When it comes to data collection, financial institutions have a rich trove of readily accessible real-time client information. When a consumer moves homes, buys a car, pays college tuition or simply swipes a credit card, the transactions are recorded in a database.

It is a massive amount of highly regulated customer data, and this makes it tailor-made for artificial intelligence decision-making, since the sheer size of the information eclipses what humans can do to properly manage and analyze it.

“The financial services industry is one in which clean, uniform customer data is routinely collected,” said Robert Jones, director of business consulting at Fair Isaac Corp, more commonly known as FICO, in an exclusive interview for this story. “It’s got some of the deepest and best data out there. Every purchase made, every credit card swipe, the time of day, the geographic location, payment history, risk levels … life milestones are what they deal with. For years, financial institutions have been collecting a massive amount of customer data. That’s one area where AI-powered decision-making excels.”

In the first of a three-part spotlight series on cloud modernization, SiliconANGLE takes a closer look at the future of dynamic decision-making, including how FICO and Infosys Ltd. are leveraging AI-powered tools to improve results for customers and businesses alike while ensuring decision transparency and compliance, powered by Amazon Web Services Inc.

This article is part of SiliconANGLE’s ongoing exploration of cloud modernization trends in 2023. This piece highlights key developments from AWS and AWS Partners. AWS is sponsor of this ongoing article series.

Advanced analytics

The financial industry is not a newcomer to the AI revolution. FICO, a leading analytics software company, was founded in 1956 and introduced analytic solutions for credit scoring. As AI began to emerge as a viable technology, including the rise of generative AI for a range of use cases, the banking world has found applications for it.

“FICO has been using artificial intelligence for the past 20 years in the form of machine learning models, generation of synthetic data and other applications,” Jones said. “AI-first institutions are using advanced analytics to generate intelligent, highly relevant messages and provide smart servicing via assisted channels to create a superior experience, which has been shown to contribute to higher rates of conversion.”

There is another measure of how AI has emerged as a significant technology in the financial world. Research data indicates that major banking firms are currently on a hiring wave to fill AI-related positions. This includes J.P. Morgan Chase & Co., which advertised 3,651 AI-related global jobs over a three-month window earlier this year, and Citigroup Inc., which posted approximately 2,100 AI-related openings over the same period.

Digital drivers

The move of the financial industry into AI-related solutions is being witnessed firsthand by large digital consulting giants, such as Infosys. The company built its global practice in more than 56 countries by identifying the key drivers of digital transformation for clients. In 2023, those primary drivers make for a short list.

“There is AI coupled with cloud; that is what’s driving digital transformation,” said Shyam Vijayan, AVP at Infosys, during a recent interview with theCUBE, SiliconANGLE’s livestreaming studio. “The cloud part gives you the scalability and the modern environment to innovate very, very fast, but the AI part gives you the insights that you can then turn into very quick decisions that can drive business growth and execution as well.”

An example of what Vijayan described can be seen in Infosys’ generative AI solution, Topaz, which was launched earlier this year on AWS, leveraging Amazon Bedrock, a fully managed service through AWS. Amazon Bedrock is designed to make it easier for users to build and scale generative AI applications with foundation models.

Topaz is an AI-first set of services and platforms using generative AI technologies to improve client service, reimagine business processes and boost productivity. Within the financial services sector, the digital consulting firm is already seeing an impact as clients adopt AI-based solutions.

“It’s helping reduce costs,” said Navdeep Gill, industry principal and segment lead for risk and computational finance at Infosys, in an interview for this story. “We implemented an AI solution for a large Australian bank to bring down the cost of operations by 40%. The decision-making is faster.”

Prompt credit decisions

The financial industry is leveraging AI in a number of key areas, including the all-important field of risk management. Making the right decision over whether or not to extend credit to a customer can have a significant impact on financial institutions and businesses over time, and it is one reason why AI is playing a significant role.

“AI-first banks have designed streamlined lending journeys, using extensive automation and near-real-time analysis of customer data to generate prompt credit decisions for retailers, small and medium-size enterprises and corporate clients,” Jones noted. “They do this by sifting through a variety of structured and unstructured data collected from conventional sources, such as bank transaction history, credit reports and tax returns, and new ones, including location data, telecom usage data and utility bills.”

Banks are also working with clients to assist them after they are in the fold. AI is being used to channel data flows into actions that can help and retain customers through closer, consistent engagement.

“Combining internal and external data sources can create a comprehensive report,” Gill said. “Banks are increasingly engaging with customers to assist them to stay on track with payments and work more closely with those who have problems. It’s building an enterprise-wide digital marketing engine to translate insights generated in the decision-making layer into a set of coordinated messages delivered through the bank’s engagement layer.”

Personalizing the experience

There are two additional areas of opportunity in the financial industry for AI, and they involve sentiment analysis and hyper-personalization. One of the hallmarks of generative AI has been its ability to funnel natural language processing into insights. In a recorded call, is the customer happy or are they upset about an unsatisfactory experience?

“A bot driven by natural language processing can predict with a high degree of accuracy what the customer was thinking and can recommend follow-up actions to prevent silent attrition,” Jones said.

Hyper-personalization extends this analysis further by using AI’s decision-making prowess to improve the effectiveness of marketing campaigns. Rather than mailing thousands of pieces of mail on the slim hope that 0.5% will respond, financial institutions can leverage new algorithms to incorporate helpful data points, such as the preferred method of communication and the best time of day to follow up.

“Banks may use AI-powered decisions to develop a smart, highly tailored servicing experience based on client microsegments, allowing diverse channels to give greater service and a compelling experience with fast, easy and intuitive interactions,” Gill said.

The collaboration between AWS, FICO and Infosys offers a glimpse into how rapid advancements in AI are reshaping the financial industry. This is a story of how technology and banking expertise come together to deliver solutions that make a difference in areas of a business that matter. It is only the first installment in what will likely be a long, innovative journey.

“We are still in early days, but I do think the more surprising, really jaw-dropping stuff will come out of collaborative partnerships, layers of technologies working with each other,” Jones said. “Groups working together combining AI technologies with other emerging or established technologies – that’s where the exciting stuff will happen.”

Image: PhonlamaiPhoto’s