Mortgage Loan Default Prediction System

#Mortgage Loan Default Prediction System | 来源: 网络整理| 查看: 265

Infosys’ Mortgage Default Prediction System uses a combination of available industry data, artificial intelligence and machine learning algorithms to provide the mortgage industry with a fairly accurate view of potential defaulters.

This is how it works. The lending bank has the customer’s loan portfolio. This includes information about any loan payment defaults already recorded in the system. In addition, our solution uses public APIs to collate historic information from mortgage investors such as Fannie Mae and Freddy Mac. Lastly, macro-economic influencers such as unemployment statistics in the area where a borrower lives are gathered from government sources.

This intelligence is passed through the machine learning model to generate a default score for each borrower. On a scale of 1 to 100, the higher the score, the greater the chances of the borrower defaulting on the loan payment.

Clients can use this score to negotiate remediations with the borrowers. This data can in turn be used to automate remediations over time.

Infosys’ Mortgage Default Prediction System: A future-proof, automated solution for mortgage default prediction

Up until now, there has been no single source of truth that could aggregate borrower and industry data to accurately predict defaults.

Our solution combines modern, scalable technology to provide a future-proof solution that uses:

Deep neural networks with multiple layers to train the machine learning model Explainable AI to highlight potential reasons for default for better risk analysis Public APIs from the US government’s Bureau of Labor Statistics to gather unemployment data at job sector as well as the metropolitan statistical area levels Correlation analysis to analyze the huge amount of data from mortgage investors Fannie Mae and Freddie Mac

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