Credit Stacking Classifier Using Transaction Stream Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Lenders face challenges in assessing credit stacking activities and accurately determining a business's revenue streams, which can impact the borrower's ability to repay loans and make it difficult for lenders to recover funds.
Innovation Solution
A system utilizing a machine learning component with a supervised-based neural network to analyze historical transaction data, applying transaction categorization and entity recognition models to identify loan activity and generate a credit stacking classifier model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual assessment methods are used to evaluate credit stacking, then the process is simple and requires minimal technology, but lenders cannot accurately assess credit stacking activities or gather accurate pictures of all revenue streams
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated machine learning system. The neural network model automatically analyzes transaction data to detect credit stacking patterns and revenue streams, eliminating the need for manual review while achieving high accuracy in detecting complex lending behaviors across multiple data sources.
Solution Approach 2:
The patent introduces a machine learning intermediary layer between raw transaction data and lender decision-making. The neural network model acts as an intermediary that processes, cleans, and analyzes complex transaction patterns, transforming raw data into actionable insights about credit stacking and revenue streams without requiring lenders to manually process the complexity themselves.
2Reliability
If comprehensive transaction data is collected from multiple sources, then lenders can assess all revenue streams and credit stacking activities, but the system complexity and data processing requirements increase significantly
Solution Approach 1:
The patent builds a universal neural network model that can process multiple types of transaction data from different sources (banks, credit cards, payment processors) through a single integrated system. The model is designed to handle diverse data formats and sources uniformly, allowing comprehensive revenue stream assessment without requiring separate processing systems for each data type.
Solution Approach 2:
The patent performs preliminary data processing and feature extraction during the training phase, where the neural network learns to recognize patterns and relationships in transaction data. This preliminary learning enables the model to efficiently analyze new transaction data without requiring complex real-time processing, reducing operational system complexity while maintaining comprehensive assessment capability.
3Measurement precision
If machine learning models are trained on extensive historical data, then the accuracy of credit stacking identification improves, but the training time and computational resources required increase
Solution Approach 1:
The patent applies partial action by training the neural network on a strategically selected subset of historical data that represents the most informative patterns for credit stacking detection. Rather than requiring exhaustive training on all available historical data, the system identifies and trains on the most relevant transaction patterns, achieving high accuracy with reduced training time and computational resources.
Data Source
AI summary
A system is configured to retrieve a set of raw transaction data. A transaction categorization model is applied to the raw transaction data. The transaction categorization model infers a category from each transaction and labels each transaction with the inferred category. An entity recognition model is applied to the labelled transaction data. The entity recognition model extracts an entity from each transaction and labels each transaction with the extracted entity. The system generates a plurality of transaction streams from the labelled transactions based on the category and entity labels. The system also labels each transaction stream with either a loan label or a non-loan label based on an analysis of the types of transactions defining the transaction stream. The system trains a supervised-based neural network using the labelled transaction streams to generate a credit stacking classifier model.