The Short Trip From Analytics to Artificial Intelligence
Decades of popular science fiction have painted artificial intelligence (AI) as futuristic, mysterious and sometimes campy – on track to become reality around the same time bankers begin driving hover cars to work. Yet AI is now reaching buzzword status, and there's not a hover car in sight.
The recent interest in AI could feel like a sudden and disjointed trend – an alluring concept without the technology to support its promises. In reality, AI is the next logical step in a long progression of innovations, in banking and across all other industries.
The basic foundation for AI is already in place. And as advancements continue at a rapid pace, new capabilities will alter the way businesses serve their customers – and the way customers expect to be served. In a time of growing complexity, AI may hold the key to helping businesses create an experience that's better, faster and more affordable.
So what, exactly, is AI? Broadly speaking, the term can be defined as software that is able to manage tasks previously believed to require skills only humans possess. It's borne out of a combination of increasingly sophisticated software and vast improvements in how growing data volumes are retained and accessed. The result is highly automated applications that mimic the way people reason, use language and build on established knowledge to get smarter over time.
At its most sophisticated level, AI theoretically could enable machines to form original ideas, exhibit nuanced social skills and teach themselves how to solve complex problems. While the full potential of AI is still coming into focus, we can be certain that AI-powered applications will someday manage many of the tasks a business's staff does now and make employees more capable in new responsibilities.
Elements of AI have already appeared in American homes and businesses. Personal voice assistants such as Alexa, Siri and Cortana have become household names, and their capabilities are expanding rapidly. Banking services using digital assistants are attracting more attention, and leading financial services providers are building out voice banking skills that connect Amazon® Alexa Voice Service to digital banking and payment services.
Innovations in data warehousing and predictive analytics have laid the groundwork for those capabilities. As financial institutions turn their attention to stronger data and analytics – creating a clear data strategy, assigning dedicated resources to manage data initiatives and partnering with data-focused technology providers – they've built a solid bedrock for coming innovations. Today's efforts in data and analytics are preparation for tomorrow's AI.
At the same time, new methods of software integration have broken down data silos, letting data flow freely. Advancements in data management and solution integration create the conditions for machine learning, which is the next step in the evolution toward full AI.
Machine learning is the point at which bots gain the ability to create new assumptions and predict outcomes based on a range of existing information, without being explicitly guided by human programming.
Behind the scenes, machine learning uses sophisticated methods of identifying trends within data sets. Rather than using simple "if, then" logic, the machine is taught to pull from a wider range of indicators and make adjustments on the fly. It's like giving a bot the capacity to develop a unique language by first teaching it how individual letters and syllables combine to form words and sentences.
Through our fintech accelerator, INV, Fiserv is identifying and testing new technologies related to AI as they come to market. The first generation of solutions that employ machine learning as an integrated addition to banking processes are now being delivered. Financial institutions are using those solutions to prevent and detect fraud more efficiently, to transform the customer experience and to offer tailored financial advice. In future posts, we'll explore the potential for using AI in each of those ways to help clients do more with existing resources and develop more satisfying customer experiences.