Adaptive and Explainable Machine Learning Framework for Real-Time Credit Scoring and Financial Fraud Detection with Privacy-Preserving Intelligence

Indrani Hazarika, K. Raghuveer, Jayanth H., J. Tamilarasu, C. Kathiravan, G. V. Rambabu

2025

Abstract

In a dynamic financial technology world, imposing requirement of stable, real time and interpretable machine learning methods in credit scoring and fraud detection is more essential than ever. This paper presents an adaptive and explainable machine learning framework, which goes beyond existing models by including real-time risk analysis, privacy-preserving intelligence, and enhanced processing of imbalanced data. In contrast to state-of-the-art systems, the model integrates attribution methods like SHAP and LIME to provide interpretable predictions, better towards regulatory compliance and user trust. The model is enriched with federated learning to ensure data privacy among different financial institutions and integrates online learning capability for adapting to evolving fraud patterns and credit behaviors. We present experimental results on modern datasets, enjoying accuracy, interpretability, and scalability in a wide range of financial situations. This paper adds an end-to-end, practical end-to-end for secure, accurate, and accountable identification of financial risk.

Paper Citation

in Harvard Style

Hazarika I., Raghuveer K., H. J., Tamilarasu J., Kathiravan C. and Rambabu G. (2025). Adaptive and Explainable Machine Learning Framework for Real-Time Credit Scoring and Financial Fraud Detection with Privacy-Preserving Intelligence. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 131-136. DOI: 10.5220/0013858800004919

in Bibtex Style

@conference{icrdicct`2525,
author={Indrani Hazarika and K. Raghuveer and Jayanth H. and J. Tamilarasu and C. Kathiravan and G. Rambabu},
title={Adaptive and Explainable Machine Learning Framework for Real-Time Credit Scoring and Financial Fraud Detection with Privacy-Preserving Intelligence},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={131-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013858800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Adaptive and Explainable Machine Learning Framework for Real-Time Credit Scoring and Financial Fraud Detection with Privacy-Preserving Intelligence
SN - 978-989-758-777-1
AU - Hazarika I.
AU - Raghuveer K.
AU - H. J.
AU - Tamilarasu J.
AU - Kathiravan C.
AU - Rambabu G.
PY - 2025
SP - 131
EP - 136
DO - 10.5220/0013858800004919
PB - SciTePress