Neural Network for Unstructured Financial Data Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Individuals and businesses face challenges in making prudent financial management decisions due to difficulties in determining available funds, managing debts, and making investment choices, often relying on inadequate sources such as friends, family, or unstructured financial advice.
Innovation Solution
A computing system that trains and deploys a neural network to process unstructured data for resource record utilization, providing recommendations on resource allocation and utilization improvements by determining optimal threshold quantities and suggesting actions through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained to process unstructured financial data and provide automated recommendations, then financial decision-making accuracy and resource utilization improve, but system complexity and computational requirements increase
Solution Approach 1:
The patent introduces a neural network as an intermediary component between raw unstructured financial data and decision-making outputs. The neural network processes complex unstructured data (emails, documents, messages) through trained layers to generate structured insights and recommendations, thereby improving decision accuracy while managing complexity through specialized data processing architecture
Solution Approach 2:
The system segments the financial data processing into distinct functional layers within the neural network architecture. Each layer processes specific aspects of unstructured data (text extraction, sentiment analysis, entity recognition, financial impact assessment), allowing the complex task to be divided into manageable computational stages that improve overall precision
2Reliability
If the neural network processes comprehensive unstructured user data to provide personalized financial advice, then the quality of financial management decisions improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring unstructured financial data before it reaches the main neural network processing stage. Data cleaning, entity extraction, and initial classification are performed in advance to reduce the computational burden during real-time recommendation generation, thereby maintaining high advice quality while reducing processing time
Solution Approach 2:
The neural network applies partial processing to different types of data based on their relative importance and processing requirements. High-priority data (e.g., transaction records, urgent communications) receive more comprehensive processing, while lower-priority data receive streamlined processing, optimizing the balance between advice quality and processing time
Data Source
AI summary
Systems and methods train machine learning model(s) of a neural network, the training including building layers of the neural network to process unstructured data associated with resource record utilization, and the neural network is deployed to process the unstructured data. Unstructured user data of a user that is associated with (i) a first user record comprising a user resource and (ii) a second user record is received, and the neural network is applied to the received unstructured user data, the applying including determining that at least a portion of the user resource should be transmitted from the first user record to the second user record based on predicted resource utilization improvement(s). A recommended user action is provided, via a user interface of a user device, the recommended user action including an indication of the resource utilization improvement(s).