Dynamic Income Validation Using ML Pattern Recognition
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Solution Overview
Problem
Current systems for validating customer income are time-consuming and resource-intensive, requiring manual parsing of transactions and bank statements, which limits efficiency and scalability.
Innovation Solution
A system utilizing optical character recognition (OCR) and machine learning models to dynamically validate income by identifying repeating sources of deposits from transaction data, generating an income amount and confidence score, and displaying this information in a graphical user interface (GUI).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parsing of transactions and bank statements is used to validate customer income, then accuracy of income verification is maintained, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent replaces manual parsing of transactions with automated machine learning models that process transaction data to identify repeating deposit sources. The system uses ML models to analyze transaction patterns, extract income information, and generate validation results automatically, eliminating the need for manual review while maintaining verification accuracy.
Solution Approach 2:
The system performs self-service by automatically validating income using embedded machine learning models that process transaction data independently. The patent implements automated pattern recognition, deposit source identification, and income calculation without requiring human intervention, allowing the system to serve itself in the validation process.
2Measurement precision
If manual parsing of transactions and bank statements is used to validate customer income, then verification accuracy is maintained, but resource requirements increase significantly
Solution Approach 1:
The patent replaces manual parsing operations with automated machine learning-based processing. The system uses ML models to automatically analyze transaction data, identify repeating deposit sources, and calculate income amounts, significantly improving processing efficiency while maintaining verification accuracy through automated pattern recognition and validation.
Solution Approach 2:
The system changes the approach from manual processing to automated processing by introducing machine learning parameters and algorithms. The patent transforms the validation process using computational parameters, statistical models, and automated decision-making mechanisms that efficiently process large volumes of transaction data without human resource constraints.
3Productivity
If automated machine learning models are used to validate income dynamically, then processing speed and efficiency improve, but system complexity increases
Solution Approach 1:
The patent segments the income validation system into distinct functional components: transaction data processing module, machine learning model layer, pattern recognition module, and result generation module. This segmentation allows each component to perform specific tasks independently, managing overall system complexity while maintaining high processing speed through specialized functional units.
Solution Approach 2:
The system introduces machine learning models as intermediary components between raw transaction data and final validation results. The ML models act as mediators that process complex transaction patterns, identify repeating deposit sources, and generate income validations, simplifying the overall system architecture by encapsulating complexity within specialized model layers.
Data Source
AI summary
Disclosed embodiments may include a method for validating dynamic income. The method may include receiving, via a first user device, estimated income amount associated with a customer, and receiving or retrieving a plurality of transactions comprising associated text data. The method further includes dynamically determining, using a first machine learning model, a repeating source of deposits by identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits, dynamically generating, using a second machine learning model, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount, dynamically generating a graphical user interface comprising the income amount and the confidence score, and dynamically transmitting the graphical user interface to a second user device for display.