The customer onboarding and maintenance experience in financial services is still difficult, tedious and expensive. 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.

 

According to Refinitiv, corporate clients report the average onboarding process requires eight separate contacts with their financial services provider, and that the customer refresh process takes an average of 20 days to complete.

 

All this effort is in service of ensuring the bank maintains correct, timely and useful Know Your Customer (KYC) data. KYC is the bedrock of effective financial crime compliance and these onboarding and refresh processes are immensely important – 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 diffaerent 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 view. The result is a poor customer experience 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 organisations are pursuing an “open data” agenda and looking to increase the transparency and usage of ownership and control data – a topic we’ll address in detail in a future blog!

 

Most financial institutions are now looking at strategic ways of leveraging this third party data to increase the efficiency of their onboarding and customer maintenance processes, as well as identify risk events 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. This, may even lead to poor quality KYC profiles 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 your internal view of your customer with the third party data, finding the correct associated reference data in a structured way, and building 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 review – 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.

 

Click here to learn more about how contextual decision intelligence and increased automation is the most effective way to bring efficiency gains for organizations and make better use of the data and information they already have access to.

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