Insurance Analytics Market Size Worth $20.6 Billion, at 15.1% CAGR by 2026

The study categorizes the insurance analytics market based on component, deployment mode, organization size, application, end user are at the regional and global level.


Northbrook, IL 60062 -- (SBWIRE) -- 08/28/2023 -- The global Insurance Analytics Market size is expected to grow from USD 8.8 billion in 2020 to USD 20.6 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 15.1% during the forecast period, according to research report by MarketsandMarkets™.

The rising need for big data and predictive modeling capability during the COVID-19 pandemic drives the adoption of insurance analytics solutions and the rise in adoption of IoT products driving the adoption of insurance market.

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186 - Tables
67 - Figures
267 – Pages

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Demand to reduce the time for efficient management of claims and insurance policies is expected to boost insurance companies segment to hold a larger market size

Insurance companies are the end-user companies that provide various types of insurance, such as consumer insurance (including life and health insurance, and vehicle/auto insurance) and commercial insurance (employee insurance and asset insurance for manufacturing companies). Insurance companies are now widely adopting various technology solutions, including analytics, for their internal and customer-facing services. For example, multi-line carriers that supply consumer policies — for homes, cars, and small groups — in addition to business services, such as P&C insurance, run several applications to handle underwriting, claims, and Customer Relationship Management (CRM). Increasing service demand coupled with critical end-of-year fiscal reporting forces these organizations to adopt advanced technological solutions, leading to a larger market size during the forecast period. Bajaj Allianz Life Insurance, a Pune, India-based life insurance provider, is among the leading insurers in India who are using predictive analytics across their insurance operations. The company is leveraging predictive analytics across its customer life cycle to assess customer risk, to determine the potential of a customer to renew or lapse a policy, and to act accordingly. Risk management, fraud detection, customer management, and personalization are some of the key application areas where the India-based insurer is applying analytics to improve operational efficiency, reduce cost, and enrich customer experience.

The growing demand to reduce the response time for various claims management processes and the increasing adoption of IoT insurance driving the demand for process optimization during the forecast period

Insurers are widely using analytics solutions to understand the potential risks and deploy countermeasures to mitigate losses, or at least screen, pre-empt, and assess the cost of risks in the underwriting process. Risk management involves the identification, assessment, and management of potential risks, incorporating analytics to support decision-making by clearly stating business goals and objectives, and facilitating precise information management with a better understanding of the trade-offs between risks and rewards. Risk management provides insurers with the risk capacity to maintain specific credit ratings, manage capital, and reduce earnings volatility across insurance companies.

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North America to dominate the market during the forecast period

North America is expected to dominate the global market from 2020 to 2026, owing to the presence of several solution vendors in the US. Canada has the highest internet penetration rate responsible for the growth of North America. Additionally, the presence of several vendors and favorable support environments has led to the foundation of a robust support ecosystem in the region. The increasing demand for fraud detection solutions and the rise in the requirement of on-time authentic information would drive the growth of the insurance analytics market in this region.

The major vendors offering insurance analytics tools are IBM (US), Salesforce (US), Oracle (US), Microsoft (US), Sapiens (Israel), OpenText (Canada), SAP (Germany), Verisk Analytics (US), SAS Institute (US), Vertafore (US), TIBCO (US), Qlik (US), Board International (Switzerland), BRIDGEi2i (US), MicroStrategy (US), Guidewire Software (US), LexisNexis Risk Solutions (US), WNS (India), Hexaware Technologies (India), Pegasystems (US), Applied Systems (US), InsuredMine (US), ReFocus AI (US), RiskVille (Ireland), Pentation Analytics (US), Habit Analytics (US), (India), CyberCube (US), and (US).

Key Dynamic Factors For Insurance Analytics Market:

The insurance sector produces enormous amounts of data as a result of consumer contacts, claims, underwriting, policy management, and other factors. This data explosion is a result of the move towards digitalization and the proliferation of linked devices (IoT). Companies may harness and analyse this data using insurance analytics tools to gain valuable insights.

Customer expectations in the insurance business were changing towards more personalised services as a result of personalization. Insurance companies were able to offer specialised products, services, and communication tactics by using analytics technologies to better understand client preferences, behaviours, and needs.

Risk Management and Underwriting: The improvement of risk evaluation and underwriting procedures was greatly aided by insurance analytics. Insurers were able to analyse risks more accurately and establish suitable rates with the aid of advanced analytics, such as predictive modelling and machine learning, which led to better risk management and increased profitability.

Fraud Detection and Claims Management: Analytics tools were being used to speed up claims management procedures. Insurers could spot possible fraud instances, speed up valid claims, and improve claims settlement procedures by examining past claims data and patterns.

Usage-Based Insurance (UBI) and Telematics: Insurance companies now have the ability to provide personalised coverage based on real-time data gathered from vehicles and other connected devices thanks to the growth of telematics and UBI. Analytics assisted insurers in creating usage-based models, accurately calculating premiums, and promoting safer policyholder behaviour.

Operational Efficiency and Cost Reduction: By automating manual procedures, lowering errors, and optimising resource allocation, insurance analytics solutions helped businesses increase operational efficiency. Costs were reduced as a result, and business performance as a whole improved.

Regulatory Compliance and Reporting: The insurance sector must abide by a number of regulatory obligations. Analytics technologies made it easier to manage and report on important indicators, lowering the risk of fines and assuring compliance with industry rules.

Market Competition and Differentiation: The market became more competitive as more insurers adopted analytics. Businesses that successfully applied analytics earned a competitive advantage by providing cutting-edge goods, first-rate customer support, and more precise risk assessments.

Data Privacy and Security: With the growing use of data, worries regarding data privacy and security have grown. Insurance companies had to make sure that the information gathered, saved, and analysed followed stringent security procedures and conformed with data protection laws.

Partnerships and Collaborations: To gain access to specialised knowledge and solutions, insurers worked with technology providers, analytics companies, and insurtech startups. These collaborations sped up industry innovation and made it easier to implement advanced analytics tools.

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Competitive and Segmentation Analysis:

The insurance analytics market is characterised by fierce competition and is going through rapid changes as a result of the industry's expanding need for data-driven insights. Multiple criteria, including deployment type, analytics type, insurance type, and geography, can be used to segment this market. since of its scalability and flexibility, cloud-based deployment is gaining popularity since it allows insurers to use analytics solutions without making significant infrastructure investments. There are many different forms of analytics, including predictive, prescriptive, descriptive, and more. Each type addresses a different requirement, such as risk assessment, customer engagement, and operational efficiency.

Numerous insurance categories, including life, property and casualty, health, and others, are served by insurance analytics. Insurance companies may adapt their plans to the particular needs of each industry thanks to the flexibility of analytics technologies. The adoption of analytics varies geographically depending on regional regulatory structures, consumer behaviour, and market maturity.

Established IT companies and specialised analytics providers compete for market share in this hostile environment. Industry heavyweights provide full analytics suites that include modules for fraud detection, claims analysis, underwriting optimisation, and customer experience improvement. Startups and insurtech pioneers concentrate on specialised solutions like telematics-driven analytics or individualised pricing structures. Continuous innovation, strategic alliances with insurtech companies or data providers, and a strong emphasis on data security and privacy are all components of competitive strategy. The market is always evolving, making differentiation, innovation, and adaptation to shifting industry dynamics vital for long-term success. This is because insurers are rapidly realising the importance of analytics in risk management, operational efficiency, and customer engagement.

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