In the last two decades, the financial services industry has experienced enormous change, along with some growing pains, thanks to the 21st century’s technological advancements. At the forefront of that change is Artificial Intelligence (AI).
In the wake of a pandemic that demands remote work, the financial industry depends more on digital operations, as it shifts to online platforms and cloud-computing. As a result, AI is adapting the way these financial services operate and invest with AI-enabled solutions for customers and employees alike.
On a fundamental level, AI can streamline a company’s ability to utilize the massive amount of data generated in daily business activities. From the algorithmic automation of standardized workflows in operations to comprehensive risk analysis in banking compliance to 24/7 customer services like talk-to-text, it’s not hard to see why so many financial services are investing in AI.
According to the 2022 AI Index Report From Stanford University, the private investment in AI in 2021 totaled around $93.5 billion—doubling the 2020 investments combined. The share of job postings that mention a need for AI skills saw a global increase, with the most demand for machine learning skills (0.6% of all job postings), followed by artificial intelligence (0.33%), neural networks (0.16%) and natural language processing (0.13%).
AI solutions foster more efficient communication, pattern identification, and easier met trend predictions telling of customer demands, thanks to the automation of backend processes. From banks to used car dealerships pre-approving a retail investor for a loan the moment they walk in the door, AI capabilities are now vital due to the need for data and the finance industry’s dependence on technology.
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Where is AI headed?
Even as investment levels have skyrocketed, the number of companies getting that money has gone down. In the 2022 Stanford AI Index report, they identified venture capitalists funded 1,051 AI companies in 2019. In 2020, that number dropped to 762 and then sank lower to 746 in 2021. As well, in 2021, only 15 companies received funding valued at over $500m. So, the question is now, can AI keep up with such inflated expectations?
For example, in Deep Learning (DL), AI expert Gary Marcus suggested that DL is “at its best when all we need are rough-ready results, where stakes are low and perfect results optional.”
As David Beyers has said, “Too many businesses now are pitching AI almost as though it’s batteries included [which may] potentially lead to over-investment in things that over-promise. Then when they under-deliver, it has a deflationary effect on people’s attitudes toward the space.”
Expectations should be more in line with the areas of finance already improving, such as employee efficiency thanks to automated workflows, customer satisfaction and problem resolution with the aid of chatbots, or increased security and transparency through contactless payments and stock trading.
With so many opportunities to apply AI in the finance sector, it’s important to prioritize clear communication on how the new technology will be used and why it is necessary in the first place. By having the customer and service delivery at the core, it can prevent unnecessary risk and harm to consumers and providers in the future.
References
Stanford University, 2022, Artificial Intelligence Index Report, https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Master.pdf
The Dirty Secret of Machine Learning – 02/2017
https://www.oreilly.com/radar/the-dirty-secret-of-machine-learning/
Gary Marcus: The Road to Artificial General Intelligence – 07/2017
https://medium.com/syncedreview/gary-marcus-the-road-to-artificial-general-intelligence-ce5b1371aa02
AI Investments Soared in 2021, But Big Problems Remain – 03/2022
https://www.techrepublic.com/article/ai-investments-soared-2021-big-problems-remain/
