By Hamza Basyouni1
1Digital Analyst, Innovation Lead and Transformation Advisor
According to the AT&T Foundry, artificial intelligence (AI) has been a research focus for over 50 years, but only in the last decade has it become popular for enterprise and consumer use.1 The market for AI has grown in recent years, with approximately 1500 companies in North America developing AI applications including leading companies such as Microsoft, IBM, Google and Amazon.2
This advent in the development and uses of AI, machine and deep learning is affecting the potential future direction of the Financial Services Industry and emphasizing the importance of human and machine collaboration. Accenture reports that 62% of senior bankers said the proportion of roles requiring people to collaborate with AI will rise in the next three years.3
AI has evolved from being a potential disruptor into a key component of digital and business transformation for long-term sustainable operational benefits. Deloitte reports that AI advances have unlocked new horizons within the Financial Services Industry, through numerous applications that are already being implemented, potentially changing business activities across operations, risk, finance, and compliance.4 Deloitte also reports that the rise of AI-based applications, such as robo-advisors, pattern recognition, AI/virtual agents and intelligent automation,5 such as robotic/intelligent process automation has had a significant impact within investment management, banking and insurance.
The banking industry is rapidly changing, with a paradigm shift to digital-only banks that leverage AI technology and data to provide digitalized offerings and services. In the banking sector, companies have adopted AI by investing in the development of specific applications and created partnerships with FinTechs,6 tech startups and digital Banks.
PricewaterhouseCoopers (PwC) describes AI as a game changer that could contribute up to $15.7 trillion to the global economy by 2030.7 It named key areas within the Financial Services Industry that have the biggest AI potential as being personalized financial planning, fraud detection, anti-money laundering and process automation.8 PwC also said that the costs of AI technology would decline over the next ten years as the software becomes more commoditized9 for mainstream uses across various other industries.
Furthermore, the International Data Corporation states that worldwide cross-industry spending on cognitive and AI systems increased by 59% in 2017 compared to 2016, reaching $12 billion, and will rise to $57.6 billion in 2021.10
The terms AI and machine learning are used interchangeably, but they are different. One expert identifies three defined levels of AI:
The current key difference is between AGI and machine learning, as AGI refers to machines generally being able to carry out tasks typically performed by humans that also includes search, symbolic and logical reasoning, and statistical techniques that aren’t explicitly deep learning-based.12
Machine learning refers to the process of computer systems that have the ability to learn, automatically discover patterns in data and improve their performance through data exposure without being explicitly programmed.13 Its subset application is deep learning, which allows a computer to recognize patterns from both labelled and unlabelled data sets.14 A comprehensive AI makes use of all these fields, although presently the enterprise uses are heavily focused on machine learning.15
This has undoubtably bought disruptive changes to the Financial Services Industry, in terms of the impact on professions and business models and technology will continue to profoundly impact the employment landscapes in the coming years. For example, in 2015, the UK media, using research by the University of Oxford, said accountants have a 95% chance of losing their jobs as machines take over the number crunching and data analysis.16 “We expect around 35% of skills will be different in the near future,” says Till Leopold, the project lead on the World Economic Forum’s (WEF) employment, skills and human capital initiative.17
In the Investment Management and Hedge Fund sectors, companies have begun and continue to deploy the latest advances in data analytics, data science and machine learning to develop complex quantitative investment and trading strategies to produce astounding results within the financial markets. For example, more than 70% of the trading today is carried out by automated AI systems.18 Another example of a paradigm shift that has already occurred is the case of a Venture Capital Fund that appointed an AI computer algorithm tool called “Vital” to its board to partake in the screening and voting of new investment opportunities.19
Significant barriers to the implementations of AI are already evident, as reported by Accenture through its surveys of senior decision-makers and banking executives. These surveys show the first signs of resistance by employees, as they believe on average only 26% of their workforce is ready to work with intelligent technologies, and mostly cited a growing skills gap as the leading factor influencing their workforce strategies.20
Additionally, Deloitte state there are current and future challenges of AI and machine learning implementations within the Financial Services Industry. These limitations include a lack of expertise and awareness regarding the technology, significant volumes of data hosted on legacy systems and an overall lack of agility in deploying digital projects that have already left the traditional Financial Services Industry behind smaller FinTechs21 and tech startups.
Clearly, a lot of work still needs to be done, and a shift from a traditional top-down management approach is required. Organizational structures and processes need to adapt and a crucial first step will be the design of effective journey maps that reinforce strategic alignment and serve to increase efficiency and deliver value through technological implementations.
AI is within Gartner Hype Cycle’s “peak of inflated expectations” and will continue to have enormous impacts and future implications within the Financial Services Industry. As Deloitte states, with the eruption of AI some of the market leaders in ten, or even five years’ time, may be companies you’ve never heard of, and in turn, some of today’s biggest commercial names could be struggling to sustain relevance or have even disappeared altogether.22 However, the Goldilocks principle still applies as finance professionals and society are advised not to be either too optimistic or too pessimistic about AI technology.