Insurance Analytics Market Growth, Expected to Reach US$ 22.35 Billion by 2030

Insurance Analytics Market Snapshot

“The Insurance Analytics Market is estimated to increase at a CAGR of 15.23% from $ 14.78 billion in 2023 to $ 22.35 Billion by 2030.”

Insurance data analytics is the process of acquiring and analyzing insurance data to identify recurring patterns and trends. This information is then used to calculate pricing, assess risk, improve operational efficiency, and detect fraudulent conduct. The goal is to make better selections for insurance firms, using specialized software and analytics solutions. This strategic tool enables insurance businesses to operate more intelligently and efficiently.

The article discusses the significant impact of data analytics on the insurance industry.

Data analytics is revolutionizing the insurance sector by enabling insurers to make more accurate pricing and risk assessments, tailor policies to individual needs, process claims faster, and improve customer service. It also helps identify future trends and hazards, lowering costs and making insurance more efficient and customer-centric. This transformation is enhancing the efficiency, competitiveness, and innovation of insurance organizations.

Insurance companies face numerous challenges, and understanding how to overcome them is crucial for their success.

The rising cost of living, inflation, and healthcare expenses are major challenges for insurers, who must maintain affordable premiums and adjust rates accordingly. Additionally, the increasing frequency of natural disasters and cybersecurity risks can disrupt business operations and strain insurance profits.

Health insurance firms face a complex regulatory environment, including federal, state, and municipal regulations, which can create uncertainty and make future planning difficult. Regulation changes could significantly impact their corporate operations.

Modern consumers demand personalized insurance app development, quick delivery, and price-consciousness, necessitating insurers to develop innovative techniques to meet these evolving expectations.

The rise of AI, ML, and blockchain technologies presents both advantages and challenges for insurance companies. They can generate new products, enhance operational efficiency, and lower costs, but their implementation requires significant investments in technology integration and staff skill development.

>>>Download Sample Report Now: https://marketreportservice.com/request-sample/insurance-analytics-market-54636 

Key Benefits

• Lead Generation

Lead generation insurance firms face numerous challenges such as competition, high-intent insurance leads, and exceptional customer service. Data analysis can help solve these problems by providing marketers and insurers with a clear picture of their Customer Acquisition Cost (CAC), which shows the amount spent on organic lead generation and third-party vendors, and indicating where corporations should focus their resources.

• Better Customer Satisfaction

McKinsey's research indicates that insurance companies that offer exceptional customer experiences are 80% more likely to retain customers, successfully cross-sell, and receive referrals, thereby reducing marketing and customer acquisition costs.

• Less Fraud

Claims fraud, a significant issue in the insurance industry, costs the US at least $80 billion annually, accounting for 10% of total reimbursements. However, recent advancements in insurance administration solutions and data analytics have improved fraud detection processes.

• Faster Underwriting

Underwriting, a challenging area in the insurance industry, is being transformed by predictive trend data for risk assessments. This helps underwriters reduce the time-consuming task of manually assessing risk profiles and establish premiums that accurately represent each policyholder's risk level. This results in fair pricing for all and reduces the likelihood of other companies making offers first. Long underwriting timeframes can lead to lost revenue and sunk costs, as customers may sign with another provider in the interim.

• Business Growth

Data analytics can lead to corporate growth through targeted marketing, increased customer satisfaction, reduced fraud, and faster underwriting. Insurance companies can improve their bottom line by marketing effectively, reducing fraud costs, and enhancing risk assessment. Predictive analytics can help businesses identify money pits and mitigate them before they become too large.

Growth Drivers

The demand for data-driven decision-making is on the rise.

Insurers are increasingly utilizing data analytics to make informed decisions, gain insights into customer behavior, assess risk, detect fraud, and optimize pricing, driven by the growing demand for data-based decision-making.

The volume of data is increasing rapidly.

Insurance generates vast amounts of data from various sources, including client contacts, policy, claims, and more. With the advent of IoT and telematics technologies, insurers can access even more data from devices and sensors, enabling them to effectively utilize this data for valuable insights.

Restraints

Insurance firms often face challenges in managing and integrating data from various sources, including legacy systems, third-party providers, and external sources. Inaccurate or inadequate data can lead to erroneous insights and decisions, making data quality and integration issues key barriers to the adoption and success of insurance analytics.

