It is no secret that the Fintech space is undergoing massive changes, especially in India. Traditional banking models have left a significant amount of people under served. Couple this with a complex web of regulations, and a slow uptake of technology, and this area is prime for disruption from ambitious Non-Banking Financial Companies (NBFCs).
AbhiPaisa is an innovative lending platform that aims to address the credit gap for the young salaried individuals. Based on extensive internal research, AbhiPaisa had concluded that most of these individuals are looking for bridge loans that get them through to the next salary. This could be for a payment of an EMI, or money for a flight ticket home or gifts during the holiday season.
These are individuals that have been left out of the current credit systems primarily due to a lack of credit history. The current models rely on credit history, transaction analysis and current worth to decide the eligibility for a loan. While these are no doubt significant parameters, they fail to paint an accurate picture given the small loan amounts and the short repayment periods. Typically, young individuals availing such loans are rejected by a lack of credit history, poor credit ratings and a lengthy loan approval/ disbursement process.
So, the problem was clearly how do you get to service these young individuals without much of a credit history and provide them access to quick, easy bridge loans? Instead of trying to solve that problem, AbhiPaisa decided to instead redefine the problem to something far more ambitious. AbhiPaisa decided that they would offer bridge loans to salaried employees and the loan amount would be disbursed to the customer’s bank account in 5 minutes. As the technology partner, it was left to us to devise a risk-free model to enable this to happen.
Based on statements of transactions, it became a fairly simple to problem to arrive at the ability of the customer to pay. The more pressing problem was to discover the intent to pay. Without a history of repayments, how were we to predict whether the customer intends to pay?
The answer came through the recent developments in Artificial Intelligence and Machine Learning. Existing literature had successfully shown that social media information was an useful data pointer for predicting intent to pay.
Put simply Artificial Intelligence (AI) is a computer system that mimics and/or replicates human intelligence. Machine Learning allows computers to learn on their own. Machine learning analyzes data and crunches numbers, learns from it, and uses that to make a prediction/truth/determination depending on the scenario. The machine is essentially being trained, or really training itself, on how to perform a task correctly after learning from all the data it has analyzed.
We started out with a base model with relevant parameters that were of interest. To obtain data to train our models, AbhiPaisa went ahead with the bold decision of giving out loans based on existing credit models. In fact, we even went ahead with issuing a few loans that against the recommendation of existing credit models. The results (defaults, successful payments) of all these loans were used to train the AI model. Once the model started predicting the results with reasonable accuracy, a second set of loans were disbursed. As expected, and much to the relief of the AbhiPaisa, the machine started outperforming the current credit models/ SMEs in the decision of issuing a loan.
It is fascinating that technology has evolved to a stage where it is able to allow us to get a look into the psyche of a person, something which SMEs are unable to do. We have been greatly encouraged by the results and are working on exciting problems that can be solved by AI & ML.