Quantifying and pricing risk: the interplay between insights and traditional analytics in insurance

By Kishan Vanmali , Business Head at SHA Risk Specialists

The terms, ‘insights’; also referred to as ‘business intelligence’, and ