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Synthetic identities combine real and fake identity (ID) information to create a “new” identity. This identity is then used to open new accounts, by which to commit various types of financial fraud and money laundering. This type of fraud is on the rise and the risk is exacerbated as Faster Payments requirements enable more fraud and money laundering activities.
De-risking customer onboarding with Know Your Customer (KYC) identity verification and following the Customer Due Diligence (CDD) rule to ascertain and verify ultimate beneficial ownership, analyze customer relationships and monitoring customer transactions assumes two important things:
- That the KYC/CDD team can look up and verify identities around the world, and
- The team will regularly upgrade the KYC registries and databases on beneficial owners.
The number and scale of recent data breaches provides more data for criminal enterprises to leverage to create synthetic identities and perpetrate fraud. How do we, as an industry, increase KYC efficiency while protecting enterprises and consumers from synthetic ID fraud? KYC teams are and will continue to be faced with real names, locations and dates of birth combined with fake government identifiers, such as social security numbers (SSNs).
As synthetic identities are being more and more used in account origination fraud, KYC and CDD operations cannot count on a “one-size-fits-all” anti-money laundering (AML) solution to detect and prevent fraud & money laundering activities.
KYC Identity Verification: Not Catching Up
One key challenge for organizations is KYC identity verification. The layman’s view is that KYC identity verification is analogous to any other identity verification. But it’s not just about checking that a government-issued ID, be it a passport or driver’s license, is not fake and is unique to the applicant. That is insufficient within the context of the massive data breaches we have experienced over the past decade. On the dark web, you can buy a driver’s license for $20, proof of identity for $29.59, or even passport information for $62.61. According to this blog post from Knowbe4, a thief can purchase your entire online identity for approximately $1,170.
In a sense, KYC is still very much in the era of static identity proofing and Knowledge-Based Verification (KBV), where opening a new corporation banking account requires utility bills, bank statements of all account signatories, full disclosure of directors’ information including names, dates of birth, and addresses, as well as legal and tax documentation. This information may also be required post-account opening for ongoing risk monitoring.
It takes between 2 and 4 weeks to complete the onboarding of a new customer
Financial Institutions (FIs) take, on average, 2 to 4 weeks to complete the customer onboarding process, according to a 2017 survey by Thomson Reuters. This is mostly due to the static identity proofing and additional KBV activities.
It costs an average of $6000 to onboard a new client
A commissioned Forrester report on the Total Economic Impact (TEI) of Regulatory Onboarding estimates an average cost of $6,000 to onboard each new client, depending on the client’s risk rating and country’s regulation.
These KYC compliance costs add up and contribute to ever-increasing overall AML costs, which are becoming progressively so high that they can impact other essential daily operating costs for financial institutions.
Customer Due Diligence (CDD) Rule: Impacting AML Compliance Costs
The CDD rule sets out four key procedures for effective customer due diligence:
- Ascertaining and verifying customer identities
- Ascertaining and verifying ultimate beneficial ownership
- Analyzing the nature of the customer relationship
- Monitoring customer transactions
As a result of this rule, two categories of expenses are expected to increase:
- Cost associated with AML personnel. As the CDD rule will increase the time it takes to open new business accounts and respond to requests from law enforcement groups on customers’ information, increased work hours at fixed resource capacity will lead to requests for increase in compensation.
- Cost associated with new compliance programs. To remain compliant, FI personnel will have to look up and verify identities around the world and will need to upgrade their KYC registries and databases on beneficial owners.
FinCEN Regulatory Impact Assessment (RIA) projects that the CDD rule will cost banks and their customers between $700 million and $1.5 billion over the next decade.
What are the modern technologies that can help reduce AML compliance costs?
According to a Celent Research Group commissioned research paper, “Path to modern AML compliance,” here are six AML capabilities that will assist in reducing AML compliance costs:
- Real-time customer risk profiling for onboarding. Unstructured data analysis and machine learning can be used to automate data gathering, compilation of profiles, and customer risk scoring.
- Beneficial ownership data automation. Beneficial owner information can be automatically gathered and assembled from internal and external sources. Network analysis techniques can provide context on relationships and transactions among beneficial owners, or any uncovered links, and deliver these insights in visual form to assist case investigation.
- Dynamic risk scoring and perpetual KYC. Integrating KYC information with ongoing monitoring is a key to more effective assessment of customer risk. Automated analysis of unstructured, external data such as negative news can be used to trigger changes in a customer’s risk profile which, when fed into the transaction monitoring process, can support more accurate results. Transaction monitoring (TM) alerts can then in turn be used to adjust customer risk scores in an ongoing, dynamic process.
- False positives reduction. Transaction monitoring that leverages next-generation analysis can be more effective in uncovering suspicious patterns. Machine learning can identify likely false positives with increasing accuracy over time based on feedback from previous decisions. These low-touch false positives can in turn be closed automatically, if desired by the bank.
- Regulatory reporting. Intelligent automation leveraging natural language generation (NLG) can be used to assemble the customer information, transaction data and analyst actions needed to create a SAR or currency transaction report (CTR). The relevant data can also be used to support the assisted creation of a SAR narrative. This can significantly reduce the time and resources needed for these arduous reporting processes.
- Cloud based big data lake. Cloud provides a powerful platform for next-generation technology by supporting the three V’s: volume to enable data lake-based big data analysis; velocity for real-time monitoring and screening; and variability to support unstructured data analysis. Cloud platforms also enable efficient delivery of the machine learning capabilities that are transforming AML compliance operations at financial institutions.
Guardian Analytics helps you control compliance costs while mitigating fraud and money laundering activities
As a pioneer in fraud detection with machine learning and behavioral analytics, Guardian Analytics is combining best-in-class behavioral analytics and biometrics methods to detect fake user digital identity with Cloud-based big data lake infrastructure called “Evidence Lake™” to enhance and rethink KYC/CDD and Transaction Monitoring processes towards a holistic AML approach.
Learn more at guardiananalytics.com/aml-solutions.
