Sanctions Screening Software for Banks and Insurers
How does software auto-match data on individuals and companies with sanctions, PEP and adverse media lists?
And what impact does AI have?
How does software auto-match data on individuals and companies with sanctions, PEP and adverse media lists?
And what impact does AI have?
Sanctions screening software checks customer data against entries in sanction, adverse media, and PEP (politically exposed person) lists using comparison algorithms.
Banks and insurers then use sanctions screening in banking during the onboarding process, but they are also obliged to review customers and business partners regularly throughout their business relationship. The need to identify and monitor new and existing customers on an ongoing basis is part of the KYC (Know Your Customer) process and helps underpin anti-money laundering regulations.
Sanctions screening tools can struggle with false positives, namely alerts generated by the software to flag potential risks. Although technically accurate, they do not always indicate high-risk individuals. Since a compliance team member must investigate each false positive, banks and insurers increasingly use machine-learning techniques. Combining compliance staff’s expertise with data insights can help streamline sanctions screening and make it more effective.
The sanctions screening software matches data from individuals and legal entities with the existing customer base. There are public lists, such as the EU list, the Bundesanzeiger list (Germany’s Federal Gazette), the OFAC list (the US Office of Foreign Assets Control), or lists provided by data providers like Dow Jones, Lexis Nexis, info 4c, LSEG, or Reguvis. In addition, internal watchlists can also be used.
During onboarding, i.e. when an account is opened, the software screens on an ad-hoc basis and analyses data from new customers accordingly in line with the KYC principle. To monitor existing customers, the software conducts regular reconciliations and checks to ensure that customer data aligns with sanctions list data.
If the software detects any customer data entries that match those on a PEP (politically exposed person), sanctions, or adverse media list during screening, it automatically generates an anomaly and assigns it to the responsible compliance manager for case processing, necessitating manual clarification.
Machine learning is an AI component that aids in streamlining the sanctions list screening process. Closed cases are utilized to train machine learning models and generate a score value. This score indicates the probability of a noticeable anomaly posing a genuine risk. Any anomalies with a high probability are subsequently flagged as top priorities for the compliance teams to investigate.
„The numerous benefits reaped through HCOB include far smoother and powerful transaction monitoring and scope to define risk criteria in much finer detail. Customer risk scoring and compliance reporting procedures have also been streamlined, with all relevant data now swiftly accessible and integrable in a data warehouse.”
Dr. Michael Sendker
Head of the Compliance Digitization Project, Hamburg Commercial Bank
ACTICO Customer Screening is an AI-powered software solution for banks and insurers. It automatically compares customer data against the sanction, PEP, and Adverse Media lists. One key advantage is fewer false positives, achieved by leveraging machine learning to automate case processing. Both on-premises and cloud operations are supported.
For compliance teams, quality is the top priority and sanctions screening software must perform flawlessly. This point is particularly crucial.
Banks and insurers hold substantial amounts of data from their customers and other contractual partners, such as parties involved in claims or claimants. As part of their compliance obligations, they must routinely review the entire dataset, including updates, and the sanctions screening tools must be capable of managing this task.
When the algorithm in the sanctions list screening software functions optimally, few false positives emerge, and it can be relied upon to find true positives. Incorporating machine learning into an AI-powered approach can further enhance the end result and reduce false positives by up to 60%.
Banks and insurers often have to manage multiple sanction, adverse media, and PEP lists. Ideally, the sanctions screening software you use should include interfaces to facilitate the import of this data.
More and more companies are opting to cloudify their compliance applications. But modern software used to check against sanctions list checks must be suitable for both on-premise and in-cloud operation.
The more standardised the software, the more rapidly security updates or new releases can be distributed.
Automatically cross-check personal data against sanctions, PEP and adverse media lists, use machine learning and reduce false positives. Our experts are happy to answer any further questions you may have.
When it comes to sanctions list checking software, banks and insurance companies have differing requirements.
Banks from this sector have thousands of customers and business relationships that need regular auditing, all of which the software has to handle regularly.
The focus for these banks is on more complex business relationships. Their clients often have complex networks. The software used must be flexible and agile enough to adapt the sanction screening rules to check against the listed clientele.
Insurers often hold data on millions of customers as well as data of contractual partners such as parties to claims or claimants. The sanction screening software must be capable of matching these high volumes of data with the sanction lists during batch operation.
Day in, and day out, banks and insurers have to match their customer data with sanctions lists. This is how they meet the regulatory requirements, gain transparency about their customer base, and help safeguard their reputation.
High data volumes make it all the more important for the software to perform well and minimise the number of false positives identified. Discover how one of the top 5 insurers in Germany maintains an accurate customer database while minimizing manual effort.
Financial institutions use software to regularly check new and existing customers for matches to sanctions and PEP lists. When it comes to reducing false positives, machine learning is in a class of its own.