Fraud Prevention With Machine Learning: Stream Processing and Real-Time Data Management

• How can organisations react quicker with stream processing to detect financial crime?
• What techniques can be used to ensure robust fraud detection?
• How can the industry be empowered to safeguard against reputational damage?

As fraudsters continually evolve tactics, traditional detection methods often need help. By leveraging these stream processing and real-time data access, organisations can revolutionise their approach to detecting and preventing fraud. Stream processing involves analysing data as it flows through systems in real-time, enabling proactive identification of fraudulent activities and swift response.

However, there are unique challenges associated with real-time fraud detection, including effectively handling large volumes of data, ensuring data accuracy, and maintaining low latency. Latest advancements in machine learning algorithms can help resolve some of these issues, specifically focusing on their unification of data-in-motion and data-at-rest. By combining these technologies, organisations can enhance fraud detection accuracy while minimising false positives.

Further, the significance of model interpretability and ethical considerations when deploying machine learning for fraud prevention must also be considered. With innovative techniques and strategies, technologies can be leveraged for robust fraud prevention effectively. The industry must stay ahead of fraudsters and safeguard organisations against financial losses and reputational damage.

Watch this Finextra webinar, hosted in association with Hazelcast, to join our panel of industry experts who will discuss the potential of stream processing and real-time data in fraud prevention.

Speakers:

• Gary Wright – Head of Research, Finextra [Moderator]
• Manish Devgan – Chief Product Officer, Hazelcast
• Chris Merz – Senior Vice President, Data Science, Cyber and Intelligence Solutions, MasterCard
• Jaco Struik – Global Head of Fraud Services, ING