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Several use cases for AI in fraud detection and management were discussed in the report. For example, AI can improve the accuracy of transaction monitoring. The analysts described how financial services provider FIS worked with Brighterion, an AI company owned by Mastercard, to improve its anti-money laundering capabilities. The provider now uses AI to vet risk when onboarding new vendors, for example. In other use cases, AI can improve the efficiency of fraud investigations by streamlining and prioritizing alerts.

Now that we've entered the largest retain season of the year, it is the perfect time to turn attention to cyber security defenses and fraud protection. Alarmingly, 96% of all ecommerce businesses have reported some form of fraud attack at their organization, with account takeover landing in the top three, according to Merchant Risk Council's 2019 Global Fraud Survey Results. Retailers need to prioritize proactive fraud management more than ever as the threat of fraudulent online activity continues to rise, especially as more consumers store payment, billing and shipping information with retailers directly. In order to catch fraudsters quickly and efficiently, retailers must consider the use of technology and machine learning – where it works, where it doesn't and where it's costing retailers. From new automated technologies to manual review processes, ensuring that these methods apply across all channels is vital, here's what retailers need to know in order to stay ahead of the fraud game.

The number of digital payments being made across the globe is increasing dramatically. Unfortunately, this volume has been matched by an increase in the number of fraudulent incidents. In fact, fraud has reached the highest levels on record, affecting more organizations than ever. The scale of the problem was revealed in last year's PWC Global Economic Crime and Fraud Survey. Nearly half (49%) of the 7,228 businesses across 123 territories that were interviewed reported that they had experienced fraud and economic crime over a two-year period.

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.

Today we are witnessing something almost unimaginable as recently as a decade ago: the mainstreaming of machine learning and artificial intelligence. All over the world, people use Alexa to assist with everyday activities. People listen to playlists tailored to their listening habits on Spotify and discover new movies via proprietary Netflix algorithms. Self-driving cars are becoming a familiar sight. Smart robots perform everything from shipping fulfillment to natural disaster management.

Stripe processes billions of dollars a year for some of the largest companies in the world. Enterprise Innovation talks to Michael Manapat, Stripe's head of data and machine learning products, about the lessons governments, large enterprises and online businesses alike can draw from Stripe's experience in building Radar, Stripe's machine-learning-powered fraud detection system. What key lessons can we learn from Stripe's experience in processing billions of dollars a year for global companies, especially in managing fraud, privacy and security? Manapat: In 2017 alone, Stripe Radar prevented US$4 billion in attempted fraud, directly helping the hundreds of thousands of companies who run their business on Stripe. From operating at this scale, we've drawn three important lessons that all online businesses, no matter where they are in the world, should consider when tackling fraud: They can be helpful, but also time consuming and not completely effective.

Using the smartest technology for fraud management in their joint effort to make online experiences smoother and safer, the two companies form a powerhouse for fighting ecommerce fraud and helping merchants maximize their revenue. The partnership creates a single platform capable of managing every type of ecommerce fraud facilitated by Sift Science. Acapture's in-house global acquiring solutions offer access to payment data and insights into the payments journey of the end consumer. This data paired up with Sift Science's cutting-edge machine learning models make the ultimate fraud management solution to protect merchants in the fast-paced fraud landscape. "We're delighted to have Sift Science as our trusted partner for our long-term mission to help merchants to fight fraud and deliver a better consumer experience" said Rudolf Booker, Acapture CEO.

In a stifling room at Mobile World Congress in Barcelona on Tuesday, Chris Reece discussed what artificial intelligence could do for the telecommunications industry. Reece, a technologist for Award Solutions, explained that AI, which telecos have already leveraged in some situations, could help solve some of communications service providers' (CSPs) most complicated problems. CSPs have been slow to adopt artificial intelligence, Reece explained, in part because the initial problems AI was developed to address didn't really affect them. When he asked the crowd for examples of problems they'd heard of AI solving, one person suggested chess, and another mentioned image recognition. Reece agreed, saying, "I don't know a lot of teleco operators who really need a computer to tell the difference between a cat and a dog." "There's a lot of opportunity to use AI in the telecom space, and we're just starting to scratch the surface," Reece added.

Let's consider what factors are driving artificial intelligence applications for payments and transaction processing: Digital banking and ecommerce channels are growing exponentially as more and more people use apps and mobile connectivity for transactions. For retailers, newer business models are evolving every day, from instant delivery of goods to digital downloads. Commerce is now operating in an omni-channel environment across multiple devices and touchpoints. Growth comes with a price, as it has led to a corresponding rise in fraud - and fraud loss - in online marketplaces that connect buyers and sellers. That's especially true in e-commerce, where it is harder and more complex to prevent fraud than in person transactions.