Insight Template Generation for Explainable Risk Analysis
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Solution Overview
Problem
Existing machine learning models for risk assessment in financial institutions often generate false positive insights due to reliance on features that are not business-oriented, making it challenging to extract actionable insights and requiring subject matter expert feedback for validation.
Innovation Solution
A system that combines machine learning feature-oriented explainability with subject matter expert knowledge to generate composite insight templates, transforming ML results into actionable insights by grouping entities with similar features, building decision trees, and applying expert input to reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models use numerous features for risk assessment, then measurement precision is improved, but false positive insights increase
Solution Approach 1:
The system implements feedback loops where subject matter experts review and validate ML-generated insights. Expert feedback is used to refine and update insight templates, which then improve subsequent ML model performance by reducing false positives while maintaining measurement precision through iterative optimization
Solution Approach 2:
Subject matter experts act as intermediaries between the ML model and final decisions. Experts validate insights generated by the model, filtering out false positives before they impact risk assessment outcomes, thus resolving the contradiction between precision and reliability
2Measurement precision
If machine learning models process large data sets with numerous parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex risk assessment process into distinct components: ML model processing, insight generation, template matching, and expert validation. This modular segmentation manages device complexity by organizing numerous parameters and processing steps into manageable, independent modules that can be developed and maintained separately
Solution Approach 2:
Insight templates serve as intermediaries that bridge the complex ML processing and the simpler expert validation stage. Templates encapsulate complex feature relationships and patterns, transforming them into interpretable rules that reduce the apparent complexity for both the system and users
3Measurement precision
If machine learning models generate detailed feature-based insights, then measurement precision is improved, but loss of time in investigation increases
Solution Approach 1:
The system performs preliminary action by pre-defining insight templates that capture common risk patterns and relationships. When the ML model generates insights, these pre-established templates enable rapid matching and validation, eliminating the need for analysts to manually analyze every feature from scratch and thus reducing investigation time while maintaining precision
Solution Approach 2:
The system uses copying by replicating proven insight patterns across multiple cases through templates. Once a valid risk pattern is identified and validated, it is copied into the template library and automatically applied to similar cases, reducing investigation time for recurring patterns while maintaining consistent measurement precision
Data Source
AI summary
A system, platform, program product, and/or method for generating new composite insight templates that includes: running a machine learning model on a data set to obtain for each of a plurality of entities a risk score and feature-based insights; generating a list of top “n” features input to the machine learning model that contributes to the risk score for each entity; grouping entities based upon similar features input to the machine learning model that contributes to the risk score for each entity; generating a decision tree for at least one of the group of entities; extracting, from the decision tree generated for the at least one of the group of entities, one or more feature-based insights; generating, by applying subject matter input, a new composite insight based upon the one or more feature-based insights; and adding the new composite insight to insight templates.