Reducing Loyalty Fraud with Big Data & Machine Learning - Webinar Recording: Fraud Fighting Technologies

Date: 14 Dec 2016    Location: Webinar Recording    Delegates: 50+

Organised in cooperation with:

With an estimated more than 70% of $238bn market at risk, CFOs and Marketing Heads understand the need for highly advanced and efficient solutions to fight loyalty fraud.

Swastik Bihani, VP of Product Management at Simility, led this webinar to help participants understand how using Big Data Analytics and Machine Learning can overcome common fraud problems like:

• Fraudulent Account Creation
• Stolen Account Usage
• Loyalty Fraud Rings
• Unauthorized Points Redemption

With this technology, you can spot a suspicious device before it commits fraud, even if it has never appeared on any blacklists before.


To obtain the link to the recording of this webinar, click on the button above for "Sign up for Conference Presentations" and request the link for the "Loyalty Fraud Prevention Webinar with Simility."
 




Swastik Bihani, VP of Product Management at Simility, is a seasoned Silicon Valley business leader with 15+ years of experience in the high-tech industry. He has a long successful tenure in cyber security spanning multiple geographies (UK, US, India) and is well versed in technologies used to thwart various attacks including fraud, spam, malware, DDoS and hacking. Swastik holds an MBA from UC Berkeley's Haas School of Business with focus in Marketing and Strategy.




Michael Smith has been writing and speaking on the topic of loyalty fraud for the past several years. Formerly at British Aiways, where he managed the non Air Partners of the loyalty program, he has subsequently worked with numerous stakeholders across the industry to understand their issues and concerns for loyalty fraud. Michael just completed an industry wide survey of loyalty fraud that will soon be published.
 



27 years of fighting fraud at Google, funded by top tier VC investors, including Accel and Trinity, Simility transforms fraud prevention with a versatile platform that combines the best of human analysis and machine learning. We combine the power of algorithms to recognize similar and dissimilar signals with the ability for humans to create meaning out of them and give front-line fraud fighters tools that empower them to put their domain expertise and knowledge to use without needing to write code.