An interview with Ajit Shah, Managing Director – APAC, Middle East & Africa at ACTICO, about why financial institutions must merge expert knowledge and agile machine learning models across the business, if they want to compete in a modern credit and lending market that is driven by increased personalisation and real-time decisioning.
What are the main challenges that lenders are facing?
In my view, there are five broad challenges.
Firstly, the changing customer behaviours and expectations that companies need to adapt to. With the advent of BigTechs and FinTechs, customers have gotten used to real-time services and they have begun to expect the same from their banks as well. For instance, when customers are looking for lending products, they are looking for almost instant decisions and servicing – nobody is willing to wait for days or months, as was the case earlier.
Secondly, financial institutions are under pressure to seize the opportunity of utilizing large amounts of available data from disparate sources (e.g. social or behavioural) in decision making. The lure of automation makes this exercise even more challenging.
Thirdly, agility or frequent experimentation has almost become a must-have in today’s dynamic, ever-changing and highly competitive business world. But, achieving nimbleness is easier said than done, especially for large banks with siloed teams and matrix organizations.
Fourthly, uncertain economic conditions and low interest rates mean that cost pressures will remain a significant factor in any transformation exercise.
Lastly, due to the pandemic situation, there’s a renewed focus on business resilience, which has become a hot topic at the C-level across industries, including BFSI.
What can banks / financial institutions do to tackle these challenges?
The importance of technology has been reaffirmed in the last few months. Digitalization, not only reduces human dependence and increases resilience, but is also the backbone of agile and innovative organizations. Most of the above challenges can be addressed by rethinking the paradigms of tools and techniques in automation projects.
From an architecture point of view, an ideal way is to visualize it in layers with predominantly three categories: while ‘systems of record’ (e.g. loan management / ERP systems) are an absolute must and ‘systems of engagement’ (e.g. mobile/web apps) have received a fair share of attention in the recent past, ‘systems of intelligence’ (e.g. decisioning systems) are a new breed of solutions that can potentially provide a competitive advantage that the businesses and investors are looking for. What makes a ‘system of intelligence’ valuable is that it typically crosses multiple data sets, multiple systems of record, even data outside the organization. The combined use of human expertise and AI & advanced analytics on these diverse datasets allows companies to build intelligence, in the form of decisioning models that can set their offering apart. In the lending lifecycle, this can mean taking instant credit decisions, offering risk-based pricing or personalized product recommendations.
In addition, organizations need to foster a culture of continuous innovation and an infrastructure that supports it. Agility can also be gained by giving more power in hands of the business user and reducing dependence on IT teams or vendors.
In your view, what does a modern credit risk platform look like?
I would look at 5 key attributes in a modern and future ready credit risk management platform.
Firstly, it should have a future ready technology stack with both established and emerging techniques like AI and big data. Secondly, it needs to be front-to-back digital with minimal (if not zero) human touchpoints. Thirdly, it needs to have a high degree of integration capability, thus allowing use of ever-increasing data sources. Fourthly, it would enable flexibility and agility to minimize time-to-market, promote ease in change management and minimize organizational friction. Lastly, the system has to be explainable and 100% auditable in compliance with regulatory and customer needs.
What does the future hold for the lending industry?
We believe that data, machine learning and AI will continue to play a more prominent role in credit decisioning and associated areas, like early warning. Another important trend, which is already visible and will continue to thrive is the emergence of what we call ‘ecosystem plays’, where more and more unrelated industries come together to deliver joint propositions. Today, we are already witnessing banks giving out loans through e-commerce platforms. In the future, these ecosystems will become more diverse and complicated.
This, in turn, will necessitate an open API and microservices movement to allow seamless flow of information, allowing financial institutions to tailor their offerings to the needs of the end customer.
How is ACTICO positioning its offerings to enable all these trends we spoke about?
We sincerely believe that a blend of expert judgment and ML / AI will drive lending technology in the foreseeable future. This is the principle idea we are propagating through our platform and solutions, while bringing agility, continuous innovation and business user empowerment to the fore in digital projects.