AML Monitoring

Explainability was a condition from the start. For every alert, your team sees what drove the score, down to a single transaction. You set your own risk appetite. Every new version of the AI is tested against the current one before it goes live, and your bank validates the results. Every decision can be traced and explained, even years later.

Discai can run as an add-on to the transaction monitoring you have today, or you can replace it. It can check transactions ex ante, before they are executed, and ex post, depending on what you need. You can start by adding our AI solution to the rule-based scenarios you already run. Later you can move your rules onto our platform, and your investigations into ours. You decide how far you go and what you leave to us.

Discai brings false positives down by 40 to 60%. That means fewer alerts reach your team that never needed to be looked at. Every alert that does arrive shows the customer, the transactions and the factors behind the score. So investigators can judge within minutes whether an alert needs a full investigation or a short note. Your team is always in control and reaches a decision on each case faster.

The other part of the cost is keeping the solution running. One subscription covers the licence, the cloud, maintenance and the retraining of the AI. You do not pay separately for servers, updates or data scientists to keep the AI accurate.

Build and manage scenarios with full transparency over how alerts are generated. Our dynamic scenario management enables teams to simulate and tune scenarios before deployment, adjust thresholds and continuously improve performance.

• Design and manage scenarios Align detection logic with the institution’s risk framework.
• Simulate changes before deployment Simulate a change to a rule and see its impact before it goes live. A lookback runs a new or changed rule over past transactions and shows which alerts it would have raised.
• Continuously improve Evaluate scenario performance and tune thresholds using actual results.
• No-code rule design Create, manage and adapt rules with an intuitive drag-and-drop interface.

Discai uses machine learning to identify behavioural patterns, relationships and risks that predefined scenarios cannot capture. Depending on the institution’s needs and targets, different models are trained and combined to provide the most effective detection coverage.

By learning from your historical investigations or looking at anomalous behaviour within peer groups, the solution brings together behavioural, transactional and network insights into a holistic risk score. This way, investigators can focus their attention on customers that warrant closer investigation.

• Broader detection Identify suspicious behaviour that remains invisible to predefined scenarios.
• Behavioural insights Detect unusual customer behaviour and transaction patterns.
• Network intelligence Reveal relevant relationships and transactional connections.
• Risk prioritisation Focus investigative capacity on higher-risk behaviour for target typologies.
• Explainable risk scoring Understand the drivers behind each risk score and document your decision-making.

AI is becoming increasingly important in AML detection, uncovering suspicious behaviour that traditional scenarios may not identify. At the same time, scenario-based monitoring remains an established approach, providing the transparency, explainability and control regulators expect.

Combining rule-based monitoring with machine learning brings both together: broader detection without giving up regulatory control, with investigators remaining firmly in the decision-making process.