Improving Credit Risk Management in SAP Systems with Machine Learning Approaches

Authors: Surya Sai Ram Parimi

DOI: https://doi.org/https://doi.org/10.5281/zenodo.12805551

Short DOI: https://doi.org/gt442s

Country: India

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Abstract: Effective credit risk management is essential for financial institutions to maintain stability and profitability in dynamic market environments. In recent years, the integration of machine learning (ML) techniques within SAP systems has emerged as a transformative approach to enhance the accuracy and efficiency of credit risk assessment processes. This survey paper explores various ML models and algorithms tailored for credit risk management within SAP environments, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, and deep learning neural networks. We discuss their applications, benefits, and challenges, highlighting key considerations such as data integration, model interpretability, scalability, regulatory compliance, bias mitigation, and operational integration. Through a comprehensive review of current literature and case studies, we examine how these ML approaches leverage the rich data stored in SAP systems to improve predictive accuracy, streamline decision-making, and mitigate risks effectively. The paper concludes with insights into future trends, including the role of explainable AI (XAI) and federated learning, in shaping the future of credit risk management within SAP systems. By navigating these challenges and embracing best practices in data management and governance, financial institutions can leverage ML-driven solutions to optimize credit risk assessment processes and enhance overall business performance.

Keywords: Credit Risk Management, SAP Financial Operations, Machine Learning

Paper Id: 230797

Published On: 2024-02-10

Published In: Volume 12, Issue 1, January-February 2024

Cite This: Improving Credit Risk Management in SAP Systems with Machine Learning Approaches - Surya Sai Ram Parimi - IJIRMPS Volume 12, Issue 1, January-February 2024. DOI https://doi.org/10.5281/zenodo.12805551

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