Federated Data Exchange for Privacy-Aware Loan Risk Scoring
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Solution Overview
Problem
Traditional loan underwriting systems operate in silos, lacking comprehensive customer data from multiple lenders and merchants, leading to limited creditworthiness assessments due to non-reporting by some lenders and inconsistent credit checks.
Innovation Solution
A federated machine learning approach that trains a deep neural network using data from various entities, generating a global risk score for customers based on pooled data without sharing sensitive information, allowing lenders to make informed loan decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional siloed loan underwriting systems are used, then each lender operates independently with limited data access, but creditworthiness assessment accuracy deteriorates due to lack of comprehensive customer data
Solution Approach 1:
The patent merges data from multiple lenders and merchants into a centralized pooled data structure, allowing comprehensive creditworthiness assessment. The system combines loan data, payment history, and transaction information from various sources to create a holistic view of customer financial behavior, resolving the accuracy-completeness contradiction by aggregating dispersed information while maintaining data privacy through controlled access mechanisms.
Solution Approach 2:
The patent introduces a data exchange system as an intermediary between lenders, merchants, and customers. This intermediary facilitates secure data sharing and access without requiring direct connections between all parties, enabling comprehensive data aggregation while maintaining system independence and security. The intermediary manages data pooling, access control, and privacy protection.
2Measurement precision
If lenders share comprehensive customer data to improve assessment accuracy, then creditworthiness evaluation improves, but data privacy and security risks increase
Solution Approach 1:
The patent segments data access and processing into distinct layers: centralized data pooling for storage, controlled access layers for retrieval, and processing layers for analysis. This segmentation allows comprehensive data availability for accurate assessment while maintaining privacy through controlled access - different users can access different levels of data detail without exposing sensitive information unnecessarily.
Solution Approach 2:
The data exchange system acts as a secure intermediary that enables data sharing while protecting privacy. It provides authenticated access to pooled data, manages user permissions, and facilitates data exchange between lenders and merchants without requiring direct data exposure between parties, thus reducing privacy risks.
3Measurement precision
If centralized data pooling is implemented to improve underwriting accuracy, then assessment comprehensiveness improves, but system complexity increases
Solution Approach 1:
The patent designs the data exchange system to serve multiple functions: data pooling from various sources, data storage, controlled access management, data processing, and privacy protection. By making the system multi-functional, it consolidates what would otherwise require separate systems into a single unified platform, reducing overall system complexity while achieving comprehensive underwriting capability.
Solution Approach 2:
The system implements self-service mechanisms for data management, including automated data collection from lenders and merchants, automatic data pooling, and user-friendly interfaces for data access and updates. This reduces the operational complexity of managing centralized data by enabling the system to maintain itself with minimal manual intervention.
4Object-affected harmful factors
If federated machine learning is used to share data securely, then data privacy is maintained, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary data processing and feature extraction at the data pooling stage, preparing data in advance for analysis. By pre-processing and structuring data before it enters the machine learning pipeline, the system reduces the computational burden during subsequent analysis while maintaining privacy through the federated learning architecture that processes data locally at participating entities.
Solution Approach 2:
The patent replaces traditional centralized mechanical data processing with distributed federated machine learning computations. Instead of aggregating all data centrally for processing, the system uses distributed computations across multiple entities, reducing the computational load on any single system while maintaining security and privacy through the distributed architecture.
Data Source
AI summary
A system may transmit an MLM to one or more entities. The system may receive, from the one or more entities, data associated with a plurality of customers, the data generated by the MLM. The system may train a federated deep NN based on the data. The system may receive, from a user, a first request for a loan. The system may generate, using the federated deep NN, a risk score associated with the user, wherein the risk score comprises a likelihood the user will satisfy condition(s) of the loan. The system may transmit, to lender(s), the risk score and a second request for loan option(s) for backing the first request for the loan. The system may receive, from the first lender(s), the loan option(s) for backing the first request for the loan, and may transmit the loan option(s) to the user.