Insurance policy structures thrive on data. Traditional models collect information about past events to guide risk assessment procedures.
However, there are instances when historical data does not apply to the current landscape. New intelligence could drastically influence policy structures. Real-time data collection provides a continuous stream of data to keep information current, revolutionizing the world of insurance.
Traditional data collection methods — such as email questionnaires, oral conservations, and web forms — have been sufficient, especially at providing personal information about customers. However, this same set of tools creates several pain points for insurance companies:
- Organization: There is a variety of information scattered throughout different collection methods, and collating the files requires a lot of resources.
- Viewing data: You may also struggle to access and view that data. Traditional intelligence piles up, requiring you to sort through files. Thus, viewing fresh information would still take time due to the backlog.
- Deriving value from data: Only 18% of insurance organizations have the tools and technologies to derive full value from the growing volume of files. Additionally, six in 10 collaborate with insurance technology companies, despite the fact that many insurance data masters view insurtechs as a threat.
Real-time data collection offers a chance to enhance insurance systems. Introducing it may improve several processes:
- Speeding up accessibility: Insurance professionals can pick out a recent file in a few clicks rather than sifting through multiple documents and scanning multiple texts.
- Verifying data reliability: Automating data collection verifies information to make it as reliable as possible.
- Accessing more accurate trends: You can also train predictive models to notice recent customer trends regarding their chosen insurance plans and claims.
- Forecasting outcomes: With more accurate data, companies could forecast outcomes and mitigate high-cost cases. They can also speed up adjustments.
- Enjoying more confidence: Insurance systems can also feel more confident in their numbers with real-time reporting. They can make more informed decisions and more accurately define patterns and trends.
See also: The True Cost of Big (Bad) Data
Risk Assessment Accuracy
Historical data offers outdated information, and inaccuracy may result in financial losses. With real-time data, you remain informed of changes to a policyholder’s risk profile and can make adjustments to their premiums. Here are a few examples:
- Driving and auto insurance: If you have real-time insight into a policyholder’s driving behaviors — such as speed and acceleration — you can adjust your assessments. If you notice any concerning behaviors, you can offer incentives for safe driving behaviors and assess whether they are effective.
- Weather and homeowners insurance: If a policyholder lives in an area that has experienced an uptick in extreme wildfires, hurricanes, floods, or other weather phenomena, real-time data can determine their present risk and indicate whether it would be beneficial to update coverage.
- Theft and homeowners insurance: Real-time data can reveal high-risk areas where higher premiums may be necessary.
- Policyholder health and health insurance: Monitoring an individual’s health history, demographic factors, and lifestyle in real time can guide insurance companies to update health insurance premiums.
Personalized Service
Real-time data collection allows insurance companies to offer personalized services to current and potential policyholders. Predictive models showcase more recent trends to see what people in different segments want in their insurance plans. You can match coverage limits, premiums, deductibles, exclusions, and other terms and conditions to the specific individual.
Updated information can also speed the claims process. With automation, there’s no need to review paperwork and find the relevant data for a case. Automated systems can compile everything you need so that the recent data is at your fingertips.
With real-time data, you’ll understand how policyholders wish to be contacted and can communicate with them effectively. You also get a better basis for your decisions and inevitably create better insurance policies.
Dynamic Pricing
Real-time data collection is also ideal for creating more dynamic pricing strategies.
Machine learning models that use historical data to guide pricing only improve profits by 1% or less. Replacing historical data with a continuous stream of information tailored to the individual policyholder can lure away potential policyholders from competitors.
Dynamic pricing allows you to act on real-time market changes and meet customer expectations.
In addition to gathering market data about competitor pricing and demand, you can offer premiums that reflect the individual policyholder, essentially offering lower premiums for low-risk customers and higher premiums for high-risk customers.
See also: Unauthorized Use of Auto Claims Data
Advanced Fraud Detection
In the U.S., more than $300 billion is lost to insurance fraud annually. Fraud results in higher premiums for all policyholders and costs each consumer approximately $900 per year.
Real-time data plays a critical role in detecting and preventing fraud. Policyholders may make illegitimate claims, whether deliberately or accidentally. With current information, insurance companies can detect anomalies and discrepancies in documents and evidence.
Whether a policyholder deliberately fakes an accident to fabricate an insurance claim, misrepresents an accident to receive a bigger payout, or accidentally omits important information when making a claim, real-time detection can help.
Machine learning (ML) models are especially useful because they can calculate large datasets. One study published in 2024 found that effective ML models could drastically reduce health care fraud and minimize the resources needed to investigate probable fraud.
When integrating real-time data collection and fraud detection technology, a human set of eyes must still assess those activities and make the final decision. Predictive analytics and models are only meant to display information and are still not perfect in their structure.
Privacy and Compliance Considerations
Insurers must, of course, comply with privacy laws. A significant example is the European Union’s General Data Protection Regulation (GDPR), which extends its protection beyond its borders.
A U.S. company may only receive personal data from an EU resident in accordance with GDPR guidelines. Insurers that provide services to individuals in the EU must comply with GDPR regulations, regardless of where the insurance company is registered.
Select U.S. data protection laws also exist and prioritize the protection of policyholders. For instance, the Insurance Information and Privacy Protection Act (IIPPA) covers California residents, while the Health Insurance Portability and Accountability Act (HIPAA) protects an individual’s medical records and personal health information. Another important law is the Gramm-Leach-Bliley Act, which requires companies that offer financial services to explain their information-sharing policies to customers.
Technological advancements like blockchain can provide data privacy advantages to insurance policymakers and holders. It’s designed to protect confidential and sensitive information by decentralizing control and using encryption to ensure data is inaccessible to unauthorized users.
Blockchain technology maintains the system's integrity and gives users peace of mind that their data is protected. Insurance companies also implement traditional cybersecurity protocols to mitigate cyberattacks.
Revamped Policy Structures
Effective insurance policies protect policyholders. The best insurance policies are tailored to the individual and take market conditions into account. These policies help foster the relationship between companies and people. However, policies created using outdated information benefit neither party. In fact, they may diminish policyholder trust and lead to adverse financial outcomes.
Real-time data collection is a key asset for insurance professionals. They can gain more updated information about their current and potential policyholders and resolve some of the issues associated with using historical data. With real-time data, your company can better assess risks and create a better relationship with customers.
