Business Situation
- A leading custodian bank sought to improve its entity resolution process to enhance accuracy and efficiency.
- Entity resolution, identifying and linking records that refer to the same entity across different data sources, was critical for the bank to maintain accurate client records and streamline operations.
SGA Approach
Technology
- Data Integration: Implemented a robust data integration platform to consolidate client data from various sources
- Model Deployment: Utilized cloud-based infrastructure to deploy the entity resolution model, ensuring scalability and real-time processing
- Customer Interaction Channels: Integrated AI-driven tools into the platform for real-time entity resolution
AI
- Chose machine learning (ML) algorithms such as decision trees and neural networks to build the entity resolution model
- Trained the models using historical data and validated them with a separate dataset to ensure high accuracy
- Used AI to automate the resolution of entity matches, reducing manual intervention and errors
Data
- Aggregated comprehensive client data from transaction records, account details, and external sources
- Ensured data accuracy and consistency by removing duplicates and correcting errors
- Developed features such as transaction patterns and account linkages to enhance the entity resolution model
Key Takeaways
- Enhanced Accuracy: Improving entity resolution accuracy by 40% ensured reliable client records
- Operational Efficiency: Reducing manual intervention and processing time by 50% helped streamline operations
- Scalability: Leveraging cloud infrastructure to handle growing data volumes maximized efficiency
- Data-Driven Insights: Comprehensive data integration and feature engineering improved model performance
- AI-Driven Automation: Automated entity resolution processes reduced errors and increased efficiency
