Intelligent Automation of Common Sense Mortgage Lending

Some lenders have adopted ‘common sense lending’ approach which involves case by case assessment of individual mortgage loan applications instead of giving weight to some basic facts and credit scores, such as in high street banks and building societies. Individualistic assessment requires underwriters to follow decline and referral rules, affordability factors, repayment and default history, and other loan details. With time a substantial of data is getting accumulated, policies have become more complex and the consequence of noncompliance is getting more severe. A manual decision making by underwriting in such situation causes stress and unrecognized biases or errors. This research proposes a transparent intelligent automation system by an expert rule-based system. It makes joint utilization of expert knowledge and heterogeneous source of credit risk data available from external agencies and internal data of
lending institutions. Both expert knowledge and credit data are independent and complementary and are used to train the parameters belief-rule-base system and machine learning model. The proposed methodology can determine the nonlinear relationships between
default features and can explicitly represent the underwriter’s domain-specific knowledge as well as the judgment from historical data. The decision of rejecting or funding an application is fine-tuned by aggregating the output from the automated system with machine learning model