AI-Driven Big Data Analytics for Financial Decision-Making: Forecasting, Risk Assessment, and Fraud Detection

DOI

10.2991/978-94-6239-772-9_32How to use a DOI?

Keywords

Artificial Intelligence; Big Data Analytics; Financial Decision-Making; Stock Market Forecasting; Credit Risk Assessment; Fraud Detection; Explainable AI; FinTech; Decision Intelligence

Abstract

Financial service sectors generate significant volumes of structured and unstructured data from stock markets, credit schemes, and digital transactions, creating both opportunities and challenges for optimal decision-making. This paper presents a Big Data analytics framework that leverages artificial intelligence (AI) to unify three essential financial analytics tasks: stock market forecasting, credit risk prediction, and behavioral fraud recognition. For stock market forecasting, a hybrid CNN-LSTM model identifies both local patterns and long-range temporal dependencies. Credit risk assessment employs Random Forest classifiers augmented with SHapley Additive exPlanations (SHAP) to produce transparent, regulation-compliant risk scores. Fraud recognition uses an autoencoder-based architecture for real-time anomaly detection. A Unified Decision Intelligence Score synthesizes outputs across all mod-ules. Experimental evaluation demonstrates strong forecasting accuracy (R² = 0.983), reliable credit risk discrimination (ROC-AUC = 0.95), and high-precision fraud detection (Precision = 97.1%).

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mathyam Supriya
PY  - 2026
DA  - 2026/09/07
TI  - AI-Driven Big Data Analytics for Financial Decision-Making: Forecasting, Risk Assessment, and Fraud Detection
BT  - Proceedings of the 2nd International Conference on Innovations and Challenges in Financial Technology (ICICFT 2025)
PB  - Atlantis Press
SP  - 439
EP  - 451
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-772-9_32
DO  - 10.2991/978-94-6239-772-9_32
ID  - Supriya2026
ER  -