Financial services and insurance leaders confront AI growing pains as focus shifts to large-scale integration

Rhea-AI Summary

EXL research reveals that 91% of financial services firms and insurance carriers have launched AI proofs of concept, but only 36% are using it widely across business functions. The research highlights challenges with accessing siloed data and concerns about data privacy as major barriers to AI adoption.

Positive

• None.

Negative

• The slow adoption rate of AI across business functions (only 36%) despite the high number of proofs of concept launched (91%) suggests potential challenges in scaling AI solutions within these industries.
• The research indicates that accessing siloed data and concerns about data privacy are significant barriers to AI adoption, which could hinder the widespread implementation of AI in financial services and insurance.

Insights

Financial Analyst

The recent research highlighting the discrepancy between AI proofs of concept and their widespread application across business functions in financial services firms and insurance carriers bears significant weight on strategic planning and competitive positioning within these industries. While the initiation of AI projects indicates a forward-thinking approach, the limited integration into everyday operations at 36% signals a gap in actualizing the potential of AI for transformative outcomes. This suggests that investors should be cautious about the optimism associated with AI announcements and consider the practical hurdles that might delay expected returns on investment.

The challenges of accessing siloed data are particularly noteworthy, as they underscore the infrastructural and cultural barriers that companies face in data management and utilization. For stakeholders, these challenges may translate into longer timelines for AI projects to contribute to operational efficiency and profitability. Moreover, inefficiencies in data integration can affect customer experience and decision-making processes, potentially impacting market share and revenue growth.

Data Management Expert

From a data management perspective, the issues with siloed data that impede the application of AI in financial and insurance sectors are a critical bottleneck. The ability to effectively harness data is a cornerstone for AI's success and the lack of integration points to a need for robust data governance frameworks and investment in data infrastructure. This is a complex endeavor involving not just technology but also changes in organizational culture and processes to promote data sharing and interoperability.

For companies that successfully address these challenges, the rewards can be substantial, including enhanced analytical capabilities, personalized services and improved risk management. As such, firms that prioritize breaking down data siloes could gain a significant competitive advantage, potentially leading to market leadership and increased shareholder value in the long term.

AI Technology Analyst

The report's findings also have implications for the AI technology sector, particularly for providers of AI solutions to the financial and insurance industries. It suggests a large market opportunity for AI platforms that can offer seamless integration with existing systems and manage disparate data sources. AI technology companies that can address these integration challenges and provide easy-to-implement solutions are likely to see increased demand for their products and services.

Furthermore, the emphasis on AI proofs of concept indicates a trend towards experimentation and innovation in the sector, which could drive advancements in AI technology tailored to the specific needs of financial services and insurance. However, the successful transition from proof of concept to full-scale deployment will be a critical factor in realizing the commercial potential of these innovations.

While 91% have launched AI proofs of concept, only 36% are using widely across business functions

NEW YORK, Jan. 25, 2024 (GLOBE NEWSWIRE) -- The largest financial services firms and insurance carriers are all-in on artificial intelligence (AI), with the vast majority (91%) launching point solutions and proofs of concept over the last year. However, according to new research conducted by EXL [NASDAQ: EXLS], a leading data analytics and digital operations and solutions company, challenges with accessing siloed data and concerns about risks associated with the technology are preventing large-scale, enterprise-wide integration.

The research, published in a report entitled 2024 EXL Enterprise AI Study: Bridging Strategy and Operations is based on a survey of 158 C-suite and other senior decision makers engaged in strategy, technology and business process at the top 20 non-bank lenders, top 100 insurance carriers and tier 1, 2, and 3 financial institutions. Its findings shine a spotlight on key focal points for AI and generative AI (GenAI) development as well as the challenges and obstacles they are facing as they implement these solutions throughout their businesses.

Following are some of the report’s key findings:

• AI Pilot Projects Abound, But Most Remain Narrowly Focused: Amid a flurry of AI experimentation, just over one-third (36%) of financial services and insurance firms have implemented company-wide AI initiatives, while the majority (55%) have implemented AI for limited functions within their organizations.
  
• Business Development, Risk Management and Internal Operations Top Use Cases: Among firms that have integrated AI more widely into core business functions, the key areas of focus have been marketing and business development (47%), risk management/fraud detection (43%) and internal operations, such as claims management (42%) and back-office billing and payments processing (37%).
  
