ML Credit Profile Simulation for Predicting Score Changes
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Solution Overview
Problem
Conventional credit profile simulation tools lack the ability to learn from historical data and predict future outcomes based on current inputs, limiting their effectiveness in simulating a consumer's credit profile changes due to various actions.
Innovation Solution
A system utilizing a machine learning model trained on a plurality of consumer credit profiles aggregates credit data to predict the impact of actions such as applying for credit products, resolving negative marks, or changing credit card balances, providing predictions on credit score changes and financial trajectories over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional tools use predefined rules and algorithms to simulate credit profiles, then the simulation process is simple and deterministic, but the ability to learn from historical data and predict future outcomes is limited
Solution Approach 1:
The patent replaces conventional mechanical rule-based algorithms with a machine learning model that automatically learns patterns from historical credit data. The ML model processes input features (income, expenses, credit history) and predicts credit score changes without requiring explicit programming of financial rules, thereby improving prediction accuracy while maintaining manageable system complexity through automated learning mechanisms.
2Adaptability or versatility
If machine learning models are used to simulate credit profiles, then the ability to learn from historical data and predict future outcomes is improved, but the system complexity increases
Solution Approach 1:
The machine learning model performs self-learning by automatically processing historical credit data and extracting relevant patterns without requiring manual programming of financial rules. The system feeds credit profile data into the ML model, which internally processes and learns from this data to improve its prediction capabilities, thereby achieving adaptability while keeping the user interface simple.
3Measurement precision
If credit data is aggregated to determine features for the ML model, then the prediction accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The system extracts and selects only the most relevant features from the aggregated credit data that are needed for accurate credit score prediction. By identifying and extracting key predictive features (such as payment history, credit utilization, and debt-to-income ratio) from the broader credit data, the system achieves high prediction precision while minimizing data processing complexity by focusing only on essential variables.
Data Source
AI summary
Using various embodiments, systems, methods, and techniques are disclosed to perform financial simulations using a machine learning model are disclosed. In one embodiment, a system receives credit data of a user and a request to perform a financial simulation of a financial profile pertaining to a consumer. The credit data is aggregated to determine one or more features required by an AI/ML model, and then submits the aggregated data to the model. The system then returns a prediction based on the financial simulation provided by the ML model.