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3 Ways Contextual KYC Helps Improve Customer Onboarding and Refresh
A contextual approach to customer onboarding and refresh is key to detecting financial crime risk.
The customer onboarding Know Your Customer (KYC) and refresh experience in financial services is often complex, tedious and costly. Despite years of investment in technology upgrades and digitization, most legacy banks and other financial institutions still struggle to provide a seamless digital onboarding experience for their prospective customers. Existing customers fare little better as they are contacted repeatedly for updated information to maintain the bank’s internal customer records.
Corporate clients report that the average onboarding process requires eight separate contacts with each of their financial services providers, and that the customer refresh process takes an average of 20 days to complete. To add to this, most corporations are multi-banked, so this process is often repeated multiple times.
All this effort is in the service of ensuring the bank maintains correct, timely and useful customer KYC data, and therefore a current customer risk rating. KYC software is the bedrock of effective financial crime compliance, and these onboarding and refresh processes are immensely important to support, amongst other things, robust Anti-Money-Laundering (AML) monitoring capabilities – but how can banks and financial institutions make better use of the data and information they already have access to?
1. Build a connected view of a customer even before they’re a customer
When onboarding a new customer, the typical approach is to start from scratch and assume that nothing is known about the customer in question – but that’s rarely the case.
For large legacy financial institutions, there is a high likelihood that your firm has seen or interacted with that new customer previously. Consider:
The new customer might hold a different product with you – in a different line of business or a different geography
The new customer might have been the counterparty to a transaction with one or many of your existing customers
The new customer might be a director/controller of a business – which you already have as a customer
The new customer might have family or associates who already are customers
These connections are powerful – in terms of improving onboarding efficiency if information can be gathered internally rather than via the customer, but also in terms of making an informed risk assessment. It is usually difficult for large financial institutions to draw these connections because they lack the Entity Resolution capability to build a true single customer or prospect view. The result is poor customer experience, a lengthy analysis process, and an impression that the financial institution does not truly know or appreciate the existing relationship they may have with a customer.
2. Use high-quality third-party data – with accuracy
The amount of high-quality third-party data available continues to increase year on year, with new providers entering the space and additional useful data being captured, gathered and curated – particularly on legal entities. More and more governmental organizations are pursuing an “open data” agenda and looking to increase the transparency and usage of ownership and control data with the creation of Ultimate Beneficiary Owner (UBO) registers.
Most financial institutions are now looking at strategic ways of leveraging this third-party data to increase the efficiency of their customer onboarding or KYC refresh processes, as well as identify change events and their potential risk implication as and when they happen.
The challenge is data overload – simply subscribing to a third-party data set and allowing your KYC analysts loose within that data to fill in customer profiles and identify risk will not improve the efficiency of your KYC process. When using a variety of data sources, consolidating the relevant information and building ownership and control views can become increasingly cumbersome. This may lead to poor quality KYC profiles, mismatching information with internal databases and bad customer outcomes if your analysts aren’t able to identify the correct, relevant and trusted data for your customers.
Entity resolution technologies help to solve this issue – automatically connecting the third-party data with a financial institution’s internal view of a customer, enriching the profile with new or more up to date information, and therefore finding the correct associated reference data in a structured way. By leveraging network generation capabilities, organizations can build context around that customer to identify higher or lower risk indicators.
3. Use context to identify risk – before it becomes a reality
Financial institutions rely on a layered approach to detect risk at onboarding and refresh – for example, the use of a customer risk assessment taking into account jurisdictional risk, product risk and structural risk is layered with screening for sanctions risk and Politically Exposed Persons (PEPs), which in turn is layered with an enhanced due diligence approach for high-risk customers.
Customer context is another layer on top of these existing controls. An analysis of the recent laundromat cases (the Troika Laundromat) and the Azerbaijani Laundromat) emphasize how important a customer’s ownership and control structure is to understanding the underlying risk of that entity being used for money laundering – and how simple contextual factors could have uncovered serious risk indicators.
For example – many of the companies involved in the Azerbaijani Laundromat scheme were Scottish Limited Partnerships (SLPs), which historically have not been subject to the same level of disclosure requirements as other UK companies. While most financial institutions would have considered this ownership structure to be of elevated risk, the customer context around these companies would have revealed other substantial risks – for example, that the companies had all been set up within a very short time, or that many of the companies had a registered address that was shared with thousands of other SLPs. Shared director names and addresses combined with unlikely financial details would have added to the suspicion that these were likely shell companies rather than legitimate customers.
This type of context – “zooming out” from a customer and focusing on who and what they are connected to – is the key to detecting financial crime risk even before a customer begins transacting.