• Data Silos Hinder Company-Wide AI Integration Efforts: Among firms that have implemented AI for limited functions, 74% say data silos have been a barrier to enterprise-wide implementation. Among that group, 33% say data is siloed within each business function and 41% say data is siloed in some business functions but shared among others.
  
• Trust Remains a Challenge for Large-Scale GenAI Projects: A total of 54% of total study respondents have implemented GenAI projects, with 27% having implemented them narrowly and 27% implementing more widely across business functions. Respondents’ biggest concerns regarding the use of GenAI are algorithms operating outside of intended parameters (44%), potential for new regulation to emerge (43%) protecting customer data (42%) and risk of biased decision making (42%)
  
• Top GenAI Use Cases Focused on Product Development, Customer Experience and Risk: Among firms that are already using GenAI, the top business functions being targeted are product development (93%), customer care/experience (82%), human resources (82%) and corporate strategy (75%). Among firms that plan to incorporate GenAI over the next 24 months, the top areas where they will be focusing the technology are regulatory compliance (52%), risk management (52%) and corporate strategy (52%).
  

“The findings of this report are very much aligned with what we’re seeing in our interactions with clients. Virtually every business leader recognizes the enormous potential in AI, particularly GenAI, and they are committing significant resources to build new solutions,” said Vivek Jetley, executive vice president and head of EXL analytics. “However, the number one obstacle preventing these projects from getting from concept to fully integrated, enterprise solution is data. Data is still too siloed and often locked in legacy systems, so businesses need help integrating that data so they can unlock the full power of AI.”

The full report, 2024 EXL Enterprise AI Study: Bridging Strategy and Operations, can be accessed here.

About EXL

EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company that partners with clients to improve business outcomes and unlock growth. By bringing together deep domain expertise with robust data, powerful analytics, cloud, artificial intelligence and machine learning, we create agile, scalable solutions and execute complex operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media, and retail, among others. Focused on driving faster decision-making and transforming operating models, EXL was founded on the core values of innovation, collaboration, excellence, integrity and respect. Headquartered in New York, our team is over 50,000 strong, with more than 50 offices spanning six continents. For more information, visit www.exlservice.com.

Cautionary Statement Regarding Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the United States Private Securities Litigation Reform Act of 1995. You should not place undue reliance on those statements because they are subject to numerous uncertainties and factors relating to EXL's operations and business environment, all of which are difficult to predict and many of which are beyond EXL’s control. Forward-looking statements include information concerning EXL’s possible or assumed future results of operations, including descriptions of its business strategy. These statements may include words such as “may,” “will,” “should,” “believe,” “expect,” “anticipate,” “intend,” “plan,” “estimate” or similar expressions. These statements are based on assumptions that we have made in light of management's experience in the industry as well as its perceptions of historical trends, current conditions, expected future developments and other factors it believes are appropriate under the circumstances. You should understand that these statements are not guarantees of performance or results. They involve known and unknown risks, uncertainties and assumptions. Although EXL believes that these forward-looking statements are based on reasonable assumptions, you should be aware that many factors could affect EXL’s actual financial results or results of operations and could cause actual results to differ materially from those in the forward-looking statements. These factors, which include our ability to maintain and grow client demand, our ability to hire and retain sufficiently trained employees, and our ability to accurately estimate and/or manage costs, rising interest rates, rising inflation and recessionary economic trends, are discussed in more detail in EXL’s filings with the Securities and Exchange Commission, including EXL’s Annual Report on Form 10-K. You should keep in mind that any forward-looking statement made herein, or elsewhere, speaks only as of the date on which it is made. New risks and uncertainties come up from time to time, and it is impossible to predict these events or how they may affect EXL. EXL has no obligation to update any forward-looking statements after the date hereof, except as required by federal securities laws.

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FAQ

What percentage of financial services firms and insurance carriers have launched AI proofs of concept?

91% of financial services firms and insurance carriers have launched AI proofs of concept, according to EXL's research.

How many of these firms are using AI widely across business functions?

Only 36% of the firms are using AI widely across business functions, as revealed by EXL's research.

What are the major challenges highlighted in the research regarding AI adoption?

The research highlights challenges with accessing siloed data and concerns about data privacy as major barriers to AI adoption in the financial services and insurance industries.

What company conducted the research?

EXL, a leading data analytics and digital operations and solutions company, conducted the research.

What is the ticker symbol for EXL?

The ticker symbol for EXL is EXLS.