Synthetic Identities affecting KYC/CDD Compliance Costs

December 5, 2019

Compliance, KYC

By admin

What is a synthetic identity?

Synthetic identity is categorized as an identity that has been made up. It combines both real and fake ID information to create a “new” identity. This type of fraud is only growing. KYC teams are faced with real names, locations, and dates of births combined with fake government identifiers, making it harder to spot potential cases of fraud.

Synthetic identity is categorized as an identity that has been made up. It combines both real and fake ID information to create a “new” identity. This type of fraud is only growing. KYC teams are faced with real names, locations, and date of births combined with fake government identifiers, making it harder to spot potential cases of fraud.

Customer Due Diligence (CDD) sets four procedures for effective CDD.

1. Verification of customer identity
2. Verification of ultimate beneficial ownership
3. Evaluating the nature of the customer relationship
4. Ongoing monitoring of customer transactions

It is expected that to maintain effective CDD two specific needs will arise. Upgrade of KYC registries and databases and increased personnel costs to keep up with the increased time for customer onboarding.

What modern technologies can companies implement to reduce AML compliance costs and how is it beneficial?

• Machine learning to automate the process of data gathering, customer profiling, and customer risk scoring.
• Automation of beneficial ownership data. Information can be automatically gathered and assembled. With the use of network analysis techniques, companies can gain context on relationships and transactions, uncover links and obtain insight to assist in case investigations.
• Integration of KYC information with ongoing monitoring for dynamic risk scoring. Automated analysis of external data eg. Negative news can trigger changes in the customer’s risk profile, which when fed into the transaction monitoring process can provide more accurate results.
• Machine learning to identify false positives with increasing accuracy as the system learns from previous decisions.