AI in Finance: Market Overview and Applications
Executive Summary
“Intelligence” is generally defined as the ability to learn or understand, or to deal with new or trying situations.1 A trait normally associated with biological beings such as chimpanzees, dolphins, and, of course, humans, recent scientific and engineering developments have enabled computers to exercise “artificial intelligence” or AI. Artificial intelligence is “the simulation of human intelligence processes [especially learning and adaptive behavior] by machines.”2
Artificial intelligence is powering a wide variety of business and consumer applications, such as sifting through mountains of Big Data to extract business intelligence or permitting a vehicle to drive itself. The most prominent of AI technologies is “machine learning” (ML), which enables a system to enhance its awareness and capabilities – that is, to learn – without being explicitly programmed to do so.
At the enterprise level, virtually all industry sectors, including healthcare, transportation, manufacturing, energy, and entertainment, are leveraging AI to improve operational efficiency, lower costs, lure customers, and establish a competitive advantage.
Perhaps the leading adopter of AI is the finance sector. Writing in the Harvard Business Review, analyst Mihir A. Desai states that “The world of finance is an obvious laboratory for exploring the potential effects of AI because information processing is the central function of financial markets. Unsurprisingly, financial institutions of all types invest heavily in technology and data well ahead of other industries in order to compete most effectively.”3
Within finance, AI is used extensively for:
- Fraud detection, identifying anomalous transactions
- Customer service, providing personalized financial advice
- Algorithmic trading, performing real-time transactions using pre-programmed rules
AI in Fintech Market
As might be anticipated, the finance community is “bullish” on AI. As projected by Grand View Research, the global artificial intelligence in financial technology (fintech) market, which was valued at $12.11 billion is 2022, is expected to reach $41.16 billion in 2030, achieving a very respectable compound annual growth rate (CAGR) of 16.5 percent during the forecast period.4
In December 2021, a survey conducted by Tableau found that 32 percent of fintech companies already use AI technologies.5 Among the prominent players in the AI in fintech market are:
- Amazon
- Google
- IBM
- Intel
- Microsoft
- Oracle
- Salesforce
For example, Amazon Web Services (AWS) offers a curated catalog of AI/ML solutions for financial services institutions that, according to the vendor, “create efficiencies and speed up innovation to realize business goals.”
Use Cases
AI has numerous applications in the field of finance, including:
- Credit scoring to enable better lending decisions
- Insurance underwriting to limit insurer liability
- Regulatory compliance to detect, for example, money laundering activities6
Among the more prominent use cases are:
Fraud Detection
Analyst Alice Ivey asserts that “AI algorithms can analyze transactions in real time, detect anomalies and patterns that may indicate fraudulent activities, and alert banks to take appropriate actions. An example of fraud detection using AI is PayPal’s fraud detection system. PayPal uses machine learning algorithms and rule-based systems to monitor real-time transactions and identify potentially fraudulent activities.”7
Customer Service
A versatile tool set, analyst Jeremy Bowman points out that “banks use AI … in a wide range of activities, including receiving queries through a chatbot or a voice recognition application. If you’ve contacted your bank recently, there’s a good chance you’ve engaged with an AI chatbot or a voice recognition system. AI chatbots [not only] help companies respond quickly to customers, [but can recommend new products].”8
Algorithmic Trading
Analyst Mihir A. Desai observes that “The hedge fund industry has been transformed by the growing dominance of quantitative investing over traditional, fundamentals-driven long-short strategies. The ability to analyze large amounts of data quickly and create relatively short-term strategies appears to be beating the slower and deeper analysis that traditionally led to long and short investment decisions. These trends in finance suggests that an AI-dominated future can create outsized winners and losers in very short order.”9
Generative AI
One of the latest and most controversial developments in the field of artificial intelligence, generative AI (GAI) refers to programs that can generate unique business, literary, and artistic content, creating brand new digital images, video, audio, text, and even computer code.
Generative AI in general – and its principal exemplar, ChatGPT, in particular – have experienced intense scrutiny owing to GAI’s ability to disrupt or even displace the human element in creative arts. Educators, for example, worry whether high school students will leverage ChatGPT or other generative AI programs to either write or substantially contribute to their term papers. Professional writers, including those serving enterprise interests, are concerned that next-gen GAI programs will rob them of their livelihood.
Compounding the problem is the issue of intellectual property. If a GAI program responds to a few well-worded and incisive questions by producing a work, who has rights of authorship? The person who posed the questions? The company that produced the program? Neither? Both? It’s a legal minefield which, ironically, might have to be negotiated by a future GAI legal aide.
It is not surprising, therefore, that CFOs and other finance professionals are approaching generative AI cautiously. The Boston Consulting Group reports that:
“So far, generative AI tools are primarily used to process and generate text and images. Their ability to generate numerical analyses with the accuracy required in finance is still evolving. The tools can perform an initial pass at analyzing limited data sets, but the reliability of outcomes must improve before human intervention is no longer required. In contrast, the traditional applications of AI in finance functions can reliably analyze numerical data for forecasting and risk assessment, among other use cases. Some use cases may therefore be specific to either generative AI or traditional AI techniques, while for others it may be possible to apply the technologies in combination.
