Proactive Risk Management: Quickly identify potential compliance issues or fraudulent activities before they escalate

CASE STUDY | June 2, 2024

Anomaly Detection Business Intelligence Clustering Analysis Data-Driven Strategy Data Analysis Data Analytics Data Segmentation Financial Regulation Financial Services Fraud Prevention Machine Learning Ocean Pearl Solutions Performance Monitoring Performance Optimization Resource Optimization Risk Management Risk Mitigation Segmentation

Background

Financial services firms face numerous risks, including compliance violations, fraud, and performance issues. Traditional monitoring methods often fail to detect subtle anomalies in large, complex datasets of advisor activities and client interactions.

Key Challenges

• Identifying potentially fraudulent or non-compliant financial advisor behaviors
• Detecting unusual patterns in client account activities across diverse portfolios
• Adapting to evolving financial products, regulations, and market conditions
• Balancing client service optimization with risk mitigation

Our Solution

We implement a powerful two-step approach:

1. Segmentation and Clustering:

• Segment financial advisors based on key performance and risk metrics
• Cluster advisors within segments to identify similar behavioral profiles

2. Anomaly Detection:

• Apply advanced algorithms to detect outliers within each cluster
• Identify advisors whose behavior significantly deviates from their peers

Value Delivered

• Proactive Risk Management: Quickly identify potential compliance issues or fraudulent activities
• Performance Enhancement: Recognize top-performing advisors and replicate best practices
• Targeted Interventions: Provide tailored training or investigations based on specific anomalies
• Adaptive Compliance: Continuously update models to address evolving regulations and market dynamics

Why This Approach Works

Segmentation/clustering followed by anomaly detection is particularly effective for financial services firms because it:

1. Accounts for natural variations across different client segments and product lines
2. Identifies subtle deviations that may be missed by broad, one-size-fits-all approaches
3. Provides context-aware insights, reducing false positives
4. Scales efficiently to handle large, complex datasets of financial transactions and advisor activities

Example Anomalies Detected

• Unusual patterns in trading frequency or volume
• Sudden changes in client asset allocation or risk profiles
• Significant deviations in fee structures or discounting practices
• Unexpected shifts in product recommendations or client communication patterns

By leveraging this advanced approach, financial services firms can perform proactive risk management, optimize advisor performance, ensure regulatory compliance, and maintain client trust in today’s complex financial landscape.