- Frontrunner financial services firms are achieving companywide revenue growth of 19% directly attributable to their AI initiatives, much greater than the 12% of follower firms achieve.
- 70% of all financial services firms participating in the study are using machine learning in production environments today, and 60% are using Natural Language Processing (NLP).
- 60% of frontrunner financial services firms are defining AI success by improvements to revenue and 47%, by improving customer experience.
- 49% of frontrunners have a comprehensive, detailed, companywide strategy in place for AI adoption, which departments are expected to follow, giving them immediate scale and speed over rival firms.
- 45% of AI frontrunner firms are investing over $5M in AI initiatives today, 3X the level of starters or late adopters.
These and many other fascinating insights are from Deloitte’s AI Leaders In Financial Services, Common traits of frontrunners in the artificial intelligence race study published on August 13th. You can see the entire report online here. The study’s focus is on how financial services firms trailing their front running competitors can jump-start or adapt their AI game plans to come up on top as the race heats up. The methodology is based on interviews with 1,100 executives from US-based companies across different industries that are prototyping or implementing AI. For an overview of the methodology, please see page 4 of the study here.
Key insights from the study include the following:
- Deloitte discovered a series of best practices that differentiate those firms who are frontrunners, known for gaining the greatest financial returns from their AI implementations versus their peers. Deloitte found that financial services firms can be divided into three clusters based on the number of full AI implementations and the financial return achieved from them. Each of these clusters identifies respondent firms based on the different phases of AI maturity each has achieved. 30% of respondent firms are frontrunners or those organizations who have achieved the highest financial returns from a significant number of AI implementations. 43% are followers who are in the middle ground of AI implementations and financial returns. And 27% are at the start of their AI journey and are lagging in the level of return achieved from AI implementations. Deloitte calls this group the starter firms or those who are late adopters.
DELOITTE’S AI LEADERS IN FINANCIAL SERVICES, COMMON TRAITS OF FRONTRUNNERS IN THE ARTIFICIAL INTELLIGENCE RACE; AUGUST 13, 2019
- The top 30% of financial services firms who are frontrunners are more adept at integrating AI into the core strategic business of their firms, delivering revenue and cost gains quicker than competitors. Frontrunners can define and execute on a clearer vision of how AI can increase revenue, reduce costs, and improve overall operations faster than competitors can. Deloitte found these high-achieving firms have three distinctive traits that include the ability to embed AI in strategic plans and emphasize an organization-wide implementation plan; focus on revenue and customer opportunities, rather than just cost reduction; and adopt a portfolio approach for acquiring AI, where they utilize multiple development models for implementing AI solutions. The following graphic compares the characteristics of frontrunners.
DELOITTE’S AI LEADERS IN FINANCIAL SERVICES, COMMON TRAITS OF FRONTRUNNERS IN THE ARTIFICIAL INTELLIGENCE RACE; AUGUST 13, 2019
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- Frontrunner firms are 12X more likely to see and act on the importance of AI to their businesses than AI late adopters. Deloitte finds that while many financial services companies agree that AI could be critical for building a successful competitive advantage, the difference in the number of respondent firms in the three clusters that acknowledged the critical strategic importance of AI is exceptional. Frontrunners’ early recognition of the critical importance of AI to their businesses motivated these firms to define AI implementation plans that sought out preemptive revenue and cost advantages over competitors. 49% of frontrunners have a comprehensive, detailed, companywide strategy in place for AI adoption, which departments are expected to follow.
- 45% of AI frontrunner firms are investing over $5M in AI initiatives today, 3X the level of starters or late adopters. Frontrunner firms believe so strongly in the value of AI initiatives that they lead all other vendor categories in investment, and they are accelerating their spending at a higher rate. 25% of frontrunners have invested $10M or more in AI initiatives today, which further signifies how strategic AI is to financial services firms. 70% percent of frontrunners plan to increase their AI investments by 10% or more in the next fiscal year, compared to 46% of followers and 38% percent of starters or late adopters.
DELOITTE’S AI LEADERS IN FINANCIAL SERVICES, COMMON TRAITS OF FRONTRUNNERS IN THE ARTIFICIAL INTELLIGENCE RACE; AUGUST 13, 2019
- 60% of frontrunner firms are defining AI success by improvements to revenue and 47%, by improving customer experience. Deloitte finds frontrunners whose business cases are balanced between revenue growth, improving customer experiences, and cost reductions are the most effective in finding and funding more diverse business opportunities. Frontrunners develop a more opportunistic mindset regarding how AI is deployed in new strategies, and they generate greater revenue and improve more customer experiences than competitors as a result.
DELOITTE’S AI LEADERS IN FINANCIAL SERVICES, COMMON TRAITS OF FRONTRUNNERS IN THE ARTIFICIAL INTELLIGENCE RACE; AUGUST 13, 2019
- Frontrunner financial services firms are achieving companywide revenue growth of 19% directly attributable to their AI initiatives, much greater than the 12% of follower firms achieve. Starter or late adopter firms are seeing revenue decline by 10% due to their early pilot phases not receiving the necessary support and funding to deliver net revenue gains. Deloitte found that frontrunner firms create an early lead in realizing revenues by relying on AI to create, test and launch new products and pursue new, high growth markets,
DELOITTE’S AI LEADERS IN FINANCIAL SERVICES, COMMON TRAITS OF FRONTRUNNERS IN THE ARTIFICIAL INTELLIGENCE RACE; AUGUST 13, 2019
- Financial Services most often acquire AI through enterprise software with Salesforces’ Einstein being a pervasive example in the industry. Deloitte finds that AI’s quickest onramp into financial services firms is via enterprise software applications and platforms already in place. Deloitte says that “with existing vendor relationships and technology platforms already in use, this is likely the easiest option for most companies to choose.” The survey found that frontrunners master a portfolio-based approach to acquiring and developing AI that provides them greater scale and speed than competitors.
DELOITTE’S AI LEADERS IN FINANCIAL SERVICES, COMMON TRAITS OF FRONTRUNNERS IN THE ARTIFICIAL INTELLIGENCE RACE; AUGUST 13, 2019
- 65% of frontrunner financial services firms have major to extreme concerns regarding the potential risks associated with AI. Frontrunners have significantly more experience with AI implementations, giving them a more realistic grasp of the degree of risks and challenges with adopting new technologies. Deloitte found that frontrunners have achieved a more pragmatic understanding of the skills required for implementing AI projects. Cybersecurity vulnerabilities of AI/cognitive technologies are the threat area frontrunners, and starters or late adopters are most concerned about.
DELOITTE’S AI LEADERS IN FINANCIAL SERVICES, COMMON TRAITS OF FRONTRUNNERS IN THE ARTIFICIAL INTELLIGENCE RACE; AUGUST 13, 2019
- 70% of all financial services firms participating in the study are using machine learning in production environments today, and 60% are using Natural Language Processing (NLP). Deloitte found the financial services firms participating in their study’s most common use cases for machine learning include the following. Predicting cash-flow events and proactively advising customers on spending and saving habits; expanding the data set for developing credit scores and applying machine learning to build advanced credit models for expanding reach and reducing defaults; providing machine-learning-based merchant analytics “as a service”; and detecting patterns in transactions and identifying fraudulent transactions as early as possible. Common NLP use cases include the following: reading documents and identifying errors for support activities such as information verification; user identification, and approvals; improving the underwriting process and capital efficiency; understanding customer queries via voice search on digital voice assistants or smartphones.
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