2022-2032
2023
2024
2024-2032
2022
CAGR of 33.1% from 2024 to 2032
Value (USD Billion)
The market is projected to reach USD 13.57 billion by 2032.
In 2023, the market was valued at USD 1.01 billion.
The market is projected to grow at a CAGR of 33.1% during the forecast period.
By end-user, banks are expected to lead the market.
Increased need for operational efficiency and cost reduction among financial institutions fuels market growth.
Accenture, SAS Institute, Inc., DataRobot, Inc., Microsoft, IBM Corporation, NVIDIA Corporation, and Salesforce, Inc. are the top players in the market.
North America is expected to hold the highest market share.
By application, algorithmic trading is expected to grow with the highest CAGR during the forecast period.
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The global generative AI in BFSI market size was valued at $1.01 billion in 2023 & is projected to grow from $1.38 billion in 2024 to $13.57 billion by 2032
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In September 2016, AIG and Hamilton Insurance Group announced a joint venture with hedge fund Two Sigma to form Attune, a data and technology platform to serve the $80 billion U.S. small and midsize commercial insurance market. Through Attune, the companies are seeking to transform the small commercial segment by harnessing data, artificial intelligence capabilities and advanced modeling techniques. Attune will partner with brokers, agents and other intermediaries to streamline the pricing, selection and underwriting of insurance for small business owners.
Insurers have historically collected a wealth of data, but they have been slower to monetize this asset—by creating new business lines or models to capture the value of data and analytics. As more insurance consumers move online to interact, compare products and prices, and make purchases, the volume of available data is increasing exponentially. Even more significantly, powerful new analytics technology enables insurers to use that data in ways they had not previously considered. However, many insurers face organizational challenges to becoming data-driven companies. Others are waiting for business opportunities to emerge before enhancing their analytics capabilities. As a consequence, insurers have lagged behind other industries in their investment in and adoption of analytics.
As first movers among insurers create new business models and seek to harness the potential of their data, those that wait will be at a significant competitive disadvantage. To become a data-driven insurance organization, firms must rethink their approach to building and managing data and analytics assets and develop distinctive go-to-market capabilities that allow them to offer clients data-centric solutions.
The explosion in available customer data (both personal and commercial), the growth in analytics technologies and the rapidly declining cost of computing power and data storage are prompting companies to invest in data analytics as a means to innovate. Forward-thinking leaders across industries are pursuing opportunities to create data-driven businesses in core and adjacent markets. 1 1. For more on how analytics is shaping a range of sectors, see “ The age of analytics: Competing in a data-driven world ,” McKinsey Global Institute, December 2016. United Healthcare’s subsidiary, Optum, monetizes its proprietary consumer data and offers technology, consulting, and other services to providers, payors, government agencies, and life science organizations. Caterpillar’s investment in Uptake, a predictive maintenance platform, allows Caterpillar to tap a quintillion bytes of data to help customers make real-time maintenance decisions that can dramatically reduce the costs of ownership and operations.
Such examples have spurred early movers in the insurance industry to employ analytics across functions such as marketing and distribution, underwriting and claims. In addition to traditional data aggregators such as Acxiom, Epsilon, and Experian, carriers are using new online data sources such as Argus, Datalogic, DemystData, and specialty providers such as Judy Diamond and ATTOM Data Solutions to create 360-degree views of consumers and channels and identify new opportunities in several ways.
Increasingly, carriers are creating entirely new business models and disruptive offerings that generate non-risk, fee-based income. These “data as a business” models allow insurers to take advantage of their vast data pools and existing investments in data and analytics to offer unique data-driven insights to partners and end customers.
The arguments for harnessing the power of data and analytics are convincing. However, the question often asked by insurance executives is, “Where and how do we start?” Insurers should follow a five-phase approach to design, launch and successfully manage a data analytics business (exhibit).
The first step in shaping a “data as a business” strategy is for an organization’s senior leaders to define a compelling aspiration for the new business. Given the enormous economic potential the data hold, the aspiration should be bold and include business-backed, strategic use cases. A rallying cry for the organization could be, “Through launching a new data business, we expect to radically redefine the homeowner’s insurance market and double our market cap in three years.” Creating a yardstick to measure progress will ensure that the organization is thinking boldly enough. A high-level business and economic model based on the aspiration should also be developed during this phase. Throughout the following four phases, these elements will be pressure-tested and adjusted as appropriate.
With the aspiration set, senior leaders must determine the most appropriate course to mobilize the organization to pursue it. This task includes appointing and visibly backing a leader to drive the next four phases.
With the aspiration and strategic use case as the foundation, insurers must next determine which of their assets they can harness to build the capabilities required to achieve the strategic use case. This process includes not only understanding the types and value of existing data but also building the analytical and business capabilities needed to transform raw data into valuable insights for partners and customers.
