Cometh the hour, cometh the machine: Time for leaner growth models in capital markets
Tony Warren | EVP head of Strategy and Solutions Management, FIS
September 21, 2020
Capital markets firms are facing tough choices as they prioritize technology spending in the wake of Covid-19. But in a world of strained budgets and resources, implementing advanced automation and machine learning is increasingly vital to supporting growth.
Capital markets executives are no strangers to navigating their organizations through choppy waters. But as they rebuild their firms in the wake of COVID-19, ‘cometh the hour, cometh the machine’ may be the recovery rhetoric ringing around industry boardrooms.
Our latest research, which surveyed 250 industry executives post-pandemic, finds that heightened pressure on capital expenditure will have the second-biggest impact on growth models post-COVID-19, with 46 percent citing this, behind concerns over prolonged market uncertainty (47 percent).
Amid the strain on budgets and resources, advanced technologies such as robotic process automation (RPA) and machine learning – which have, in recent years, moved from promising nascent solutions to delivering tangible returns – offer a business case that is hard to ignore.
Now, more than ever, capital markets firms need to automate non-differentiating activities in the middle and back office to free up precious talent for strategic issues. Meanwhile, delivering growth in today’s conditions demands an ability to onboard new asset classes or launch new products without requiring huge amounts of resources.
Unlike in previous crises, these advanced technologies are now readily available and are delivering measurable return on investment for early adopters.
For instance, we recently deployed a machine learning solution within a reconciliation platform for a private equity house that has enabled substantial cost savings and efficiency improvements. By digesting historical data showing the reconciliations that had fallen through the cracks of rules-based processing, and had required manual interventions, the machine learning tool is able to suggest the appropriate matches itself when similar exceptions arise in future.
Through automating what is often one of the most time-consuming pieces of reconciliation, the firm was able to able to enhance productivity and make cost savings. This has important implications for growth strategy too, as it reduces the extra resourcing capacity required to support the launch of a new fund or, in the case of a fund administrator, to onboard a new client.
Artificial intelligence (AI) and machine-learning solutions are enhancing efficiency and stripping out cost for sell-side firms too. For instance, in securities financing, optimization algorithms are being used to improve the matching of borrowers and lenders to improve financial outcomes. And in commercial lending, natural language processing (NLP) is being applied alongside optical character recognition (OCR) to consume and interpret documents to streamline loan applications.
Meanwhile, within auto finance, AI is playing a critical role in stripping out the need for physical, in-person customer support in some areas. Image recognition technology is enabling remote, self-service assessments of vehicle damage at the end of lease contracts, which is critical during the pandemic and cost effective going forward.
Sophisticated automation can play an integral role as buy- and sell-side firms pivot their product and service offerings too. For instance, it can help enable the mass customization of the client experience in areas such as onboarding and service support for new products, without the need to hire an army of people.
For asset owners, meanwhile, this infrastructure can help support growth ambitions by enabling the onboarding of new asset classes amid resource pressures. We recently completed a transformation project with a sovereign wealth fund – whose global portfolio contained a mix of liquid and illiquid asset classes – with ambitions to further diversify its portfolio. By applying advanced automation to reconciliation and exception management services to support cash, total equity and margin/collateral reconciliation and net asset value (NAV), the fund is positioned to achieve leaner growth of its portfolio.
Covid-19 has amplified the challenges facing an industry that was already facing huge pressures on its margins and routes to growth. The firms that succeed will be those that master the art of doing more with less as they support growth initiatives — and that is only possible with the aid of advanced technologies.