Our client, a large retail bank, was interested in assessing the data quality of their customer data, specifically with respect to understanding how any data gaps or data quality issues might impact the effectiveness of their sanctions and PEPs screening.

The client was aware of certain data quality issues related to the completeness and accuracy of customer data, but was unsure of the extent of the issues, and with over 80 different data sources of customer data and more than 10 million personal and organisation customer records, undertaking a comprehensive data quality review of all customer data presented a challenge.


Our client, a payments company, had concerns that their transaction monitoring was not effective & could be opening them up to the risk of failing to identify suspicious activity. They were also concerned about their regulatory risk should a regulator investigate & find them to be sub-standard.

We worked with our client to scope a review of the transaction monitoring & controls as follows:

  • An assessment of the policy & procedures covering transaction monitoring
  • An assessment of the quality & coverage of TM rules
  • A gap analysis to assess alignment between the TM rules & the firm’s business-wide risk assessment


Have you faced a situation where something has triggered a large increase in screening alerts?

For example, an expansion of your business requires a new set of customers to be screened or maybe there has been a change in list provider or screening solution, or perhaps you have the requirement to rescreen your customers for adverse media or PEPs etc.

It is highly unlikely there will be the resource capacity to deal with spike in alerts. As a result, it can be extremely unnerving to be faced with a large set of unprocessed name matching alerts waiting to be worked. Could there be a sanctions match lurking in the pile or a new PEP which will require enhanced due diligence?

SQA Consulting has developed a new screening tool called the Eliminator.  This takes your alerts, matches and reprocesses them, applying further logic and rules to eliminate vast quantities of false positives.

This following case study showcases how the Eliminator tool successfully reduced a back book of alerts, such that the client only had to manually review 2% of the alerts. The other 98% could be automatically closed as confirmed false positives.

The Challenges

A client was faced with a massive volume of name screening alerts, over 1 million matches, relating to Sanctions, PEPs and Adverse Media after an exercise to rescreen the client base was carried out. These needed to be processed and closed but this would not be possible manually.


A law firm had been appointed as a Skilled Person to conduct a Section 166 review of various financial crime processes at an e-payments company. The law firm required support and subject matter expertise to assess the adequacy and robustness of the company’s sanctions monitoring. This included: 

  • The coverage of sanctions lists used by the company 
  • The calibration of the sanctions screening system, including thresholds and matching logic 
  • The adequacy of investigations undertaken to discount/escalate potential sanctions matches 
  • The adequacy of controls in place to maintain, update and test sanctions list data feeds 
  • The effectiveness of compliance oversight and quality assurance 
  • The adequacy of training and communications relating to handling potential sanctions alerts. 


Included within the scope of a recent customer screening assurance review for a client was testing the approach to PEPs.

The client wanted confirmation that the PEP screening was working as designed and had raised concerns about the high level of false-positive alerts that were being generated by the system.


A provider of sanctions screening engines was interested in assessing how effective their screening engine was at identifying sanction list names and how the various system configuration setting options might affect screening effectiveness.

In short, the screening provider wanted to improve the performance of their screening engine and approached SQA for assistance.


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