Understanding the ecosystem of analytics partners is critical to generating impact from analytics. Carriers should identify best-in-class companies that deliver impact through data, analytics, and insights across the industry value chain. Equally important is finding “white spaces” in the market where no solutions exist. The key here is to understand how to create value in these opportunity areas and which analytical capabilities matter most to the solution. For example, partners to evaluate can include those with:
At the conclusion of Phase 2, management should align on the data assets and capabilities that best fit the strategic use case, the gaps that will need to be addressed and the high-level business case. To rally stakeholders, leaders need a compelling reason for the changes they intend to make, and must clearly describe the impact that analytics can have on the organization, its clients, and employees. The key challenge is to ensure that these assets link clearly to business value; without this connection, the resulting assets will have no real impact.
Monetization business models.
Organizations are exploring a number of business models to monetize data and deliver against business-backed use cases. These models range from providing raw data to providing insight-based consulting solutions and services to customers and channel partners. Across industries, five categories of business models are emerging (Exhibit A).
In insurance, some early movers are aspiring to develop “utilities” by taking advantage of their size and access to a disproportionate share of data to create solutions that improve industry economics and firms’ ability to serve clients. For example, Aon’s Global Risk Insight Platform (GRIP) contains a proprietary database of insurance industry placement data, a source of insights across carriers, industries and products from individual transactions to global trends. This collection of data enables Aon to benchmark similar risks worldwide, including pricing information, to help clients evaluate performance and anticipate shifts in the market.
Insurance executives must consider many factors when exploring potential business models, such as the use cases, the ultimate customer value proposition, the specific business problems being addressed, and the profit formula (for example, how much profits depend on quickly achieving scale). In data-centric business models, a key factor is data quality and how much processing will be required to make the information usable. In general, moving from the data provider model toward the others requires more processing of the underlying raw data, and hence higher levels of investment.
The good news is that the more companies refine the data, the more value is created for end customers and the higher the resulting pricing power. To get started, management teams should evaluate potential models based on the data that can be monetized with little additional processing (for example, data provider) versus putting all the organization’s efforts behind a longer-term play (for example, full solution provider).
The next step is defining specific use cases and associated customer value propositions—in other words, the building blocks of the aspiration and strategic use case. Where applicable, management should consider further refining the list of potential use cases through market research with potential partners and customers. This exercise gives the team a clearer understanding of potential demand for each use case and the primary monetization mechanisms (for example, price premiums for current products or incremental subscription fees for new data products). As building the necessary capabilities might also require partnerships or acquisitions, insurers should conduct the external assessment with an eye toward this possibility. This assessment can help to identify potential business models (see sidebar, “Monetization business models”).
Phase 4—conduct pilots.
The team proves the value of the new business by piloting two or three minimum viable products (MVPs). Carriers should take an iterative, test-and-learn approach to pilots, with each lasting no more than 8 to 10 weeks. It is important to keep the scale of these pilots manageable and not attempt to perfect final offerings. Also, metrics to gauge a pilot’s success should include a mix of learning and financial impact (with more emphasis on the former) as well as test-versus-control experiments where applicable to measure the incremental value delivered by analytics.
Scaling successful prototypes and establishing foundational capabilities by recruiting talent and building the “data factory” will position an insurer to formally launch the new venture and begin the scaling process. The new venture will call for new roles in the organization, including not only data scientists who can analyze big data and solution architects to manage the delivery road map, but also experts who can translate business needs into analytics language.
As the data-driven business matures, firms should explore establishing a new branded unit based on its capabilities and revenue growth projections. Insurers should also establish and manage key metrics to assure implementation is on track and that the business unit is delivering anticipated value. The primary focus should always be on value delivered and on investments based on clear value realization milestones.
Building a data-driven business is often a multiyear journey requiring parallel efforts in such areas as data and analytics modeling and business building, along with a heavy customer engagement and go-to-market component.
Developing a data monetization business calls for strong go-to-market capabilities. Insurers must quickly develop the data analytics offerings, conduct tests with real customers, refine them in quick iterations, and price solutions based on the value delivered and the customer’s willingness to pay. For most insurance carriers, this approach will represent a change from their traditional way of operating. However, leading digital companies around the world are using such agile approaches to deliver business and customer value quickly and effectively. Carriers should consider taking the following actions:
Since this approach to developing and running a business represents a sharp break for most carriers, executives should consider establishing the new business so that it does not get burdened by the larger organization’s requirements and processes. For example, a new business will need to attract different talent profiles and develop unique partnerships, so established processes and lead times (for instance, hiring processes) might not be effective. Senior leaders should proactively think about addressing these issues while building momentum, generating excitement, and celebrating successes.
The exploding volume of data available to insurance carriers is giving rise to new business models, revenue streams, and enormous opportunities to increase value. Embarking on the journey to monetize data requires insurers to rethink their approach to building and managing data and analytics assets and to develop distinctive go-to-market capabilities to bring new data-centric offerings to their clients. Executives that can manage investments in analytics while identifying new business lines can capture significant rewards.
Ari Libarikian is a senior partner in McKinsey’s New York office, where Ani Majumder is an associate partner; Kia Javanmardian is a partner in the Washington, DC, office, where Doug McElhaney is a vice president.
The authors wish to thank David Rose for his contributions to this article.
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