Activity Baseline Scoring for Financial Risk Detection
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Solution Overview
Problem
Financial institutions face difficulties in identifying ongoing bank fraud and other nefarious activities until they have escalated, leading to significant negative financial impacts, and current credit risk assessments are limited to credit-related information, lacking transactional and asset data across multiple financial institutions and products.
Innovation Solution
A system that determines customer or customer segment activity baseline scores based on financial institution data from multiple institutions, monitoring deviations from these scores to generate risk alerts, and assessing risk throughout the credit lifecycle by incorporating transactional and asset data across multiple financial products and channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional credit risk assessment methods are used, then credit-related information can be assessed, but transactional and asset data across multiple financial institutions are not captured
Solution Approach 1:
The patent merges data from multiple financial institutions and multiple data types (credit, transactional, asset data) into a unified risk assessment system. This consolidation enables comprehensive information collection while managing system complexity through integrated architecture.
Solution Approach 2:
The risk assessment system is designed to handle multiple functions: assessing credit risk, detecting fraud, analyzing transactional patterns, and evaluating asset data across different financial institutions. This multi-functional approach improves information completeness without proportionally increasing complexity.
2Reliability
If fraud detection is performed only after escalation, then simple monitoring can be used, but significant negative financial impacts occur
Solution Approach 1:
The system performs preliminary fraud detection by establishing baseline scores for normal customer behavior and continuously comparing actual transactions against these baselines. This early detection mechanism identifies fraudulent patterns before they escalate, improving reliability while reducing detection delay.
Solution Approach 2:
The system implements continuous feedback loops where transaction data is constantly monitored, compared against baselines, and used to update risk assessments in real-time. This feedback mechanism enables timely fraud detection and response, preventing financial losses before escalation.
3Measurement precision
If activity baseline scoring is implemented across multiple financial institutions, then comprehensive risk assessment is achieved, but data integration complexity increases
Solution Approach 1:
The patent segments the risk assessment process into distinct components: baseline score calculation, deviation detection, and risk evaluation. Each component handles specific data types and functions independently, improving measurement precision while managing data integration complexity through modular architecture.
Solution Approach 2:
The system introduces intermediary processing layers that standardize and normalize data from multiple financial institutions before analysis. These intermediaries handle data integration complexity by providing unified interfaces and transformation rules, enabling accurate cross-institutional risk assessment.
Data Source
AI summary
Embodiments of the present invention relate to systems, apparatus, methods and computer program products for integrated risk management. More specifically, embodiments of the present invention provides for determining one or more activity baseline scores, each score associated with one or more activities and based at least in part on financial institution data from multiple financial institutions. The activity baseline score defines a normal risk or baseline activity for a customer, a customer segment or customer population. Further, the invention provides for monitoring at least the financial institution data to determine deviations from the activity baseline score(s) and generating and initiating communication of risk score alerts and/or activity baseline deviation alerts based on predetermined activity baseline deviations. Additional embodiments provide for determining a risk score based on risk patterns associated with financial institution data and, in some embodiments, non-financial institution data.