AI for Financial Fraud Detection & Prevention workshop will explore the cutting-edge artificial intelligence and machine learning approaches for combating financial fraud, addressing one of the most critical challenges that financial institutions face. Participants will gain both theoretical understanding and practical insights into AI techniques specifically tailored for fraud detection and prevention. The workshop aims to tackle financial sector complexities including managing vast, imbalanced datasets where fraud is rare, adapting to evolving threats, ensuring model interpretability for compliance, and protecting sensitive financial data privacy. The workshop will feature research, applications, and case studies from banking, fintech, payment processing, and cryptocurrency sectors demonstrating real-world AI implementation, illustrating both opportunities and challenges across different organizational and regulatory contexts.

Submission Guidelines

All submissions must be PDFs formatted in the Standard ACM Conference Proceedings Template. Submissions are limited to 4-8 content pages, including all figures and tables but excluding references.  Accepted papers will be posted on the workshop website. Following the conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed.

Papers should be submitted by 01st October 2025, 11:59 PM, Anywhere on Earth.

List of Topics

• Adaptive & Autonomous Systems - Reinforcement learning and agentic frameworks that enable self-evolving security strategies and decompose complex fraud investigations into manageable autonomous tasks

• Multimodal Intelligence - Integrating language models, computer vision, graph frameworks, multimodal architectures and traditional machine learning approaches for comprehensive fraud detection across communications, transactions, documents, and behavioral patterns

• Anomaly Detection & Graph Analytics – Traditional and cutting-edge deep learning approaches for identifying sophisticated fraud patterns in complex transaction networks and money laundering schemes

• Generative & Adversarial Systems - Leveraging GANs for synthetic fraud data generation and developing robust defenses against AI-powered attacks targeting financial systems

• Real-Time Detection & Synthetic Intelligence - Advanced ensemble methods for high-speed fraud detection in digital payments and cryptocurrency, enhanced by AI-generated synthetic data to overcome rare fraud event limitations

• Explainable & Collaborative AI - Interpretable models for regulatory compliance and federated learning approaches that enable privacy-preserving fraud detection across multiple financial institutions