Data analytics is the key to KYC By Delphine Masquelier, KYC Solution Manager at Quantexa

With businesses racing to digitalise, they amass enormous volumes of data to help their strategic and operational decisions. However, most of these huge swathes of data exist in silos, meaning organisations struggle to extract meaningful value.

Currently, 95% of businesses are experiencing the effects of the ‘data decision gap’, which is the difference between the data they have and the data they can use to bolster business-critical decisions and drive optimal results. Alongside constantly changing regulatory pressures, this ‘data gap’ exposes them to compliance risks . This becomes increasingly important when it comes to ‘knowing your customer’ (KYC).

Financial institutions can transform KYC processes through the better use of data and its analysis, enabling better spotting of both risks and opportunities. However these organisations must first re-address their internal KYC processes.

KYC as we know it

When looking at the way risk is assessed, the traditional and most widely used KYC model still heavily leans on ‘profile data’, which is when it depends on specific profile characteristics of a legal entity or individual based on categories, such as geographic locations. This method is plagued with many inaccuracies and inefficiencies when considering all growing complexities within the global enterprise ecosystem.

On top of this, the way many organisations still apply KYC processes is vastly done through human manual monitoring. Despite the hard work risk analysts put in to ensure total compliance, it is still inevitable that human capacity cannot process the gargantuan amount of data flowing into the enterprise. Manual onboarding, refresh and remediation processes also require big teams, driving up the cost of compliance whilst also resulting in poor customer experience.

Aware of the race to keep up with regulation changes as well as the pressure to digitise rapidly, most organisations are launching themselves into the dark. Many are throwing both money and human hours at these issues without addressing the root problem. The solution lies in smarter data analytics and context - and the role technology plays is critical in a highly constrained cost and regulated environment.

Understanding customer behaviour in context

To enable a more intelligent way of staying compliant and driving effective and efficient KYC processes , whilst staying in tight control of finances, businesses must turn to contextual methods of collecting and making sense of data- such as contextual decision intelligence (CDI). This is based on how real risk in any organisation often hides within indirect connections and transactional behaviour with other entities and organisations in the network.

Businesses must build a single network picture to create a single customer view based on dynamic layers of context from every available bit of internal and external data. Entity resolution can help with this overall enhancement of data analytics - the process of working out whether multiple records are referencing the same real-world person or organisation - and implementing automation on top of this takes it a step further.

How businesses build customer risk profiles can also provide a key to the solution. There needs to be a major move from categorising according to 'old school risks' to a new model of looking at the behaviour of every customer to be able to detect changes and anomalies as risk arises. This requires a fundamental shift from profile-based and static ID element data use, towards more behavioural approaches.

Once behavioural risk profiles have been properly established, enterprises must be able to continuously monitor and constantly update the data in KYC record systems to reflect changes in information and risk profiles. By applying dynamic network generation to emerging or changing risks associated with customers, analysts can refresh and reassess customer risk scores in real-time, before it is too late to act.

This moves away from the ‘traditional’ KYC processes, which could risk enterprises missing major shifts in their customers’ activities, and forms the foundations of ‘perpetual KYC’ (pKYC). pKYC methods boost capabilities to make processes far more informed from an internal data perspective. This helps build a single customer view within an organisation, which can then be enriched with external information that is relevant to the customer to drive better awareness of the entire value chain.

Risks and opportunities

It is unquestionable that once enterprises have the ability to better know their customers, they can better identify risks and opportunities related to them.

Enhanced KYC processes help transactional monitoring, so that the organisation will be able to seamlessly reveal the origin and whereabouts of funds. This can help uncover hidden risk among customer activities, such as money laundering, fraud detection, tax evasion and even terrorism financing. A robust KYC process clearly helps companies avoid becoming embroiled in economic crime, providing security to remain compliant with ever-changing regulations.

KYC, however, is not only used for enterprises looking to remain safe from regulatory scrutiny. These processes can also increase operational efficiency and enhance customer experiences. In the enablement of helping businesses better understand their customers, they will consequently be able to provide them with exactly what they are looking for. In enhancing an overall customer experience, and thus company loyalty, businesses reap the rewards in the form of overall business development and return-on-investment.

Looking forward

KYC remains at the heart of financial crime compliance, forming the most solid base for other use cases across financial institutions, including regulatory pressure, fraud detection, as well as growth opportunities. The right data analytics technology significantly reduces the need for ongoing due diligence reviews, making businesses more trustworthy to both regulators and customers.

Context and technology within wider data analytics is the ‘secret sauce’ to every KYC process, as it helps organisations shift towards better behavioural-based monitoring. It can also enable the development of a critical ‘single view’ of customers amidst the vast silos of data many organisations are still drowning in. With this, businesses will be able to make smarter data-driven decisions and finally close the data decision gap.

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