In future to succeed in utilizing and bringing value out of data analytics in insurance, data scientists and actuaries must collaborate to understand the analytics models. At present only 3% of the insurance companies in the US are doing the same.
- Definition / Scope
- Market Overview
- Top Market Opportunities
- Market Trends
- Industry Challenges
- Technology Trends
- Pricing Trends
- Regulatory Trends
- Other Key Market Trends
- Market Size and Forecast
- Market Outlook
- Distribution Chain Analysis
- Competitive Factors
- Key Market Players
- Strategic Conclusion
Definition / Scope
Big Data has become a catchall phrase used colloquially to represent the capture and analysis of large databases to glean useful results. A singular, comprehensive definition of Big Data is elusive.
A common definition of Big Data is the analysis of data demonstrating such volume, velocity, and variation, that standard data-processing equipment is not sufficient for the task. Given the speed of technical advancement in data-processing hardware and software, the concept of a large database is a moving target; however, much faster increases in production and collection of data suggest this definition will be valid for years to come.
Therefore, it is common for Big Data analysis to occur on a server (perhaps in a cloud), rather than on a desktop or laptop computer.
- With data analytics in Insurance, the scope of this report covers use of data analytics across various areas in P&C (Personal and Commercial) Insurance as well as L&H (Life & Health) insurance businesses respectively.
- The term 'Big Data' is being used as a common phrase at present times where the technology captures and analyzes large databases to gain useful results. The analysis of data involves such volume, velocity and variation for which a standard data-processing equipment is not enough. Thus, big data analytics is generally conducted on a server (cloud) rather than on hardware (computer).
- Most common data analytics software packages use Microsoft Excel or spreadsheet format. The hard drive storage must be huge as the amount of data will be high. Second factor to consider is velocity or speed at which data is created and analyzed. For instance data used to monitor audio and video recordings can be used to alert authorities in case of some danger approaching. The third factor is variation which considers the formatted and organized data where massive data in form of texts, numbers, words, images and sounds are used to draw inferences.
- Uses of big data in Insurance:
- Underwriting and pricing: The role of big data in insurance operations is to match the premiums and losses through pricing and underwriting more accurately. For instance, the use of big data in pricing is use of telematics data. The plans based on telematics are often known as PHYD or pay-how-you-drive plans. The benefits of PHYD is high where it is fully transparent to customers. Through the feedback of the insights from data analytics the behavior of the customers can be monitored and improved.
- Satisfaction of customers: Another use of big data in insurance is to improve customer satisfaction. Two major approaches in customer satisfaction is first, to resolve service related problems and second, to provide personalized offers as per customers' preferences. For instance, mitigating customer service issues and addressing customer queries in real time. In addition, advance data analytics is also able to detect customer satisfaction from data produced during telephone conversation where software detects if there is some problem and automatically transfer the call to an authority to solve the problem
- Increasing coverage: One example of big data use in insurance is computerized catastrophe models for pricing and underwriting extreme weather conditions such as windstorm or earthquake. Data driven catastrophe models estimate loss outcomes and probabilities more accurately. In absence of such models, private insurance for such exposures will be expensive and cannot be made available too.
- Mitigating fraud: In the US the annual estimates for costs of insurance fraud totals around $40 billion. In 2018, the number of fraud detection used by insurers in the US was also around 70% made possible by big data methods. The big data analytics has capabilities for fraud detection and mitigation and two major approaches used by the businesses are text analytics and social media analytics.
- Operational efficiency: Technologies are being used to make businesses run more efficiently. Big data is used to improve insurer efficiency in ways that benefit consumers. With the help of data analytics insurance agents are able to complete the majority of personal line insurance application fields by requesting only one or two items from prospective customer. By restructuring the application process, the search costs of the insurers are being lower than before.