Three key data challenges
For starters, poor data quality can lead to bad alerts or false positives. Sometimes data is wrong. And sometimes data is missing, which can skew accuracy by its omission.
Second, there's the issue of disparate data sources. When data comes from different places it is often defined and measured differently in each source, which can lead to false positives. Today's investigators spend a lot of time collating data. Many pull up one screen for their case management system, only to minimize it and open a dozen different windows to collect all the data they need.
As a result, it can be difficult to ferret out whether an alert is legitimate. With no single 360-degree view of the customer to rely upon, due diligence becomes a time-consuming task. This is particularly challenging for larger institutions dealing in multiple jurisdictions or across many different product lines. And consolidation across the banking sector has only intensified the problem.
Finally, there's the issue of data volume. In some cases, particularly at larger banks, the sheer volume of data prohibits the transaction monitoring systems from scaling to it. And as part of the volume challenge, there's a lack of skilled resources to mine that data.