Adaptive Graph Learning for Customer Risk Analytics
Analysis
This paper addresses the critical problem of identifying high-risk customer behavior in financial institutions, particularly in the context of fragmented markets and data silos. It proposes a novel framework that combines federated learning, relational network analysis, and adaptive targeting policies to improve risk management effectiveness and customer relationship outcomes. The use of federated learning is particularly important for addressing data privacy concerns while enabling collaborative modeling across institutions. The paper's focus on practical applications and demonstrable improvements in key metrics (false positive/negative rates, loss prevention) makes it significant.
Key Takeaways
- •Proposes a federated learning approach to customer risk analytics, addressing data privacy concerns.
- •Combines graph neural networks and personalized PageRank for improved customer network segmentation.
- •Employs hierarchical reinforcement learning for optimizing intervention strategies.
- •Demonstrates significant improvements in false positive/negative rates and loss prevention compared to traditional methods.
“Analyzing 1.4 million customer transactions across seven markets, our approach reduces false positive and false negative rates to 4.64% and 11.07%, substantially outperforming single-institution models. The framework prevents 79.25% of potential losses versus 49.41% under fixed-rule policies.”