Research indicates that many businesses are reluctant to embrace a data-driven culture, but anyone who fails to do so will miss out on the benefits of data-led 'know your customer' controls. HNW individuals do not have static identities and risk profiles, so the data-led approach is the way forward.

By harnessing large, cross-industry data sets, one can now authenticate the identities of people faster than ever before and do so with more accuracy and lower KYC costs than ever before, while also being more 'compliant.’ This is available to any business that is prepared to change.

Customers’ changing identities

Humans are continuously changing their risk attributes and occupy complex interconnected networks that are also in a more-or-less constant state of flux; they do not have static identities and risk profiles. It’s therefore surprising that some firms are still failing to look any deeper than the basic legal data attributes of an individual – usually collected during On Boarding – when building online identity checks to fulfil their ‘Know Your Customer’ (KYC) obligations. If humans are the sum of complex attributes that are changing all the time, then a firm cannot expect its KYC records and customer risk profiles to be effective if it relies only on a few, static attributes of identity.
 
One's name, address and date of birth establish little more than a legal identity. Even if we expand these data attributes to include nationality, gender and passport number, we are not really building up a clear picture of who the HNWI is. His identity is built up of a plethora of other niche and more complex aspects: his biology, the devices he uses, the people to whom he talks and even his patterns of behaviour, among other things. If the private bank he approaches for business increases the number of data attributes that it wants to collect on him, it will be more certain about who he is and the risk profile that suits him best. To be effective, a customer verification and risk profiling process must therefore take into account all these aspects of his digital footprint to establish his full identity.

Reducing risk and the cost of compliance

Data is crucial to any financial firm that wants to know its customers better than in the past; the more data it can obtain and evaluate, the more it can learn about its customer base. If the data attributes it uses are too few and too static, it will fail to gain an accurate risk profile of its customers and will not truly know who they are. Ineffective data is also at the heart of so many of the very costly ‘false positive’ problems faced by firms, which tend to arise because the financial firm in question is not certain about the entities involved. However, rather than using effective data-led IT to 'screen' and otherwise authenticate entities effectively, far too many firms continue to resort to employing more and more people to investigate risk-profiling problems or screening shortfalls and, somewhat bizarrely, they expect them to do so with the same ineffective data sets.

Firms are only embracing data science for KYC purposes at a slow pace. This can be attributed to a misplaced anxiety about whether new software packages are compliant, to confusion over the volume of software programmes available on the market and to fear of the unknown.

Despite widespread hesitancy, the solution is simple and readily available: strengthen the records you keep of your customers by using large sets of cross-industry data attributes (including screening records) and compare them with consolidated internal data attributes for each customer. If you keep those attributes dynamically enriched, you will know about any changes to identity and risk immediately.

This will enable your firm to identify duplicate accounts in its own records quickly, trace lost customers and update files to ensure that its view of its customers is accurate.

This sophisticated data-matching capability is available now. It is being embraced by some, but many more firms continue to use less effective, more costly methods.
 
Overcoming ‘explainability’

Artificial intelligence or AI, in the broadest sense, is simple: it is the use of computers to perform tasks that could otherwise be performed by humans. More sophisticated forms of AI, such as machine learning, use algorithms to analyse large sets of data and thereby spot patterns that lead to specific undesirable things (e.g. fraud/money laundering/payment default/corruption etc), all without human intervention. The more data that one feeds into the machine-learning programme, the more sophisticated the pattern analysis can become and the less easy it is for people to perform the same analysis.

At the most complex end of the analytic process, ‘deep learning’ can use either structured or unstructured data to analyse patterns on many layers in order to spot events that might have otherwise been impossible to predict or identify. A bank can use the same thing to 'reverse-engineer' some analysis from an event, thereby spotting the attributes that could have indicated that event (e.g. fraud or money laundering). Such complex, multi-layered analysis is helping firms to fix problems in a broad range of industries – from medicine to aeronautical design and manufacturing.
 
Despite its considerable successes, the financial services sector has so far failed to reap the benefits of this approach. This is largely because ‘deep learning’ struggles to explain exactly how it arrived at a particular conclusion. This jars with financial institutions, which must be able to explain how and why they reached this-or-that decision to their regulators. Before long, the industry will have to decide whether it is preferable to have ‘explainability’ or to have more effective, game-changing risk-management IT.

In the meantime, some banks are using simple forms of data-led statistical modelling and machine learning AI software that ‘comply’ with regulations fully to find patterns and associations in large data sets. They are doing this most successfully and already improving their traditional control models no end.
 
To understand the possibilities

In an increasingly data-led world, it is crucial for organisations to understand the value of data (when used correctly) and all its possibilities. By acquiring the right technology to manage the vast amounts of data that they need to help them make decisions, banks can truly reach their full potential. Knowing one's customer is no longer an option; instead, it is a pre-requisite for the survival of one's financial business.

It is clear that richer data sets are capable of improving both the experience of customers and the insights that their banks have about risks. They can also reduce the cost of data-related errors, duplicate accounts and inefficient decision-making processes. With that in mind, banks would do well to consider the competitive advantages they could gain by taking up such technology.