Market Trends

The US is prioritizing digital transformation through online platforms, mobile apps, and data analytics to enhance customer experiences, streamline processes, and enhance risk assessment.

Advanced analytics techniques like machine learning, artificial intelligence, natural language processing, and predictive modelling are being utilized by insurance businesses to extract insights from vast data, automate procedures, and make data-driven decisions, transforming traditional operations and enhancing client experience.

AI and ML are revolutionizing the insurance industry by enabling automation, personalized consumer experiences, and efficient claims processing.

Technology, including AI, machine learning, and IoT, is revolutionizing the insurance sector by automating processes like underwriting, claims processing, risk assessment, and client engagement.

Insurance Analytics Market Segment Analysis

Segmented by Component

The insurance analytics market is expected to grow at a CAGR of 13.7% due to the growth of the solution sector, which offers benefits like performance optimization, customer satisfaction, risk management, and cost reduction.

The market for services is also growing rapidly, as analytical services are crucial for operational efficiency and client satisfaction, contributing to the global insurance analytics market's expansion.

The business is expected to experience significant growth in the coming years due to the growing demand for insurance analytics services, particularly customer analytics, which aids in early fraud detection.

Segmented by Deployment

The on-premise market segment is expected to grow at a CAGR of 10.8% in the future, driven by the secure storage and use of data by insurance companies, government agencies, third-party administrators, brokers, and consultancies.

This deployment is driven by the need for authorised personnel access. Meanwhile, cloud services are the fastest-growing market, as they help insurance companies reduce risks and integrate risk assessment reports, thereby encouraging the cloud segment of the global insurance analytics market.

Competitive Landscape of the Insurance Analytics Market Analysis

• IBM Corporation (United States)
• Oracle Corporation (United States)
• SAP SE (Germany)
• SAS Institute Inc. (United States)
• Verisk Analytics, Inc. (United States)
• TIBCO Software Inc. (United States)
• com, Inc. (United States)
• Guidewire Software, Inc. (United States)
• Microsoft Corporation (United States)
• Accenture plc (Ireland)
• Tableau Software (United States)
• QlikTech International AB (Sweden)
• MicroStrategy Incorporated (United States)
• FICO (Fair Isaac Corporation) (United States)
• Pegasystems Inc. (United States)
• LexisNexis Risk Solutions (United States)
• OpenText Corporation (Canada)
• Cognizant Technology Solutions Corporation (United States)
• Deloitte Touche Tohmatsu Limited (United States)
• WNS Global Services (India)
• ExlService Holdings, Inc. (United States)
• Palantir Technologies Inc. (United States)

New Developments in Insurance Analytics Market

Aug 24, 2023- Fortitude Re Engages IBM to Transform and Optimize Its Life Insurance and Annuity Third Party Administration Operations.

Aug. 24, 2023 – IBM and FGH Parent, L.P.,  (together with its subsidiaries, “Fortitude Re”), announced they have entered into a $450 million agreement to transform Fortitude Re’s  life insurance policy servicing operations with the implementation of AI technology and other automation tools designed to deliver a best-in-class customer experience for policyholders and insurers.

April 2023: Guidewire launched the Garmisch solution to provide developers with more self-service tools on the Guidewire Cloud Console. Insurance companies can easily create and implement seamless, digital claims experiences using this solution. With ready-to-use bulk data connectors from top global data platforms, Garmisch reduces the time it takes for an organization to gain insight.

In March 2023, Munich Re announced a partnership with Google Cloud to develop and deploy new insurance analytics solutions. The partnership will focus on using Google Cloud's artificial intelligence (AI) and machine learning (ML) capabilities to improve Munich Re's risk management and underwriting processes.

In February 2023: MS Amlin Insurance S.E. adopted a data analytics solution by Sapien. Sapiens IDITSuite is an award-winning, end-to-end, modular insurance platform driven by technology. Combined with the insurer's data produces actionable insights that enhance risk selection during underwriting and lower claim expense ratios. MS Amlin Insurance will likely first implement the concept in France and then expand to their markets in Belgium and the Netherlands.

In February 2023, Willis Towers Watson acquired Sapient, a global technology and consulting firm. The acquisition will give Willis Towers Watson access to Sapient's AI and ML capabilities, which it plans to use to develop new insurance analytics solutions.

