow is also expected to go live in 2023. Such integration will enable users to transfer their money overseas easily, facilitating remittance.
The payments industry is still rapidly evolving as we expect to observe new and exciting developments. With greater growth, the various payment offerings are also expected to come under greater scrutiny in lieu of stricter government regulations moving forward.
This morning at the Department of Information Systems and Analytics Seminar, Alex Tuzhilin (NYU) on his recent work entitled “Predicting Consumer Choice from Raw Eye-Movement Data Using the RETINA Deep Learning Architecture” (the paper is available on SSRN: https://ssrn.com/abstract=4341410) with his colleagues Moshe Unger (Tel Aviv University) and Michel Wedel (University of Maryland).
The essence of his talk was that it was possible to utilize advanced deep learning (Dl) techniques (transformer + metric learning) with “raw” eye-tracking data (i.e., just the x-y coordinates of the eye gaze without any additional contextualizations including area of interest (AOI) or content semantics) to predict consumers’ product choices (on a webpage that showed four products in a consideration set along with their specifications) with higher accuracy than conventional state-of-the-art machine learning approaches calibrated with fixation data (which includes raw gaze data, scanpath data, AOI data and image data). There were three additional surprising results — 1. the proposed DL model with only raw gaze data did NOT require a huge dataset — the superior performance was demonstrated with only 112 subjects’ eye-tracking data, 2. prediction reached very good accuracy very fast — we don’t need to observe the full interaction with the webpage to predict the product choice; at least in their experiment, product choice could be accurately predicted after about 5 seconds of subjects perusal of the webpage where the average total duration for the choice task was around 10 seconds, and 3. the SHAP analysis of the AutoML models showed that among the different types of data (raw, fixation, AOI and image), raw gaze data had the highest overall importance.
The study in and of itself was very interesting. But to me what was more interesting were the implications of such technologies.
First, if it is possible to accurately (maybe not perfectly but with sufficiently high accuracy) predict consumers’ product choices, wouldn’t it be possible to rearrange / redesign webpages strategically to make (or at least strongly nudge) consumers to choose a preferred product (e.g., perhaps one with the greatest profit margin, or ones with excess inventory, etc.) Such technology could easily lead to anticompetitive behaviors especially when large platforms control the display of product offerings. I am reminded of the US Department of Justice’s antitrust investigation into SABRE in the 1980s. Will these technologies also need to be regulated? If so, how? What is acceptable nudging behavior? Isn’t marketing and advertising pretty much the same thing? How can we clearly distinguish between simple nudges and manipulation (i.e., loss of the exercise of free will in choices)?
Second, although the study used eye-tracking data collected using specialized infrared eye tracking machine, the rate at which webcams are improving in quality and resolution along with more research in the Computer Vision area in inferring coordinate gaze data just by looking at someone’s eyes, makes me nervous that such technologies can quickly be deployed at large scale and technology companies/platforms that control the data interface (e.g., cell phone manufacturers since most phones have front-facing cameras; video conferencing providers such as Zoom, AR/VR headset manufacturers etc.) will gain immense power. For example, its not difficult to imagine analyzing individuals gazes during zoom meetings to predict behaviors (e.g., negotiation strategies/outcomes) and value-added services can be offered to productize strategic insights in real time.
Third, there is definitely a “creep” factor but I’m wondering what the legal norms might be for evaluating such technologies. Advanced AI techniques are just being used to create accurate prediction models about human behaviors. But isn’t generating insights about human behaviors simply psychology research (I know I am oversimplifying)? How is this application of AI and eye tracking data different from using theories derived from psychology laboratory studies to create more favorable (profitable) setups? For example, is the widely adopted practice of the decoy effect a problem? Perhaps, the difference is based on the general behavior vs. a specific person’s behavior. Psychological theories offer us a general understanding of behaviors whereas the kinds of AI techniques used in this study give the user of such technologies predictions about specific / targetted individuals.
Well, the genie is already out of the bottle. We really need to think about governance mechanisms such that technologies are used safely and for good.
The Hong Kong Monetary Authority (HKMA), Hong Kong’s central bank, announced that it is pressing ahead with the regulation of stablecoins, unveiling a slew of measures in a report released on 31 January — almost a year after it issued a discussion paper on the subject and invited feedback from stakeholders.
Starting with the regulation of stablecoins backed by fiat currencies, HKMA said that it will supervise key activities such as the issuance of stablecoins and the provision of wallet services. Entities that conduct such activities will soon be subject to licensing requirements, which will apply not only to entities that conduct such activities in Hong Kong, but also ones that market to the public in Hong Kong or are involved in stablecoins backed by the Hong Kong dollar.
HKMA will also require the issuers of stablecoins to maintain reserves matching the amount of cryptocurrency in circulation and no longer allow algorithmic stablecoins, a significant development.
In seeking to regulate stablecoins, Hong Kong is not alone — with various regulators currently engaged in the process of contemplating and working out stablecoin regulations. In Singapore, the MAS published a consultation paper about their proposed approach to stablecoin regulations and sought feedback from stakeholders late last year. In the US, Stablecoin legislation is high on the list of priorities for the newly formed subcommittee on digital assets, financial technology and inclusion in the Republican-led House of Representatives. A strengthened regulatory framework for stablecoins is also expected to be implemented in the EU, subject to formal rubber stamping by the European parliament.
But what exactly are stablecoins, and why is there such a rush to regulate them?
More-stable coins
Very simply, stablecoins are cryptocurrencies whose value is pegged or tied to that of another currency, commodity or financial instrument.
