What is insurance analytics?

When carrying out insurance analytics, insurance companies use predictive analytics tools that collect relevant data from a wide variety of internal and external sources to try and understand, and then predict the insured's behavior. The property insurance sector, for instance, collects data from sources such as social media, intelligent homes, customer interactions, agent interactions, and telematics. This data is then analyzed in order to provide insights.

Insurance analytics also utilizes predictive modeling. This helps insurance companies determine the effect of implementing a particular change on the insurer's business books, or how a change in policy price will affect sales.

Traditional insurance vs. insurance data analytics

There is a large difference between the insurance process carried out using data analytics and the process done manually using traditional digital technologies.

Manual processing or the use of basic computer programs can only deal with small amounts of predominantly localized data. Consequently, calculating premiums and underwriting was relatively slow and mainly depended on human judgment. Since the process depended on human agency and heuristics, there was also a tendency towards making mistakes in the calculations, requiring other people to check the numbers.

Conversely, insurance data analytics automates most of the calculation process. Consequently, the process is more accurate and relies less on human input since data analytics software makes calculations based on the predetermined formulas fed into the system.

Insurance analytics in the insurance business processes

Insurance analytics is applied in all stages of insurance coverage, including policy creation, marketing, and even in fraud investigations. Insurance firms need insurance analytics to optimize the processes that are involved in the evaluation and calculation of insurance risks and decisions around insurance products. There are six stages to using insurance analytics.

1. Problem framing

First, the insurance organization must frame the problem. That means translating the problem into a format that analytics can assess. One of the biggest reasons for analytics project failure is that the understanding of the problem was off, so the analytics solution was not accurate.

First, contextualize the problem. Who are the stakeholders, what are their assumptions, and what is their point of view? Then, describe the business needs. What are the overarching metrics and goals?

Sometimes, in order to understand the problem, the “five whys” can be used. This technique is simply asking why something is happening, five times. By the fifth “why”, the cause of the problem should be clear.

For instance, if the problem is that no one is buying a particular package:

The problem: No one buys the insurance package ABC

1. Why? Prospects say it’s too expensive.
2. Why? Because leads think there should be a bigger discount.
3. Why? The percentage of income to package is too high.
4. Why? The marketing was created with the incorrect target market in mind.
5. Why? Market research and avatar creation was not completed correctly.

Once the actual cause of the problem is ascertained, the organization can add context and business requirements.

2. Data collection

For the most accurate results, data must be relevant, holistic, include first and second party data, and appropriate. For instance, in determining marketing avatar information, information like location and household income are important.

All the data should either be stored in one place, or easily accessible by the analytic software.

3. Data extraction, cleaning, and crunching

Historical data and legacy systems often involve old and archaic systems that call for knowledge and expertise to extract meaningful and useful data. For instance, maybe a customer filed a claim on their home insurance seven years ago, and again just recently. Legacy systems may not be able to make connections between their old claim and the new claim to recognize patterns.

This need for data extraction is solved by efficient and agile insurance analytics software. When using appropriate software, data can be extracted, transformed, and stored in a more accessible dataset for modeling and validation.

While data is being processed, it should be checked for errors and any problems fixed. This includes resolving:

• Errors
• Duplication
• Outliers
• Incomplete/ missing data
• Irregular structure
• Irrelevant data points

4. Model development and data analysis

Once the data scientist has formulated the question, collected and cleansed the data, it is time to analyze it. There are a range of analysis types:

Descriptive analysis: Describes something that has already occurred. How many people purchased a certain insurance package, and in what time period?

Diagnostic analysis: Diagnoses why something happened. For example, if there was a drop in people purchasing car insurance; was there an internal change, such as an increase in policy price, or was it during the pandemic when many people stopped using their vehicles or found they no longer needed their second car?

Predictive analysis: Predictions of future trends based on previous performance. For example, if there have been a number of tornado events recently in a certain area, analysis will predict more, and insurance premiums may increase.

Prescriptive analytics: Prescribes recommendations for the future. Is there a new type of insurance product that people are buying?

5. Results and sharing

Insurance companies have many stakeholders. The results of the data analysis will likely need to be shared with a range of people other than just management. As a result, information needs to be shared in an easily-digestible manner, with intuitive visualizations and clear outcomes.

6. Evaluation of success or failure

Data analytic processes are messy. They are not easy to implement. Even if the outcome was successful, to ensure the results can be replicated analysts need to understand why the process worked.

Were there patterns in the data that have resulted in more questions? Were there problems with the data that needed more cleansing or collection?

Uses of insurance analytics

The leading insurance providers utilize insurance analytics to perform various roles traditionally carried out by human agents, to augment current processes, and to create new opportunities and processes.

Better risk evaluation

Insurance analytics is crucial in reimagining the risk evaluation process. By including more data and scenarios and using complex mathematical models, organizations can better assess risk in terms of fraud, but also in terms of risk of events and claims. This creates better pricing of policies to ensure no losses.

