AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review

AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Review

Introduction

Financial networks constitute complex systems that enable global monetary transactions and investments, including banks, payment processors, credit unions, and cryptocurrency platforms. These entities are vulnerable to multiple forms of fraud such as identity theft, transaction fraud, payment fraud, and securities trading fraud. The rapid digitization and scale of financial ecosystems have increased the urgency for sophisticated fraud detection mechanisms. Artificial intelligence (AI), particularly machine learning (ML), deep learning (DL), and hybrid models, has emerged as a critical technology for addressing this challenge by analyzing vast datasets, uncovering hidden patterns, and adapting dynamically to evolving fraudulent tactics.

AI Techniques in Financial Fraud Detection

The review classifies AI techniques into machine learning, deep learning, reinforcement learning, and hybrid models.

Machine Learning (ML)

Machine learning is fundamental in identifying fraudulent patterns:

• Supervised learning relies on labeled data to train models such as Random Forests, Support Vector Machines (SVM), and Gradient Boosting Machines. These models excel at distinguishing fraudulent from legitimate transactions with high accuracy (often exceeding 90%).
• Unsupervised learning detects anomalies in unlabeled data using techniques like K-Means clustering and Isolation Forests, allowing detection of novel fraud schemes.

Deep Learning (DL)

Deep learning employs neural networks with multiple layers to capture complex data relationships:

• Convolutional Neural Networks (CNNs) process grid-like data (e.g., images or geospatial information) aiding in biometric verification and QR code fraud detection.
• Recurrent Neural Networks (RNNs) and LSTM networks analyze sequential transactional data to identify temporal fraud patterns.
• Graph Neural Networks (GNNs) model relationships in transaction networks, effectively detecting coordinated fraud rings such as money laundering.

Reinforcement Learning (RL)

RL adapts through trial and error by interacting with environments and receiving feedback. It is particularly useful for real-time adaptive fraud detection where tactics evolve continuously.

Hybrid Models

Hybrid approaches combine multiple AI methodologies to leverage their complementary strengths. Ensemble techniques such as stacking integrate outputs from various models to improve overall accuracy and reduce false positives. For example, combining deep learning with Random Forest algorithms has shown superior results in fraud detection tasks.

Bastian Schwind-Wagner_Follow "AI technologies are revolutionizing financial fraud detection by providing accurate, real-time identification of complex fraudulent activities. Addressing challenges around transparency, bias, and scalability remains critical for widespread adoption."

Applications Across Financial Fraud Types

AI-driven methods target diverse fraud types:

• Payment Fraud: Detection of credit/debit card misuse, mobile payments, and online payment fraud using supervised/unsupervised ML enhanced by biometric authentication.
• Identity Fraud: Identification of synthetic identities and account takeovers through behavior profiling and anomaly detection.
• Transaction Fraud: Real-time monitoring of unauthorized withdrawals or transfers via sequential analysis using RNNs/LSTMs.
• Cryptocurrency Fraud: Use of graph-based methods and GNNs to detect Ponzi schemes and fraudulent ICOs within blockchain transactions.
• Money Laundering: Visualization and analysis of transaction graphs combined with explainable DL models to identify suspicious money flows.
• Insurance and Claims Fraud: Integration of blockchain for secure data sharing alongside ML to detect false claims.
• Securities and Trading Fraud: Application of ML and natural language processing (NLP) to analyze market data and detect illegal trading activities.

Metrics for Evaluating Fraud Detection Models

Key performance metrics include:

• Accuracy: Overall correctness of predictions but may be misleading in imbalanced datasets.
• Precision: Proportion of correctly identified fraud cases among flagged transactions; important to reduce false alarms.
• Recall: Ability to identify all true fraud cases; critical to minimize missed fraudulent events.
• False Positive Rate (FPR): Rate at which legitimate transactions are incorrectly flagged, impacting user experience and trust.

Balancing these metrics is essential for effective fraud detection systems.

Legal and Ethical Considerations

AI-based fraud detection must address:

• Data Privacy: Ensuring compliance with regulations like GDPR to protect sensitive customer information.
• Algorithmic Bias: Preventing unfair outcomes by using diverse datasets and explainable AI methods.
• Regulatory Compliance: Adhering to legal standards across jurisdictions.
• Accountability: Clear roles for stakeholders responsible for AI system outcomes.

Fraud Detection Approaches

• Anomaly Detection: Identifies transactions that deviate from normal behavior.
• Rule-Based Systems: Use predefined rules but struggle with evolving fraud tactics.
• Graph-Based Detection: Analyzes relationships between entities to uncover complex fraud rings.
• Social Network Analysis: Maps connections to detect collusion and organized crime.
• Behavioral Analytics: Profiles user behavior to spot unusual activities indicative of fraud.

Open Issues and Challenges

• Evolving Fraud Patterns: Rapidly changing tactics require continuous model updates.
• Imbalanced Datasets: Scarcity of labeled fraud instances complicates model training.
• Model Interpretability: Need for transparent AI models to satisfy regulatory scrutiny.
• Balancing Metrics: Managing trade-offs between false positives and detection rates.
• Scalability: Handling large volumes of real-time transactions efficiently.
• Bias and Fairness: Mitigating discriminatory effects from biased data or models.
• Resource Constraints: Smaller institutions may lack capacity for advanced AI solutions.

Industry Applications and Standards

AI is increasingly integrated into banking, payment processing, insurance, and cryptocurrency platforms for fraud detection. ISO standards provide frameworks ensuring regulatory compliance, security, and interoperability.

Conclusion and Future Work

This comprehensive review highlights the transformative role of AI in financial fraud detection. Combining machine learning, deep learning, blockchain, big data analytics, and federated learning can significantly enhance fraud prevention capabilities. Future research should prioritize improving model interpretability, integrating privacy-preserving methods like federated learning, and developing scalable, adaptive real-time systems. Cross-sector collaboration among financial institutions, regulators, technology providers, and academia will be essential to sustain advancements in combating financial crime.

Dive deeper

• Research ¦ N. Jahan Sarna et al., “AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review,” in IEEE Access, vol. 13, pp. 141204-141233, 2025, doi: 10.1109/ACCESS.2025.3596060. ¦ Link