Cognitive Automation for Financial Exception Processing
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Solution Overview
Problem
Conventional hardware and software tools used in processing deposit items and associated exceptions in financial institutions face limitations in accuracy and speed, making them inefficient for enhanced exception processing.
Innovation Solution
A computing platform employs cognitive automation tools to receive interaction data from analyst user devices, trains machine learning models to resolve exceptions, and generates configuration commands to implement these models for processing additional exception items, improving processing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hardware and software tools are used to process deposit items and exceptions, then the system structure is simple and easy to implement, but the processing accuracy and speed are limited
Solution Approach 1:
The patent replaces conventional hardware and software tools with a machine learning-based cognitive automation system. The machine learning model processes exception items by learning from interaction data, substituting traditional mechanical processing methods with intelligent algorithms that achieve higher accuracy while maintaining manageable system complexity through modular architecture.
2Speed
If conventional processing tools are used, then the system is easy to implement, but the processing speed is slow
Solution Approach 1:
The patent replaces conventional processing tools with a machine learning-based cognitive automation system. The machine learning model processes exception items by learning from interaction data, substituting traditional mechanical processing methods with intelligent algorithms that achieve higher speed while maintaining manageable system complexity through modular architecture.
3Productivity
If manual intervention is used to resolve exceptions, then the system requires less complex processing logic, but productivity and efficiency are reduced
Solution Approach 1:
The patent implements self-service automation where the machine learning model autonomously processes exception items by learning from interaction data. The system automatically resolves exceptions without requiring manual intervention, achieving high productivity while maintaining appropriate automation levels that can be adjusted based on confidence thresholds and exception complexity.
Data Source
AI summary
Aspects of the disclosure relate to performing enhanced exception processing using cognitive automation tools. In some embodiments, a computing platform may receive interaction data identifying one or more actions performed by one or more users in resolving a plurality of exception items associated with an exception queue. Subsequently, the computing platform may train, using a learning engine, a machine learning model to resolve a first exception and a second exception of one or more exceptions based on the interaction data. Based on training the machine learning model, the computing platform may generate one or more configuration commands directing a processing module to implement the machine learning model to process additional exception items associated with the exception queue. The computing platform then may send the one or more configuration commands to the processing module.