Adaptive Fraud Detection in Online Transactions Using Machine Learning Techniques

Abstract

Financial institutions suffer substantial losses dueto fraudulent online transactions. Traditional fraud detection methods often fail to identify evolving fraud patterns due to dataset imbalance and high false negative rates. This paper proposes an adaptive fraud detection system integrating a value- at-risk (VaR) metric with machine learning techniques. The system uses historical simulation to estimate potential fraud- relatedlossesandemploysK-NearestNeighbors(KNN)toclassify fraudulent transactions. The proposed approach enhances fraud detectionaccuracywhileminimizingfalsenegatives,providingan effective fraud prevention framework for financial institutions.

Introduction

Overview:
Financial fraud has grown more complex with the rise of digital banking and e-commerce, exposing vulnerabilities in traditional rule-based detection systems. These outdated systems often fail to detect new fraud tactics and generate high false positives, impacting both security and customer experience.

Proposed Solution:

To address these issues, a hybrid fraud detection model is introduced, combining:

• Value-at-Risk (VaR): Assesses the financial impact of each transaction and prioritizes high-risk ones.

• K-Nearest Neighbors (KNN): Classifies transactions based on similarity to past fraud cases for better adaptability.

This model improves fraud detection accuracy, reduces false negatives, and supports real-time, intelligent decision-making.

Related Work:

• Traditional Models: Logistic regression and decision trees are interpretable but inflexible to evolving fraud patterns.

• Advanced Methods: Ensemble models (Random Forest, Gradient Boosting) and deep learning (ANNs, CNNs) offer better accuracy but face high computational costs and lower interpretability.

• Data Imbalance Problem: Fraud cases are rare, skewing model learning. Methods like SMOTE and cost-sensitive learning help but are not always sufficient.

The proposed model balances accuracy, transparency, and real-time performance—critical for financial institutions.

Methodology:

A. Data Preprocessing:

• Challenge: Highly imbalanced dataset (few frauds vs. many genuine).

• Solution: Use SMOTE and undersampling to balance the data.

• Feature Engineering: Includes transaction amount, location, timestamp, payment method, and user behavior.

• Techniques Used: Min-Max scaling, PCA to reduce noise and improve model efficiency.

B. Fraud Detection Model:

1. Feature Extraction: Behavioral patterns like irregular spending and location shifts.

2. VaR Metric: Quantifies potential fraud losses and prioritizes transactions.

3. KNN Classification: Identifies fraud based on nearest neighbors in feature space.

C. System Implementation:

• Built with Python, Django, and SQL database.

• Real-time fraud monitoring and alert system.

• Five-step pipeline:

  1. Data Collection

  2. Preprocessing

  3. Fraud Classification

  4. Risk Assessment (VaR)

  5. Alert Generation

Results and Discussion:

• True Positive Rate: 0.95

• Fraud Detection Rate: 0.9406

• Performance Metrics: High precision, recall, and F1-score.

Key Outcomes:

• VaR effectively prioritizes risky transactions.

• KNN provides adaptability to new fraud behaviors without constant model updates.

• Outperforms traditional models in accuracy and real-time applicability.

• Demonstrated consistent success across various datasets.

Conclusion

This paper presents an adaptive fraud detection system that integratesValue-at-Risk(VaR)riskassessmentwithK-Nearest Neighbors (KNN) classification to enhance fraud detection accuracy.Theproposedapproacheffectivelyidentifiesfraudu- lent transactions while minimizing false negatives, ensuring a balancebetweensecurityanduserconvenience.VaRquantifies financial risk, prioritizing high-risk transactions for further analysis, while KNN adapts to evolving fraud patterns by learning from historical transaction data.Futureworkwillexploretheintegrationofdeeplearn- ing techniques such as Long Short-Term Memory (LSTM) networks and Transformer models to improve fraud detection accuracy, especially for sequential transaction patterns. Additionally, real-time optimization using parallel computing and edge AI can enhance scalability and reduce processing delays. Another key focus will be on improving explainability and interpretability, ensuring that fraud detection decisionsare transparent and aligned with financial regulations. These advancementswillcontributetoamorerobust,real-timefraud prevention system with higher accuracy and adaptability.

References

This paper presents an adaptive fraud detection system that integratesValue-at-Risk(VaR)riskassessmentwithK-Nearest Neighbors (KNN) classification to enhance fraud detection accuracy.Theproposedapproacheffectivelyidentifiesfraudu- lent transactions while minimizing false negatives, ensuring a balancebetweensecurityanduserconvenience.VaRquantifies financial risk, prioritizing high-risk transactions for further analysis, while KNN adapts to evolving fraud patterns by learning from historical transaction data.Futureworkwillexploretheintegrationofdeeplearn- ing techniques such as Long Short-Term Memory (LSTM) networks and Transformer models to improve fraud detection accuracy, especially for sequential transaction patterns. Additionally, real-time optimization using parallel computing and edge AI can enhance scalability and reduce processing delays. Another key focus will be on improving explainability and interpretability, ensuring that fraud detection decisionsare transparent and aligned with financial regulations. These advancementswillcontributetoamorerobust,real-timefraud prevention system with higher accuracy and adaptability.

Copyright

Copyright © 2025 Ammati Anil Kumar, Ellanti Jaideep Chand, Velidi Sriram Chowdary, Vemireddy Mani Venkata Chaitan, R Kanimozhi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.