Published Research
How AI Helps Prevent Financial Fraud in Indian Banking
A practical overview of machine learning, anomaly detection and deep learning for fraud prevention, based on our 2026 published research.
Why traditional rules are not enough
Rule-based systems remain useful, but fraud patterns evolve faster than static thresholds. A transaction may look normal by amount yet become suspicious when timing, device, location and behavioural history are considered together.
AI methods help analyse these relationships at scale and identify patterns that a single rule may miss.
Which models are used
Logistic Regression provides an interpretable baseline. Random Forest and XGBoost capture non-linear relationships. LSTM networks can analyse sequences over time, while CNN-style approaches can learn complex feature relationships.
The best model is not selected by accuracy alone. Fraud datasets are imbalanced, so precision, recall, F1 score and false-positive impact matter.
What our research examined
Our paper, “Artificial Intelligence in Financial Fraud Prevention: Enhancing Security in the Indian Banking Sector,” was published in IJFMR, Volume 8, Issue 4, in 2026.
The study compares traditional and AI-driven approaches and considers both technical performance and the perspectives of banking customers and staff.
The responsible path forward
A bank cannot treat an algorithm as an unquestionable judge. Models need monitoring, explainability, privacy safeguards and escalation to trained people.
The winning system is therefore not AI instead of humans. It is AI helping people detect risk earlier and investigate it with better evidence.
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