Fraud Detection Solutions

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Alaric Systems "Fractals" card fraud detection and prevention systems using proprietary inference techniques based on Bayesian methods. Analyst's Notebook 6, from IBM, conducts sophisticated link analysis, timeline analysis and data visualization for complex investigations. Aptelisense Compliance Automation Server, advanced real-time fraud prevention and data compliance that requires zero change to applications or systems. Business Data Miners builds highly effective data-driven models and rules to mitigate credit risk and fraud losses; saved its clients over $100 million in the past 2 years. Centrifuge, offers analysts and investigators an integrated suite of capabilities that can help them rapidly understand and glean insight from new data sources.


Het vizier op de tech industrie

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Cosmos Bank in India recently had $13.5 million siphoned off by hackers linked to the Lazarus Group in North Korea. They exploited and succeeded in compromising two of the bank's payment systems – the ATM Switch and the SWIFT payments system. The group is also alleged to have orchestrated the $81 million cyber heist at Bangladesh Bank by siphoning off SWIFT payments from the bank's Federal Reserve account. These attacks emphasize the need for layered fraud defences and controls that effectively mitigate such risks going forward. As the adage says: "There is no silver bullet!"


How machine learning enables real-time commerce The Paypers

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There is no question about it: the real-time, on-demand economy is disrupting ecommerce. These days, you can order rides, buy groceries, rent a car, make a dinner reservation, and more with a single tap on your smartphone – and each service arrives in as little as minutes. Against this backdrop of speed, more consumers are expecting – and even demanding – a fast and frictionless user experience. The challenge for businesses is to meet these high expectations and stay competitive – all without increasing risk. All types of ecommerce companies struggle with payment fraud, but time-sensitive businesses that fulfill orders in real time face unique challenges.


Machine learning innovations for fighting financial crime in an Open Banking era The Paypers

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The fight against financial crime is changing and banks are struggling to keep up. Financial institutions are already losing ground in the adoption of open banking initiatives like PSD2. Coupled with the increasing market demands for compliance and transparency brought on by regulations like the GDPR, it's clear that banks have a lot to deal with. The financial industry is quickly shifting towards real-time payments and instant services, two key aspects of a frictionless customer experience. However, these frameworks present serious challenges to the security side of things – particularly where financial crime is concerned.


Why today's real-time economy needs machine learning

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"Machine learning" is not just a buzzword for futuristic applications; it is the concept of machines carrying out tasks on their own that would typically require human intelligence. Its emergence is very much happening now. It is at the top of Gartner's hype cycle. In fact, Gartner predicts that by 2022, more than half of data and analytics services will be performed by machines instead of human beings, up from 10 percent today. And while not all machine learning use cases include real-time analytics, there is a definitive growth trend in the market for real-time decision making powered by machine learning.