Credit Assessment Analytics in Banking and Data Science Applications in the Digital Transformation Process

Objective of Training

The most important concepts in the credit assessment process in banking are knowledge accumulation and experience gained from past examples. However, producing quality outputs by processing data analytically using scientific methods and using these outputs to support decision-making will increase the quality and efficiency of the process. On the other hand, the implementation of IFRS 9 in the Turkish Banking Sector has necessitated that banks manage the analysis, allocation, marketing, monitoring, tracking, and reporting of Loans, which are the most important component of the active composition of bank balance sheets, in serious interaction with digital transformation. In this regard, banks are prioritising practical data science applications by developing credit decision support systems based on their own initiatives, as well as integrating modelling algorithms required by legislation into their systems. This programme aims to provide banks with a reference point for developing both mandatory and initiative-based systems and algorithms in the digital transformation process, featuring assessments and applications that blend expert experience and data science.