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Transforming Paradigms

A Global AI in Financial Services Survey

Lukas Ryll, Mary Emma Barton, Bryan Zheng Zhang, Jesse McWaters, Emmanuel Schizas, Rui Hao, Keith Bear, Massimo Preziuso, Elizabeth Sege, Robert Wardrop, Raghavendra Rau, Pradeep Debata, Philip Rowan, Nicola Adams, Mia Gray, Nikos Yerolemou

This report presents the findings of a global survey on AI in Financial Services jointly conducted by the Cambridge Centre for Alternative Finance (CCAF) at the University of Cambridge Judge Business School and the World Economic Forum in Q2-Q3 2019. Representing one of the largest global empirical studies on AI in Financial Services, a total of 151 respondents from 33 countries participated in the survey, including both FinTechs (54 per cent of the sample) and incumbent financial institutions (46 per cent of the sample). The study was supported by EY and Invesco.

Highlights from the report

The key findings of this empirical study are as follows:

  • AI is expected to turn into an essential business driver across the financial services industry in the short run, with 77 per cent of all respondents anticipating AI to possess high or very high overall importance to their businesses within two years. While AI is currently perceived to have reached a higher strategic relevance to FinTechs, Incumbents are aspiring to catch up within two years.
  • The rising importance of AI is accompanied by the increasingly broad adoption of AI across key business functions. Approximately 64 per cent of surveyed respondents anticipate employing AI in all of the following categories – generating new revenue potential through new products and processes, process automation, risk management, customer service and client acquisition – within the next two years. Only 16 per cent of respondents currently employ AI in all of these areas.
  • Risk management is the usage domain with the highest current AI implementation rates (56 per cent), followed by the generation of new revenue potential through new AI-enabled products and processes, adopted by 52 per cent. However, firms expect the latter to become the most important usage area within two years.
  • AI is expected to become a key lever of success for specific financial services sectors. For example, it is expected to turn into a major driver of investment returns for asset managers. Lenders widely expect to profit from leveraging AI in AI-enabled credit analytics, while payment providers anticipate expanding their AI usage profile towards harnessing AI for customer service and risk management.
  • With the race to AI leadership, the technological gap between high and low spenders is widening as high spenders plan to further increase their R&D investments. These spending ambitions appear to be driven by more-than-linear increases in pay-offs from investing in AI, which are shown to come into effect once AI investment has reached a ‘critical’ mass of approximately 10 per cent R&D expenditure.
  • FinTechs appear to be using AI differently compared to Incumbents. A higher share of FinTechs tends to create AI-based products and services, employ autonomous decision-making systems, and rely on cloud-based offerings. Incumbents predominantly focus on harnessing AI to improve existing products. This might explain why AI appears to have a higher positive impact on FinTechs’ profitability, with 30 per cent indicating a significant AI-induced increase in profitability compared to seven per cent of Incumbents.
  • FinTechs are more widely selling AI-enabled products as a service. Successful real-world implementations demonstrate that selling AI as a service may allow large organisations to create ‘AI flywheels’ – self-enforcing virtuous circles – through offering improved AI-driven services based on larger and more diverse datasets and attracting talent.
  • AI Leaders generally build dedicated corporate resources for AI implementation and oversight – mainly a data analytics function – to work with their existing IT department. On average, they also use more sophisticated technology to empower more complex AI use cases.
  • Leveraging alternative datasets to generate novel insights is a key part of harnessing the benefits of AI with 60 per cent of all respondents utilising new or alternative forms of data in AI applications. The most frequently used alternative data sources include social media, data from payment providers, and geo-location data.
  • Incumbents expect AI to replace nearly nine per cent of all jobs in their organisation by 2030, while FinTechs anticipate AI to expand their workforce by 19 per cent. Within the surveyed sample, this implies an estimated net reduction of approximately 336,000 jobs in Incumbents and an increase of 37,700 jobs in FinTechs. Reductions are expected to be highest in investment management, with participants anticipating a net decrease of 10 per cent within five years and 24 per cent within 10 years.
  • Regardless of sectors and entity types, quality of and access to data and access to talent are considered to be major obstacles to implementing AI. Each of these factors is perceived to be a hurdle by more than 80 per cent of all respondents, whereas aspects like the cost of hardware/software, market uncertainty, and technological maturity appear to represent lesser hindrances.
  • Almost 40 per cent of all respondents feel that regulation hinders their implementation of AI, whereas just over 30 per cent perceive that regulation facilitates or enables it. Organisations feel most impeded by data sharing regulations between jurisdictions and entities, but many also deem regulatory complexity and uncertainty to be burdensome. Firms’ assessments of the impact of regulation tend to be more positive in China than in the US, the UK, or mainland Europe.
  • Mass AI adoption is expected to exacerbate certain market-wide risks and biases, and at least one in five firms do not believe they are well placed to mitigate those. Firms are particularly wary of the potential for AI to entrench biases in decision-making, or to expose them, through shared resources, to mass data and privacy breaches. Nevertheless, many firms are involving risk and compliance teams in AI implementation, and those who do tend to be more confident in their risk mitigation capability as a result.
  • Long-established, simple machine learning algorithms are more widely used than complex solutions. Nonetheless, a large share of respondents is planning to implement natural language processing (NLP) and computer vision, which commonly involve deep learning, within two years.
  • Nearly half of all participants regard ‘Big Tech’ leveraging AI capabilities to enter financial services as a major competitive threat.