Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes

Operational Processes are defined as the core business of companies and firms: drug companies consider them to be drug testing and approval, manufacturing firms identify them in the product assembly process, while banks and financial firms have their own core business in risk management and evaluation. In order to be able to concede loans, financial institutions are compelled to predict whether an applicant is likely to repay the debit. In such a framework, Credit Scoring plays a huge role in ranking applicants based on their likelihood to pay back the loan. Each person is associated with a credit score value, namely a "number that summarizes its credit risk, based on a snapshot of its credit report at a particular point in time" [1]. Behind the scenes, CRM is employed to reach the goal: scoring models, or "scorecards", are generated from historical data, employing well-established statistical techniques. The cornerstones of a reliable scorecard are well depicted by Loretta Mester in [2]: "the model should give a higher percentage of high scores to borrowers whose loans will perform well and a higher percentage of low scores to borrowers whose loans won't perform well". Several advantages stem from risk modelling, among the most important there are an increased profitability of financial corporations due to more reliable loans conceded, the chance of evaluating new loan programs based on the data collected and the enhancement of the credit-loss management capability [1]. Therefore, over the years, some institutions arose to accomplish the task. CRIF is a global company specialized in credit bureau and business information, outsourcing and processing services, and credit solutions.