In January 2023, Travelers announced a partnership with Palantir Technologies, a data analytics company. The partnership will focus on using Palantir's data analytics platform to improve Travelers' claims processing and fraud detection capabilities.

In November 2022: IBM revised its Business Analytics Enterprise hub by introducing new analytics tools to assist businesses in overcoming data silos and analytics bottlenecks so they can quickly make data-driven choices and handle unforeseen interruptions. The new IBM Analytics Content Hub was to link internal stakeholders to various B.I. solutions for simple self-service data access to improve support for clients' end customers. Regardless of the vendor or location of the data, IBM Business Analytics Enterprise offered a solution to combine analytics tools in a single view.

Regional Outlook

The Asia-Pacific region is expected to experience the highest growth.

The Asia-Pacific region's insurance analytics markets are being driven by the growing demand for customer and behavioral analytics, machine learning, and algorithm development. In India, Max Life Insurance uses real-time analytics technology to detect fake medical reports and provide health rankings.

The growing urban population in the region presents health risks associated with sedentary lifestyles, making insurers a significant opportunity to target this growing urban population. Data analytics can help insurers research this consumer base before offering insurance.

Insurance companies in Asia are focusing on back-end automation and digitalization of distribution channels, with Prudential partnering with Google Cloud for data analytics.

This collaboration aims to simplify protection, health, and savings solutions in Asia. Additionally, foreign ownership restrictions in Asia-Pacific markets have loosened, allowing foreign investors to control and own a majority stake in domestic insurers in six of the seven rising economies.

The United States is emerging as a key hub for insurance analytics due to its robust data and analytics infrastructure.

The US insurance market's popularity is attributed to its mature and competitive insurance sector, which benefits from advanced analytics solutions. Insurers are using data-driven insights to improve underwriting processes, pricing, and claims administration.

The country's technology infrastructure and data resources also contribute to the market's growth. The regulatory framework places high value on data compliance and consumer protection, emphasizing the need for accurate data management. The rising demand for personalized services, transparency, and fast claim processing has also compelled insurance companies to employ big data.

China is a profitable market for insurance analytics solution providers due to its high demand and potential for growth.

The Chinese insurance market is experiencing significant growth due to the country's expanding middle class and increased affordability of insurance products. Insurers are recognizing the value of data analytics in enhancing risk assessment, streamlining underwriting processes, and increasing client interaction. The country's emphasis on technology and innovation has led to increased usage of advanced analytics techniques like AI and machine learning.

Segments Covered in the Insurance Analytics Market Report

By Component

• Solution
• Service

By Deployment

• On premise
• Cloud

The above information has been provided for the following regions and countries:

North America

• U.S.
• Canada

Europe

• UK
• Germany
• France
• Italy
• Russia

Asia Pacific

• China
• India
• Japan
• South Korea

Latin America

• Brazil
• Mexico

MEA

• UAE
• Saudi Arabia
• South Africa

Frequently Asked Questions

What is the expected growth rate of the Insurance Analytics market over the next 7 years?

The Insurance Analytics Market is estimated to increase at a CAGR of 15.23% from $ 14.78 billion in 2023 to $ 22.35 Billion by 2030.

Who are the major players in the Insurance Analytics market and what is their market share?

IBM Corporation (United States),Oracle Corporation (United States),SAP SE (Germany),SAS Institute Inc. (United States),Verisk Analytics, Inc. (United States),TIBCO Software Inc. (United States),Salesforce.com, Inc. (United States),Guidewire Software, Inc. (United States),Microsoft Corporation (United States),Accenture plc (Ireland),Tableau Software (United States),QlikTech International AB (Sweden),MicroStrategy Incorporated (United States),FICO (Fair Isaac Corporation) (United States),Pegasystems Inc. (United States),LexisNexis Risk Solutions (United States),OpenText Corporation (Canada),Cognizant Technology Solutions Corporation (United States),Deloitte Touche Tohmatsu Limited (United States),WNS Global Services (India),ExlService Holdings, Inc. (United States),Palantir Technologies Inc. (United States), are prominent players operating and dominating in the market.

What is the demand projection for Insurance Analytics by 2030?

Demand for Insurance Analytics is projected to reach a market value of US$ 22.35 Billion by 2030-end.

