Breaking Barriers: Micro-Mortgage Analytics

Providing affordable housing to a rapidly increasing low income group population in Urban-Semi Rural India remains one of the biggest challenges as well as opportunities faced by the Housing Finance Sector. Several new housing finance companies such as Shubham Housing Finance have pioneered a "small ticket" loan product to address the market gap. They recognize that these customers are not "high-risk" as perceived by conventional financiers, but "unknown risk". To assess this "unknown risk", they rely on detailed, field-based verification rather than on formal financial documentation. The primary objective of this case is to analyze the past data from these field level interactions and the eventual credit evaluation decision to determine the factors which result in a favorable decision. The application scoring model is expected to deliver a competitive edge to Shubham's operations by enabling faster decisions earlier in the assessment phase, targeting applicants more likely to pass through to the credit worthy status, standardize applicant evaluation across the nation and enable Shubham to offer competitive products. The objective of the case is to predict the probability of loan sanction using the socio-economic attributes of prospective loan applicants by employing techniques such as chi-squared automatic interaction detection (CHAID) and binomial logistic regression.

• Statistical analysis
• Microfinance
• Financial statements
• Loans
• Business failures