The banking and financial services industry has always been at the forefront of technology adoption. This industry has kept pace with the evolution of technology and as a result has seen its process and speed of execution change over time.
A few decades ago, the focus was on improving the efficiency of transaction processing engines. In many areas, especially in capital markets, the technology race has shifted to a race to reduce latency. Later, the flavor of the season shifted to structured data analytics, in the form of data warehouses and MIS systems.
Just as technological improvements at banks and financial institutions peaked somewhere around 2015, we saw the start of the next wave of technological disruption. This current wave is transformative in more than one way. This is an AI-led disruption in banks and financial institutions. What we have witnessed so far is only the tip of the iceberg. We’re in an interesting phase right now where adoption, learning curve, and technological maturity are happening at the same time.
We believe the path to a mature AI state in the banking and financial services industry is through intelligent automation. Currently, the world is experiencing more and more intelligent process automation. Some of this automation takes the form of chatbots, while other initiatives focus more on structuring relevant information from unstructured sources for process efficiency or decision making.
Future prediction is one area that also finds its actual use cases in the form of market prediction or default prediction in the lending space. What we are seeing now is the early stage of disruption. It will take us about a decade to reach a point where disruption caused by AI is pervasive.
Along with these changes in the tech world, the last two years of the pandemic have also helped to give further impetus to smart automation and the adoption of these technologies. The post-pandemic world is expected to be very different from the pre-pandemic era in terms of our working model. We may not be completely reverting to the old ways of working, nor will we be in the current mode which is largely remote working. Most likely, the reality of the post-pandemic world will fall somewhere in between. Which means the momentum for AI adoption will continue to grow.
If we move quickly to 2030, many changes are expected in the functioning of our financial institutions. All of these changes would not only make us more efficient in our processes and decision making, they will also lead to a huge transformation of the workforce. Some of the elements of such a transformation are covered in the World Economic Forum’s report on the future of jobs.
According to the WEF report by 2025, the time spent on common tasks at work by humans and machines will be equal. The report further estimates that by 2025, 85 million jobs could be displaced by a change in the division of labor between humans and machines, while 97 million new roles could emerge that are more suited to the new. division of labor between humans, machines and algorithms. . 90% of financial services companies polled by WEF’s Future of Jobs survey indicated that AI technologies would likely be adopted by 2025.
Against this 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 experience more requalification than others. The role of analysts would be to enter into a more subjective and nuanced analysis than what it is currently. The human mind will always have a role to play, but instead of focusing on structuring data from unstructured documents, such as corporate financial results, an activity on which 80% of energy is currently focused, it is will lead to a more nuanced analysis. financial results. The manual generation of financial spreads will therefore be a thing of the past. More news would be pulled and analyzed at the time of the subscription decision than is currently happening.
The human mind will be made more efficient in decision making, as the ability to process unstructured information increases. So we see the role of analysts in 2030, but analysts will focus their energies on finer analysis and not so much on collecting the data that occupies the majority of analysts’ time today.
The operations team will see many processes shrink due to AI-driven automation. These teams would experience a retraining. Preparing the data for the creation of the model will be a role that will emerge within the operations teams. Analysis and testing of models will be an important aspect that will require skill development.
The roles and efforts of the maker-checker will undergo a change. While the manufacturer’s effort will be reduced due to machine support, the required verifier bandwidth will remain the same. In some processes and organizations, we can see the coverage of the controllers increase as the decision makers become the controllers.
Overall, the ROI will be in favor of the machine, but that would not be the only reason for adopting AI technology. The main reason will be the reduction of sub-optimal decisions made due to the current limitation of the scale that the human mind brings to process a large amount of information, especially unstructured information.
Return on investment based on cost savings can be easily estimated and various organizations have attempted to do the same. It is the cost of reducing suboptimal decisions that will provide the greatest return on investment. Although it is difficult to estimate this return on investment, it will be much higher than the economic return on investment. The amount of information coming from unstructured sources is only increasing exponentially and hence the reduction in risk due to the ability to process a large amount of unstructured data will make change a much needed change.
(Pravin Lal is founder and CEO of Capital Quant Solution, a startup supported by NSE)
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Posted on: Thursday September 23, 2021 11:44 am IST