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Advanced Analytics: Unlocking Insights for Smarter Insurance Decisions

Mar 13, 2024
Insurance Analytics

According to a study by the Swiss Re Institute, the amount of data being generated globally is expected to grow by 27% in the next two years. This digital data will be generated automatically by sensors, transaction records, and social media platforms.

With business intelligence and analytical tools, insurance companies can now analyze structured and unstructured data, yielding valuable insights about individuals and businesses. By augmenting internal data with external, semi-structured data sources, P&C insurers can price new markets and risk classes.

Insurers using advanced analytics to gain insights into customer behavior, identify emerging risks, and improve operations will gain a competitive advantage.

Data Growth

In this blog, we will explore the transformative impact of advanced analytics on the insurance sector, delving into key applications and benefits.

5 Use Cases of Advanced Analytics in Insurance

Data analytics in insurance can support several business needs. These include enabling growth by understanding new market opportunities, engaging customers more effectively, augmenting portfolio data through multiple linkages with external datasets, and improving efficiency by automating underwriting and claims processing functions.

Use Case 1: Building Risk Evaluation Models

Insurers with access to analytical expertise can build detailed risk evaluation models, especially when moving into new markets where they lack underwriting expertise.

Let’s understand this through an example.

Insurers seeking to expand into the small and medium enterprises (SME) segment would need to combine many fragmented data sources to assess the different risks faced by SMEs. This complex task would require risk approximations based on various factors (business, industry, revenue bands, number of employees, etc.) to arrive at an appropriate risk-scoring methodology.

Building analytical dashboards can:

  • Combine an insurer’s exposure data.
  • Help insurers conduct competitor pricing analysis.
  • Grow as per industry standards and segment customer data.

Continuous enrichment of such analysis with qualitative expertise can help shape optimal strategies for accessing new opportunities.

Use Case 2: Constructing Customer Behavioral Science and Predictive Models

Insurers can use behavioral science and predictive models to engage customers and employees. By applying behavioral science’s insights, insurers can gain:

  • Better understanding of consumer behavior in sales, underwriting, claims, and retention.
  • Ability to make even slight modifications based on such behavioral insights for a significant impact.

Let’s understand this through an example.

A subtle re-wording of a button labeled “Request a pamphlet” to include the words “If you don’t know how much cover you need, request a pamphlet” can significantly increase clicks.

Widespread sharing of these learnings through behavioral insights and predictive models can encourage insurers to amplify and even replace traditional customer research methods with focused studies.

Use Case 3: Optimizing Portfolio Performance

Insurers often have underperforming portfolios where the primary cause behind poor profitability and high volatility is unknown. With business intelligence and analytics, insurers can investigate trends in underlying loss drivers, while data enrichment using external data (from websites, news, and public datasets) can help refine segmentation and underwriting strategy. 

Inadequate customer segmentation may also contribute to portfolio underperformance, especially if the portfolio mix needs to be adjusted to respond to changes in market loss trends.

Let us give you an example of this.

Insurers trying to meet the demand for flexible cover from transport ecosystems may find that while the volume of claims resulting from accidents involving electric scooters and bicycles is just a small fraction of the total, they could represent a growing share of total claim costs. Analytics can reveal that accounts with certain features face more significant losses, and with more granular segmentation, insurers can take remedial action.

Use Case 4: Automating Underwriting and Claims Management Processes

Insurers have made significant headway in automating underwriting and claims management processes.

Here’s an example:

A global insurance service provider is automating low-value, high-frequency commercial claims to start paying simpler claims in a single day from the current average span of weeks. Others have deployed bots to assist claims teams by cross-checking customer emails with claims records.

Analytics can help insurers to:

  • Streamline the review process of policy wordings using text mining tools. This may help them identify where clauses were deleted, inserted, or modified, saving significant time comparing, drafting, and reconciling policies.
  • Look for trends, dependencies, and additional information to enhance their propensity to bind with document intelligence tools.
Use Case 5: Detecting Fraudulent Claims

Insurance companies incur huge losses every year due to fraudulent claims. With data analytics in insurance, it is now possible to detect fraudulent activities, suspicious claims, and behavioral patterns.

Whenever a claim is made by a person with a history of false claims, the system halts the claim process and recommends an investigation. Predictive modeling techniques are used to analyze patterns in fraud and in the screening of false claims.

Let’s understand this through an example. 

Consider a scenario where an insurance company utilizes advanced analytics to detect fraudulent claims. By analyzing patterns in historical data, the company identifies anomalies, such as claims for accidents in remote locations where the insured rarely travels.

The system flags these suspicious claims for further investigation. Text-mining techniques can also be employed to scrutinize claim descriptions for inconsistencies or exaggerated details. Through such sophisticated analytics, insurance companies can effectively mitigate losses, ensuring fair premiums.

Conclusion

The insurance industry has traditionally been slow to adopt new technologies, but times are changing. A steady shift in technological advancements is significantly influencing data analytics techniques. This ongoing technological evolution is poised to enhance the insurance value chain.

Moreover, the integration of data intelligence within the insurance sector is providing comprehensive insights from substantial data volumes in key areas such as underwriting, claims management, customer experience, and policy administration.

Insurance analytics are facilitating advanced predictive analysis, thereby empowering companies to exhibit informed decision-making in their internal processes and business transactions. However, the full potential of these advancements will only be unleashed with the development of industry-specific infrastructure, resources, and expertise.

Connect with our experts to learn how to improve your insurance operations through advanced analytics and predictive modeling.

Let’s discuss your project. Connect with us.

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