CUSTOMER CASE STUDY

Fraud Detection: Data Ingestion and Mapping at a Top US Bank

Executive Summary

This case study details the implementation of a sophisticated data ingestion and mapping system by a top US bank to enhance its fraud detection capabilities. Facing challenges with the integration of disparate data sources and visibility across-lines-of-business, the bank adopted the DataWalk platform. This initiative significantly improved the bank's ability to detect and prevent fraud across multiple lines of business, leading to substantial financial savings and operational efficiencies.

Introduction

Fraud detection in the banking sector has become increasingly complex, requiring the integration and analysis of vast amounts of data from diverse sources. The bank recognized the need for a more robust system to manage data ingestion and mapping to enhance its fraud detection efforts. The implementation of DataWalk represented a strategic move to address these challenges.

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Challenge

The bank's pre-existing system lacked the capability to provide cross-line-of-business visibility of fraud incidents. This limitation hindered the bank's ability to detect and prevent fraud effectively, as information silos prevented a holistic view of fraudulent activities. Additionally, the bank struggled with the integration of data from various sources, including loan applications, deposit accounts, and physical security logs, into a coherent system for fraud analysis.

Solution

The DataWalk platform was selected for its advanced data ingestion and data mapping capabilities. Key features of the solution included:

Continuous Data Ingestion: Unlike traditional systems that operated on nightly batch runs, DataWalk enabled constant data ingestion, ensuring real-time data availability for fraud detection.
Cross-Line-of-Business Visibility: DataWalk provided a unified view of customer activities across all business lines, enabling the detection of fraudulent activities by individuals previously identified for fraud in different areas.
Sophisticated Mapping Tools: The platform included a source mapper tool that streamlined the process of integrating data from various sources into the system, enhancing the efficiency and accuracy of fraud detection algorithms.

Implementation

The implementation process spanned 19 weeks, from conception to going live. This rapid deployment was critical in addressing the bank's urgent need for an improved fraud detection system. Key achievements during the implementation phase included:

Integration of Diverse Data Sources: Data from SAS data warehouses, Amazon S3 landing sites, and external systems like NCFDA Siphon were successfully integrated into DataWalk.
Real-Time Fraud Detection: The system began registering fraud cases within minutes of receiving data, significantly reducing the response time to fraudulent activities.
Scalability: The system was tested with data sizes ten times larger than the initial load, proving its capability to scale according to the bank's growing data requirements.

Outcomes

The implementation of DataWalk had a profound impact on the bank's fraud detection capabilities:

Enhanced Fraud Detection: The bank reported stopping approximately $3 million in fraud per month, with unofficial estimates suggesting even higher savings.
Operational Efficiency: The constant data ingestion process eliminated delays in fraud detection, enabling the bank to respond to threats more swiftly.
Award-Winning Innovation: With the success of the implementation, the bank gave DataWalk a prestigious supplier award recognizing its innovative approach to fraud detection.

Conclusion

The DataWalk implementation at the bank represents a significant advancement in the use of data ingestion and mapping technologies for fraud detection. By enabling real-time analysis of comprehensive, cross-line-of-business data, the bank has significantly improved its ability to detect and prevent fraud, resulting in substantial financial savings and operational improvements.