A Credit Risk Assessment System for Financial Institutions Utilizing Deep Learning

According to the banking supervision and regulation of the worldwide, credit risk approximation of a bank/financial institute is not only confined within a product line or value chain anymore. Today’s banking regulation is putting pressure on the banks to deliver the risk measure values by regarding the risk considerations more broadly than just product and trading desk level together with the changes in the market environment and customers’ commitments. Hence, it is deprived of fixed parameters and wholly relies on different parameters and huge data documents. Given very fast progress in the field of data availability, generation, storage, and data processing, machine learning now occupies a strategic position in all areas of the banking business and technologies. The usage of machine learning is extremely appreciable in the construction of credit risk modelling and loss estimations. Risk practitioners are inclining their focus toward applying the advantage of deep learning for the nonlinear relationships of substantive variables and default risk.