Senior Manager - Financial Crime, EY
Banks have been quick to seek the promised benefits of using AI techniques in their financial crime operations, yet few have achieved widespread deployment and continue to face the traditional challenges.
Join our line-up of experts for this short, informative session that will help you:
- understand how to transform your AI vision into reality with real life examples from Banking and beyond
- discover industry perspectives and recommendations to improve the global fight against financial crime
- build an action plan around how to operationalise AI in your AML operations
|8:00 a.m.||Registration & breakfast|
|8:30 a.m.||Welcome & Introduction||Talya Sella, Senior Business Solutions Manager, AML, SAS|
|8:40 a.m.||Precogs: AI empowered AML|
Practical examples of how AI can empower analysts to better combat money laundering and financial crime
|Iain Brown, Head of Data Science, SAS|
|9:10 a.m.||The benefits & challenges of AI in the FinCrime world|
Why have attempts to use AI not seen the promised results? A look at why we might need to fundamentally reassess our approach
|Matthew Reed, Senior Manager - Financial Crime, EY|
|9:40 a.m.||Industry perspectives on Machine Learning and AML|
A review of the IIF survey on adoption of AI for AML purposes along with industry recommendations to improve the overall effectiveness of the global regime to combat illicit financial activity.
|Matthew Ekberg, Senior Policy Advisor for Regulatory Affairs, Institute of International Finance|
|10:10 a.m.||Q&A Followed by networking and refreshments|
Successful execution of an analytics, and thereby AI strategy, needs the right balance of choice and control.
Creativity and innovation flourish in open spaces. You need flexibility and freedom to attract the best analytical talent, use a wide variety of techniques and develop processes that work best. You need the flexibility to use multiple programming languages and analyze any data in any environment and to keep up with accelerating demands.
Analytics chaos can creep up. Once you lose control of your data, you lose trust in the system and its outputs. Transparency, governance and security become essential for maintaining trust in models and analytical results. Becoming even more critical as you scale development, monitoring and refinement of analytics applications and their associated processes.
At the intersection of data, software, and ingenuity, the future is being redefined. After all, when curiosity meets capability, progress is inevitable.