Capital markets ripe for disruption by AI

Capital markets ripe for disruption by AI

Ciarán Daly

September 18, 2017

5 Min Read

Founded in 1784, BNY Mellon is one of the longest-standing financial institutions in the world, enduring and innovating through every economic event and market shift of the last 230 years.

Kumar Srivastava is BNY's VP of Products & Strategy. Kumar has spent his career building big data, analytics, machine learning, API and app products as part of a diverse and broad portfolio. 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 contributes thought leadership regarding the convergence of big data, analytics, cloud technology, mobile and digital, and their impacts on opportunities 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.

In this exclusive interview, we picked his brain for the latest insights into how the financial services sector is approaching AI - a sector which he argues is ripe for disruption.

Capital markets, meet AI

Data has long been vital to the operations of financial institutions. It’s the bedrock of effective investments, the bread and butter of teams of analysts and portfolio managers. However, the way that data is processed and used to produce insights has undergone many transformations in the last decade.

Today, every company in every sector is effectively drowning in data. Its growth is accelerating exponentially. AI technologies represent an obvious mechanism for trawling through this data and delivering fast, accurate insights—and this is how it’s already being deployed in other sectors. Despite this, capital markets as a whole are slow on the uptake, Kumar argues.

“Penetration and awareness of AI is very uneven in capital markets. Though advanced statistics and mathematics are deeply embedded in these markets, AI, machine learning, and natural language processing (NLP) are used sporadically by a few companies,” he explains. “Most enterprises are struggling to understand the implication of AI to their products and services and are either experimenting with these technologies or considering doing so.”

Kumar believes that combination of finance’s wealth of data and the reluctance of industry leaders to truly exploit its potential can only mean one thing: the sector is ripe for disruption.

“The financial services industry realizes that it has been a laggard in terms of adoption of disruptive technologies. It is also one of the most manually operated industries, where technology has either been used only in trading scenarios or used to ‘keep the lights on’. This means that the industry is ripe for disruption and incumbents have a huge advantage to gain and solidify their position by being the first movers in the adoption of AI.”

“AI will fundamentally change how these enterprises do business. However, that is not all. These technologies will fundamentally change how these enterprises are organized, how they make decisions, how they plan, strategize and execute. AI and ML will impact every function in the company either directly or indirectly.”

First contact

So where can industry observers expect to see the first impacts of AI? “The first area of impact and the most applicable area of investment is in the application of AI and ML to making the customer / user experience better. Being able to predict intent, understand satisfaction, and determine needs is and will continue to be a popular application of AI. It will automatically lead to a broader application across the enterprise in how investment, planning, and operational decisions are made and measured.”

“AI-assisted technologies are likely to make organizations more aware and perceptive about their customers and users,” predicts Kumar. “This awareness will lead to more client-centric decisions, products, and services. This will lead to higher satisfaction, lower attrition, and—inevitably—more revenue.”

“On the other hand, by using AI in operational planning and execution, enterprises will be able to predict problems, issues, and outages—and mitigate them. In addition, they will be able to automate using predictive insights, workflows, and value delivery; ensuring highly consistent service quality levels delivered at a cheaper cost, which can and will improve operating margins.”

We can already witness these early developments in the deployment of chatbots by BNY Mellon, but in anticipation of these changes, they’re also using robotic process automation to transform their operations.

From what Kumar tells us, these investments are anything but premature. He expects that the applications of AI in financial services is poised to grow exponentially. He observes five trends that will either begin soon, or continue to develop:

  • Higher investment in AI, AI vendors, and AI partnerships in order to reduce the latency of AI adoption in the enterprise

  • Customer service and relationship management will be driven by AI to detect dissatisfaction and systemic QOS issues

  • Transaction processing and servicing will be made more error-free and efficient using AI

  • Better understanding of internal, external, and market events to feed strategic corporate planning efforts

The extent to which these trends will impact the industry are unclear, but it’s obvious that forward thinking is required from major and minor players across the sector. Ultimately, it’s up to industry leaders to futureproof themselves against the disruptive effects of AI in the coming decade—before the disruptors of the future force their hands.

To gain more of Kumar’s industry-leading insights, make sure to attend his keynote speech at this year’s AI Summit, entitled ‘The State of Artificial Intelligence in Capital Markets’. On the Summit, he has this to add: “I am very excited about this year’s AI Summit and am looking forward to the early trailblazers in the space of AI and learn from their success and failures.”


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