Segmented Machine Learning Ensembles for Personalized Credit Risk
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional risk assessment models are inaccurate and fail to account for unique trends within smaller subsets of users, leading to inefficient characterization of credit risk and increased losses for financial institutions.
Innovation Solution
The method segregates users into different clusters based on arbitrary filters, generates machine learning sub-models for each cluster, and combines their outputs into an ensemble model to generate personalized risk scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models are used for risk assessment, then the system can process data from multiple sources, but the models remain sub-optimal due to gap between loss function and evaluation metric
Solution Approach 1:
The patent divides the risk assessment system into multiple specialized sub-models, each trained on specific subsets of data from different sources (e.g., transactional data, behavioral data, external data). Each sub-model focuses on a particular aspect of risk assessment, allowing them to optimize for their specific evaluation metrics while the ensemble model integrates their results. This segmentation resolves the contradiction by enabling specialized optimization without requiring the entire system to be overly complex.
Solution Approach 2:
The patent combines multiple sub-models into an ensemble model that integrates their individual risk predictions. The ensemble model synthesizes results from different sub-models, each trained on different data sources and optimized for different metrics. This merging approach allows the system to achieve high measurement precision by leveraging the strengths of multiple specialized models while maintaining manageable complexity through modular architecture.
2Measurement precision
If expert-written rules are used for risk assessment, then the system is easy to implement, but it fails to account for unique trends within smaller subsets of users
Solution Approach 1:
The patent segments users into different subsets based on characteristics such as transaction behavior, demographic factors, or risk profiles. For each subset, a dedicated sub-model is trained to capture unique trends and patterns specific to that group. This segmentation enables the system to detect and respond to niche patterns within smaller user subsets that would be invisible to a single monolithic model, thereby improving measurement precision while maintaining operational simplicity through automated data-driven subset identification.
Solution Approach 2:
The patent dynamically adjusts model parameters and training approaches based on the specific characteristics of each user subset. Instead of using fixed expert rules, the system automatically identifies relevant features and optimizes parameters for each subset based on their unique risk patterns. This parameter adaptation allows the system to capture subset-specific trends while maintaining ease of operation through automated parameter tuning rather than manual rule configuration.
3Measurement precision
If a single monolithic model is used, then the system structure is simple, but it cannot accurately characterize credit risk for diverse user populations
Solution Approach 1:
The patent implements a segmented model architecture where a single monolithic model is divided into multiple specialized sub-models, each handling specific user segments or data types. This segmentation enables accurate risk characterization for diverse populations by allowing each sub-model to specialize in specific patterns, while the ensemble framework maintains overall system coherence. The segmentation resolves the contradiction by enabling detailed specialization without requiring complete architectural complexity.
Solution Approach 2:
The patent applies local quality by allowing different parts of the model to have different characteristics optimized for their specific purposes. Each sub-model is tailored to the specific needs of its target subset, with customized features, parameters, and evaluation metrics. This local optimization enables high measurement precision for diverse user populations while keeping the overall system complexity manageable, as each local component can be developed and maintained independently.
Data Source
AI summary
Systems and methods for training a machine learning model to assess risk as disclosed. The machine learning model includes a plurality of machine learning sub-models and an ensemble model. The method includes: receiving a plurality of user data records, each user data record comprising data collected for an individual user from multiple data sources; creating the plurality of machine learning sub-models based on the plurality of user data records; assigning at least a subset of the plurality of user data records to each of the plurality of machine learning sub-models; training each machine learning sub-model using the assigned subset of the plurality of user data records, each sub-model trained to accurately determine a risk score based on a given user data record; providing the risk scores generated by each of the plurality of machine learning sub-models to an ensemble machine learning model, the ensemble machine learning model being trained to combine the risk scores from the sub-models to obtain a combined risk score; using the trained machine learning model to determine a risk score for an individual user data record; and reusing the determined risk score for the individual user record to retrain the machine learning model.