AI Rules-Based Transaction Modeling for Real-Time Balance Projection
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Solution Overview
Problem
Administrators of computing systems face challenges in analyzing and acting upon transaction data in real-time, leading to potential exposure risks due to delayed decision-making.
Innovation Solution
A method and system utilizing a predictive machine-learning model to capture historical transaction data, extract item-level features, identify patterns, and generate projected balances for client accounts, enabling automated decision-making and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If administrators manually analyze transaction data, then decision-making accuracy may be maintained, but response time increases and real-time visibility is lost
Solution Approach 1:
The patent replaces manual administrative analysis with an automated machine learning system that processes transaction data. The ML model automatically captures historical data, extracts features, identifies patterns, and generates projected balances without human intervention, thereby eliminating the time loss associated with manual analysis while maintaining or improving accuracy through consistent algorithmic processing.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously perform data analysis and generate insights without requiring administrator intervention. The automated pipeline continuously processes transaction data, updates projected balances, and provides real-time visibility, freeing administrators from manual analysis tasks while maintaining high accuracy through the model's self-learning capabilities.
2Loss of time
If real-time data processing is implemented, then response time is reduced, but system complexity increases
Solution Approach 1:
The patent segments the complex real-time data processing system into distinct modular components: a data capture module that collects historical transaction data, a feature extraction module that identifies relevant patterns, a machine learning model that generates predictions, and a projection module that calculates future balances. This segmentation reduces overall system complexity by allowing each module to be developed, maintained, and optimized independently while working together to achieve real-time processing.
3Measurement precision
If comprehensive historical data is captured, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent applies the extraction principle by selectively identifying and isolating the most relevant features from comprehensive historical transaction data. The feature extraction process filters out unnecessary information and focuses only on the key patterns and variables that significantly impact prediction accuracy. This selective extraction reduces data processing complexity while maintaining high prediction accuracy by concentrating computational resources on the most informative data elements.
Data Source
AI summary
A method for rules-based modeling may include capturing a plurality of historical transaction data of a client account. The method may further include extracting a plurality of item level features from the plurality of historical transaction data. The method may further include providing the plurality of item level features to a predictive machine-learning model. The predictive machine-learning model may be trained to identify patterns within the plurality of item level features and generate a projected balance for the client account based on the identified patterns. The method may further include transmitting the projected balance to a user interface.