Transactional Metadata Analysis for Customer Identity Updates
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Solution Overview
Problem
Financial institutions face challenges in detecting outdated or invalid customer identity data and maintaining confidence in customer information, as third-party data aggregators fail to identify changes in mutable characteristics, leading to stale information.
Innovation Solution
A system and method that utilize transactional data to identify changes in customer mutable characteristics by extracting metadata from transactions, determining potential changes, requesting confirmation, and updating the data, employing a trained machine learning engine to assess confidence levels and leveraging geolocation data to detect address and other changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If third-party data aggregators are used to verify customer identity data, then regulatory requirements are met and fraud is mitigated, but outdated or invalid customer information persists because aggregators cannot detect changes in mutable characteristics
Solution Approach 1:
The patent introduces an intermediary system that acts as a bridge between transactional data sources and customer identity data. This intermediary analyzes transactional metadata (such as geolocation data from mobile devices) to detect changes in mutable characteristics like address, phone number, and employment status, then communicates these changes back to update the customer profile, thereby solving the limitation of third-party aggregators that cannot detect such changes
Solution Approach 2:
The system enables self-service by automatically monitoring transactional data generated by customers during normal banking activities. The system autonomously detects changes in customer mutable characteristics without requiring customers to proactively update their information, and automatically updates the customer profile when changes are confirmed, eliminating the need for manual customer intervention
2Reliability
If customers are required to update their addresses and information manually, then data can be kept current, but customers do not always update their information leading to stale data
Solution Approach 1:
The system enables self-service by automatically monitoring transactional data generated by customers during normal banking activities. The system autonomously detects changes in customer mutable characteristics without requiring customers to proactively update their information, and automatically updates the customer profile when changes are confirmed, eliminating the need for manual customer intervention
3Measurement precision
If transactional data is analyzed to detect changes in mutable characteristics, then data accuracy is enhanced and fraud risks are reduced, but system complexity increases due to metadata extraction and machine learning engine requirements
Solution Approach 1:
The patent applies multi-functionality by using a single system to perform multiple functions: extracting metadata from various transactional data sources, analyzing geolocation data to detect address changes, monitoring transaction patterns to identify employment status changes, updating customer profiles, and reducing fraud risks. This consolidates what would otherwise require multiple separate systems into one unified platform
Solution Approach 2:
The system implements feedback loops where transactional data is continuously analyzed, changes in mutable characteristics are detected and communicated back to update the customer profile, and updated profiles are then used to inform future transaction analysis. This closed-loop feedback mechanism ensures continuous improvement of data accuracy while automating the process to manage complexity
Data Source
AI summary
Systems and methods for identifying changes in customer mutable characteristics in customer identity data using transactional data are disclosed. According to an embodiment, a method may include: (1) receiving, by a computer program executed by an electronic device, a plurality of transactions conducted with a financial instrument issued to a customer; (2) extracting, by the computer program, metadata from one of the plurality of transactions; (3) retrieving, by the computer program, a plurality of stored mutable characteristics for the customer; (4) determining, by the computer program, that the metadata indicates a possible change in one of the plurality of stored mutable characteristics; (5) requesting, by the computer program, confirmation that the one stored mutable characteristic has changed; and (6) updating, by the computer program, the one stored mutable characteristic in response to receiving confirmation that the one stored mutable characteristic has changed.