Exclusive Trusted Magazine Q&A with Aditi Subbarao, Global Financial Services Industry Lead.
How could you describe your career path in a few words?
I’ve been fortunate enough throughout my career to work with some brilliant people at great organizations, across industries and geographies, in different business lines, on different products, covering a wide spectrum of client types. What has driven my career path is the motivation to, and fortunately also the opportunities to satisfy my curiosity and keep extending my learning to the next logical phase of development.
My first job was as an FX Sales person with Barclays Capital in Singapore. I moved to London in 2008, just as Lehman Brothers went bankrupt. What followed then was more than a decade of interesting roles in Global Markets and Transaction Banking across Barclays, Deutsche Bank and JPMorgan working with both institutional and corporate clients all over the world, on both investment and risk management products. I was doing a lot of work with payment providers and tech companies in the last few years of my time in banking - and, keen to explore this world more closely and experience a much more agile environment, I moved to a Fintech company in 2019. I have been with Instabase, my current employer for the last 3 years.
While my career path has been anything but linear, my advice on career paths would be - (i) seek an area that you want to learn about and get familiar with as the next phase of your career progression (ii) find or create a role that allows you to retain or apply part if not all of your previous experience, in the new field.
What are the highlights of banking transformations in 2023? Can you give us some major examples?
Continuing on from the push for transformation in banking during and after the pandemic, 2023 brought more than its fair share of innovation and transformation. While significant investments and development happened in the Payments, BNPL, Cybersecurity, Open Banking, Embedded Finance and Web3 spaces - the area I’d like to highlight and focus on could almost be digital transformation’s person of the year - Artificial Intelligence.
While banks have been using ML and AI for decades now, the emergence of Generative AI and Large Language Models has created a huge leap in the capabilities of machines. This expanded the spectrum of applications and use cases these technologies can now be leveraged for in the banking sector. Last year, I’ve spoken to several large global banks investigating, experimenting with, and now implementing Generative AI solutions. This started as a tentative toe-in-the-water earlier in the year, fuelled by the OpenAI’s launch of ChatGPT - customers expected their banks to keep with the times, and no bank wanted to be left behind its competitors. At that stage however, there were several concerns and questions still on the robustness, reliability and security of these models. Hence, banks were very much in ‘experimentation’ mode - trying it out on use cases that were not business critical, using only public data - for e.g. research reports and news articles summarisation, some forms of code development etc.
Some public examples were Morgan Stanley using it for Wealth advisors, Goldman for writing code, ING in customer service, JPMorgan for security recommendation through IndexGPT etc…
Over the course of the year, as the technology itself developed, and solutions came up for the biggest areas of concern around data security, and reliability (i.e. avoiding hallucinations). Banks began to train their own LLMs, using only their own data, while providers of LLMs like OpenAI and Google and others also built in safeguards to cater to requirements of regulated clients. Banks realized that just having access to an LLM does not solve an end to end business problem - several functions need to be strung together for this to happen, of which the LLM is just one part. For e.g. at Instabase too we spent a lot of time incorporating validations and human review into our AI solutions. This helped clients expand usage of Generative AI across several use cases and problem sets that were, until now, completely manual due to their complexity - not just onboarding, KYC, loan and mortgage underwriting, settlements automation, contract and legal automation, but also more complex activities like true personalisation of product recommendations, automatic email triage, portfolio optimisation suggestions, dispute resolution accelerations and more.
And finally, with all that, transformation mindsets started changing their objective - from process optimisation, to true data intelligence. Building on the cloud migrations, core-banking upgrades and many other changes of the last decade, 2023 has set the stage for banking to change completely with the new engine of Gen AI.
In your opinion, what are the transformation axes that are becoming more important for banks in the context of 2024?
Keeping with the same topic of AI - I think adoption and assimilation of AI into day-to-day operations is going to be a huge trend we will see in 2024. This will finally enable banks to leverage the massive amounts of data that the financial services industry has - to provide better customer experiences, improve returns and better manage risk.
I believe we will see this play out on two fronts:
AI becoming mainstream:
Data to Insights translation:
These two trends will then help fuel significant progress across several goals for the financial services sector - including supporting and promoting more responsible ESG investments and operating models and combating climate change, driving more financial inclusion, and making the global economy more resilient to geopolitical and other challenges - because working towards all of these ideals needs the ability to access, understand and act on data. That’s what 2024 is going to be all about - all that data.
Based on your recent experiences, and if you have one piece of advice to give for the success of transformation projects, what would it be?
Have you seen the movie, Mary Poppins? One of its many wonderful songs begins when Julie Andrews tells the children - ‘Well begun is half done’.
With statistics like 70% failure rates of transformation projects etc being common knowledge, the associated wisdom of requiring the right leadership, alignment and culture is all still true and important. Especially for transformation projects related to AI though - my one piece of advice would be to have the right vision, and the right definition of the problem upfront. Begin well!
All too often I have seen organizations limit their focus to a problem statement that is too narrow, for e.g. automating a certain specific process or task. With all the technology that is available and will be soon - that entire business function can be rethought of. An entire end-to-end customer journey can be redesigned. Hence, it is important to define your end objective and optimisation function correctly right in the beginning. Another part of this is to have the right vision over the longer term - this isn't about savings for this quarter or next, it is about how an entire industry will realign itself in the course of the next few years, and how you can equip your organization to stay future proof to those changes.
The advantage here, is that everyone is going through the same revolution - change makers can learn so much from the experiences of others. Most importantly - none of the difficulties are reason enough to stay away from this amazing new change - so I’d definitely encourage banking transformation enthusiasts to embrace the new world of AI - and good luck in 2024!
