Payment Platform Predictive Model for Bill Statement Categorization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing payment processing systems face inefficiencies in categorizing and processing electronic bill or invoice statements, particularly due to limited computing resources and the need for accurate data extraction from images, which can lead to increased processing times and resource consumption.
Innovation Solution
A payment platform utilizing a predictive model that categorizes bill or invoice statements received as electronic messages, such as emails or SMS, by employing image processing techniques like neural networks to identify format attributes and self-train using confidence thresholds, thereby conserving resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a predictive model is used to categorize bill statements, then categorization accuracy is improved, but computing resources are consumed
Solution Approach 1:
The system performs preliminary categorization using a predictive model before full processing. The model predicts whether an image is a bill or not based on format attributes (logos, headers, sections, text alignment) extracted in advance, allowing the system to consume computing resources only when necessary for confirmed bills.
Solution Approach 2:
The system applies partial processing by using the predictive model for initial classification and only performing full data extraction and processing on images categorized as bills. This partial action approach reduces overall computing resource consumption while maintaining high accuracy for actual bill processing.
2Measurement precision
If image processing techniques are applied to extract payment data, then data extraction accuracy is improved, but processing time is increased
Solution Approach 1:
The processing pipeline is segmented into distinct stages: (1) image cleaning, (2) format attribute extraction for categorization, (3) full data extraction only for confirmed bills, and (4) payment processing. This segmentation allows the system to spend time on detailed extraction only when necessary, reducing overall processing time.
Solution Approach 2:
Format attributes are extracted and used for preliminary categorization before full payment data extraction. This preliminary action filters out non-bill images early, preventing time-consuming full extraction processes from being applied to irrelevant images.
3Measurement precision
If the predictive model is trained using more images, then categorization accuracy is improved, but training time and resources are increased
Solution Approach 1:
The system performs self-training by using newly processed bill images to continuously improve the predictive model. The model trains on actual processed images rather than requiring external training datasets, enabling continuous improvement with minimal external resources and time investment.
Solution Approach 2:
The system implements feedback loops where processed images are used to retrain and improve the predictive model. This feedback mechanism allows the model to continuously learn from actual bill examples, improving accuracy over time without requiring additional manual training data collection.
Data Source
AI summary
A platform and process for electronic payment processing using electronic communications from different communication channels or bands. The system and process can generate alerts using fraud detection and verify payment requests using historical data and pattern recognition. The system and process can categorize images and extract payment data.