Nine out of 10 acquiring banks reported transaction fraud increased during COVID-19, according to PYMNTS.com. Meanwhile, U.S. lenders are doing business amidst rising interest rates – with household debt at an all-time high of $15.84 trillion. Now these problems can be managed quickly and accurately with out-of-the box AI solutions that are ready to deploy in as little as 30 days. With Mastercard's global network of 210 countries and territories, the breadth of transaction data is vast. Using transaction data for financial data analytics while respecting customer privacy is a core value for Mastercard.

Sudhir Jha, senior vice president and head of Mastercard's Brighterion unit, told Karen Webster in the most recent On the Agenda discussion that artificial intelligence (AI) can strengthen credit and risk management and broaden its value well beyond simply improving day-to-day operations. But to get there, enterprises need a bit of guidance. "What used to be cutting-edge technology five years ago is no longer cutting edge," he said, and enterprises that try to keep up with the rapid changes in data science and analysis on their own can be quickly overwhelmed. The enterprise that starts with regression and pattern analysis solutions might scale rapidly and find benefit from neural networks. For banks, acquirers and healthcare payments executives, he said, using vendors' AI-based solutions help to avoid undue losses from fraud, the abuse and misallocation of funds and poor underwriting decisions.

It's been a while since cash was king here in the U.S., but in other parts of the world, such as South America, paper money has managed to retain its grip, albeit slightly diminished as a result of the pandemic's many lifestyle changes that exposed cash transactions as cumbersome and risky. It's a reality that Brighterion's Sudhir Jha told PYMNTS has resulted in a pan-regional progression that is moving more Latin American consumers into digital payment solutions, even though credit penetrations remain low. "There's not a lot of historical data about consumers who are new to digital ecosystem -- that's why there is a desire to go directly to an AI-based solution in many cases, because you want a solution that works today, but also scales really well and attracts more and more customers to your system," Jha explained. That growing regional need for artificial intelligence (AI)-based solutions is what motivated Brazilian insurer Porto Seguro to team with Brighterion. Announced recently, the engagement leveraged Porto Seguro's analytical expertise in combination with Brighterion's AI technology to build high-performance models custom-created to identify risks better.

In a rapidly changing financial environment, the race is on for AI implementation. It's no longer if an organization is using AI, it's when they get it and how they implement it in business. The big question is should they develop in-house, buy off the shelf or get a custom solution? Here's what the options offer and what to look for when choosing an AI solution. Surveys of major financial organizations show they recognize the need to use AI to leverage their complex data and mitigate business risks. MIT Sloan Management Review did a survey of more than 3,000 managers and interviewed executives, learning that a majority of companies have tried developing AI, but only 1 in 10 gained significant financial benefits.

While many fintech platforms focus on risk assessment, Brighterion has been solely dedicated to AI-powered decisioning for over 20 years. With a sharp focus on financial irregularities, Brighterion's AI decision-making algorithms provide real-time detection in financial fraud, credit risk, healthcare fraud, waste and abuse, and money laundering (AML). The role of artificial intelligence is taking top billing in the search for software that detects fraud and credit risk. Legacy solutions like rules-based decisioning are hard pressed to stay ahead of bad actors as fraud evolves and becomes more sophisticated. Machine learning rises to the top for its ability to learn from complex and widely varied data.

Artificial intelligence (AI) is what all those 1980s killer robot movies were trying to warn us about…right? For financial institutions (FIs), AI has many beneficial aspects. With the right platform and proper optimization, AI can enhance the experience for both the institution and the customer. From credit risk monitoring to customer behavior predictions and everything in between, AI solutions can provide services that were lacking in the pre-pandemic world. Thanks to the accelerated digitization sparked by COVID-19, those killer robots may be on our side, at least when it comes to assessing credit portfolios.

Because COVID-19 made it difficult for consumers to venture out and run their usual errands, FIs needed to find other ways to provide their services. The only way for them to really keep up with the speedy digitization was through the implementation of AI systems. To further discuss all things AI, PaymentsJournal sat down with Sudhir Jha, Mastercard SVP and head of Brighterion, and Tim Sloane, VP of Payments Innovation at Mercator Advisory Group. Jha believes that there were two fundamentally big changes that occurred in banking during the pandemic: the environment began constantly shifting, and person-to-person interactions were abruptly limited. "Every week, every month, there were different ways that we were trying to react to the pandemic," explained Jha.

How do AI models compare to rules-based decisioning? Brighterion shows how its AI models outperform and gives client metrics.

Fintech startups and banks have always been at the forefront of tech adoption, and they've been curiously following the growth and development of AI for many years. And there's a good reason for it -- we, the consumers of their services, want to have access to cutting-edge technology while dealing with our finances, as well as making sure that the companies dealing with our savings be equipped with the best of what tech can offer. AI and ML have recently moved from the realm of futurism to the very crux of the conversation in the Fintech sector, and many aspiring businesses have started integrating it into their services. In this article, we wanted to touch on the ways various Fintech businesses and startups implement this technology in the services they provide their customers with and how it benefits their users. Let's dive right in, shall we?

You may have read previously on this blog that Brighterion collaborated with PYMNTS.com to analyze how AI and ML are being used by financial institutions (FIs) across the U.S. We surveyed 200 financial executives from banks and credit unions with assets ranging from $1 billion to over $100 billion to get a feel for the industry as a whole. In a series of reports, we've presented analysis of the 12,000 collected data points. The top four use cases for learning systems were supporting banking services (79.1 percent), enhancing payments services (53.7 percent), customer life cycle management (46.2 percent) and credit underwriting (42.5). Banks also reported using machine learning for compliance and regulation, preventing internal fraud, merchant services, collections and supplier onboarding. These findings also revealed a gap: financial institutions aren't accessing true AI with these tools.