As its name suggests, this makes their valuation more “stable” than other cryptocurrencies like Bitcoin, for instance, whose prices are more volatile and prone to huge swings.
By making cryptocurrency more predictable, stablecoins can be suitable for use in daily transactions; be it for crypto traders who want to go in and out of different crypto investments in a DeFi exchange and preserve their portfolio’s fiat value without having to cash out of the crypto market entirely, or the average individual who wants to participate in a DeFi project or pay for everyday goods and services like buy a pizza with cryptocurrencies.
There are two main methods by which the value of a token is fixed to a stable figure: via the backing of assets or via algorithms.
The first category of cryptocurrencies, which includes fiat, crypto and commodity-backed stablecoins, are cryptocurrencies whose value is pegged to that of fiat currencies, other cryptocurrencies and commodities, respectively. The issuer of the cryptocurrency holds in their reserve the asset the token is pegged to — with the reserves ideally equal to the amount of stablecoins in circulation, allowing any stablecoin holder to redeem their token for the asset it is pegged to.
This, while highly stable and safe, requires issuers to hold in their reserves a large amount of assets and is thus very capital intensive.
Algorithmic stablecoins, on the other hand, are not backed by any real-world commodities and instead utilise algorithms to maintain the value of a given token. By burning or minting tokens according to supply and demand, the value of a token can be dynamically maintained at a fixed level.
However, in extreme market conditions, the algorithms may fail to keep up — resulting in the value of a token de-pegging.
This happened to TerraUSD, an algorithmic stablecoin that was pegged to the US dollar not by cash reserves but with the use of a stabilisation mechanism involving another cryptocurrency, Luna.
Last May, it lost its peg to the US dollar after a series of large dumps of the token prompted a broader sell-off in the market, leaving them unable to reinstate the peg of TerraUSD to the US dollar.
It is against this context that HKMA has announced plans to outlaw algorithmic stablecoins and require stablecoins to be backed by “reserve assets … of high quality and high liquidity” — seemingly to minimise the risk of stablecoins de-pegging and creating turmoil in the financial system in the future.
Why regulate stablecoins?
One large reason regulators are moving to regulate stablecoins is the contagion risks they pose to the broader financial system.
Eswar Prasad, a professor of economics at Cornell University, pointed out in a recent interview with CNBC that “if there were to be a loss of confidence in stablecoins, maybe because some exchanges come down or for other reasons, then we could have a wave of redemptions (of stablecoins), which would in turn mean that stablecoin issuers have to redeem their holdings of treasury securities, and the large volume of redemptions even in a fairly liquid market can create turmoil in the underlying securities market”. He added that regulators were therefore right to be concerned about stablecoins, especially given the importance of the treasury securities markets to the broader financial system.
“Risks will increase as … (stablecoins) become more interconnected with the existing financial system,” an IMF report notes, adding that this is especially the case if, in the future, stablecoins become more widely accepted and, therefore, interconnected with existing financial entities and payment infrastructures, a scenario not unlikely given Stablecoin’s potential to be used to improve the efficiency of cross-border transactions.
Should the substitution of currency through cryptocurrency markets accelerate, stablecoins could also be the source of spillovers into exchange rates markets, giving regulators much reason to step in and exercise oversight.
How about CBDCs
Another key consideration for central banks seeking to regulate stablecoins is how their own digital currencies fit into the broader picture.
Central Bank Digital Currencies (CBDCs) are essentially digital tokens not unlike cryptocurrencies but are issued and backed by the central bank.
An Atlantic Council tracker shows that 100 nations are developing, researching or have already launched a digital currency. CBDCs will potentially make the financial system more efficient by decreasing reliance on intermediaries such as clearing houses and banks, while also promoting the financial inclusion of the disenfranchised.
Despite their similarities, some experts believe that CBDCs and stablecoins can co-exist, with the use of stablecoins for specific purposes being complementary to the use of CBDCs as a general-purpose currency.
Central banks, however, may decide that even stablecoins backed by assets in a 1:1 ratio are not necessary in light of CDBCs and thus disallow stablecoins entirely.
A broader reckoning
While governments have an imperative to regulate stablecoins given the heightened risks they pose to the broader financial system, it remains to be seen if stablecoins will be an exception or if the regulation of stablecoins will create the impetus for governments to exercise further oversight over other areas of the cryptocurrency space, in the same way they regulate other financial institutions.
Questions about the appropriate regulatory approach to the broader cryptocurrency space are particularly pertinent in the aftermath of the crash of FTX. The crypto-exchange, once among the most high-profile ones in the world, was forced to suspend withdrawals and subsequently file for bankruptcy late last year and currently faces serious allegations, including the improper use of customers’ funds. Temasek Holdings wrote down its 275 million dollar investment in FTX last November and Sam Bankman-Fried, the founder of FTX, has since been extradited to the US and faces fraud charges.
Speaking at a panel at the World Economic Forum in Davos, Tharman Shanmugaratnam, the Chairman of the Monetary Authority of Singapore (MAS), said that there is a need to step back and ask a basic philosophical question before considering regulating cryptocurrencies the same way we do banks and insurance companies.
“Does (regulation of cryptocurrency) legitimise something that’s inherently purely speculative and in fact, slightly crazy? Or are we better off just providing ultra clarity as to what’s an unregulated market and if you go in, you go in at your own risk,” he said, adding that he leans a bit more towards the latter view.
With regards to the wider crypto space, the HKMA said in its report that it will continue its discussion with other stakeholders, adding that while it embraces financial innovation and encourages companies to explore the potential of distributed ledger technologies, it “will continue to monitor market developments and the risks that different categories of crypto-asset may pose to monetary and financial stability”.
