AI Business recently caught up with Kumar Srivastava, VP of Products & Strategy at US banking giant BNY Mellon.

Founded in 1784, BNY Mellon is one of the longest-lasting financial institutions in the world, enduring and innovating through every economic event and market move over the past 230 years.

Kumar has spent his career building big data, analytics, machine learning, API and app products as part of a diverse and broad area set, and it is these technologies that he and his team are focusing on at the BNY Mellon Silicon Valley Innovation Center.

As well as holding several patents, Kumar regularly writes thought leadership articles about the convergence of big data, analytics, cloud, mobile and digital and its impact on and opportunity for entrepreneurship and innovation. He has been published in Forbes, Wired, Entrepreneur, Bloomberg and other publications and has authored two books on the subjects of Digital Transformations and API Product Management.

Kumar will be calling upon his expertise and experience at The AI Summit in San Francisco on 28-29 September, where he will deliver his keynote entitled ‘Beyond the Tech: Rethinking product development in the age of AI and Machine Learning.’

Kumar believes that adaptability in business is the only factor that really matters: “Businesses in general tend to be complex systems and their longevity and continued profitability depends on these systems being adaptive. The concept of anti-fragility is also interesting when applied to business planning and strategy.”

But how can AI make businesses adaptable?

“AI has the potential to make businesses both adaptable and anti-fragile by offering analysis and organization of information that is beyond human capability of comprehension and analysis. AI powered by computers can be used to determine and detect subtle changes in consumer or user behaviour and preferences. On top of this, their expected outcome can be used to adapt existing systems to the user’s needs and plan or define new solutions to satisfy future needs.”

Kumar finds that the main challenge in adopting AI technologies is twofold. The first part is related to the development of AI software:

“First, enterprises need to understand that the development of an artificially intelligent systems is very different from typical software development. Without digesting this point, enterprises risk failing entirely in their initiatives. Symptoms of this disconnect can range from businesses expecting to ‘replace’ entire key functions with AI to businesses expecting the AI driven system to perform at 100% accuracy for the enterprise’s current environment and context."

The second part of the challenge is the internal education and training required within an enterprise, Kumar explains:

“AI requires a retraining of almost the entire internal value chain (people, processes, products) to include the AI delivered value at key points in the value chain. Without understanding the impact of including and not including AI-driven insights at key points in the value chain, business run the risk of either completing missing the benefits of AI or mishandling the AI and unintentionally driving their users away along with their business.”

When it comes to BNY Mellon, Kumar keeps his cards close to his chest, though he does reveal that the initiative is all-encompassing:

“We are constantly analyzing our entire value chain and experimenting with new and better techniques to drive client value and satisfaction.”

And their strategy for the near and distant future is in line with this:

“Our strategy is driven by our quest to always increase client value and satisfaction. Technologies like AI, machine learning, advanced analysis, APIs and apps enable us to build new or improve our existing products and services to deliver value in the manner and channel expected and desired by clients.”

Within enterprises in general, Kumar tells us that he believes the positioning of AI-enabled systems and processes is key to adoption. He reflects on an earlier point as he expands on this:

“Enterprises should focus on an ‘aid and assist’ strategy as opposed to a ‘replace’ strategy in the adoption. Earlier version of AI enabled systems should make employees more productive, efficient and intelligent in their ability to meet and exceed client needs. This is not just important from an employee or human perspective but key for a learning system to be able to ‘observe’ the human employee and improve itself.”

He makes a parallel to the early adoption of self-driving cars:

“The concept of self-driving cars is not new or recent as over the last few decades: slowly and steadily new enhancements such as ‘automatic braking systems’ or ‘cruise control’ have been paving the way for fully automated, self-driving cards. In the early phases, these technologies existed to ‘aid and assist’ drivers while getting better in its ability to predict and adapt to changing road and pedestrian conditions.”

Thinking about the finance industry specifically, Kumar sees the space feeling a “huge impact” from AI. He initially breaks his explanation into macro- and microeconomic branches:

“At the macro level, we will see a shift in how the industry is organized. The speed to adopt and deliver AI-enabled value will have a direct impact on the longevity and sustainability of the incumbents.

“At the micro level, the services part of the financial industry, across B2B, B2C and B2B2C, will see a lot of activity through new and enhanced products and services that understand and predict the needs of the users and clients. Within these it will be the implementation of AI that will enable services to craft an experience that exceeds expectations.”

True to his role at BNY Mellon, Kumar also shares further thoughts on the products side:

“I expect innovations that are driven by new patterns being detected in how complex global markets function and operate and such patterns being bundled as a new class of products to improve financial performance."

Kumar concludes by putting himself in the position of the consumer, drawing upon the insights and transparency that AI can help achieve:

“As a consumer, I expect technology to progress so that it can analyse and determine the best path to my own financial goals having observed me, my past behaviour, behaviour of other people that resemble me, my predicted future (professional and personal) and my currently used products.”

“I am quite sure that, similar to ABS and cruise control, we are going to start seeing early innovations, and not too far in the future, we will look back and wonder why we wasted so much time driving around when we could be driven around.”

AI Business recognises the huge opportunity that AI presents the finance industry. At The AI Summit in San Francisco on 28-29 September, some of the most exciting Fintechs will meet with CxOs from the world's biggest banks, insurance companies, accounting and private equity firms.

At the event, Kumar Srivastava will deliver his keynote entitled 'Beyond the Tech: Rethinking product development in the age of AI and Machine Learning.'

To find out more, and to register to join us at the event, visit: theaisummit.com

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