Leveraging AutoML for advanced feature engineering in financial risk assessment

Beem, Varun Reddy (2025) Leveraging AutoML for advanced feature engineering in financial risk assessment. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 846-853. ISSN 2582-8266

AutoML has revolutionized credit risk assessment in financial technology by automating feature engineering and pattern recognition. The integration of machine learning automation has transformed traditional risk assessment methodologies, enabling financial institutions to process vast amounts of data efficiently while improving accuracy in default prediction. Through advanced pattern recognition and automated feature selection, institutions can now identify subtle risk indicators and counter-intuitive payment behaviors that were previously undetectable. The implementation has resulted in substantial improvements in operational efficiency, cost reduction, and risk management effectiveness, while maintaining regulatory compliance and data security standards. Furthermore, the adoption of AutoML has enabled financial institutions to leverage sophisticated algorithms for real-time risk assessment, enhanced decision-making capabilities, and predictive modeling, leading to improved customer experiences and more precise credit evaluations across diverse market segments.