Credit Suisse sees client demand for data-driven answers – Spark + AI Summit

By Sunniva Kolostyak

Swiss bank Credit Suisse is increasingly utilising data to create digital solutions, as a result of exponential demand from clients, a director said at the annual Spark + AI Summit.

In his keynote speech at Databricks’ Spark + AI Summit 2020, Anurag Sehgal, Managing Director at Credit Suisse, said data is transforming all aspects of how the bank is doing business.

Seghal, who is in charge of data analytics, machine learning and AI within the bank’s global markets division, explained that the financial institution has moved from a traditional, human-centric approach towards a model where digitally connected client journeys augment human expertise.

“There’s an exponential demand for insights and data-driven answers from our clients,” Seghal said. “Data analytics is at the core of everything we’re doing.”

Despite the increasing embrace of big data and AI, most financial services companies still experience significant challenges around data types, privacy, and scale. Credit Suisse is according to Seghal overcoming these obstacles by standardising on open, cloud-based platforms, including Azure Databricks. This is to increase the speed and scale of operations and ensure democratisation of machine learning.

During the conference, which this year was arranged as a virtual event, Seghal explained that Credit Suisse’s old strategy was predominantly bespoke and expert judgement driven.

However, utilising data and digital platforms, it is entering new markets, discovering new routes to customers, and developing value propositions for sales trading and research.

“Our trading teams are looking for real-time intelligence on how to price, how to manage inventory, how to drive RfQs. Our sales teams are looking for insights in what clients are interested in, what products they are interested in, what stocks they are interested in. And, our research teams are looking to differentiate our content and our advice to clients with distinct and differentiated analytics,” Seghal said.

In order to achieve this transformation, the bank’s mindset has been to foster the generation of ideas, allowing for experimentation, rapid prototyping and having a VC mindset in leveraging and driving mature, commercially viable ideas into products and business ventures.

“We want to continue to turbocharge data analytics, embedding machine learning and AI in all aspects of how we do business – in creating value for our clients as well as for our internal sales, trading and research teams,” he said.