In this literature review, Bonnie Buchanan from Economics and Finance at Seattle University, details the artificial intelligence (AI), machine learning (ML) and deep learning (DL) taxonomy as well as their various applications in the financial services industry and summarizes the current academic, practitioner and policy related AI literature.
She specifically discusses four ways in which AI is changing the financial services industry: (1) fraud detection (how AI is used to keep criminal funds out of the financial system); (2) banking chatbots; (3) algorithmic trading and (4) regulatory and policy aspects.
AI can mimic actions it has seen or previously have been taught about without any new intervention. ML is defined as a particular approach to AI able to take the data and algorithms and apply it to new scenarios and patterns without being programmed directly. Deep learning (DL) is viewed as a branch of ML. DL provides machines with algorithms necessary to understand the underlying principles of an action and significant portions of data. They can then be combined to learn on their own and deepen the knowledge and skills with which they are provided.
High frequency trading (HFT) and algorithmic trading use high-speed communications and computer programs in the financial services industry. For at least a decade banks have been using ML to detect credit card fraud. The UK Financial Conduct Authority (FCA) is utilizing ML to help individuals manage their current accounts. Approximately 9% of all hedge funds use ML to build large statistical models. And:
- in 2016, Aidyia launched an AI hedge fund to make all its stock trades.
- Sentient Investment Technologies uses a distributed AI system and DL as part of its trading and investment platform.
- Fukoku Mutual Life Insurance uses IBM’s Watson Explorer AI to calculate pay-outs.
- Feedzai uses ML to detect fraudulent transactions.
- UK PropTech start-up Leverton applies AI to automatically identify, extract and manage data from corporate documents such as rental leases.
- In October 2017, exchange traded funds (ETFs) were launched that use AI algorithms to choose long-term stock holdings.
Like other Fintech sectors, AI offers many opportunities and challenges. In terms of financial inclusion, the increased application of AI technology to capital markets is likely to reduce barriers to entry for many individuals who might not have previously had access to financial markets.
Some of the world’s most valuable big tech companies such as Apple, Amazon, Tencent and Alibaba have been pouring money into AI research. But as Robert Shiller’s remarks at the 2018 Davos Forum indicate, AI also presents a great deal of uncertainty as a disruptive technology. In 2016, the GIS-Liquid Strategies group was managing $13 billion with only 12 people.
In 2017 Standard & Poor’s (S&P) acquired Kensho for $550 million in the biggest AI acquisition to date. Kensho was founded in 2013 with the intention of replacing bond and equity analysts. Its algorithm is dubbed “Warren” (after Warren Buffet) and it can process 65 million question combinations by scanning over 90,000 events such as economic reports, drug approvals, monetary policy changes and political events and their impact on financial assets. And:
DeepMind Technologies was purchased by Google and Intel has acquired Nervana Systems. In 2017 Opimas LLC estimated that AI would result in approximately 230,000 job cuts in financial firms worldwide by 2025, with the hardest hit area being asset management (with an estimated 90,000 job cuts).
A literature survey of AI and financial services cannot ignore the econometric aspects and implications. ML methods are about algorithms, more than about asymptotic statistical processes4 . Unlike maximum likelihood estimation, ML’s framework is less unified. To that end, I will discuss the ML approaches of unsupervised and supervised learning.
In unsupervised learning, ML can help issue account alerts such as low balance warnings. It can also be applied to bank overdraft charges to help ascertain what is happening to individual customers and what might be the causes of the situation. This is accomplished by using clustering algorithms. Regulators can also use clustering algorithms to better understand trades and categorize business models of banks in advance of regulatory examinations.
The SEC is using topic models to detect accounting fraud. Topic models help us understand the behavioral drivers of different market participants. Topic models draw on text mining and natural language processing (NLP). Both of these unsupervised techniques are precursors to predictive analytics (or supervised ML).
Supervised ML entails teaching an algorithm to learn from past breaches of regulations and predict new breaches, insider trading and cartel detection. In this literature survey Buchanan also discusses Random Forests, neural networks (a type of deep learning), as well as least absolute selection and shrinkage operator (LASSO) regressions.
Additionally, she plans to address the following knowledge gap – ML is anticipated to have a far greater potential impact if it is combined with the processing capabilities of quantum computing. If quantum computing becomes a reality, it has the potential to disrupt blockchain. Once this is achieved, what does this mean for the financial services industry? AI continues to become more sophisticated and complex, but so do the financial markets and this presents major challenges in regard to regulation and policy-making.
Finally, she discusses how ML is making an impact on the tools regulators use to set policy, detect fraud, estimate supply and demand and ensure compliance. In the future, regulators will still need to have procedures in place for determining whether a firm or person is at fault. She details the international regulatory responses to AI and financial services, with an emphasis on the UK and its agencies such as the Bank of England, the FCA, Serious Fraud Office and the Competition and Markets Authority.