Attribute Value Estimation via Transaction Categorization
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Solution Overview
Problem
Existing systems face challenges in estimating certain attribute values from user account data, requiring significant user interaction and struggling with inefficiencies in data retrieval and interface usability.
Innovation Solution
The system retrieves and processes large amounts of data via API requests, normalizes and efficiently provides it, and generates interactive user interfaces that allow for dynamic and efficient human-computer interactions, enabling improved access and analysis of account data by categorizing transactions and predicting attribute values like income streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data retrieval methods are used, then user account information can be accessed, but the process requires significant user interaction and documentation submission
Solution Approach 1:
The system automatically retrieves user account information from external user account systems without requiring user action. The processor autonomously queries transaction data, categorizes transactions, and estimates attribute values, eliminating the need for users to submit documentation or complete forms.
Solution Approach 2:
The system performs preliminary data retrieval and processing by automatically accessing user account systems and extracting transaction information before any user action is required. This preliminary automation of data collection removes subsequent barriers to attribute estimation.
2Adaptability or versatility
If proprietary APIs from multiple external systems are accessed, then comprehensive account data can be retrieved, but interface complexity and data normalization challenges increase
Solution Approach 1:
The system implements a universal data access layer that handles multiple proprietary APIs through a single standardized interface. The processor is configured to query different external user account systems using their respective APIs while presenting a unified data structure to the attribute estimation process, enabling multi-source data integration without proportionally increasing system complexity.
3Measurement precision
If manual attribute estimation processes are used, then accuracy can be improved through user input, but productivity and efficiency decrease
Solution Approach 1:
The system replaces manual mechanical processes of attribute estimation with automated computational processes. The processor automatically queries transaction data, categorizes transactions using predefined criteria, and estimates attribute values through algorithmic analysis, substituting human manual work with automated mechanical-computational systems that maintain accuracy while dramatically improving productivity.
Data Source
AI summary
Systems and techniques are disclosed for accessing accounts associated with a user and estimating a value of an attribute associated with the user based upon the retrieved account information. Transaction data associated with an account at an external user account system is received. The transactions are categorized into transaction groups. For each transaction group, a confidence value that the group is associated with the attribute is estimated, based at least in part upon a distribution of transaction amounts for the transactions of the group over a time period associated with the group. An attribute value is estimated for each group, based at least in part upon the transaction amounts of the transaction of the group. In addition a value of the attribute for a future time period may be predicted based upon the transaction groups.