Why Data Analysis Matters in the Insurance Industry

December 6, 2022
Why Data Analysis Matters in the Insurance Industry

Data transformation in the insurance industry

Insurance is a centuries old industry. The first known insurance contract covering maritime trade can be traced back to Genoa in 1347. Life insurance policies were introduced in the early 18th century. The increasing popularity of life, property and commercial insurance then inspired the development of actuarial science, a means of assessing and managing financial risks.

Using statistics and mathematics, actuaries estimate the risks of uncertain future events. It is based on collecting historical statistical data and developing forecasting models. There are many areas of overlap between actuarial science and data science. Insurers rely on insights gleaned from data to develop products and services that meet customer’s needs while achieving profits.

The insurance industry, almost by definition, is risk averse, and slow to adopt new technologies, but it has been unable to resist digital transformation, especially since the pandemic. More people are relying on online channels to shop for and buy insurance products. Digitalisation, which is causing an explosion in extractable data, is pushing us all to adopt different ways of working. Big data and the ability to capture and analyse huge volumes of structured and unstructured data represent both an opportunity and challenge to the insurance sector.

According to YFS Magazine, leveraging big data insights can result in:

  • 30% improvement in access to insurance services
  • 40-70% savings
  • 60% better fraud detection

Uses of big data in the insurance industry

  1. Customer acquisition – Targets specific customer segments more effectively and increases lead generation
  2. Customer retention – Keeps customers engaged by better understanding their needs and desires, and connecting with them over multiple channels (social media, email, advertising, customer service) and therefore, extending the relationship
  3. Risk assessment – Increases the efficiency of risk assessment by identifying risk patterns through predictive analytics
  4. Fraud prevention – Detects and prevents fraud through examining transaction and behaviour patterns
  5. Cost control – Digitalisation, including automation of manual process, can help in slashing costs
  6. Personalisation – Customers can be offered opportunity to personalise insurance plans and products
  7. Customer service or aftersales – Data analytics can help identify customer pain points and aid in developing effective solutions
  8. Operation improvement – Analysis of internal processes can identify areas for improvement

Big data: Critical challenges facing the insurance sector

The insurance industry is being forced to change traditional ways of working. Digitalisation is the core driver of this change. But the sector is facing several complex and overlapping challenges in changing and in accepting change, including:

1. Data fragmentation

Insurance companies must deal with multiple sources of information ranging from the structured, like customer information forms to the unstructured (including emails, PDFs, images, social media posts). Unstructured data may represent almost 80% of the information that must be processed and analysed to gain insights.

Insurance companies already have many different information gathering systems in place, and many teams working on analysis, which can create data silos, and a lack of coordination in terms of analysis.

2. Legacy systems

Most insurance companies have multiple legacy systems and platforms that are not integrated. This is related to the challenge of data fragmentation.

3. Data Quality

Insurance companies rely on advanced statistical models to generate risk assessments. The accurate functioning of these models depends on the quality of the data that is input at the start. Many companies still rely on manual data entry, which raises many issues regarding accuracy, and consistency.

4. Agility in responding to changing customer preferences

Insurers may be still operating in traditional mode, but the customer base has moved on. Customers are using digital channels to access information on insurance products. They want to buy, upgrade, and renew policies online. They also want to pay premiums, submit and follow up on claims digitally. Increasing digitalisation has created an expectation of fast response and resolution times. We are talking about hours, rather than days or weeks.

5. Maintaining customer loyalty and engagement

As customers can access more information about insurance, and as they are able to leverage on pricing differences, the competition within the industry is becoming cutthroat. It is not enough to generate leads and to achieve sales, insurers have to maintain relationships with customers, including after sales support, re-selling and upselling.

6. Benefiting from automation and advanced technologies

There are many insurance related processes that can be made more efficient through the use of automation, artificial intelligence and machine learning. Machine learning is already being used in customer analytics and claims processing. Recognising the areas where advanced technologies can be used, and implementing them effectively remains a challenge.

7. Business intelligence

Having the data is not enough. It has to be analysed and the insights generated have to be integrated within the company’s processes and strategies. Insurance companies need high calibre employees who are not only tech savvy but are able to capture the opportunities for growth and improvement presented by Big Data. These are people and teams who are able to work with the latest software and platforms to crunch the data, as well as to move beyond generating insights to data-driven actions and improvements.

Big data and data analytics: The future has already arrived

The insurance industry suffers from a traditional mindset. Moving into a digital ecosystem requires transforming many of the sector’s core operations. Digitalisation is creating the opportunity of new distribution and customer service models, including virtual assistants, robo advisers and chatbots. Automation, artificial intelligence and machine learning will allow employees to offload routine tasks and to concentrate on more value focused activities.

Big data and data analytics are the future of this industry. Start extracting value from your data by hiring your own internal data team, or by hiring freelancers. Pangaea X can make your search for the best data talent easier. Browse through our rich roster of data experts or post your project to get started.

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