How to Harness Insurance Data to Power Better Decision-Making

10 August 2022
8 min read
Are you using your insurance data to its fullest? Discover how to benefit from this precious commodity and make your business data-driven.

Among others, insurance is a data-driven industry, which has access to enormous volumes of data, including real-time data. From historical claims data to hazard data to policy data, insurance companies are sitting on an untapped goldmine — insurance data. And only those that are able to convert this data into useful insights could unleash the value from it.

According to IBM, 47% of insurance businesses consider data and analytics a very important factor when it comes to their competitive advantage. This percentage is projected to grow up to 58% in a few years. This isn’t surprising, though, if we consider that data can help insurers enhance decision-making, contribute to innovation, increase customer satisfaction and operational efficiency.

Let’s explore why insurance data is a valuable asset for your company and how you can unlock its potential with the help of big data analytics and machine learning technology.

The importance of insurance data

5 ways to get the advantage of your insurance data

The research on the insurance industry reports that from 10 to 55% of the tasks performed by insurers could and will be automated over the next decade, including claims, underwriting, marketing, and operations. We’ve already been witnessing a prominent shift in how insurers approach their business strategy, making an emphasis on data-driven insurance. The major values that data analytics and data science in insurance bring include the following:

Better lead generation

Data and analytics, including unstructured data, offer valuable insights to insurers about customer behavior and market opportunities. Insurers can use this info to extract valuable insights and get a fuller picture of their ideal customer and the customer journey. As a result, an insurance company can focus on and personalize interactions with leads, provide in-context offers, and choose a marketing tactic depending on the value of the customer.

In particular, marketing insights received from insurance data could be used in:

  • Building innovative products and services that would target specific customer segment

  • Choosing the best channels and touchpoints to contact the leads

  • Providing more personalized communication to attract leads, for example, by writing customized warm-up messages

Read also: Predictive Lead Scoring: Benefits and Use Cases

Real-life examples

  • Paired with machine learning, data science in business has empowered our customer to improve lead quality by 5% and insurance agent efficiency by 3%

  • A French multinational insurer AXA relies on data analytics and predictive modeling to identify future product and service priorities for different customer segments. By far, this big data strategy has allowed the company to increase its sales by 40%

Higher customer satisfaction and loyalty

No doubt the secret weapon to business success is to keep your customers happy. As McKinsey finds out, satisfied policyholders are 80% more likely to go for policy renewals. And there are lots of ways how to increase customer satisfaction in insurance, from providing your services faster and more efficiently to making the services more personalized.

For sure, data analytics could be of the greatest use in this case, helping an insurer to predict the needs of its customers. By looking through the data trends and patterns, insurance companies would discover more opportunities how to improve their customer service. Another good idea is to use data to predict customer churn. This way, an insurer could provide a timely, appropriate action to prevent customers from leaving.

Real-life examples

  • A US-based insurance company, Prudential Finance, has started offering life insurance policies to HIV-positive customers after their extensive data analysis proved the longer life span of this segment

  • AIA life insurance business from China uses data analytics to personalize insurance coverage for their customers. Out of thousands of possible coverage combinations, customers receive the ideal insurance offer that is the closest to their needs

  • A large US insurer has conducted data analysis based on the available customer data, transaction data, and call-center interactions to develop new product offers. As a result, the company experienced a 40% increase in its retention rate.

New cross- and up-selling opportunities

Insurance data is also a substantial source of insights if the company seeks cross-selling and up-selling opportunities. With the aid of data science, an insurer can analyze customer behavior and track critical life events to target the customer with new types of policies and coverages that match the customer’s needs better.

The most illustrative example is the discount on bundled auto insurance when the company discovers that a policyholder’s child is near the driving age. Or the customer could receive an individual life insurance policy offer after the company discovers they have been exploring different options online. If it’s more advanced analytics, like the one powered by machine learning, the insurer can even predict the likelihood of whether the customer accepts the offer.

Real-life examples

  • AEGON Hungary Composite insurer had access to tons of raw customer data, which the company planned to use for cross-selling opportunities. The insurer used statistical analysis and modeling solutions to connect customer life events to their insurance needs. And as soon as it started to send individual insurance offerings to the customers, the insurer improved its response rate by 78% and sales by 3%.

  • John Hancock Financial, a life insurance company, has used an unusual data-driven approach to cross- and up-sell to its customers. The insurer offers a discount on new premiums based on how their customers are reducing their unhealthy and risky behaviors.

Claims optimization and fraud prevention

Insurance data analytics could also be very helpful in claims processing. Combined with a machine learning model, data analytics allows insurers to handle claims faster and more accurately. Most importantly, it can help you categorize risks and predict fraud likelihood in real time.

