Abstract-In Dempster-Shafer evidence theory (DST) based classifier design, Dempster's combination (DC) rule is commonly used as a multi-attribute classifier to combine evidence collected from different attributes. The main aim of this paper is to present a classification method using a novel combination rule i.e., the evidence reasoning (ER) rule. As an improvement of the DC rule, the newly proposed ER rule defines the reliability and weight of evidence. The former indicates the ability of attribute or its evidence to provide correct assessment for classification problem, and the latter reflects the relative important of evidence in comparison with other evidence when they need to be combined. The ER rule-based classification procedure is expatiated from evidence acquisition and estimation of evidence reliability and weight to combination of evidence. It is a purely data-driven approach without making any assumptions about the relationships between attributes and class memberships, and the specific statistic distributions of attribute data. Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show high classification accuracy that is competitive with other classical and mainstream classifiers.

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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...

Considering that the uncertain linguistic variable (or interval linguistic term) has some limitations in calculation, we extend it to the continuous interval-valued linguistic term set (CIVLTS), which is equivalent to the virtual term set but has its own semantics. It has the advantages of both the uncertain linguistic variable and the virtual term set but overcomes their defenses. It not only can interpret more complex assessments by continuous terms, but also is effective in aggregating the group opinions. We propose some methods to aggregate the individual decision matrices represented by CIVLTSs to the collective matrix. The extended Gaussian-distribution-based weighting method is proposed to derive the weights for aggregating the large group opinions. Furthermore, the general ranking method ORESTE, is extended to the CIVL environment and is named as the CIVL-ORESTE method. The proposed method is excellent by no requirements of crisp criterion weights and the objective thresholds. A case study of selecting the optimal innovative sharing bike design for the "Mobike" sharing bikes is operated to show the practicability of the CIVL-ORESTE method. Finally, we compare the CIVL-ORESTE method with other ranking methods to illustrate the reliability of our method and its advantages.

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