Aggregation Based Credit Decision Using Machine Learning
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Solution Overview
Problem
Existing credit decision systems rely on incomplete views of users' data, as more transactions are recorded electronically but not fully aggregated, leading to inaccurate creditworthiness assessments.
Innovation Solution
An apparatus and method for aggregating transaction data from multiple third-party sources using machine learning to determine a credit metric, providing a more comprehensive view of a user's creditworthiness to interested parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If credit decisions are based on traditional credit bureau scores, then the decision process is simple and fast, but the view of the user is incomplete and creditworthiness assessment is inaccurate
Solution Approach 1:
The patent combines transaction data from multiple third-party data sources with traditional credit bureau data to create a comprehensive view of user creditworthiness. This merging of diverse data sources resolves the contradiction by improving assessment accuracy through broader data coverage while managing complexity through systematic integration processes.
Solution Approach 2:
The system creates a universal credit assessment framework that can process and analyze multiple types of data sources (transaction data, credit bureau data, financial behavior data) through a single machine learning model. This multi-functional approach improves accuracy without proportionally increasing system complexity.
2Loss of information
If transaction data from multiple third-party sources is aggregated and analyzed using machine learning, then a more comprehensive view of creditworthiness is achieved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent replaces traditional mechanical credit scoring methods with machine learning algorithms that can automatically process and analyze aggregated transaction data from multiple sources. This substitution reduces the manual complexity of data integration while improving the completeness of the financial view through automated pattern recognition.
3Measurement precision
If a broader range of financial behaviors and trends is considered in credit scoring, then credit decision accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary aggregation and preprocessing of transaction data from multiple third-party sources before credit decision-making occurs. By preparing and organizing the data in advance using machine learning models, the system can quickly retrieve and analyze relevant financial behaviors when credit decisions are needed, reducing real-time processing time while maintaining comprehensive analysis.
Data Source
AI summary
Apparatuses, systems, methods, and computer program products are disclosed for aggregation based credit decisions. An apparatus includes a data module configured to receive transaction data for a user that is aggregated from a plurality of different third-party data sources where the user has accounts. An apparatus includes an analysis module configured to analyze aggregated transaction data using machine learning to determine a credit metric describing a credit worthiness of a user. An apparatus includes a credit module configured to provide a determined credit metric to one or more interested third parties.