Sionic’s Financial Crime & Compliance team has today launched a new series of Sionic Fraud Talks looking at the most important fraud prevention techniques and technology financial services firms should focus on now, and in the immediate future.
These discussions are designed for the senior leaders in banks and financial firms who have responsibility for any aspect of client or business-focused fraud and financial crime prevention, cyber security or trade and transaction monitoring.
Each event is hosted by Sionic Head of Fraud Gareth Evans, who will be joined by leading industry experts to discuss behavioural profiling, transaction monitoring and open banking, artificial intelligence and machine learning.
“Fraud has never been more widespread or more sophisticated. It’s already effectively and ‘industry’ and one that’s growing fast. Fraudsters are expert innovators and it’s important not to underestimate their determination to stay several steps ahead of the individuals and institutions they target. It’s essential for everybody’s commercial and economic well-being for financial firms to reverse that balance. These sessions will help any leader responsible for tackling fraud to increase their understanding and the range of techniques and technologies they can use to fight back.”
The events all take place as Zoom-enabled webinars under Chatham House rules on 9th July, 13th August and 10th September 2020.
9th July: Behavioural Profiling
Gareth Evans, will be joined by Asaf Yacobi, Solutions Architects Director at behavioural profiling experts Buguroo. This session will explore how behavioural science is used in fraud prevention, in understanding cyber fraud techniques and in the interaction between cyber and user behaviours. The discussion will focus on new account opening, user-centric detection, cyber fraud and fraudster hunting.
13th August: Transaction Monitoring & Open Banking
Gareth will be joined by a specialist from IBM to discuss the advancement in transaction monitoring, holistic omni-channel fraud detection and how open banking, remote banking and new payment methods impact the capabilities of traditional transaction monitoring solutions.
10th September: AI & Machine Learning
Gareth will be joined by a leading data scientist from ThetaRay to discuss the application of data science in fraud detection and to explore different data science techniques and their applications across different fraud prevention applications.
Notes to Editors
- These events are for senior leaders in fraud and financial crime prevention and are by invitation only. Anyone wishing to receive more details should contact Sionic direct.
- Sionic’s specialist Financial Crime & Compliance Practice is led by Sionic Managing Partner Joe Cataldo. The team help financial institutions worldwide to formulate strategic approaches and demonstrate measurable progress in reducing risk exposure and maximising the efficiency and effectiveness of their financial crime compliance programs, applications, and solutions.
- Gareth Evans is a fraud prevention specialist with over 20 years’ experience protecting financial institutions, banks and their customers from attack. Gareth is a well known media commentator on all aspects of fraud. His most recent blog is on Pandemic profiteering: how to fight the fraudsters.
- Buguroo is a behavioural profiling, cyber and fraud firm established in Madrid, Spain in 2004. They have over 50m end users across more than 30 banks and offer products to tackle new account fraud and account take over, malware detection and behavioural biometrics.
- IBM is one of the world’s largest companies. Their product range includes IBM Safer Payments – a transaction monitoring solution for monitoring real time payments which is geared towards large volumes of payments with minimum latency tolerance for decisioning.
- ThetaRay is a cyber security and big data analytics company that provides artificial intelligence (AI) solutions for combating financial crime. ThetaRay specialises in unsupervised machine learning, identifying anomalies due to patterns within data.