There are two types of companies operating today – those that acknowledge the impact poor data has on AML screening and those that act on that knowledge. What could possibly excuse the lack of action on data quality when it exposes you to so much risk?
Compliance teams repeatedly struggle to tackle data quality issues at their sources. Let’s take Matt for example. Matt is the compliance officer for a large bank that operates in over 50 countries, each country having its own responsibility for the capture and entry of customer data, with frequent manual data entry points. Because customer data is captured in different languages, character sets and name styles, there is limited standardization on how customer data must be formatted.
Matt knew that the quality of his own region’s data was insufficient, and he had tried to address this individually, but without some global sense of structure and compliance, it was a futile effort. While senior management agreed there was likely a cost associated with the problem Matt described, without specific details and some analysis of real data, he was unable to get approval to address the problem.
While the response was often that such a project would “cost too much” or “wasn’t a priority right now,” Matt realized that by leaving their customer data to deteriorate, it was increasing their costs by wasting resources reviewing false positives and increasing their risk of missing a true hit.
Matt knew that more needed to be done and decided to take matters into his own hands to research the impact that data quality could have on AML screening, asking himself some basic questions:
- Are the data elements to which we are matching (name, date of birth, country of birth, etc.) all complete, consistent, and accurate?
- Are we able to monitor the quality of data in real time and receive alerts if it deteriorates?
- Are we able to adapt our data’s format to more easily match the lists against which we’re screening?
Matt’s answer to each of these was, sadly, no. As he explored potential solutions, he fortunately stumbled upon FinScan’s data quality article that highlighted to Matt the catastrophic effects of poor data quality. It was here he realized that several common data quality issues directly affected him and his operations:
- Joint accounts where one person poses a higher risk to his business than the other account holders
- Names entered in address fields or incorrectly filled out forms
- Spelling mistakes in names, addresses, or company names
- Inconsistent or stylized words like titles, company abbreviations, or grammar
- International conventions for names (surname sequence and capitalization, matronymic or patronymic names, etc.)
Rather than waiting for the problem to grow, Matt reached out to FinScan and spoke to Robin, a sales representative that specializes in helping banks be more effective in their AML efforts. Robin arranged for an audit on Matt’s data and confirmed that it was in a bad state. She helped Matt develop a plan for a solution to cleanse the data that existed now and to keep new sources of data equally clean as they were added.
Thanks to Matt’s initiative, he found FinScan, the only AML screening software with a built-in data quality platform, which allowed him to identify and correct his data quality issues. When he began, Matt did not know exactly how much these issues reduced AML screening efficiency and increased his risk of missing a true hit. As a result of his efforts, he was not only able to provide his management the specifics of how their data quality impacted AML effectiveness but was also able to illustrate how it could reduce overall costs and risks across the entire bank.
This journey of understanding the impact of poor data quality does not always have a happy ending. Sometimes people don’t lend the necessary weight to the issue at all and in other instances, they choose to take no action. Either alternative can result in significant risk if your organization misses a true hit as most regulators will not accept “poor data” as an excuse.
Data quality can always be improved – but not without acting. The sooner you start, the sooner you’ll be able to improve your data and your AML effectiveness.
Contacting your IT partner or AML solutions provider is a good place to start. A data audit will allow you to profile your data, give you a good idea of any potential issues and start working on implementing long-term fixes. If your current AML solutions provider does not have an integrated data quality component, get in touch with FinScan. We can do a data quality analysis on a standalone basis and help you quantify the quality of your current data and the value that cleaner data will provide your organization.
We understand the importance of data quality to AML compliance because data quality is our foundation. FinScan has an integrated data quality engine – helping to identify and cure the mistakes that poor data entry, missing data elements or variations in the way names are stored or represented. We provide more than great technology solutions. We provide confidence that you have the cleanest data possible and increased accuracy in your AML screening processes.