A major credit card issuer was faced with a daunting challenge: quickly and accurately screen over 375 million records from 14 separate data sources. Finding a solution that was powerful enough to process the data at lightning speed while maintaining a delicate balance between efficiency and accuracy and avoiding disruptions to their Anti-Money Laundering (AML) operations would be difficult. The company sought out FinScan, who could offer expert support and training, refine their screening algorithms for optimal accuracy, and resolve any issues in a timely fashion.

The initial obstacle involved consolidating data from the 14 sources, two of which held an astonishing 100 million records each. Navigating through the different data sources can pose a significant challenge when each source has its own distinct format and level of detail. To tackle this, FinScan conducted a bespoke data quality assessment on each source to understand the state of the data. Then, the FinScan Data Prep application was used to unify the data into standardized formats such as naming patterns, dates of birth, and country formats. Moreover, the team set up alerts to ensure that if an error during the data uploading process occurred it would trigger a notification to the organization and be promptly rectified to avoid delays in the workflow.

The next issue was the sheer quantity of records that needed to be screened and monitored on a regular basis, which can prove difficult for some providers. As a credit card issuer, speed and accuracy of screening is vital to maintain smooth onboarding processes and daily operations. FinScan’s systems can scale for high volume batch processing, meaning processing 375 million records overnight was no issue. FinScan also has the scalability to manage even larger amounts of data as the business grows.

Because the credit card issuer had only a small analyst team for reviewing cases, it was imperative that their screening process be as efficient as possible and reduce the number of alerts without increasing the risk of missing a true hit. This was done by customizing the matching criteria using a test file of 5 million records. By narrowing down the alert criteria they were able to find the most efficient point of reducing alerts while still detecting hits. Moreover, FinScan enabled them to filter the AML list content and select specific risk attributes including geographical regions and regulatory lists when screening, which drastically reduced the potential for false positives. With the combination of the right AML dataset and FinScan’s advanced matching technology, the number of alerts was reduced from a staggering 16 million records down to a manageable 5.3 million, massively reducing their review time.

FinScan also uncovered an additional 353 confirmed sanctions and PEP alerts post implementation, reducing the organization’s risk of fines from non-compliance and reputational damage.

Throughout the implementation, FinScan worked closely with the client to train them on how to effectively use the software, building a strong bond between the teams in the process. The training offered ultimate flexibility, with a combination of in-person sessions and online videos that were available 24/7. This allowed for a customized and convenient learning experience, ensuring the client had all the support they needed to succeed.

With the implementation of FinScan, the credit card issuer was able to transform their compliance process by efficiently screening hundreds of millions of records with ease. FinScan’s flexible configuration allowed the issuer to customize their matching criteria and strategically select the list categories to screen against, reducing false positives by 67%. Throughout the entire process, the FinScan team was extremely supportive, which gave the client confidence in their new processes.

Discover the power of efficient and effective sanctions screening with FinScan. Our cutting-edge software can quickly screen tens to hundreds of millions of records, ensuring that you remain compliant and protected from financial crime.