Built based on your historical data, an ML-powered model can analyze data patterns to seek these trends in new claims. If any suspicious trend is noticed, the company will know immediately and inform investigators. The insurer can also use alternative data sources, such as geospatial data or social media. For example, geospatial data could be useful to check whether the policyholder is honest about the accident details.

Real-life examples

  • After Santam Insurance deployed a predictive analytics solution for fraud detection, the company managed to save $2.5 million in payouts to fraudsters and almost $5 million in total repudiation.

  • ZhongAn Technology, a Chinese insurance company, uses image recognition to mitigate claims fraud. The users are asked to upload a photo of their cracked phone screen, for example.ML-driven technology then helps the company decide whether the actual damage took place and also lets you automate claim processing with machine learning.

  • Allianz Insurance, the Chech insurer, reports saving up to $4.5 million a year thanks to reducing fraud via data analytics.

  • A similar experience was in Poste Assicura, an Italian insurance company, which estimates savings of 5 to 10% of claims since it introduced insurance data analytics to its fraud detection.

  • The insurtech company Lemonade is using machine learning to compare claims against each other in its database and detect fraud. More complex cases are then transferred to an insurance investigator. The simpler ones are solved in a few seconds.

Improvements in insurance underwriting

Historically, underwriting was associated with a document-rich assessment process, which undermined any effort to automate this process. However, data and analytics have changed this approach, bringing automated data extraction, business intelligence, and predictive modeling to insurance underwriting.

In intelligent underwriting, insurers are able to prioritize submissions for quoting and start from the most valuable ones as per data. In those sectors where the workload is the highest, such as in life insurance, carriers can decline the least profitable submissions at once and improve turnaround times.

Insurance data can increase the accuracy of risk assessments as well. An insurance company would have access to more data sources to analyze the customer’s risk profile, such as credit agencies, social media, and third-party vendors. So, for example, if it’s revealed that the customer was involved in rough driving, they will receive a higher premium accordingly.

Real-life examples

  • A Scandinavian insurance company, Tryg A/S, decided to use data analytics to check the effectiveness of its risk parameters. In 2021, the company discovered that 0.6% of its quotes needed reassessment since this share of customers proved unfavorable risk behavior. Unfortunately, this resulted in extra expenses for the insurer. Tryg A/S solved this challenge by increasing the renewal price to compensate for the risk.

  • AIA Life Malaysia succeeded in reducing its underwriting submission process to less than an hour by introducing a set of specific, profile-based questions on the digital platform.

  • US-based Allstate insurance company relies on telematics to collect and analyze mobility and driver data. With the help of technology, the insurer then transforms this data into behavioral insights and uses it in optimizing premiums for auto insurance.

What should I do to unleash the power of my insurance data?

If you agree it’s the right time to use the data for the benefit of your organization, here are some recommendations from where to start with:

  • Invest in data analytics and big data: You won’t be able to get the advantage of your insurance data unless you put in order your data sources, data collection process, data preparation, and so on.

  • Use machine learning: There are many use cases for machine learning in insurance. We’ve mentioned some of these, but the idea behind this is to build algorithms that will automatically process your insurance data, find patterns behind them, and help you improve decision-making.

  • Build data expertise: At least half of insurance companies report the lack of data specialists in their organization, even though creating value from data is hardly possible without having one in your team. A good data scientist is worth their weight in gold since they don’t only have important technical skills but domain knowledge. A talented data scientist will see the broader picture of your business strategy and advise on how to solve your challenge with the help of data.

  • Go paperless with OCR: Automatic data extraction is probably the first you should think of when improving your business processes in insurance. ML-based OCR software reduces any manual entry and search for sources and accelerates the insurer’s work a few times.

  • Pay attention to ethics and governance: Whenever data is involved, the issue of data governance, including data leakage and data privacy, becomes urgent. That’s why you should think about your company’s reputation and build customer trust and loyalty. Aside from committing to the principles of ethics and governance, try to walk the talk on ethics and be transparent with your customers when it comes to the use of data.

Wrap up

As an insurance business owner, you probably have access to a large volume of data. This provides you valuable insights: from powering up your company’s decision-making to handling your claims in the most efficient manner to optimizing premium rates.

Still, having access to insurance data is one thing. Being able to unlock the power of this data is something entirely different. In the process, the insurer can meet a range of challenges, such as more global ones like how to introduce data analytics into your business strategy to trivial ones like building data pipelines and machine learning models.

If you need help with any of these, reach out to us with your specific business challenge, and our expert team of data engineering experts will come back to you with an answer.

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Oleksandr Stefanovskyi
Head of R&D Department
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