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Data quality’s role in smarter AML decision-making

Anti-money laundering compliance requirements are getting tougher, which is putting more pressure on AML teams. As more and more data needs to be collected and documented about every customer, the need for data to be accurate and up-to-date increases.

This is a guest blog from our data partner Roaring. Visit their website for more information.

In many cases, we’ve found customer-related processes in companies, where the customer itself is responsible for providing the AML information, such as their own PEP status or declaring the UBO’s (ultimate beneficial owners). Giving the customer the responsibility to answer these questions themselves is risky, if not verified or checked.

With manual inputs from the customer itself, the risk of bad data increases rapidly. Not only due to people lying to get through a certain process, but mainly due to faulty entries, misspellings etc. In turn, bad data costs a lot of money and can also lead to poor decision-making. One example is allowing “high-risk” companies, from a money laundering perspective, to become customers, another is key strategic decisions being made based on bad data.

So what is data quality and how does it apply to making wise AML decisions?

Data Quality Explained

Data quality is a measure of the condition of data, normally assessed in five different criteria; validity, accuracy, completeness, consistency and uniformity. When data fails to meet the quality demands listed in the criteria of assessment, it is deemed “bad”.

Using high-quality data to make smarter AML decisions

In the era of data-driven businesses and big data, there has never been a better time to invest in data quality. Not only to secure your business and reduce risks, but to improve processes as a whole. We list a couple of examples where accurate customer data can improve the comfort and quality of your process.

Customer Onboarding

With automated data collection and verification from high-quality sources, based on a company id or social security number for example, data such as UBO’s, PEP and sanctions screening, company activity, board members and representatives can easily be checked.

This enables avoiding high-risk companies and people becoming your customers, rejecting signups or applications from such entities in an early stage of your process. Rejecting the high-risk customer early on, is a key decision based on data, as it not only reduces risk, but also reduces the need for backend administration and costs related to those types of activities.

Recurring data cleanses and monitoring

Customer data changes regularly. People changing their address or workplace, a change in PEP or RCA status or board members being replaced are just a few examples. It is therefore key to keep track of changes and to ensure customer data quality and compliance over time.

Data cleansing refers to the process of replacing, correcting, modifying or deleting corrupt, inaccurate, irrelevant, duplicated, incomplete or incoherent records from a set of data. It is done with regular intervals in order to keep information up to date. However, new ways of monitoring customer data, such as webhook monitoring and push notifications, have grown rapidly in recent years.

When customer data changes, you can use monitoring services or cleanses to collect the new data, and keep your customer profile and risk status up to date, which is crucial to consistent and smart decision-making in the long run.