Widespread adoption of automated decision making by artificial intelligence (AI) is witnessed due to specular advances in computation power and improvements in optimization algorithms especially in machine learning (ML). Complex ML models provide good prediction accuracy; however, the opacity of ML models does not provide sufficient assurance for their adoption in the automation of lending decisions. This paper presents an explainable AI decision-support-system to automate the loan underwriting process by belief-rule-base (BRB). This system can accommodate human knowledge and can also learn from historical data by supervised learning. The hierarchical structure of BRB can accommodates factual and heuristic rules. The system can explain the chain of events leading to a decision for a loan application by the importance of an activated rule and the contribution of antecedent attributes in the rule. A business case study on automation of mortgage underwriting is demonstrated to show that the BRB system can provide a good trade-off between accuracy and explainability. The textual explanation produced by the activation of rules could be used as a reason for denial of a loan. The decision-making process for an application can be comprehended by the significance of rules in providing the decision and contribution of its antecedent attributes. Manual Underwriting Challenges: Manual underwriting task is a very much paper-based process. It is an inconvenient process of circulation of loan application files within different departments of a lending institution. Full attention to details is requisite to give sound judgment on an application. Human underwriter evaluates scenarios by analysing a large amount of dynamic information in a loan application. This could be a source of inconsistency, inaccuracy, and biases (Peterson, 2017). Manual underwriting is often successful in processing non-standard loans. Many lenders like high street banks and building societies follow strict rules and do not offer personalized underwriting. However, there are some lenders who exercise common sense lending approaches for assessing both standard and nonstandard cases such as non-standard properties, non-standard income/employment, and less than perfect credit scores. The underwriting process of non-standard cases is very detailed and individualistic. Common senses lending approaches serve underserved people Advantages and Concerns of Automated Underwriting Systems: In 1995 Fannie Mae, a US largest mortgage lending company introduced first Desktop Underwriter that applied both heuristics and statistics to process mortgage loan in less time, cost, and paperwork (Cocheo, 1995). In the same year, another US lending company, Freddie Mac introduced an automated underwriting system called loan prospector (Cocheo, 1995). Most lenders use an automated system which contains coded underwriter guidelines which provide the decision of acceptance or rejection when certain default rules in the rule base are triggered. Early statistical methods were limited t...

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Data mining requires a pre-processing task where data are prepared, cleaned, integrated, transformed, reduced and discretized to ensure data quality. Incomplete data are commonly encountered during data cleaning, which can have major impact on the conclusions that will be drawn from the data. In order to effectively carry out inferential modelling or decision making from incomplete independent variables or explanatory variables and consider different types of uncertainties, this paper adopts a data-driven inferential modelling approach, Maximum Likelihood Evidential Reasoning (MAKER) framework, which takes advantage of incomplete datasets without any imputation that may be required by other conventional machine learning methods. The MAKER framework reflects the plausibility of different values of missing data and expresses data-driven support for different values of missing data.