User-Level Credit Decision Model with Segmented Evaluation
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Solution Overview
Problem
The financial industry faces inconsistencies in credit decision-making, leading to frustration for users, as decisions are often made transactionally without considering prior interactions or user behavior, resulting in inconsistent treatment and potential punishment for good repayment habits.
Innovation Solution
Implementing a user-level credit model that employs two distinct credit extension models: a conventional transactional model and a user-level model that considers relationship information and past interactions, allowing for consistent and behavior-based credit decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transaction-level credit decisions are made independently for each transaction, then each transaction can be evaluated on its own merits, but user-level consistency and loyalty rewards are lost
Solution Approach 1:
The patent segments the credit decision-making process into two distinct models: a transaction-level model that evaluates individual transactions, and a user-level model that evaluates overall user creditworthiness and behavior. This segmentation allows each model to serve its specific function while together they provide both transactional accuracy and user-level consistency.
Solution Approach 2:
The patent introduces a credit decision service as an intermediary layer that coordinates between the transaction-level credit model and the user-level credit model. This intermediary manages the interaction between the two models, ensuring that user-level decisions provide consistent guidance across multiple transactions while maintaining the autonomy of individual transaction evaluations.
2Measurement precision
If multiple credit extension models are employed, then more comprehensive credit assessment is achieved, but system complexity increases
Solution Approach 1:
The patent divides the credit assessment function into specialized models: one focused on transaction-level risk evaluation and another on user-level behavioral patterns. Each model is optimized for its specific purpose, improving overall measurement precision while keeping individual model complexity manageable through clear functional separation.
Solution Approach 2:
The credit decision service acts as a universal coordinator that manages multiple credit extension models. It provides a unified interface and decision framework that handles both transaction-level and user-level assessments, allowing the system to leverage multiple models without proportionally increasing operational complexity.
Data Source
AI summary
A method for employing user-level credit decisions in relation to extension of a loan to a user may include receiving prequalification information associated with a user and employing a first credit extension model to execute a first credit extension decision based on the prequalification information, where the first credit extension decision is associated with a first credit limit. The method may include employing a second credit extension model, different from the first credit extension model, to execute a second credit extension decision based on the prequalification information and relationship information associated with current and prior interactions between the user and a lending entity making the first credit extension decision and the second credit extension decision, where the second credit extension decision is associated with a second credit limit. The method may also include determining, based on the first and second credit extension decisions, whether to extend a predefined credit limit to the user.