Target Account Validation with Machine Learning for Transfers
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Solution Overview
Problem
Current financial service systems lack adequate confirmation of the target account for electronic transactions, failing to validate the intended recipient account before completing a transfer.
Innovation Solution
A banking application that receives user inputs, performs lookups in various databases, and provides validation results using machine learning to confirm the target account accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If electronic transfer systems are implemented to enable remote banking transactions, then convenience and accessibility of banking services are improved, but the risk of incorrect target account transfers increases
Solution Approach 1:
The system performs preliminary validation of the target account by comparing the entered account number with the customer's contact information (phone number, email address, or physical address) associated with the beneficiary. This preliminary check occurs before the transfer is completed, allowing the system to verify the correctness of the target account without requiring the customer to physically visit the bank.
2Measurement precision
If multiple database lookups are performed to validate target account information, then validation accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The system combines multiple validation approaches into a unified process: it merges the comparison of account numbers with contact information from multiple sources (phone number databases, email address databases, physical address databases) into a single comprehensive validation step. This integration allows the system to perform thorough validation without requiring separate, time-consuming lookup operations for each data source.
3Reliability
If comprehensive validation using multiple data sources is implemented, then transaction security is improved, but device and system complexity increases
Solution Approach 1:
The system introduces an intermediary validation layer that acts as a mediator between the customer's transfer request and the execution of the transaction. This intermediary component automatically retrieves and compares information from multiple external databases (phone number databases, email address databases, physical address databases) without requiring the customer to interact with each database separately. The intermediary synthesizes the information from these sources and presents a single validation result to the customer, simplifying the user interface while maintaining comprehensive security checks.
Data Source
AI summary
A system and method for validating target account data based on inputs from a user. The system may include a memory storing instructions, and a processor configured to execute the instructions to perform operations. The operations may include providing an interface; receiving a first input; receiving a second input; enabling selection of an activatable element; conducting a lookup associated with the received inputs; receiving a result of the lookup; transforming the result of the lookup into a transformed result that predicts the probability of verification; and displaying transformed result to demonstrate the validity of the received inputs.