“Current and near-term applications across the finance value chain include the following:
“Finance Operations – Creating preliminary drafts for tasks that are text-heavy or require minimal analysis.
“Accounting and Financial Reporting – Offering initial insights for successive iterations of financial statements.
“Finance Planning and Performance Management – Performing ad-hoc variance analysis of the company’s structured or unstructured data sets.
“Investor Relations – Supporting most aspects of the quarterly earnings calls.”10
The Future of Finance and GAI
Looking forward, Deloitte sees the enormous potential of generative AI in several Finance spheres:
“Strategic Finance – Assess corporate development deals and run due diligence.
“Financial Planning and Analysis – Produce management reporting analysis, commentary and presentation.
“Business Unit Finance – Produce competitor analysis and insights.
“Transactional Finance – Process invoices, management payments, billing, and collections.
“Controllership – Automate data entry and reconciliations.
“Internal Audit – Identify potential risks and [detect] fraud.
“Treasury – Automate cash forecasting, cash management, and better visibility into cash flows.
“Tax – Automate tax preparation and reporting.
“Investor Relations – Develop investor report and communication.”11
Understand the Risks of Generative AI
While financial firms are eager to incorporate generative AI into their programs and procedures, any wholesale embrace of GAI will likely wait until certain significant problems are resolved. At present, GAI programs are:
- A poor steward of personal privacy
- Regularly guilty of plagiarism, copyright violations, and other intellectual property sins
- Capable of spreading misinformation (owing to occasional “hallucinations”)
- Not sufficiently reliable for investment decision-making
Recommendations
“It appears that the client side of finance retains a preference for humans.” – Mihir A. Desai12
To realize the full benefits of artificial intelligence, analyst Simon Skinner believes that CFOs and other Finance managers should:
Let Data Drive Decisions -“Financial institutions need to prioritize data management and analytics, fostering a data-driven culture that encourages innovation and the effective use of AI technologies.”
Invest In AI Talent – Hire individuals skilled in implementing and maintaining AI systems.
Develop Ethical AI Frameworks – “To ensure responsible AI adoption, financial institutions should establish ethical AI frameworks that address issues such as bias, fairness, transparency, and accountability.”
Monitor AI Regulatory Developments – “Keeping a close eye on regulatory changes related to AI in finance is crucial. Financial institutions should proactively engage with policymakers and regulators to help shape the regulatory landscape and ensure they remain compliant.”
Measure & Monitor AI Performance – “As AI becomes more integrated into financial processes, it is essential to continuously measure and monitor its performance.”13
Finally, AI can be scary to some. Financial institutions should preserve the “personal touch.” Be sensitive to the reality that many customers are wary of the influence of AI in people’s everyday lives, and especially in money management matters.
Web Links
Deloitte: https://www.deloitte.com/
International Organization for Standardization: https://www.iso.org/
US National Institute of Standards and Technology: https://www.nist.gov/
References
1 Webster’s Dictionary.
2 TechTarget.
3 Mihir A. Desai. “What the Finance Industry Tells Us About the Future of AI.” Harvard Business Review | Harvard Business School Publishing. August 9, 2023.
4 “Artificial Intelligence in Fintech Market Size, Share & Trends Analysis Report By Component (Solutions, Services), Deployment (Cloud, On-premise), By Application (Fraud Detection, Virtual Assistants), and Segment Forecasts, 2022 – 2030.” Grand View Research.
5 Ibid.
6 Alice Ivey. “Nine Examples of Artificial Intelligence in Finance.” Cointelegraph. April 6, 2023.
7 Ibid.
8 Jeremy Bowman. “Five Examples of Artificial Intelligence in Finance.” The Motley Fool. July 13, 2023.
9 Mihir A. Desai. “What the Finance Industry Tells Us About the Future of AI.” Harvard Business Review | Harvard Business School Publishing. August 9, 2023.
10 Michael Demyttenaere, Alexander Roos, Hardik Sheth, Marc Rodt, Matt Harris, Shervin Khodabandeh, Ronny Fehling, Daniel Martines, and Juliet Grabowski. “Generative AI in the Finance Function of the Future.” Boston Consulting Group. August 22, 2023.
11 Jonathan Englert, Soumen Mukerji, Omosede Ogiamien, Adrian Tay, and Robyn Peters. “Novel and Exponential Technologies in Finance.” Deloitte Development LLC. 2023:16.
12 Mihir A. Desai. “What the Finance Industry Tells Us About the Future of AI.” Harvard Business Review | Harvard Business School Publishing. August 9, 2023.
13 Simon Skinner. “The Future of Artificial Intelligence in Finance: Opportunities and Challenges.” Linkedin. March 23, 2023.