Credit Risk Modeling Using Alternative Data Attributes
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Solution Overview
Problem
Traditional credit scoring models are antiquated and fail to capture modern consumer behavior, particularly for underserved segments with limited or non-traditional credit data, leading to inaccurate risk assessments.
Innovation Solution
The system enhances credit risk modeling by identifying new attributes using machine learning methods and alternate performance definitions, expanding the scope of consumer segments by incorporating short-term credit usage patterns and unconventional data, thereby developing more effective scoring models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional credit scoring models are used, then the modeling process is simple and well-established, but the models fail to capture modern consumer behavior and are inaccurate for underserved segments
Solution Approach 1:
The patent segments the consumer population into traditional and underserved segments, applying different modeling approaches to each. Traditional segments use conventional scoring models while underserved segments receive alternative modeling based on non-traditional data, allowing each segment to be evaluated with the most appropriate methodology.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between raw alternative data and credit risk assessment. This intermediary processes non-traditional data (utility payments, rental history, social media) and transforms it into risk predictions that can be integrated with traditional scoring models.
2Quantity of substance
If standard performance definitions are used, then the scoring process is consistent and comparable, but data is limited for individuals with no or minimal credit history
Solution Approach 1:
The patent expands the data dimensions beyond traditional credit reports by incorporating alternative data sources such as utility payment histories, rental payment records, telephone payment patterns, and social media behavior. This dimensional expansion provides data points for consumers who lack traditional credit history.
Solution Approach 2:
The patent changes the parameters used for risk assessment by replacing or supplementing traditional credit score parameters with alternative parameters derived from non-traditional data sources, enabling evaluation of consumers based on their behavior patterns rather than conventional credit metrics.
3Adaptability or versatility
If traditional attributes are used, then the scoring model is straightforward and easy to interpret, but the attributes do not capture current consumer behavior adequately
Solution Approach 1:
The patent makes the attribute set dynamic by continuously updating and refining alternative data sources to reflect changing consumer behaviors and economic conditions. The model adapts to new data types and emerging patterns, allowing it to capture evolving consumer behavior while maintaining interpretability through structured data categories.
Data Source
AI summary
The system and method of the present invention expand/enhance modeling for credit risks scores. The expanded modeling system and methods discussed herein identify new credit risk related attributes using limited and/or unconventional credit data. In an aspect, the system and method enhance performance definitions to capture incremental insight, expanding the applicable consumer segments previously inaccessible.