Case Study: VP Bank Group halves payment screening workload and improves hit quality with ACTICO Machine Learning

VP Bank Group uses fuzzy payment screening with machine learning to produce better-quality hits while cutting its workload in half

VP Bank Group, an internationally active private bank based in Liechtenstein, needed to modernize sanctions-list screening for payment transactions by replacing exact searches with fuzzy matching to catch typos, name-order swaps and deliberate misspellings without overwhelming staff. To address this challenge it partnered with ACTICO and adopted ACTICO Machine Learning’s payment screening solution.

ACTICO implemented a calibrated, ML-driven fuzzy matching system that combines multiple similarity algorithms and targeted whitelisting; over a seven-month project this produced better-quality hits, significantly reduced false positives and halved the payments team’s workload. After going live on 1 February 2021 daily hits were already lower and, from February to June 2021, total hits fell while the proportion of transactions forwarded to compliance rose by more than 15%, demonstrating ACTICO’s measurable impact.

VP Bank Group

Fabian Wälte

Head of Payments and Transaction Services