What is the sales growth projection for Insurance Analytics?

Revenue from sales of Insurance Analytics is projected to increase at a CAGR of 15.23% through 2030.

1.SUMMARY

1.1. Insurance Analytics Market Overview

1.2. Key Insights

1.3. Report Scope

1.4. Research Methodology

1.5. Frequently Asked Questions

1.6. Chapter Outlines

2. DECISION-MAKING SUMMARY
3. INTRODUCTION

3.1. Overview of Insurance Analytics

3.2. Key Historical Events

3.3. Structure

3.4. Biochemical Interaction

3.5. Target Indications

3.6. Advantages of Insurance Analytics

3.7 Challenges associated with Insurance Analytics

3.8. Future Perspectives.

4. MARKET LANDSCAPE

4.1. Methodology

4.2 Insurance Analytics: Developer Landscape

4.2.1. Analysis by Year of Establishment

4.2.2. Analysis by Company Size

4.2.3. Analysis by Location of Headquarters (Region-wise)

4.2.4. Analysis by Location of Headquarters (Country-wise)

4.2.5. Analysis by Year of Establishment, Company Size and Location of Headquarters (Region-wise)

4.2.6. Most Active Players

5. COMPANY

5.1. Company Overview

5.1.2 Management Team

5.1.3. Product Portfolio

5.1.4. Recent Developments and Future Outlook

6. PARTNERSHIPS AND COLLABORATIONS

6.1. Partnership Models

6.2. Insurance Analytics: Partnerships and Collaborations

6.2.1. Analysis by Year of Partnership

6.2.2. Analysis by Type of Partnership

6.2.3. Analysis by Year and Type of Partnership

6.2.4. Analysis by Type of Partner

6.2.5. Analysis by Year of Partnership and Type of Partner

6.2.6. Analysis by Type of Partnership and Type of Partner

6.2.7. Analysis by Type of Partner and Parent Company Size

7. ACADEMIC GRANTS ANALYSIS

7.1. Methodology and Key Parameters

7.2. Insurance Analytics: Academic Grants Analysis

7.2.1. Analysis by Year of Grant Award

7.2.2. Analysis by Amount Awarded

7.2.3. Analysis by Funding Institute Center

7.2.4. Analysis by Support Period

7.2.5. Analysis by Funding Institute Center and Support Period

7.2.6. Analysis by Type of Grant Application

7.2.7. Analysis by Purpose of Grant

8. PUBLICATION ANALYSIS

8.1. Analysis Methodology and Key Parameters

8.2. Insurance Analytics: Publication Analysis

8.2.1. Analysis by Year of Publication

8.2.2. Analysis by Type of Publication

8.2.3. Analysis by Type of Conjugate

8.2.4. Analysis by Target Indication

8.2.5. Analysis by Copyright Holder

8.2.6. Word Cloud: Emerging Focus Area

8.2.7. Most Active Publishers: Analysis by Number of Publications

9. SUCCESS PROTOCOL ANALYSIS

9.1. Methodology and Key Parameters

9.2. Key Assumptions and Scoring Criteria

9.3. Success Protocol Analysis

9.4. Assessment of Approved Insurance Analytics

10. Appendix 1: Tabulated Data
11. Appendix 2: List of Companies 

1. IBM Corporation (United States)
2. Oracle Corporation (United States)
3. SAP SE (Germany)
4. SAS Institute Inc. (United States)
5. Verisk Analytics, Inc. (United States)
6. TIBCO Software Inc. (United States)
7. com, Inc. (United States)
8. Guidewire Software, Inc. (United States)
9. Microsoft Corporation (United States)
10. Accenture plc (Ireland)
11. Tableau Software (United States)
12. QlikTech International AB (Sweden)
13. MicroStrategy Incorporated (United States)
14. FICO (Fair Isaac Corporation) (United States)
15. Pegasystems Inc. (United States)
16. LexisNexis Risk Solutions (United States)
17. OpenText Corporation (Canada)
18. Cognizant Technology Solutions Corporation (United States)
19. Deloitte Touche Tohmatsu Limited (United States)
20. WNS Global Services (India)
21. ExlService Holdings, Inc. (United States)
22. Palantir Technologies Inc. (United States)

Research Methodology