Fraud crosses many use cases and affects multiple industries. These use cases are inter-linked and sometimes cross each other and include: AML, transactions & credit card, cyber & malware, financial crimes, insider threat, and compliance & audit. In addition, Fraud is connected to Retail and Financial Services as fraudsters become more sophisticated and use cyber data, to create rings of thieves that can attack via transactions or credit cards for legitimate goods and services. The sophistication has only increased pressure from world health events, the need for real-time analytics, and prevent and intervene strategies.
The focus of this webinar is to identify how Machine Learning, Visualizations, and new technology can directly increase the accuracy and output of systems and include 'Human in the Loop' to get ahead of fraud. This event is designed in a 'Speed Dating' format with a focus on key topics in order to maximize the insights. During this online meetup, you'll learn from our experts on how Expero can unlock the potential in your organization. We will feature Expero technology lightning talks followed by a short Q&A.
Key Learning Topics:
- Key Challenges in Complex Fraud: illustrate why Visualizations, Machine Learning, and Graph still utilize 'Human in the Loop' for maximum accuracy and productivity
- Methods to Reduce False Positives by 10%: review Machine Learning combination techniques with Graph and other platforms to reduce false positive signals
- Increase Accuracy of Fraud Identification and Intervention: strengthen and increase accuracy with combinations of technique and technologies
- Creation of Fraud Based Data Products for Preventive & Predictive Analytics: access to different roles from fraud management, investigators to data and analytics teams
- Use of Visualization for Explainable Machine Learning: show practical uses and methods for fraud identification, complex dependency, and case management
Session 1 (10 min): Why & how ML and visualizations combined with 'Human in the Loop' can be game changers for fraud detection, investigation, and prevention
Session 2 (20 min): Identify visualizations and human interaction for fraud: 'the art of the possible' fraud workflow demonstrations - management, investigations, and ML & data practitioner views
Session 3 (20 min): Details on how ML with other technologies can reduce false positives, increase accuracy, and move towards predictive forecasting and active scenario analysis
Session 4 (5 min): Tie together the different elements of ML, visualizations, & other technologies for a cohesive approach to fraud identification, analysis, and prevention
Session 5 (5 min): Open Q&A