Customer screening engines continue to throw up a deluge of both unwanted and spurious matches, even when enhanced with solutions such as secondary screening and automated alert decisioning. Baringa’s Watch List Optimisation (WLO) tool allows watch list content to be tailored to your risk appetite prior to screening, preventing the generation of worthless alerts and reducing total alert volumes by up to 40%.
Banks require methods of identifying individuals and entities that pose an elevated financial crime risk, and/or are subject to local or international sanctions.
One of the ways banks carry out this identification is by screening existing and potential customers against a watch list of persons and entities of supposed interest.
These watch lists typically contain three (potentially overlapping) groups:
- Those subject to sanctions
- These are individuals and entities that have been identified by applicable sanctions bodies, which for a UK bank are typically those issued by the UK, USA, the United Nations and the European Union.
- Politically exposed persons (PEPs) and their relatives and close associates (RCAs)
- While there is no hard and fast definition of a PEP, the designation generally covers politicians, senior law enforcement figures and others in public life, whose possible exposure to bribery, corruption or coercion heightens their risk of involvement in financial crime.
- Persons or entities of special interest (SIPs / SIEs)
- Special interest persons or entities are those that may be of interest from a financial crime perspective, including those with current or previous criminal convictions or linked to criminal groups or terrorist organisations.
Obtaining information on these three groups is challenging. With the exception of sanctions, there is no officially prescribed definition or database of PEPs, SIPs or SIEs. Even with sanctions, the multitude of lists that need to be considered, the various formats that these are provided in and the high frequency with which they are updated (due to the ever-changing nature of the geo-political landscape) result in a complex landscape that needs to be navigated.
For these reasons, most organisations turn to specialist third-parties to provide a financial crime watch list.
Flaws in third party lists
Why then are there so many false alerts?
Third-party watch list suppliers are incentivised to make their lists as broad as possible so they can be used by clients of all sizes, types (i.e. financial or otherwise) and geographies. This means that they include entries with minimal risk, and many records will have no relevance for clients.
These lists then tend to be used wholesale by banks to screen their existing and prospective customers, rather than being tailored to their particular circumstances.
As a result, entries of no consequence are included in the watch list, which has a twofold impact:
- Alerts being generated for true matches (i.e. customer record is the same person/entity as that on watch list)
- Alerts being generated for false matches (i.e. customer record is similar but not the same person/entity as that on the watch list)
In both instances, these are worthless alerts, as the watch list record is of no consequence (i.e. the individual/entity is not relevant for the bank, or they sit within the bank’s risk appetite with no action required).
In addition, the data quality of watch lists provided by third party vendors can be poor, with variations in formatting, missing / incomplete fields and a tendency to populate key information within free text fields (as opposed to explicitly defined fields where the context of the data becomes obvious). The problems with data quality and completeness can result in additional false matches, as well as potentially missed true matches, depending on how the screening engine matching algorithm is configured.
Optimising your screening
Our Watch List Optimisation tool can improve your ability to hone-in on genuine risks by preventing the generation of worthless alerts. Your AML and sanctions policy can be easily codified using the front-end of the WLO. This allows rules which control the removal of unhelpful records to be created and managed without any technical understanding.
For example, our WLO tool allows your analysts to set rules on the seniority or location of judges, politicians and other PEPs to include on the watch list in line with your risk appetite. It also allows analysts to specify key words and phrases to be identified within free text fields and ‘wildcard’ searches to help you pinpoint activity that could pose a financial risk to your organisation (and ensure this is included within the watch list), whilst simultaneously removing entries which pose no interest. We’ve already seen that our WLO tool can cut the volume of screening alerts by 40%, while validating that only worthless alerts have been culled.
The WLO tool also has built-in procedures to remediate issues with data completeness and quality that tend to be specific to each of the main third-party watch list vendors. This enrichment and standardisation reduces the volume of false matches (where the previous lack of watch list information prevented a potential match being ruled out by the system). The tool also improves the effectiveness of the system in alerting true matches (as additional information is available for use by the screening engine in determining a match).
Our WLO tool is quick and easy to deploy to existing customer screening implementations, pre-processing the latest watch list content prior to it being passed through your existing screening tool. The outputs are fully auditable, including itemisation of which entries have been included or excluded from the original third-party list and the reasons why. The agile nature of WLO means it can also be easily updated to reflect changes in risk appetite, matching rules or the source watch list.
If you would like to know more about our Watch List Optimisation solution and how it could benefit your organisation, please contact Cat Morris.
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