Machine Learning: The digital compliance advantage
Be quicker than criminals, reduce the cost of false positives
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.
Key Features
Machine Learning 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 a Compliance Suite module integrated into the existing software modules.
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.
Benefits
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.