The Dataiku for Anti-Money Laundering (AML) Alert Triage Solution uses historical, labeled alerts to predict “true positive” AML alerts, generating priority scores for new, unlabeled alerts and ranking them for financial crime teams to prioritize effectively.
The Dataiku for AML Alert Triage Solution offers ranked alerts based on the likelihood of them being true positives, making it easy to triage and pass this information into any existing case management systems used by your financial crime team.
Now, compliance analysts can focus on the highest risk alerts, reduce alert fatigue, and increase escalation accuracy.
The prioritization model gives you easy-to-understand insights into potentially relevant variables to use when creating new business rules or updating existing ones.
The Dataiku for AML Alert Triage Solution remains fully operational over the long term thanks to its integrated model revaluation and streamlined redeployment.
Because you can easily review the prioritization model and use the same process to reevaluate it over time, you save on maintenance overhead and costs.
Your teams can begin seeing benefits quickly. Using integrated and simple data requirements, you can quickly and transparently deploy updated models, all while maintaining full governance and control at all times.
Leverage Dataiku’s powerful integration capabilities to embed the resulting scoring model in your AML setup, fueling prioritization scores into your case management system.
The Dataiku for AML Alert Triage Solution helps answer a broad range of questions like:
Alert triaging is just the start. With confidence built in ML impact, smoothly move to a deeper AML setup, including rules recalibration, ML-enriched rules, and enhanced investigation activities through graph analytics — all with full governance and control.
A composite organization in the commissioned study conducted by Forrester Consulting on behalf of Dataiku saw the following benefits:
reduction in time spent on data analysis, extraction, and preparation.
reduction in time spent on model lifecycle activities (training, deployment, and monitoring).
return on investment
net present value over three years.