Cloud-Based ID Verification System Reducing Fraud Costs
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Solution Overview
Problem
Large financial corporations face significant costs and time-consuming processes when implementing scanning devices for ID validation across thousands of branches, leading to substantial losses due to fraud from counterfeit IDs.
Innovation Solution
A platform-agnostic identification verification module utilizing scanning technologies, AI/ML models, and APIs to validate IDs across various formats, reducing the need for extensive hardware deployment and streamlining the validation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If purpose made scanning devices are deployed at each branch office for ID validation, then fraud verification capability is improved, but implementation cost increases significantly
Solution Approach 1:
The patent replaces physical scanning devices at each branch with a centralized cloud-based ID verification service that processes digital images of IDs. Instead of deploying expensive hardware copies to thousands of locations, the system uses a single centralized service that receives and validates ID images transmitted electronically, dramatically reducing hardware costs while maintaining verification capability.
Solution Approach 2:
The patent introduces a centralized cloud-based verification service as an intermediary between branch offices and the fraud detection system. Rather than direct local verification hardware, branch employees submit ID images through a web interface or API to the centralized service, which then performs validation and returns results, eliminating the need for expensive local scanning devices.
2Reliability
If purpose made scanning devices are deployed across thousands of branches, then ID validation capability is improved, but implementation time increases to several years
Solution Approach 1:
The patent replaces the mechanical deployment of physical scanning devices with a software-based solution. Instead of installing hardware at each branch location, the system uses web interfaces and APIs that can be accessed from any device with a web browser, reducing implementation from years to weeks or days of software configuration and deployment.
Solution Approach 2:
The centralized verification service provides universal ID validation capability accessible from any branch office through standard web browsers or APIs. The single centralized service handles verification for all branches, eliminating the need for branch-specific hardware installations and enabling rapid deployment across the entire organization.
3Measurement precision
If multiple scanning devices are installed at each branch, then fraud detection accuracy is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent extracts the complex fraud detection logic and validation algorithms from local branch devices and consolidates them in a centralized cloud service. Branch employees only need to capture ID images using simple devices and submit them through a web interface, while the complex verification processes run remotely, reducing local device complexity while maintaining high detection accuracy.
Solution Approach 2:
Instead of deploying multiple complex scanning devices to each branch, the system uses simple image capture devices (even standard printers with scanning capability) and relies on the centralized service to perform the complex verification. The centralized service maintains the sophisticated fraud detection algorithms, while local devices remain simple image capture tools.
Data Source
AI summary
Various methods and processes, apparatuses/systems, and media for fraud verification across user identification formats are disclosed. A processor generates a digital image of an identification document presented by a customer; transmits the digital image to a server; calls a first API to read the digital image from the server and requests validation of the digital image with a second API; calls the second API to transmit the digital image to an SaaS; receives, by the second API, an identification validation response from the SaaS using existing data corresponding to the customer; implements an AI/ML model to generate a confidence score based on predefined rules and historical data; determines that the confidence score is equal to or more than a configurable threshold value; and validates the digital image based on determining that the confidence score is equal to or more than the configurable threshold value.