Data | Data in Insurance | Use of Data in Insurance | ABI (2024)

What data is used by insurers?

Insurers can gather data from information you give them, via an online form, a price comparison website, or from other sources. One example where data is gathered from alternative sources can be through data brokers, which are companies that collect data from multiple sources and anonymise it.

Insurers are now able to collect, process and use data across various stages of the insurance product lifecycle,such asproduct design, marketing, sales and distribution, pricing and underwriting and claims handling. This data can offer you, as a consumer, many benefits.

How does that impact you?

The more data which is provided to insurers and how specific it is usually means a more accurate analysis of it. This can lead to more personalised and affordable insurance products as well as more efficient servicing for customers. Insurers can expand their distribution reach, ensuring more accurate pricing and help insurers better detect fraud which can, in turn, lower the cost of premiums.

There are stages between an insurer creating a product and a customer buying an insurance policy. Insurers have invented ways in which data can be used throughout this product journey which can oftenimprovethe customer journey. This can be through the creation of new innovative products or distribution channels, orthrough the use ofnew technologies in customer services that can speed up claims. There are many examplesofwhere this has been done, but we have included specific ones below.

Developing new products

People are changing the way they interact with financial services, like their banks or insurance companies. Customers who already engaged with technology expect services to be immediate, convenient, and available in a variety of forms (like apps or online). As an industry, we adapt our products and processes to ensure that different generationsare able tofind the right insurance and protection. This can help individuals whose finances might be stretched find affordable cover. The use of wearable technology, such as smart watches, to encourage policyholders to exercise to reduce their health insurance premiums with the promise of other incentives, like discounts and offers, is one example of this. Another example is the use of telematics for young drivers which can help provide affordable car insurance, through rewarding safe driving practices with a lower premium. These benefits can apply to older drivers too. Alongside telematics, there has been an increase in ‘buy as you drive’ policies that can offer flexible cover for financially stretched generations who might not drive enough to warrant the expense of an annual policy.

People who work irregularly or part time might not be covered by company insurance, so insurance providers are working on ways to offer these people protection.Insurers are developingmore flexible products to ensure that the needs of the changing labour force are met. For example, delivery drivers are not normally covered by personal insurance policies due to increased risks, so some providers have developed specific policies for these workerswho work irregularlyto ensure that they are adequately and affordably covered. Some of these policies can even be calculated per hour to ensure that policies meet the needs of flexible working patterns.

Flexible and on-demand insurance products are increasingly becoming more widespread; for example, some providers offer monthly rolling contracts for contents insurance. Another flexible product is travel insurance which is tracked, with the customer’s consent, through the GPS in the policyholder’s smartphone, so their cover automatically switches on when they enter a different country. This ensures a slick and convenient service which automatically switches to an annual cover when it reaches a threshold amount. These types of flexible products have emerged to meet the changing needs of customers, particularly those who are technologically-savvy. We expect on-demand insurance and usage-based insurance (UBI) to become more widespread and for new products to emerge.

The Internet of Things has helped create products which focus on prevention or situational insurance, for example, a sensor will be able to monitor a household's water consumption patterns, detecting potential leaks and interrupting the flow before the basem*nt is flooded. This can prevent major damage and a potentially costly claim.

Customer experience and engagement

Insurers are increasingly using Artificial Intelligence (AI) to automate simpler stages of the claims process (i.e. automatically filling in paperwork) can enable claims handlers to spend more time on more complex cases.The use of Robo-advice, AI, and chat-bots can ensure a smooth customer journey.Someinsurers can have triggers for chatbots to offer additional help if a customer is having difficulty filling out forms.

There have also been AI programs developed for insurance claims lawyers which can assess medical claims by taking data automatically from medical reports, for example, and compare that data to which other claims of the same type were approved. From that data, it would determine ifthe claim should be approved. This can all be done by sending the AI program and email with all the relevant documents attached, so it can then open a case and to all thelegal paperwork. Once the paperwork is complete, the AI lawyer sends an email to the insurer to confirm everything and settle the case. If the insurer doesn’t confirm in a set amount of time, the case is allocated to a live handler. This can benefit customers as it will mean a case that would take several days to close willnowtake minutes.

Ways to find insurance

The rise in insurance aggregators(orprice comparison websites)has enabled customers to make more informed decisions when picking their insurance policies. Customers can enter their data once intoawebsite and compare hundreds of policies within minutes. This can improve the customer’s journey as it gives them more choice and the ability to have an overview of the market. It can also show customers what products are available to them, which they may not have realised existed before.

