As the world confronts new challenges, it is a unique, unprecedented time to recast old ways of working and redefine industries. At this moment, although it may seem that global business is at its quietist, beneath a veneer of calm, feverish transformations are taking place across markets, behind the closed doors of companies and their workers, all evolving to these new conditions.
Financial services are fertile ground for this type of transformation and wild growth. In a recently published IBM white paper, A point of view Financial services: Data and AI at the core, IBM experts say that the “post COVID 19 crisis will mark a drastic acceleration of three critical domains for banks and insurers:”
– Transforming client experience (CX)
– Reinventing intelligent workflows
– Spurring cultural change
Finance leaders need to leverage the “symbiotic relationship” between AI and data to enable and accelerate the AI journey of their businesses, enterprise-wide, to keep pace. But to do so, we must first shrug off a few misconceptions about AI in banking and finance that hold back progress. What does the financial services sector get wrong about AI? Here are 3 misconceptions about AI in financial services explained.
Misconception #1: AI only needs data
Data is essential to AI in financial services. It is the necessary fuel that AI burns to generate “thought.” But like an automobile or passenger airline, premium fuel is not the only factor for performance, efficiency and raw speed. The pilot behind the wheel plays a critical role.
Just as much as data, successful AI requires learning and methodical training. “Today’s professionals believe the more data they have, the better. In fact, less is more in many cases. Qualitative data sets are what’s required, as learning and performance are deeply linked to specific usage,” according to IBM SMEs in A point of view Financial services: Data and AI at the core.
AI is not a simple equation of gathering the right data. Learning conducted by the trainers teaching these artificial thinkers to think is equally essential. Successful AI requires optimizing the combination of data and learning. The transfer of contextual knowledge, relevant skills and experience—by humans to AI—ensures AI interprets data accurately, fairly and in a way that is useful.
Misconception #2: AI mandates a singular set of skills
You may have heard alarm bells for the AI talent war, as financial institutions eager to begin reaping AI benefits absorb as much data science talent as they can. While data science expertise remains crucial currency to help transact AI dreams into reality, effective AI takes more than data science.
In a sense, AI takes a village: “Large transformational AI and data projects require data scientists, and a wide range of business and industry experts, such as sociologists, semantics specialists, psychologists, linguists among others,” according to the recent IBM point of view white paper. A multidisciplinary set of skills possessed by a diverse group of people is needed to create effective AI, as well as to mitigate bias—vital for financial services institutions.
Misconception #3: AI technology trumps people
AI technology and people are two sides of the same coin. To focus on one without the other is to limit the potential of your AI initiatives, because the integration of both leads to success. “Often reduced to its technological dimension, AI is—above all—a human revolution,” say IBM experts. “Increasing collaboration between technology and humans means new mindsets, new behaviors and new skills. AI changes everything for everyone.”
Financial institutions are unique in that the rulebook to which they must conduct their operations constantly changes. “Additional manpower” or a ratcheting of technology in isolation is not enough to adapt to dynamic compliance and regulatory conditions. The collaboration of humans and technology are needed to adapt quick enough to stay ahead of not only regulations, but competitors also.
Download the white paper, A point of view Financial services: Data and AI at the core, to gain insights from IBM experts and see proven results from client case studies in the financial services sector. With lessons learned, dispel AI myths in financial services, ready for a robust DataOps and data platform redesign, and drive AI rapidly at scale.