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Next-generation technologies and the future of trading

Next-generation technologies and the future of trading

At a Risk.net webinar in association with capital markets technology provider Numerix, panellists discuss the potential for increased adoption of the public cloud to boost investment performance, its impact on risk management and overcoming barriers to implementation

The panel

  • Satyam Kancharla, Executive Vice-President, Chief Product Officer, Numerix
  • Daryush Laqab, Head of Product Management, OmniAI Ecosystem, JP Morgan
  • Moderator: Phil Albinus, Contributing Editor, Risk.net
Satyam Kancharla, Numerix
Satyam Kancharla, Numerix

New technology is transforming how financial institutions operate, invest and trade. In the quest for increased efficiency and profitability, firms worldwide are rethinking trading infrastructures and generating a vast array of technology and data-driven analytics.

Cloud services in particular have played a significant role in driving efficiencies in back offices for more than a decade – but front-office teams have generally lagged behind for a host of reasons.

What can the industry gain from stepping up adoption of the public cloud – the open resource available across multiple businesses that Satyam Kancharla, executive vice-president and chief product officer of Numerix, says has become “just too powerful an offer” for financial institutions of all sizes not to use?

The benefits of the public cloud are widely touted: greater agility, real-time data and better risk mitigation, to name a few. The financial services industry is keen to capitalise on these efficiencies and boost performance, particularly after the Covid-19 pandemic forced organisations to quickly adapt to the challenges of remote working.

With this in mind, the webinar audience was asked how their use of cloud technologies in the front office had changed over the past three to five years as a result of technology transformation projects, market dynamics and regulatory pressures.

Many respondents – 43% – said they were more aggressively using cloud-based outsourcing as the operational model. Fundamental changes in the landscape had added pressure to legacy technologies struggling to meet demands for compliance, data and analytics. A total of 37.5% said they were starting to explore broader adoption of outsourcing across the platform, while one-fifth said they had not migrated any front-office operations to the cloud model.

“Cloud, as a general technology, has gone from strength to strength in the financial services industry, starting with the buy side, insurance and then in banking and the sell side as well,” said Kancharla.

“We have seen a lot of adoption on the post-trade side, but recently we are seeing increasing interest on the pre-trade side as well, including for pre-trade analysis, requests for quote (RFQs), order management/execution and related front-office workflows.”

Vast infrastructure

The public cloud offers tools unavailable on-premise, providing organisations access to vast new infrastructures that provide greater flexibility and efficiency.

These include serverless cloud technology, which allows developers to run applications without managing servers, and containerisation – packaged software code that can work seamlessly on any infrastructure and enable faster and more secure application creation.

“[The public cloud] gives us elasticity … it gives us linear scaling,” said Daryush Laqab, head of product management on the OmniAI ecosystem for JP Morgan. “All of that gives our architects a set of new tools, a set of new options to better design highly available, real-time systems and solutions.”

The cloud-based environment also enables organisations to sustain a far higher pace of innovation than would be possible with on-premises products. Improvements such as the creation of customer behaviour analytics and the use of artificial intelligence (AI) and machine learning allow users to more rapidly build additional front-office decision support infrastructure, said Kancharla.

AI enables users to capture additional data such as behavioural patterns, the timings of trades, success or failure rates of RFQs, and information about different asset classes. 

“It is clearly too much for anyone to be able to process without the aid of AI and machine learning,” said Kancharla. “So, there is a lot of interesting work going on in terms of teasing out insights from structured and unstructured data, and making sure data is available where it can be accessed and properly tagged, and that the data taxonomy is understood.”

Risk management

The efficiencies of the public cloud can clearly lead to performance gains and enhanced risk management, said Kancharla.

For example, the public cloud can help make the most of AI big data analytics, which can be a game-changer at the pre-trade stage for risk management by giving users far greater insights than previously thought possible.

One of the core principles of financial risk management is the multiple lines of defence concept, Kancharla highlighted. This comprises trading as the first line of defence, risk management as the second, and audit, compliance and regulation as the third.

“How do you then still provide insights to the first line of defence, not end of day, not end of quarter, but insights as close as possible to trading decision time?” he asked.

Big data analytics, which use large, diverse datasets, are helping front-office teams with more detailed insights close to the point of trade. 

“The more you can do in the first line of defence, the less you have to worry about in the second and third lines of defence,” said Kancharla. 

Even approximate models are valuable to front-office teams when used indicatively to support a trading decision.

“They are not meant to provide numbers that are fit for regulatory approval or submission, but they are providing early indicators to the trading desk in terms of where they want to go and how they want to attune their trading strategies, taking into account regulation, compliance and collateral,” he said.

AI and machine learning can also help here by adopting and simplifying large and complex portfolio models in order to provide insights at the trading time.”

Trading and risk systems enabling these insights include Numerix Oneview, a real-time trading and risk management platform launched in 2016 that aims to give users a multidimensional view of their derivatives and structured products activities. 

Meanwhile, sandboxing ring-fences the risk modelling process from the rest of the data. A risk model in production may require a tweak or even a significant change, which can run separately in a sandbox to avoid affecting anything in production.

“You are able to do a lot of analysis, such as what-if analysis, regressions, backtesting, various steps of model validation analysis to ensure that this change is introduced in a safe manner,” Kancharla said.

What cloud offers is the ability to create hundreds or thousands of compute nodes and test models at scale – not just locally in a spreadsheet or a Python notebook – leading to greater innovation and much faster implementation of new systems.

“These cycles can be compressed significantly if you are truly taking advantage of the sandboxing and scale-up capabilities of the cloud, which is what we are bringing to our clients as part of our new NxCore Cloud offering at Numerix,” Kancharla said.

Robustness

But is the cloud robust enough for the front office? It is much less of a concern today than in earlier years of adoption, said Kancharla: “All infrastructure has to be designed for robustness. Whether it is on-premise, whether it is on the public cloud, there is a lot of effort that needs to be put into designing your entire system for that robustness.”

Users’ only concern regarding the public cloud, he added, should be that it’s designed to be fail-safe: “There are things that might go wrong, and it is really important to design your cloud infrastructure in anticipation of overcoming a potentially adverse event. In fact, with the cloud we have seen that the ability to recover from an event can be much, much faster because you have multiple zone availability to absorb shocks.”

Replacing legacy technology

Creating the front office of the future will mean overcoming challenges – from investment constraints to skill sets and personnel.

But, of all the hurdles, replacement of legacy technology came out on top, cited by 39% of audience poll respondents, followed by competing priorities and multiple strategic issues for the front office.

“You will never have an application in isolation; you will never have a component in isolation,” Laqab said. “It is always part of an end-to-end flow of multiple applications that are tied together, and together they will give you a capability. 

“It is hard to get the entire flow end-to-end lifted, shifted to public cloud and then re-architected there. There need to be bits and pieces of it that move to public cloud. I am not surprised we say that the legacy applications, legacy infrastructure, legacy end-to-end workflow, are the major impediments to moving everything to the public cloud one day.”

However, Laqab stressed that organisations would need people with keen knowledge of how to set up apps in both environments, and how to correctly architect a hybrid app to move an app or end-to-end system from one environment to another.

“The more you have folks who can better architect these types of hybrid systems, the more you can bring down the barriers posed by legacy infrastructure,” he said.

Watch the full webinar, Next-generation technologies and the future of trading

The panellists were speaking in a personal capacity. The views expressed by the panel do not necessarily reflect or represent the views of their respective institutions.

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