Harness data to restore profitability metrics
Once stellar contributors to investment bank profits, sales and trading businesses have been struggling to adjust to a new set of constraints imposed upon them by regulators and stakeholders after the Global Financial Crisis (2008). A decade of continuous waves of job cuts have failed to fix the problem: high Operating Expenses (OpEx), high capital consumption, compressed margins and increased competition have resulted in illiquid markets and structurally low Return on Equity (RoE). But there is hope.
A massive increase in available computational power and cloud-based computing capabilities, combined with advances in Artificial Intelligence (AI)/Machine Learning (ML) techniques offer exciting perspectives to completely transform the way financial markets operate. By harnessing data and analytics, investment banks have a historic opportunity to future proof sales and trading operations and restore the profitability and RoE metrics of their rewired trading floors.
Trove of unexploited data within banks
Banks are sitting on a trove of mostly unexploited transactional and alternative data which can be leveraged using AI/ML algorithms to fundamentally transform the way sales and trading activities are run. The scope of possible use cases for ML algorithms covers all aspects of trading floor performance, including market making, trading strategies, risk management, client services and capital consumption.
Optimal fintech partner to build a data-centric business
Empirical evidence is emerging of successful attempts by banks and asset managers to use ML techniques to improve business performance. Once mostly tasked with a fintech surveillance mission, bank innovation labs increasingly focus on business transformation projects. But despite these early wins, too many initiatives nurtured inside the labs never make it to production despite a clear willingness from senior management to invest. Why is that? We see 4 pre-requisites to building data-driven businesses on the trading floor.
Bringing significant innovation to a functioning business from within is notoriously difficult. Innovators often face strong resistance to change from incumbents and are subject to conflicts of interest. Driving change requires independence from these internal headwinds.
Activities conducted on a trading floor are both highly complex and highly regulated. The rewiring of the trading floor must be led by market practitioners with a deep and current knowledge of global markets. Seasoned experts will pick the right upgrade opportunities and avoid the classic pitfall of generalist engineers designing unrealistic projects. A subtle blend of advanced data analytics and operational market experience is essential.
Transforming a sales and trading operation is a complex endeavor which cannot be developed using a box-standard approach. It requires a dedicated, heavy duty data infrastructure and a full ML algorithm toolkit to design, test and code scalable data-driven solutions for maximum economic impact. The end goal is to deliver proprietary expert systems to be used in production.
Market operations are result-oriented. Solutions must be practically implementable and measured against pre-agreed industry metrics. We are far from prototypes and research projects.
The qbridge platform
At qbridge, we believe that harnessing data is key to future proofing the trading flor, and that we are only at the beginning of a deep data-driven transformation of the financial sector. Over the years, we have grown our expertise from an initial focus on credit scoring in granular private credit markets, to a much broader universe of ML use cases where analyzing large data sets brings considerable economic value to market participants. Today, we use our proprietary data & analytics platform to help banks and asset managers future proof their businesses in the data era.