Larry Cao
Senior Director, Industry Research, CFA Institute
According to our data and field research,1 an increasing number of firms and professionals in the financial services industry are seeing the benefits of AI (artificial intelligence) and big data.
Where and how these technologies are being applied and the factors that differentiate successful adopters from the less successful have also emerged.
Uses of AI and big data in finance
AI is most effective in replacing mechanical and repetitive work processes. We observe many use cases at banks and insurance companies where AI scientists are adding computer vision algorithms, a form of AI, to the routine processing of deposits and insurance claims. This allows them to redeploy employees from these mundane tasks to more productive and interesting work.
NLP (natural language processing), another popular AI technique, is also widely used by asset managers and securities brokers alike in processing financial filings, news articles and social media to get a complete and updated picture of a company.
The scalability of AI technology allows analysts to follow far larger numbers of companies than before, enabling them to enhance the quality of their research and/or enter new markets. Voice recognition can now do dictations faster and more accurately than an average person. Analysts use the technology to turn corporate earning calls into transcripts quickly which are also fed into NLP programs for analysis.
AI is most effective in replacing mechanical and repetitive work processes.
Key success factors for AI adoption in finance
Over the years we have observed that successful financial services companies in AI adoption have one thing in common: they have all built what we call T-shaped teams.2 This is an AI adoption project team including both specialists from finance and AI, connected by an innovation function, a group of professionals well-versed in both finance and AI that can help specialists communicate with each other and prioritise projects. All three types of professionals are needed for these projects to excel and for companies to stay ahead of the game.
Of course, before they get to developing T-shaped teams, successful financial institutions had senior management teams who understood the importance of embracing AI technology, offered their support and built a collaborative culture. Without that foundation, few projects of any kind can succeed.
[1] Source: Larry Cao, AI Pioneers in Investment Management, (CFA Institute, 2019)
https://www.cfainstitute.org/research/industry-research/ai-pioneers-in-investment-management and Artificial Intelligence
and Big Data in the Financial Services Industry (Hong Kong Institute for Monetary and Financial
Research, 2021) https://www.aof.org.hk/docs/default-source/hkimr/applied-research-report/aibdrep.pdf
[2] Larry Cao, T-Shaped Teams: Organizing to Adopt AI and Big Data at Investment Firms, (CFA Institute, 2021)