Overview

A sophisticated name screening system that helps financial institutions identify potential money laundering risks through advanced matching algorithms and machine learning capabilities. The system processes millions of transactions daily against various watchlists and sanctions databases.

Challenge

Financial institutions faced critical challenges in AML compliance:

• Complex name matching requirements
• Multiple language support needed
• High volume of transactions
• Strict regulatory requirements
• False positive management

Solution Architecture

Core Components

• Machine Learning Engine
• Name Matching Algorithm
• Risk Scoring System
• Case Management
• Audit Trail
• Reporting System

Key Features

• Fuzzy Name Matching
• Multi-language Support
• Real-time Screening
• Risk-based Approach
• Automated Alerts
• Compliance Reporting

Project Team

• 2 Data Scientists
• 3 Backend Developers
• 2 ML Engineers
• 1 Solution Architect
• 1 Compliance Expert

Technical Implementation

Machine Learning

• Name Variation Models
• Pattern Recognition
• Risk Scoring
• Anomaly Detection
• Continuous Learning

Data Processing

• Real-time Processing
• Batch Processing
• Data Enrichment
• Quality Assurance
• Data Lineage

Performance Features

• High-speed Matching
• Scalable Architecture
• Load Distribution
• Caching Strategy
• Optimization Techniques

Results & Impact

Compliance Benefits

• 99.9% screening accuracy
• 60% reduction in false positives
• Real-time compliance
• Automated reporting
• Enhanced risk management

Operational Improvements

• 90% faster screening
• Reduced manual review
• Better resource allocation
• Improved audit trails
• Cost efficiency

The AML Name Screening System has significantly improved financial institutions' ability to combat money laundering while reducing operational costs and ensuring regulatory compliance.