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Microfinance Credit Prediction Using Machine Learning Techniques

In the domain of microfinance, it is difficult to predict the creditworthiness of an individual without previous credit data. In the past, many financial institutions have relied on the famous Five C’s credit scoring method, namely, Character, Capacity, Capital, Collateral, and Conditions. Character refers to individual reputation, past track record in paying for debts, and credit score (I-Score). Capacity is the borrower’s income against debts which helps calculate an individual’s debt-to-income (DTI) ratio. The DTI ratio helps in assessing a borrower’s financial capability to repay a loan after covering current expenses. Capital is the amount the borrower pays against a potential investment, such as a down payment for the purchase of an asset, which helps ensure the timely repayment of a loan at a given interest rate. Collaterals are assets a borrower provides against a loan -- land, property, a bank savings deposit, etc. Finally, conditions, the internal and external financial conditions including the interest rate, market rates, etc. These conditions may or may not be under the borrower’s control.

Machine Learning Model Plug and Play

BrainWise uses an AI-powered social behavior-based credit-rating system to assess the goodness quotient of the borrower and their ability to repay.

Young working professionals who are either near-prime or subprime borrowers with or without a prior credit history. It leverages a combination of Big Data Analytics and proprietary algorithms to analyze non-traditional data derived from multiple online and offline data points, like smartphone metadata, social media footprint, education, remuneration, career, and financial history, and calculate the borrower’s creditworthiness. The platform measures a borrower’s propensity to default based on their current behavioral information instead of traditional credit scoring systems that deliver a score based on historical financial behavior. The scores are generated in real-time, enabling customers to know, within a few seconds, if they qualify for a loan with BrainWise AI. A higher score represents a lower propensity to default.

Financials leverages AI and ML algorithms to provide small-ticket loans to ensure credit access to the underbanked and underserved sections. In addition, they use AI for digitizing and streamlining various operations such as delinquency prediction, and fraud detection, which includes both KYC – information extraction and face match, income prediction, SMS parsing and categorization, collection scorecard, assisted customer support, and cost optimizations.

Benefits of Implementing AI From a Customer Point of View

It is also useful from a customer point of view offering benefits such as:

  • Quick KYC – Instead of spending hours on physical and time-consuming KYC, now micro-lending platform users can do quick e-KYC in the comfort of their homes. By using automatic ID verification, it becomes easier to process the loan application.

  • Non-traditional credit assessment – Credit history and credit score were the most basic factors to qualify for a loan earlier. But now with the help of advancement in the microfinance sector through AI, it is also possible for the unbanked to take a loan. Instead of traditional credit checks, your social behavior, utility payments, and other non-traditional parameters will be checked.

  • Quick credit and repayment – Unlike traditional loan approval, AI can process your loan quickly. Instead of waiting for several days to get the amount credited into your account, your application will be approved instantly. After the loan gets credited into your account, you’ll be instantly notified. Another advantage of using AI-based microfinance platforms is that you will be automatically notified whenever your due date

Benefits of Implementing AI From a MicroFinance Enterprise Point of View

Traditional risk assessment models used to evaluate credit risk for microfinance borrowers are based on historical data and do not take into account changes in borrower behavior or the effects of financial shocks. In recent years, there has been increasing interest in using machine learning techniques to help evaluate credit risk for microfinance borrowers.

This approach has been successful in predicting defaults for other types of loans, and it is likely that it will be effective in predicting defaults for microfinance borrowers as well. However, there are some limitations to this approach. First, the neural network must be trained thoroughly, which can take a long time. Second, the neural network may not be able to recognize changes in behavior that take place quickly.

Overall, machine learning technology is being used to improve the accuracy of microcredit risk assessments and to improve the delivery of microcredit support services. This technology has the potential to transform the way microfinance is delivered and help low-income individuals and families access affordable credit products that can help them improve their lives.

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