Improved customer experience

Data analytics can be used to improve customer experience. One way they can improve the customer experience is by implementing new processes such as allowing customers to make their applications or claims online. Another way might be by streamlining existing processes so there are no delays; for example, approving pay-outs for claims immediately.

Enhanced decision making

The use of insurance analytics enhances the decision-making process and efficiency throughout all the stages of the underwriting process. Analytics can predict risk, fraud, package pricing, and a huge range of other metrics that drive data-based decision making.

Integration of third-party data

Insurance analytics facilitates the ability to carry immense loads of third-party data collected during risk evaluation and other processes. This information can include government data, location data, industry-specific data, and environmental data.

Model re-invention

Advanced insurance analytics enables insurance companies to reinvent underwriting models by facilitating the use and reuse, obtaining, maintaining, and testing data collected from multiple sources. This can help with profitability, but also provide customers with far more effective and targeted products.

Benefits of insurance analytics to businesses

There are several benefits that the insurance data analytics technology gives to insurance companies. The five main benefits are:

1. Better lead generation

Insurance analytics aids insurance firms in generating more leads within a highly competitive market. Using insurance data analytics, data becomes comprehensible to insurers by identifying potential customers, their needs, and the best insurance solutions for different categories of clients.

2. Better customer satisfaction

Insurance data analytics is helpful in increasing the overall state of customer satisfaction. Insurance firms can accurately predict the needs of customers by identifying trends among the current client base. Analytics can also help to identify issues which cause customer churn, and resolve them.

3. Accurate identification of insurance fraud

Analytics is helpful in the process of mitigating claims fraud. One of the main challenges facing the insurance sector is fraudsters that seek to claim insurance through various nefarious means. However, insurance data analytics has improved the process of detecting fraud by automating the process and increasing the accuracy of identification. Machine learning uses previous data to see patterns and predict when a claim is fraudulent.

4. Accurate risk prediction

Insurance data analytics facilitates a better prediction of risk levels in the process of underwriting. The use of data analytics software identifies trends within data and can better link customer characteristics to their risk profile. For example, customers that drive at higher speeds can be identified as having a higher risk profile compared to insurance customers that have been identified as more careful drivers.

5. Business growth

Overall, with more customers, more streamlined processes, and less risk, insurance companies can grow their organization. Increased revenues, profits, and a larger market share are all achievable goals using data analytics.

Challenges with insurance analytics

Despite the immense benefits that insurance analytics can provide to businesses, there are still many challenges that insurance companies experience when using this emerging technology.

Compartmentalization of departments

In most insurance firms, data, and subject matter experts are scattered across different departments within the same organization. Department experts, and all data, must avoid being stuck in silos to allow for the best analysis and interpretation.

Data needs to be integrated at a central location where all data is combined and analyzed. This will enable different departments to synchronize their findings, so that the insights in one department can be helpful to other departments.

A gap between business process and data analytics

The gap between business sense and data analytics expertise has not been bridged adequately within the insurance sector. Insurers still have difficulties relating their business offerings and data analytics.

This can be solved by educating staff on the importance of using technology as a means of boosting performance and increasing profitability. There should be seminars and workshops in which staff are taught about the value and uses of insurance analytics. There can also be a focus on hiring tech-savvy staff that are familiar with different principles of insurance analytics, and their strategic placement throughout the organization.

No method to measure impact objectively

There is no way to measure and define data analytics solutions from a structural perspective. In this regard, it is impossible to justify an investment decision and the profitability of maintaining the investment.

Part of analytics use is assessing the impact it has. With the rollout of each project, there should be objective measures of outcomes. What were sales of a particular package before and after analytics helped to refine the customer avatar? What was the decrease in processing time of claims once the system was optimized? Have staff noticed customers are happier, or are they themselves happier in their work?

Non alignment to organizational vision

Sometimes, there is no drive or direction for data analytics projects since there is no synchronization of a singular data analytics strategy and vision within a company.

Organizations must spend time developing comprehensive data, analytics, and information technology practices and standards. These must be interwoven throughout the organization. Implementation should be methodical and gradually spread across the organization.

Use of insurance analytics will determine industry leaders

Insurance analytics offers key insights for organizations who are willing to invest the time and effort into implementing a thorough strategy. The emergence of big data makes it possible for companies to use vast sources of data to collect information about their clients and competitors, bolstering their market position.

The use of insurance analytics has a positive correlation with an increase in revenues. This can be attributed to better fraud detection, accurate marketing and cross-selling, and price optimization.

Many insurance companies have not yet taken advantage of their data or analytics capabilities. These firms cannot respond to changes in the market in an agile fashion, nor can they accurately diagnose problems or predict future opportunities. As a result, these organizations fall behind their competition with insurance companies and insurance tech start-ups that have already implemented big data, artificial intelligence, and machine learning.