Empowering Financial Inclusion In Bangladesh

The most promising and applicable one is probably AI-based credit scoring. In a word, credit scoring is an assessment of a customer's ability and willingness to pay back debt. There is a clear demand for more intelligent credit scoring systems given that there are 2.5 billion unbanked persons in the world and that only around half of those who are banked are considered creditworthy.

AI credit scoring decisions are made using a variety of data sources, including total income, credit history, transaction analysis, work experience, and even Google Analytics. In essence, scoring is a mathematical model that is based on statistical methods and accounts for a large amount of data. As a result, credit scoring with AI provides more sensitive, individualized credit score assessments based on a variety of additional real-time factors, allowing more people with income potential to access finance.

Most financial institutions still base their credit rating models on the scorecard approach, or the dynamics prevalent at the time of their creation. To be regarded as "scorable," a prospective borrower must have a significant amount of historical information on prior borrowing behavior. Even creditworthy consumers cannot obtain credit if such historical information is missing (which is normal for new clients of the banking industry). AI credit scoring is more sensitive to real-time indicators of the potential borrower's creditworthiness, such as the current level of income, employment opportunities, and their potential earning capacity than traditional credit scoring methods (such as the scorecard method), which focus on the borrower's past performance. As a result, credit programs only accept high-potential borrowers; individuals who technically pass the traditional credit scoring examination (such as credit card churners) are not eligible. In other words, using intelligent AI models, AI-based credit scoring enables accurate profit estimates.

Loans are always associated with risks, such as the recipient's failure to repay the loan. As a result, the goal of credit scoring is to reduce risk exposures, which has resulted in a systemic problem of limited access to credit finance. In many ways, AI solutions transform credit scoring, signaling a paradigm shift in this area. Here are some obvious benefits of incorporating AI into credit scoring.

AI applications, namely in credit scoring, are becoming more and more popular as a result of their capacity to expedite the process of making loan choices without sacrificing accuracy or quality. To create the client's credit score in the past, banks used decision trees, regression, and intricate mathematical studies. In order to make better credit-related judgments nowadays, massive amounts of redundant, unstructured, and partially structured data are incorporated into analysis (e.g., social media use, mobile phone activities, etc.). However, with the aid of AI, the speed of data processing remains high.

Credit scoring has become more future-oriented, as opposed to old-school past-oriented approaches, thanks to data science. As a result, more borrowers (e.g., students, founders of promising businesses, and foreign residents) now have access to credit, which stimulates their businesses and helps them jumpstart their ideas. Acquiring one's first credit has also become easier, as it is now done based on AI financial projections of the client's income potential and employment opportunities.

Traditional credit scoring methods have been repeatedly criticized as being out of date, owing to their standardization and lack of sensitivity to individual differences and nuances. With the incorporation of AI into credit scoring systems, banks gain unique insights into their customers' financial behavior based not only on historical data but also on potential income forecasting. Such large-scale customer data analyses enable better customer segmentation and credit risk rating, allowing financial institutions to price and sell credit products to the right types of customers.

The use of AI tools for credit scoring and lending decisions can help banks not only increase the number of customers while decreasing risk, but also make data-driven decisions, focus on margin maximization rather than risk minimization, and analyze smooth risk vs. profit curves rather than relying on pre-calculated scoring card brackets. This was virtually impossible prior to the widespread implementation of AI and data collection techniques. Adoption of AI in credit scoring benefits both banks and customers: banks gain more customers and profits, while customers in need of credit gain more access to better lending products.