Machine Learning: The digital compliance advantage.

Be quicker than criminals, reduce the cost of false positives.

Machine Learning for Compliance

Compliance meets machine learning

Compliance systems in banks and insurance companies must quickly determine which clients and transactions pose a compliance risk. To this end, many of them use ACTICO Compliance Suite. It is based on rules that detect money laundering, terrorist financing, market abuse, and insider trading. Machine learning procedures now complement this rule-based system.

Broschüre Compliance Suite

Key Features

Machine Learning (ML) in ACTICO Compliance Suite

ACTICO Compliance Suite uses rules to detect unusual client behavior and potential compliance risks. Combining this with machine learning links the expert knowledge of compliance officers with knowledge that is automatically learned from data (data knowledge). ACTICO Machine Learning is now a Compliance Suite module integrated into the existing software modules. Key features are: 

  • Prioritizing decisions

    ACTICO Machine Learning evaluates the probability that a given hit is a true positive. 

  • Learning from monitoring models

    ACTICO Machine Learning learns precise models from historical data. 

  • Detecting exceptions

    ACTICO Machine Learning detects unfamiliar transactions in a mass of transactions.


Fewer false positives

Machine learning is able to learn complex models. That is why it is very precise in analyzing compliance risks. False positives are reduced. 

Optimizing resources 

Machine learning helps with prioritization. Compliance officers can prioritize hits that have a high probability of being true positives. 

More true positives

Machine learning complements existing rule-based approaches and detects previously unknown cases. Together, the two methods deliver more true positives.

Satisfied customers

ACTICO Compliance Suite has covered compliance requirements for banks and insurance companies for many years.