Using Hidden Markov Model to Monitor Possible Loan Defaults in Banks

Purpose: Banking business faces a major challenge with defaults. This may not be practical, as there is no control over the borrower’s financial situation or their intents to repay. However, if the banks get to know the possible defaults ahead of some actionable time frame, with a certain degree of accuracy in such prediction, Banks may apply any a possible risk mitigation strategy to remediate possible defaults. Willingness to repay the debt and the capability to repay the debt are two primary reasons for the loan default. The subject of this paper is to closely monitor the Facebook activities and check if we can predict if the borrower may become a defaulter any soon, by applying the sentiment analysis on Facebook data and use Hidden Markov model to compute the probabilities of the possible default. Approach/Methodology/Design: The loan dataset was used for the borrower details and the Facebook data for all those borrowers were gathered. The data from Facebook posts, likes and shares on a borrower were subjected to sentiment analysis, considering income-related information of spend related information on neutral. Hidden Markov model was applied to the polarized data based on the sequence of the sentiment analysis. Findings: Hidden Markov Model gives the transition probability of state, default or regular, for the observed polarized sentiments from Facebook data for borrowers. Practical Implications: This mechanism can be integrated into the bank's credit risk management system and could help predict the possibility of a borrower becoming a defaulter. This is very much useful where the tenure of the loan is longer. This research paper fills the gap of active monitoring of the credit risk for long term loans, where the financial status of the borrower could change but the lender doesn’t get to know until the borrower stops the repayment.