IBM to tackle fraud with Iris Analytics

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IBM is going to apply machine learning to fraud busting with Iris Analytics. While that makes it sound as though it will be using Watson AI systems to identify fraudsters by gazing deep into their eyes, this is really about its acquisition of a German software firm called Iris Analytics. Iris monitors banking transactions and uses machine learning to spot previously unknown patterns of fraudulent transactions in real time. With only one bank in six equipped with real-time fraud detection systems, and even those taking a month or more to learn to stop new attacks once they are identified, IBM sees a big market for integrating systems like that of Iris with its existing antifraud products. This is far from IBM's first move into the antifraud market.

This Year's Model: ML Catches CNP Fraud Faster


With eCommerce driving 39% of luxury sales growth in 2017, a number that is expected to rise to 59% by 2023, even venerable brick-and-mortar retailers are succumbing to the heat. In New York City, retail icons Barneys New York and Dean & Deluca are in bankruptcy, victims of rising rents and a dramatic increase in online purchases of luxury goods. Those online purchases are big--for example, nearly half of the online luxury fashion dollar sales comes from footwear, which has the highest average annual spend per buyer and annual purchase frequency. In 2017, the average online luxury footwear spend increased 4% to $794, and purchase frequency also increased slightly to 1.6 times per year. These trends are catnip for fraudsters looking to perpetrate high-value card-not-present (CNP) fraud.

Making real-time fraud detection and prevention possible


ElasticSearch delivers real-time updating (fast indexing) with astonishing search/read response times, while Apache Kafka addresses event-at-a-time processing with millisecond latency as well as stateful (memory) processing including distributed joins and aggregations. Apache Spark is then used to process hundreds of thousands of records per node per second and offers near-linear scalability.

TSYS Enhances Real-Time Fraud Capabilities with Machine Learning Technology


"We will incorporate these capabilities across the credit risk lifecycle, enabling our issuers to catch more fraudulent transactions while dramatically reducing false-positive alerts for genuine transactions -- a sharp contrast to the industry paradigm of blocking more valid transactions in order to detect actual fraudulent activity." The new agreement allows TSYS to strengthen its position in faster payments by leveraging machine learning to provide clients with actionable insights in real time, using adaptive behavioral analytics that result in operational efficiencies. "TSYS has a long-standing leadership position in authorization processing and fraud management and we are excited to integrate our ARIC engine for TSYS' clients," said Martina King, chief executive officer, Featurespace. "We are proud to be working with TSYS to deliver world-leading machine learning fraud protection and exceptional customer management to their clients." The collaboration with Featurespace is yet another example of TSYS' partner-centric approach with new, innovative technology partners, particularly in the fraud- and risk-management space.

Artificial Intelligence at the changing face of Payment Fraud


A continuous strive towards making payment faster and easier is embracing technology innovation, like never before, which at the same time is creating newer risk exposures for frauds and money laundering. As technology evolves and new form & channel of payment emerge, it creates newer loop-hole for previously unknown pattern of fraud to sneak peek. Initiatives towards Faster & Easier payments are keeping financial industry on its toe to safeguard payments originated anywhere & anytime. Rising trend of P2P and m-commerce are fueling growth in Real-time Retail Payment Systems (RT-RPS). The growth has been encouraging, with 18 countries now having a'live' RT-RPS system in place.