Automated Financial Application Data Population
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Applying for financial products is time-consuming and prone to errors due to the need for extensive data entry, and existing methods require purchasing third-party credit scores, which can be costly and unnecessary.
Innovation Solution
A system that automatically populates financial application data structures with transactional data from electronic banking transactions, eliminating the need for manual input and third-party credit scores by using transactional data stored within the lending institution's database to assess creditworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used to complete financial applications, then the application can be filled out, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs preliminary actions by automatically gathering and storing transactional data, personal identification information, and financial account data in databases before the application process begins. When an application is initiated, this pre-collected data is automatically populated into the application fields, eliminating the need for manual data entry and significantly reducing completion time
Solution Approach 2:
The system enables self-service by automatically retrieving and populating application data from existing institutional databases without requiring external third-party credit reports. The application system serves itself by utilizing internally stored transactional data, personal information, and financial account data to complete the application process
2Reliability
If third-party credit scores are purchased to assess creditworthiness, then credit assessment can be performed, but costs increase unnecessarily
Solution Approach 1:
The system extracts the necessary credit assessment functionality from external third-party services and relocates it internally by utilizing transactional data, personal identification information, and financial account data already stored in the institution's own databases. This extraction eliminates the need to purchase external credit reports while maintaining assessment capabilities
Solution Approach 2:
The system achieves multi-functionality by using the same institutional databases that store transactional data for other purposes to also provide creditworthiness assessment data. The existing data infrastructure serves multiple functions including transaction recording, customer profiling, and credit evaluation, eliminating the need for separate third-party credit scoring services
3Loss of information
If extensive manual data entry is required, then complete application data can be collected, but errors increase and user experience deteriorates
Solution Approach 1:
The system performs preliminary data collection and validation by automatically gathering complete application data from institutional databases before the user begins the application process. Personal identification information, transactional data, and financial account data are pre-retrieved and populated, ensuring data completeness while requiring minimal user input and reducing errors
Solution Approach 2:
The system implements feedback mechanisms by automatically verifying and validating data as it is populated from databases into the application. The system checks data integrity, formats information correctly, and ensures completeness through automated validation routines, reducing errors while maintaining data quality
Data Source
AI summary
Methods and systems for populating an application are described. Data for an application is accessed, including transactional data associated with a user from a computerized database in a system. Systems and methods operate to automatically populate a financial application data structure in a computer using the transactional data stored in the computerized database. In an example, these systems and methods operate to determine a lending decision by the institution on the transactional data without purchasing a third party credit score. In an example, a financial notification is triggered using the automatically populated financial application data structure.