How are major Asian lenders leveraging artificial intelligence?
In Asia-Pacific (APAC), artificial intelligence (AI) and machine learning (ML) are increasingly being deployed in credit and risk functions to improve credit assessment, credit scoring and fraud detection.
Going forward, AI will no longer be an option for banks and financial institutions, but rather a necessity, enabling them to meet rising customer expectations, tap into new business opportunities, and cope with the The fraud landscape is rapidly changing, data scientists and senior finance officials said in a statement. recent webinar.
During Fintech Fireside Asia last round tableC-level executives representing Union Bank of the Philippines, credit bureau TransUnion, lending startups Funding Societies and data solutions provider Mobilewalla discussed the state of AI adoption in the financial ecosystem of APAC, exploring how predictive modeling and ML are now being used in the lending process.
Improve financial inclusion
For Anindya Datta, Founder, CEO, Chairman of Mobilewalla, AI offers an opportunity to deliver innovative business models that can go beyond traditional solutions and reach the unbanked, a potential particularly relevant in Southeast Asia. given that over 70% of the region’s adult population remains either unbanked or underbanked today.
“A big part of loan decision making is determining how likely someone is to repay and whether they will repay on time. Why this is so attractive in emerging markets, especially in APAC, is because the credit footprint is low [and a lot of people don’t] have credit ratings,” Anindya said.
“In terms of technology adoption, ML is probably one of the biggest framework technologies adopted in fintech that does this to assess creditworthiness.”
By leveraging non-traditional data such as consumer mobility and average household phone price, AI can reduce information asymmetry for those without a credit history, expanding the availability of credit to people whose creditworthiness cannot be measured using traditional measures, Anindya said.
For Dr. David R. Hardoon, Chief Data and AI Officer, Union Bank of the Philippines, it’s about “making lending more relevant,” supporting personalization at scale, and realizing that banks are processing with vastly different underlying cohorts and individuals with varying degrees of circumstances and necessities.
AI is about “the principle of hyper-personalization, and really [focus on] understand the specific customer from a behavioral perspective, not from a repayment ability but also from an ability in terms of ability and need,” David said. “Understanding the use and timing of lives associated with it… [and moving away from] a lending perspective to more of a credit management perspective.
Possibilities at all levels
Ishan Agrawal, Group CTO, Finance Companies, Modalku, said that beyond credit scoring, AI and ML can also be applied to every stage of the loan lifecycle, starting with acquisition and request, up to electronic knowledge of your customer (eKYC), detection, subscription and collection of fraud.
“AI really has applications across the loan lifecycle,” Ishan said.
“In today’s world, if you’re in the fintech space or lending in any form, whether it’s buy, pay now, pay later (BNPL), from credit cards, to mortgages, to loans to small and medium enterprises (SMEs), AI is going to be super essential, not just in credit scoring, but in all areas. [and] I’m really excited about the next five years of what AI will enable.
In the area of customer acquisition, advanced analytics enable banks to deliver highly personalized offers and superior experiences, resulting in higher conversion rates. This is made possible by gaining a deeper understanding of each new customer’s journey to the bank and gaining an accurate view of the context and direction of a customer’s movement.
In credit decision, AI and ML can be used to analyze large and diverse data sets in near real time, enabling banks to qualify new customers for credit services, quickly determine loan limits and pricing, as well as reduce the risk of fraud by detecting anomalies early.
“On a large scale, finding fraud is a very nuanced problem,” Ishan said. “For instance, [you need to look at] Behavioral analytics, collect data on how a person completes an application, their typing speed, their typing pattern, how long it takes them to complete an application. There is no way to detect fraud other than with AI on these kinds of issues.
Credit Bureaus Embrace AI and Open Banking
Along with incumbent fintechs and banks, credit bureaus themselves have begun to actively mine alternative data and use ML to produce better insights and expand their coverage.
In the Philippines, TransUnion Philippines spear last year CreditVision Link, a solution capable of assessing the credit of Filipino adults using traditional credit data and alternative data, including telecommunication data such as recharges, payments, data usage mobile and device data.
“In Hong Kong, we have 85% coverage, … but in the Philippines, we only catch about 25% [of the population]said Jerry Ying, chief product officer, Asia-Pacific, TransUnion. “[With CreditVision Link,] instead of covering about 25 million [Filipino] population, we can now envisage around 70 to 80 million [people]. It’s a major improvement.
“This is where the need for AI is very important: to compensate for customer coverage where some of the newer [customers] to the credit segment are not able to obtain loans.
Another key technology that TransUnion is actively exploring is open APIs. In the UK, the credit bureau has launched an open banking service that allows consumers to share their data with third parties when applying for credit. This aims to improve the consumer experience by providing faster loan decisions and greater accuracy in risk assessment.
“We are starting to look [introducing] than in Hong Kong,” Jerry said. “We’re looking at how we can access data, … use AI to interpret survey transactions and interpret things like an individual’s cash flow and income level, [and pulling that data directly from third parties sources.]”