VP Bank & ACTICO

ML-powered fuzzy payment screening improves hit quality and cuts workload in half in sanctions list checks

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Capital Markets | Sanctions & PEP Screening | Europe

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For us, it was important that fuzzy matching would increase the quality of hits for incoming and outgoing payments without unnecessarily increasing the number of transactions that had to be checked. We have achieved this goal.

Fabian Wälte

Head of Payments and Transaction Services, VP Bank Group

Challenge

Modern Payment Screening in Private Banking – VP Bank Group opts for new payment screening system based on ACTICO Machine Learning

The mounting pressure on financial institutions to meet ever-stricter compliance rules has led VP Bank Group to update its sanctions list screening. A review of the payment screening system used at VP Bank Group revealed that it no longer met the current requirements for fuzzy matching.

Solution

Installing a new Payment Screening System based on ACTICO Machine Learning

To ensure an accurate identification of potential risks without increasing the work and costs involved, the Liechtenstein-based bank decided to install a new payment screening system. It is based on ACTICO Machine Learning and is able to improve the quality of fuzzy matching significantly. At the same time, artificial intelligence has helped to halve the workload of compliance and payments staff at VP Bank.

Impact

Effectiveness and Efficiency in fuzzy Screening – An intelligent monitoring system optimizes cost and risk at the same time

Fuzzy matching means that it should still be possible to identify risky transactions even if the name in the transaction does not exactly match the name on the sanctions list. A fuzzy search always produces more hits than an exact search. Non-risky false positives increase the workload in payment and compliance teams and lead to a trade-off between effectiveness and efficiency in payment screening.

  • Effectiveness = As many risky transactions as possible can be found
  • Efficiency = The least possible transactions that, on closer inspection, turn out to be innocuous can be found

By using ACTICO’s intelligent monitoring system, VP Bank Group managed to overcome this conflict of goals. Since the system was implemented, it was able to continuously reduce the hit volume and increase the hit quality.

The above average efficiency of the ACTICO Machine Learning model was confirmed during a benchmarking exercise.

ACTICO client VP BANK

About our customer

VP Bank Group is an internationally active private bank and one of the largest banks in Liechtenstein. It has offices in Vaduz, Zurich, Luxembourg, Singapore, Hong Kong and Tortola in the British Virgin Islands. VP Bank is listed on the SIX Swiss Exchange and the group’s financial strength is confirmed by its A rating from Standard & Poor’s.

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