Composite Activity Ranking for Loss Risk Identification

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Solution Overview

Problem

Financial institutions face difficulties in identifying similarities between clients and predicting which clients may default on their accounts until a default occurs, making it challenging to streamline services and manage risk effectively.

Innovation Solution

A system and method that evaluates client activities, applies weights to data fields, and generates a composite activity ranking to identify potential loss risk clients, allowing for the targeting of similar clients within a larger pool of accounts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If financial institutions evaluate all client accounts individually to identify loss risks, then prediction accuracy improves, but processing time and computational resources increase significantly

Engineering Contradiction:

Improveloss risk prediction accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the client portfolio into distinct risk groups (e.g., watch list clients, loss risk candidates) based on composite activity rankings. This segmentation allows the institution to focus detailed analysis on high-risk segments while applying more efficient screening to the broader population, thereby improving prediction accuracy for critical cases without proportionally increasing processing time for all accounts.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary filtering and ranking of all client accounts using automated composite activity scoring before detailed loss risk analysis. This preliminary action identifies high-risk candidates in advance, allowing subsequent focused evaluation to occur only on a subset of accounts, thus maintaining high prediction accuracy while reducing overall processing time and resource consumption.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If financial institutions use detailed client activity data to identify loss risks, then identification accuracy improves, but data processing complexity increases

Engineering Contradiction:

Improveloss risk identification accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent extracts and isolates specific activity indicators from comprehensive client data that are most strongly correlated with loss risk (e.g., transaction patterns, account balance changes, deposit/withdrawal frequencies). By extracting only the most relevant features rather than processing all available data, the system maintains high identification accuracy while reducing processing complexity and computational burden.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system transforms raw client activity data into standardized composite activity rankings and risk scores through parameter changes. This transformation converts diverse, complex data types into uniform metrics that are easier to process and compare, thereby maintaining identification accuracy while simplifying the overall data processing architecture.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If financial institutions manually analyze client accounts to identify similarities and loss risks, then analysis depth improves, but productivity decreases

Engineering Contradiction:

Improveanalysis depthVSAvoidclient account processing throughput

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical analysis with automated computational systems that calculate composite activity rankings and identify loss risk candidates. This substitution maintains analytical depth through sophisticated algorithms while dramatically increasing processing throughput, allowing the institution to evaluate far more accounts than would be feasible through manual analysis alone.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates standardized profiles and composite activity rankings that serve as reusable templates for comparing multiple clients. Once a loss risk pattern is identified in one client, the same analytical framework can be rapidly applied to copy and evaluate similar clients across the portfolio, maintaining consistent analysis depth while scaling productivity across the entire client base.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS11138664B1Identification of loss risk candidates for financial institutions

Publication Date: 2021.10.05 WELLS FARGO BANK NA

AI summary

Client activity can be analyzed and risk candidates identified based on the client activities. A client database can be analyzed for determination of clients having a high risk for a financial institution. The determination is based on known activities of filtered clients where the activities are historically indicative of resulting in a loss for the financial institution. A target client profile representative of a high risk client may be selected. The client database can be mapped to find similar clients to be identified as a high risk of loss.