The future of consumer credit is in AIBy Grace Chia
Credit cards have long been a key source of unsecured credit for consumers in developed markets. Many regard credit cards as a convenient payment mechanism to finance big-ticket purchases and emergency expenses. Although Singapore enjoys a high credit card penetration of 1.5 cards per capita, penetration remains low across Southeast Asia’s emerging economies, averaging at only 0.1 credit cards per capita in Indonesia and Vietnam. Traditional credit risk assessment models employed by mainstream financial institutions have limited incumbents’ risk appetite for lending. Risk scoring models often rely on consumers’ income and credit history. As the large majority of consumers in the region do not meet the minimum income threshold or lack credit history, consumers are either not eligible for a credit card or are charged a very high price to compensate for the high default risk. Hence, traditional credit risk assessment models have limited consumers’ access to affordable credit. However, the emergence of artificial intelligence (AI) and data analytics is expected to democratise access to credit in Southeast Asia.
Redefining credit risk scoring through the inclusion of dynamic data points
The data points used in existing credit scoring models are often limited and static in nature, relying heavily on consumers’ income and credit history. AI is set to redefine credit risk assessment. The inclusion of dynamic data points like customer purchasing and payment behaviour will provide lenders with a more holistic view of consumers. Besides the identification of new customer profit pools amongst the underbanked, AI will also enable lenders to price risk more appropriately. The success of Ant Financial’s ‘buy now, pay later’ service, Huabei, is largely attributed to its in-house credit scoring system, Sesame Credit. The scoring system leverages on purchases made on Alibaba Group’s ecosystem, data from external agencies and social media interactions to assess an individual’s creditworthiness. This has enabled the unbanked and underbanked in China to gain access to credit that was previously limited to traditional financial institutions.
Consumer technology platforms are best positioned to offer ‘buy now, pay later’ services
Buy now, pay later’ or deferred payment services like Afterpay and Affirm have won over the hearts of many in the West. However, unlike in the West where pure-play fintechs are the key providers of these digital credit services, ‘buy now, pay later’ services will be best served by consumer technology platforms in Southeast Asia. Consumer technology players like super apps and e-commerce platforms have seen explosive growth in recent years, amassing a critical mass of customers from both the banked and underbanked. Whilst banks have found it difficult and expensive to reach these customer segments, technology platforms on the other hand, are well-positioned to provide digital credit services to their wide user base.
Consumer technology players have access to customer behavioural data like usage frequency and transaction history, enabling them to leverage on AI to assess the creditworthiness of customers. The seamless integration of ‘buy now, pay later’ services into the core offering will provide a strong use case for instant financing at the point-of-sale. Offering deferred interest-free payments or instalment purchase plans for e-commerce purchases, ride-hailing and food delivery services will value-add to consumers. Gojek’s digital credit service, PayLater, was first made available for Gojek’s services like ride-hailing and food delivery in Indonesia. The super app has since expanded its credit services outside of its ecosystem, enabling consumers to use PayLater at both online and offline merchant partners.
Lenders can offer more affordable consumer credit by embracing a different revenue model
Asians are known to have an aversion to debt due to high interest rates and fees on credit cards. To overcome this, disruptors can offer affordable credit solutions by adopting a different revenue model from traditional lenders. As digital credit services will enable consumers to make big-ticket purchases that they otherwise could not afford in one payment, lenders can charge merchants a higher margin on every successful purchase. Merchants stand to benefit from an increased order value and a lower likelihood of cart abandonment. Establishing a robust revenue stream from merchants will in turn enable lenders to offer more affordable consumer credit solutions.
‘Buy now, pay later’ solutions that operate under an AI-driven risk scoring model will give the underbanked access to credit. In other words, the average consumer may now be able to purchase a home appliance that he otherwise could not have afforded in a single payment. Whilst the initial adoption and usage of digital consumer credit is expected to first gain traction for online purchases, we may soon witness spillover effects into the offline environment. The democratisation of credit through AI will help to pave the way for financial inclusion in Southeast Asia.