Data disruption in insurance

Insurance industry is using data in new ways!

Data can be used to disrupt the insurance business in a number of ways, including:

• Personalizing insurance products and pricing: Data can be used to personalize insurance products and pricing for individual customers. This can make insurance more affordable and accessible for everyone. For example, some insurance companies are using telematics data to track how customers drive and to offer discounts to safe drivers.

• Improving fraud detection: Data can be used to improve fraud detection in the insurance industry. This can help to reduce costs for insurance companies and customers alike. For example, some insurance companies are using machine learning to identify fraudulent claims.

• Developing new insurance products: Data can be used to develop new insurance products that meet the changing needs of customers. For example, some insurance companies are offering parametric insurance products that pay out based on pre-defined triggers, such as a certain amount of rain or snowfall.

• Disrupting existing insurance business models: Data can be used to disrupt existing insurance business models by creating new ways to sell and distribute insurance. For example, some insurance companies are offering peer-to-peer insurance products that allow customers to insure each other directly.

Leveraging data to disrupt the insurance industry is a growing trend. Here are some ways in which data is being used to disrupt the insurance business, along with references where available:

Telematics and Usage-Based Insurance (UBI):

• Insurers are using data from telematics devices in vehicles to offer UBI policies. These policies assess risk based on actual driving behavior, which can lead to lower premiums for safe drivers. Reference: "Usage-Based Insurance (UBI) in the Auto Insurance Market"

Predictive Analytics for Underwriting:

• Insurance companies are using predictive analytics to assess risks more accurately during the underwriting process. This can lead to more competitive pricing and better risk management. Reference: "The role of predictive analytics in underwriting"

Fraud Detection:

• Data analytics is employed to detect insurance fraud by identifying unusual patterns and anomalies in claims. This can save insurers significant amounts of money. Reference: "Using data analytics to combat insurance fraud"

• Fraud detection - Detect fraudulent claims using pattern recognition across networks and databases. Saves billions in false claims.

IoT and Home Insurance:

• IoT devices like smart home sensors can provide data for home insurance policies. Insurers can use this data to prevent losses by monitoring and mitigating risks in real-time. Reference: "The Internet of Things: Revolutionizing Homeowners Insurance"

Health and Life Insurance Data:

• Health and life insurers are increasingly using data from wearable devices and health records to offer personalized policies and incentivize healthier lifestyles. Reference: "How wearables will disrupt life insurance"

Peer-to-Peer Insurance:

• Data can be used to facilitate peer-to-peer insurance models, where groups of individuals come together to insure each other. Social connections and data can play a role in risk assessment. Reference: "The Growing Trend of Peer-to-Peer Insurance"

Climate and Catastrophe Modeling:

• Insurers use climate and catastrophe data to assess and price risks associated with natural disasters. This data is crucial for property and casualty insurance. Reference: "Climate Change: The Growing Risk for Insurers"

Auto Insurance:

• Usage-based insurance - Auto and home insurance premiums based on real-time driving behavior and IoT home data instead of demographics. Helps price risk more accurately.

• Predictive analytics - Identify risk factors that lead to claims. Informs pricing, underwriting and mitigation strategies.

• Automated claims - Use image recognition, natural language processing and machine learning to read documents, assess damage, validate claims and speed up payouts.

• On-demand insurance - Ability to turn coverage on/off and customize it to immediate needs through mobile apps. Appeals to shifting consumer expectations.

• Telematics - Capture and analyze driver data through IoT devices to create customized policies. Useful for fleet insurance.

Here are some specific examples of how companies are using data to disrupt the insurance business:

• Lemonade is a digital insurance company that uses data to personalize insurance products and pricing for individual customers. Lemonade also uses data to improve fraud detection and to develop new insurance products.

• Metromile is a pay-per-mile insurance company that uses telematics data to track how customers drive and to offer discounts to safe drivers.

• Root is an auto insurance company that uses telematics data to price insurance premiums for individual customers based on their driving habits.

• Hippo is a homeowners insurance company that uses machine learning to identify and price risks. Hippo also offers parametric insurance products that pay out based on pre-defined triggers, such as a certain amount of rain or snowfall.

These examples illustrate how data is transforming the insurance industry by enabling more precise risk assessment, personalized policies, and innovative business models.

Sash Barige

Mar/03/2020

Photo: unsplash.com

References:

McKinsey article: "How insurers can use data to disrupt the market"

Swiss Re article: "The future of insurance is data-driven"

Deloitte article: "The impact of data and analytics on the insurance industry"

Accenture article: "Data-driven innovation in the insurance industry"

https://www2.deloitte.com/us/en/insights/industry/insurance/insurance-industry-trends.html

https://www.mckinsey.com/industries/financial-services/our-insights/turning-disruption-into-opportunity-in-insurance

https://www.capgemini.com/wp-content/uploads/2017/07/Digital_Disruption_in_Insurance_Study.pdf