Following numerous hype cycles, artificial intelligence (AI) is now gaining widespread adoption, with the potential to radically transform many areas of the financial services industry. In 2017, consultancy Opimas expects financial firms to spend more than US$1.5 billion on AI-related technologies. By 2021, this will rise to US$2.8 billion, representing an increase of 75%. Opimas also estimates a 28% improvement in financial institutions’ cost-to-income ratio by 2025 as they automate routine processes currently performed by employees.
AI could represent a disruptive technology that reduces the barriers to entry that protect larger incumbents through lean, tech driven business models that reduce the need for expensive staffing. This could see financial institutions disrupted by either existing tech giants or new entrants that possess advantages through improved trading strategies and client servicing, driven by emerging technologies.
Many of the large tech firms such as IBM and Google are now making their AI platforms such as Watson and DeepMind available as open source products. This democratises the ability to build solutions using the massive computing power and AI engines offered by these firms. It will result in an explosion of new AI based solutions in the coming years. For these reasons, senior executives need to closely monitor developments in AI. They should view AI both as a transformative tool to improve return on equity, and as a future threat to their firm’s business model.
Another key factor is that AI thrives on large amounts of data. In our industry, securities finance trade reporting under SFTR and derivatives transaction reporting under EMIR/Dodd Frank will result in large quantities of structured and tagged data. According to ESMA, this information will be made publicly available on the Tues following a Fri cut off. ESMA will also require trade repositories to publish data for the past 52 weeks.
Furthermore, the International Swaps and Derivatives Association (ISDA) is now promoting what it calls a ‘common domain model’. The derivatives market has undergone multiple rapid sprints in recent years to comply with regulation. This result is tactical solutions and a lack of standardization. ISDA is now looking to address this through a more strategic, efficient and standardized approach to data management.
This could provide an opportunity to apply cognitive computing algorithms against SFTR and EMIR data. However, this will depend on whether the depth and timeliness of data publicly available allows interpretation of market trends in a way that can guide future decision making. Due to the large volumes of very granular data being collected, some work may still be required to standardise this to a degree it can provide value.