How to reduce false negatives in AML with graph and AI tools

Описание: In this webinar, you'll learn how combining graph analytics and AI can help organizations effectively reduce missed detections, cut down on false alerts, and speed up AML investigations.

How important is it to reduce false negatives in AML?

Traditional rule-based systems often struggle to detect advanced money laundering schemes, which can let criminals exploit weaknesses. Financial institutions and government agencies frequently face difficulties in uncovering complex money laundering networks, leading to wasted resources on handling false positives and lengthy investigations.

Enhancing AML systems with graph analytics and AI

Linkurious Enterprise and Dataiku, can significantly enhance traditional AML systems. By combining graph analytics and machine learning, these tools work alongside existing systems to generate more accurate alerts and eventually replace outdated methods. Graph visualization capabilities accelerate and improve the quality of investigations, while an efficient, agile back-testing environment allows you to define appropriate indicators and scoring approaches to effectively reduce both AML false positives and false negatives.

Get to know more: https://linkurious.com/blog/reducing-...
You can also contact us for further assistance: https://linkurious.com/contact-us/?ut...

Chapters:

00:00 Introduction and Webinar Overview
00:55 Introduction to Linkurious
02:35 Introduction to Dataiku
05:27 Agenda and Key Topics
06:24 Challenges in Anti-Money Laundering
09:36 How Linkurious and Dataiku Can Help
11:24 Modular Approach to System Enhancement
13:55 Dataiku and Linkurious Integration
22:14 Dataiku's Capabilities and Demonstration
29:45 Leveraging Dataiku for Machine Learning and Alert Prioritization
30:30 Dashboarding and Performance Evaluation in Dataiku
31:02 Connecting Dataiku to Neo4j
33:18 Exploring Linkurious Enterprise Integration
34:32 Investigating Alerts in Linkurious Enterprise
38:04 Analyzing Suspicious Transactions and Connections
42:44 Exporting and Sharing Investigation Results
44:42 Q&A Session