Fraud detection

Insurance fraud costs the UK billions every year, adding an extra £50 onto yourmotorinsurance policies. To help stop this, software is being developed, with backing from the Government, which combines AI and voice recognition technology to detect fraud and assess the credibility of insurance claims. This will help insurers detect fraudulent claims more easily and will save customers more money in the long term.

Data | Data in Insurance | Use of Data in Insurance | ABI (2024)

FAQs

How is data used in insurance? ›

There are many practical applications for data analytics in insurance. These include mapping risks, setting pricing, targeting prospects, tracking sales and service, analyzing claims, detecting fraud and studying consumer behavior.

How does data analytics work in the insurance industry? ›

Data analytics create new capabilities that empower insurers to optimize every function in the insurance value chain with the help of data-driven decision-making. It can also analyze a customer's risk and determine which client is trustworthy or may cause great loss.

How can big data be used in insurance? ›

Moreover, big data plays a pivotal role in fraud detection. By analyzing patterns and anomalies in data, insurers can flag potentially fraudulent claims for further investigation. This proactive approach helps prevent financial losses due to fraudulent activities, ensuring the integrity of the insurance ecosystem.

Why is data so crucial for underwriters? ›

Internally as well, accurate claims analytics models can reduce instances of fraud and increase profitability through more efficient underwriting processes. Making use of internal claims data to build these models comes with two major benefits: Existing data requires less investment than acquiring new sources of data.

How is data used in policy? ›

A data-driven policy is a set of rapid policy actions that lead to improvements in the functioning of policies and influences budget decisions leading to cost-saving and optimum utilization of resources in the right geography, on the right challenge(s) and for the right set of beneficiaries within appropriate timelines ...

Why is data quality important in insurance? ›

Scalable Data Quality Systems Drive Profitability

Insurers that use technology to improve the accuracy of their data can mitigate risk, improve operational efficiencies, and achieve end-to-end financial reconciliation.

What is data visualization in insurance? ›

Data visualizations allow insurers to monitor key performance indicators (KPIs) in real-time through interactive dashboards. This enables proactive decision-making, early detection of anomalies or fraud, and timely intervention to mitigate risks.

What do data scientists do in insurance? ›

Data Science in Insurance — Decision-Making

Here, data science in insurance is used to evaluate the client's risk. Insurers set premiums that accurately reflect the risk by analyzing age, health, driving records, etc. Data science in insurance streamlines the process.

What are the responsibilities of insurance data analyst? ›

They use computer software to evaluate insurance policies to determine risks for both a policyholder and an insurance company and meet with clients to recommend a policy that suits their needs. Insurance analysts also review policyholder applications to ensure they are filled out completely and accurately.

What type of data is collected by an insurance company? ›

Financial information: This includes income, assets, debts, and credit scores. This information is important for assessing risk and determining premiums. Medical information: This includes health history, current health status, and any pre-existing conditions. This information is important for underwriting purposes.

How do insurance companies use data mining? ›

With the information uncovered through data mining, adjusters can focus on claims that may yield larger adjustments, and are less likely to waste time investigating legitimate claims. With data mining, your adjusters can focus on recovering money so your organization's bottom line is less affected by fraud.

What is big data for policy? ›

By using data and leveraging advancements in generative AI, organisations can develop policy options and strategies, and by applying advanced analytics techniques, they can identify patterns and correlations within the data and predict trends.

Why is data important in insurance? ›

To calculate how much a premium should be and the probability of an event happening, insurers need data. This data can be specific to you or could be more general, but it all helps build a picture for insurance companies to provide the cover you need if the unexpected happens.

What data is used in underwriting? ›

By integrating historical data, demographic information, claims data, and external sources, insurers can identify hidden correlations and risk factors that may not be apparent through traditional underwriting practices. Enhancing risk assessment is one of the primary benefits of predictive analytics in underwriting.

Why is data integrity important in insurance? ›

Data integrity, then, is the very foundation of effective insurance risk management. It brings accuracy and credibility to the historical trends, market conditions, and other relevant factors. This in turn helps risk managers to make more informed evaluations of the likelihood and impact of various risks.

How is data science used in insurance? ›

Risk management is one of the critical functions of the insurance industry. Insurers use data science to predict the likelihood of future events and assess the associated risks. They analyze historical data to identify patterns and trends, which helps them to develop more accurate predictive models.

How is data being used in healthcare? ›

It helps health care organizations to evaluate and develop practitioners, detect anomalies in scans and predict outbreaks in illness, per the Harvard Business School. Data analytics can also lower costs for health care organizations and boost business intelligence.

Why is data important in health insurance? ›

Uses of data analytics in healthcare

Additionally, predictive analytics can identify members at high risk for developing health problems, including chronic diseases and suggest additional screenings or lifestyle adjustments, resulting in improved health outcomes for the member and reduced costs for the insurer.

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