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.
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:
ACTICO Machine Learning evaluates the probability that a given hit is a true positive.
ACTICO Machine Learning learns precise models from historical data.
ACTICO Machine Learning detects unfamiliar transactions in a mass of transactions.
Machine learning is able to learn complex models. That is why it is very precise in analyzing compliance risks. False positives are reduced.
Machine learning helps with prioritization. Compliance officers can prioritize hits that have a high probability of being true positives.
Machine learning complements existing rule-based approaches and detects previously unknown cases. Together, the two methods deliver more true positives.
ACTICO Compliance Suite has covered compliance requirements for banks and insurance companies for many years.