Unified Customer Transaction Profile for Cross-Channel Fraud Detection
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Solution Overview
Problem
Financial institutions face challenges in detecting fraud and managing risk across isolated decision areas, known as silos, which can lead to missed fraud events when transactions occur across multiple channels or services, as existing solutions focus on individual silos rather than interconnected activities.
Innovation Solution
A system and method that connects decisions through customer transaction profiles by generating a hierarchy of relationships among services, summarizing transactional behaviors, and creating a customer profile that aggregates information from multiple services, enabling a single score for financial risk decisions and facilitating information sharing across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fraud detection focuses on individual silos to maximize detection capabilities within each channel, then detection precision within that silo is improved, but the ability to detect cross-channel fraud events deteriorates
Solution Approach 1:
The patent merges multiple siloed fraud detection systems into a unified customer profile that aggregates transactions and risk scores across all channels (credit cards, loans, deposits, etc.). This integration allows the system to detect cross-channel fraud patterns while maintaining the specialized detection capabilities of individual channels through their respective risk scoring models.
Solution Approach 2:
The customer profile serves as a universal data structure that handles multiple functions: storing transaction data across different channels, maintaining risk scores from various silos, generating consolidated risk decisions, and supporting both fraud detection and collections strategies. This multi-functional approach enables a single system to address both within-silo and cross-silo detection needs.
2Reliability
If multiple separate decision areas are maintained for different financial channels, then specialized risk management for each channel is improved, but the complexity of managing interconnected decisions deteriorates
Solution Approach 1:
The system segments the overall risk management process into distinct components: individual channel risk scoring models that maintain specialized logic for each financial product, and a higher-level customer profile aggregation layer that consolidates these scores. This segmentation preserves channel-specific expertise while reducing management complexity through clear separation of concerns.
Solution Approach 2:
The customer profile acts as an intermediary data structure between individual channel risk systems and the overall customer risk decision-making process. It receives and standardizes risk scores from various channels, then provides a consolidated view that simplifies cross-channel decision-making without eliminating the specialized risk management capabilities of individual channels.
3Reliability
If information is stored in isolated silos for each financial service, then data security within each service is improved, but the ability to share risk information across channels deteriorates
Solution Approach 1:
The system implements a nested information structure where customer-level data (name, SSN, address) contains aggregated channel-level data (transaction histories, risk scores), which in turn contain account-level details. This nesting allows information to be shared across channels at appropriate levels of abstraction while maintaining security through controlled access to sensitive underlying data.
Data Source
AI summary
An apparatus and method for developing financial risk decisions for a customer associated with a number of different financial services/channels are disclosed. A hierarchy of relationships among the financial services/channels is generated. Transactional behaviors of the customer related to each of the financial services/channels is summarized, using one or more analytical approaches executed on the hierarchy of relationships, to generate a customer level transactional behavior summary. A customer profile associated with the customer is generated which includes the transactional behavior summary and aggregated information on recent financial transactions associated with each of the financial services/channels. A score for a risk decision can be generated for one or more specific services/channels, based on the customer profile.