In the 2019 MIT Sloan Management Review and Boston Consulting Group (BCG) Artificial Intelligence Global Executive Study and Research Report, 9 out of 10 respondents agree that AI represents a business opportunity for their company.
While at the same time when they were asked:
“What if competitors, particularly unencumbered new entrants, figure out AI before we do?”
In 2019, 45% perceived some risk from AI, up from an already substantial 37% in 2017. More and more leaders are viewing AI as a risk if they are behind in adoption.
Financial Institutions have been at the forefront of AI adoption. But within financial institutions, we are seeing varying trends. Deloitte surveyed 1,100 executives from US-based companies across different industries that are prototyping or implementing AI. Out of which 206 respondents were working for financial services. On the basis of the responses received the Organizations were categorized in 3 segments Frontrunners Followers and Starters.
The traits observed of the Frontrunners were:
Embed AI in strategic plans: Integrating AI into an organization’s strategic objectives has helped many frontrunners develop an enterprise wide strategy for AI, which different business segments can follow. The greater strategic importance accorded to AI is also leading to a higher level of investment by these leaders.
Apply AI to revenue and customer engagement opportunities: Most frontrunners have started exploring the use of AI for various revenue enhancements and client experience initiatives
Utilize multiple options for acquiring AI: Frontrunners seem open to employing multiple approaches for acquiring and developing AI applications. This strategy is helping them accelerate the adoption of AI initiatives via access to a wider pool of talent and technology solutions.
The Frontrunners across the globe are leaving no stone unturned in adopting and harnessing AI in their businesses processes. With more than
Million Fintechs registering every year reimaging financial processes by using AI, Financial Institutions are at the big risk of losing to the competition. Banking in the current world is very different from banking of last century.
Adoption of AI in financial Institutions was led by the use of Analytical models in Risk and fraud management but today AI/Ml models have become the core to each process ranging from Sales to Operations to Marketing.
The Big Data space is set to reach over $273 Billion by 2023 which is the clear indication of the faith that Organizations are putting in data and Analytics. In last 2 decades analytics has evolved from low key data visualization dashboards to full blown machine learning interactive models. We have moved from descriptive to predictive to now prescriptive analytics. Organizations doesn’t want to just stop at the prediction they look forward to solutions prescribing them on what measures needs to be taken. Though we cannot completely do away with the manual interventions but adding more and more insights helps in making better decisions.
Some of the areas in which we have witnessed the remarkable progress are
Risk Scoring in Banking: In early 2000s, we were talking about visualizing the Non-Performing Loans (NPL) across the branches through dashboards and reports. By 2014 we had solutions which could predict the Non-Performing Loans basis the customer attributes, then next few years went in training those models to predict accurately. Today we are talking about using ML to not only predict the NPLs but also to prescribe the major reasons for Non-Performing Loans. The critical role which these AI/ML based early warning solutions are playing in credit decisions is noteworthy.
Similarly the Manufacturing, Oil & gas and Energy sector are banking on Industry 4.0 tools. Oil refineries are saving millions by using predictive maintenance and Anomaly detection solutions. These solutions not only help organizations in optimizing maintenance schedules but also increase the residual life of equipment.
As per the Mckinsey report
“Predictive maintenance typically reduces machine downtime by 30 to 50 percent and increases machine life by 20 to 40 percent”
Every 10 percent of the saving on machine downtime is equivalent to Millions in revenue for the Organizations. Hence 30 t0 50% would be hundreds of million dollars over the year.
As we are moving forward organizations not only want the solutions to predict when the maintenance will be due but also to prescribe and fix the maintenance schedules basis data from different equipments in the plant. Solutions are also offering the inbuilt feature to trigger auto email to the maintenance engineers for fixing appointment.
Though we are embarking new heights and unleashing new peaks with the help of AI but to benefit from these solutions, organizations need to reimagine their Organization Strategy in the light of AI and embrace the change which comes enroute in this journey.