Banking and financial services industry has always been on the forefront in adoption of technology. This industry has kept pace with the evolving technology and as a result it has seen its process and execution speed change over time.
Few decades back, the focus was on making the transaction processing engines more efficient. In many areas, especially in Capital Markets the technology race shifted towards race for reduction of latency. Later the flavour of the season shifted to structured data analysis, in the form of data-warehousing and MIS systems.
Just as the technology enhancements in banks and financial institutions was plateauing somewhere around 2015, we saw the beginnings of the next wave of technology disruptions. This current wave is transformative in ways more than one. It is an AI led disruption in banks and financial institutions. What we have witnessed so far is just the tip of the iceberg. We are currently in an interesting phase when adoption, the learning curve and technology maturity are happening at the same time.
We believe, that the road to a mature AI state in the banking and financial services industry is through intelligent automation. Currently, the world is experiencing more of intelligent automation of processes. Some of this automation is in the form of conversational bots, while some other initiatives are focusing more on structuring relevant information from unstructured sources for process efficiencies or for decision making.
Prediction of future is an area which is also finding its real life use cases in the form of market prediction or default prediction in the lending space. What we are witnessing now is the early stage of disruption. It will take us about a decade to reach to a point where AI led disruption would be all pervasive.
Along with these changes in the technology world, the past two years of pandemic has also had a role in giving an extra push towards intelligent automation and adoption of such technologies. It is expected that the post pandemic world will be very different from the pre pandemic era in terms of our working pattern. We may not revert to the old ways of working completely, neither would we be in the current mode which is largely remote working. Most likely the reality of the post pandemic world will be somewhere in between. Which means that the impetus on AI adoption will continue to grow.
If we fast forward to 2030, many changes are expected in the way our financial institutions operate. All these changes would not just make us more efficient in our processes and in decision making, these will also lead to a huge transformation in the workforce. Some of the elements of such transformation are covered by the world economic forum’s report on the future of jobs.
As per WEF report by 2025, the time spent on current tasks at work by humans and machines will be equal. The report further estimates that by 2025, 85 million jobs may be displaced by a shift in the division of labour between humans and machines, while 97 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms. 90 percent of the financial services companies surveyed by the WEF future of jobs survey indicated that AI technologies are likely to be adopted by 2025.
With this as the background, let us project the future of the workforce in the banking and financial services industry, a decade from now. We believe that some functions would witness more reskilling than the others. The role of the analysts would be to get into a more subjective and nuanced analysis than what it is now. The human mind will still have a role to play but instead of focusing on structuring data from unstructured documents, such as the financial results of companies, an activity on which currently 80% of the energy gets focused, it will shift towards a more nuanced analysis of the financial results. So manual generation of financial spreads will be a thing of the past. More news would be extracted and analysed at the time of underwriting decision than what is happening now.
The human mind will be made more effective in decision-making, as the ability to process unstructured information will increase. So we do see the analyst role in 2030 but the analysts will be focusing their energies on finer analysis and not so much on collating data which occupies majority of the analyst time today.
The operations team will see many processes shrinking because of the AI led automation. These teams would experience re-skilling. Preparation of data for model creation will be a role that will emerge within the operations teams. Analysis and testing of models will be an important aspect which will require skill development.
The maker-checker roles and effort will undergo a change. While the maker effort will be reduced due to the machine taking over the checker bandwidth required will remain the same. In some processes and organizations, we may see checker coverage going up as a result of the makers becoming the checkers.
Overall, the ROI will be in favour of the machine but that would not be the only reason for AI technology adoption. The big reason is going to be the reduction in the sub optimal decisions that get taken because of the current limitation of scale that the human mind brings to process vast amount of information especially the unstructured information.
The ROI based on cost saving can be easily estimated and there are various organizations which have attempted the same. It is the cost of reducing the sub optimal decisions which will provide a bigger ROI. Though it is hard to estimate this ROI but it is going to be much higher as compared to the cost saving ROI. The amount of information from the unstructured sources is only increasing exponentially and hence risk reduction because of the ability to process vast amount of unstructured data is going to make the shift a must have shift.
(Pravin Lal is Founder, and CEO of Capital Quant Solution, an NSE-backed startup)
