The IIT researchers will develop algorithms which, when combined with observations by subject matter experts, will recommend financial products.
The applied research and decision engine is expected to lead to products and plans that would have a lower delinquency rate compared with standard loan plans.
CHENNAI: IIT Madras is collaborating with the Dvara Group, an institution that works in the financial inclusion space, to devise AI solutions that will target customised financial products to the group’s 800,000 customers in remote locations.
An agreement in this regard was signed between the Robert Bosch Centre for Data Science and Artificial Intelligence at IIT Madras and Dvara KGFS, a group entity, on Tuesday. The IIT researchers will develop algorithms which, when combined with observations by subject matter experts, will recommend financial products.
As a first step, the researchers will identify groups of people in Dvara’s database. These groups maybe a consequence of similar demographic details, household financial goals or transaction patterns. Then, through ‘active learning’ approach, an algorithm will iteratively query an expert committee formed by Dvara to seek recommendations on the products that can be offered to select customers.
Dvara offers financial products like enterprise working capital loans, livestock loans, joint liability group loans, money market mutual funds, term life insurance policies, and gold investment, among others, to low-income families.
Nandan Sudarsanam, the lead researcher on the project, said, “We’re using ideas from active learning to create better segments or classes of customers. The goal is essentially not just to group customers but also match them based on which product portfolios are most suited for them.” Sudarsanam is also assistant professor at the department of management studies, IIT Madras.
The algorithm-based framework will lead to a decision engine in the next phase, said Samir Shah, group president of Dvara Trust. The applied research and decision engine is expected to lead to products and plans that would have a lower delinquency rate compared with standard loan plans.
IIT Madras and Dvara are assigning a higher focus to financial health or wellness as a metric instead of the ‘likelihood of repayment’, which they suggested is an important factor typically in the retail banking loan assignment process.
According to Sudarsanam, delinquencies, among a gamut of reasons, can be a result of genuine financial stress in some cases and oversight in others. However, under the metric of ‘likelihood of payment’, the end result is the same. Hence the research methodology does not view repayment as a perfect proxy of financial health, he said. “Especially in a microfinance concept it is important to model and understand financial health than just the likelihood of repayment,” Sudarsanam said.
Shah said the outcome of the research would be directly applicable to business. “It would allow for segmentation work, credit-scoring and behavioural analysis, and to develop a decision engine that would make for customised and highly suitable solutions for low income households.