Multi-Stage Customer Identity Verification Using Segmented Data Sources
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Solution Overview
Problem
The existing KYC processes face challenges in efficiently verifying customer identities when initial verification results are inconclusive, leading to resource-intensive manual processes and potential regulatory and business risks, as well as the risk of rejecting legitimate customers.
Innovation Solution
The technology implements a multi-step verification process by querying additional verification resources using new fields of customer identity data, determining if cumulative results support continued processing, and performing out-of-wallet verification to confirm customer identities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If initial verification is performed using standard KYC processes, then verification speed is improved, but verification accuracy deteriorates when results are inconclusive
Solution Approach 1:
The verification process is divided into multiple sequential stages: initial automated verification using standard KYC processes, followed by conditional secondary verification for inconclusive cases. This segmentation allows fast processing for clear cases while enabling deeper verification only when necessary, thus maintaining speed for most cases while improving accuracy for ambiguous cases.
Solution Approach 2:
The system performs preliminary automated verification using standard KYC processes before determining if manual review is needed. This preliminary action filters out clearly verifiable cases, allowing the system to maintain high speed for straightforward cases while reserving resource-intensive manual processes only for inconclusive cases where additional accuracy is required.
2Measurement precision
If manual verification processes are used for inconclusive cases, then verification accuracy is improved, but resource consumption increases
Solution Approach 1:
Instead of applying full manual verification to all cases, the system applies partial automated verification initially, then uses excessive (manual) action only for the subset of inconclusive cases. This partial/excessive approach ensures high accuracy is achieved only where necessary rather than universally, reducing overall resource consumption while maintaining verification quality for problematic cases.
Solution Approach 2:
The automated verification system serves itself by initially processing cases and automatically identifying which ones require manual review. This self-service mechanism eliminates the need for all cases to undergo manual verification, reducing resource consumption while ensuring that only cases needing human judgment receive manual attention, thus balancing accuracy with efficiency.
3Productivity
If automated verification is used exclusively, then processing efficiency is improved, but reliability deteriorates for edge cases
Solution Approach 1:
The system implements feedback by having automated verification evaluate its own results and automatically refer inconclusive cases to manual review. This feedback loop ensures that the automated system maintains high processing efficiency for clear cases while automatically triggering reliable manual verification when the automated process encounters uncertainty, thus preserving both efficiency and reliability.
Solution Approach 2:
The verification system dynamically adjusts its approach based on case characteristics: using automated processes for straightforward cases and transitioning to manual processes for inconclusive cases. This dynamic adaptation allows the system to maintain high processing efficiency for the majority of cases while ensuring reliability for edge cases that require human judgment.
4Measurement precision
If multi-step verification with additional fields is implemented, then verification accuracy is improved, but device complexity increases
Solution Approach 1:
The verification process is segmented into distinct stages with different levels of complexity. The first stage uses standard KYC fields for quick automated verification. The second stage, activated only for inconclusive cases, introduces additional verification fields and manual review processes. This segmentation improves accuracy for difficult cases while keeping the overall system complexity manageable by not requiring all fields for all cases.
Solution Approach 2:
The verification system uses a nested structure where basic KYC verification is the inner layer, and additional verification fields are nested within it for inconclusive cases. This nested approach allows the system to maintain a simple base process while having the capability to layer additional complexity only when needed, thus improving accuracy without permanently increasing the complexity of the standard verification path.
Data Source
AI summary
Receiving a request for verification of a customer's identity. The request includes fields of customer identity data. Requesting, from a first verification resource, verification of the customer's identity using the customer identity data. Receiving from the first verification resource, first verification results including at least one new field of customer identity data. The first verification results being insufficient to verify the customer's identity. Requesting, from a second verification resource, verification of the customer's identity using the at least one new field of customer identity data. Receiving, from the second verification resource, second verification results. For second verification results sufficient to verify the customer's identity, communicating to the customer a successful verification of the customer's identity.