Credit Risk Evaluation Using Decision Support System

Abstract

In order to determine the possibility that a borrower will not fulfil their financial commitments, financial institutions must evaluate credit risk. Making educated judgments about loan approvals and credit extensions is made easier with the help of the credit risk assessment. Decision Support Systems (DSS) are computer-based solutions that help businesses with difficult decision-making processes by supplying pertinent data and analytical models. DSS is essential for improving the precision and effectiveness of decision-making processes in the context of credit risk evaluation.

This study investigates the use of DSS in credit risk assessment and how it affects the decision-making process. In particular, it looks at data integration, risk assessment models, credit scoring, decision assistance, and portfolio management as important elements of a Credit Risk Evaluation DSS. A complete picture of the borrower's financial situation is possible thanks to the integration of several data sources, including financial statements and credit reports. In order to evaluate this data and find patterns or signs of credit risk, DSS uses sophisticated statistical and machine learning algorithms, which results in more precise risk assessments. Through the use of credit scoring, lenders may divide applicants into several risk groups and make well-informed judgments on loan approvals and interest rates.

The part of a credit risk evaluation that supports decision-making DSS assists in the assessment of credit applications by providing suggestions and conclusions to loan officers or credit committees. Decision-makers are able to comprehend the potential repercussions of extending credit to diverse applicants thanks to the system's presentation of numerous scenarios and related risks. DSS may also be used for continuing portfolio management and credit monitoring, following borrower financial performance, and spotting early indications of default.

Financial institutions may improve their overall risk management procedures, increase the accuracy of their decision-making, and streamline their credit evaluation processes by utilizing the capabilities of a Credit Risk Evaluation DSS. However, for implementation to be successful, difficulties with data quality, model validation, and regulatory compliance must be addressed. This study advances knowledge on the function of DSS in credit risk assessment and offers guidance to businesses looking to improve their credit assessment procedures.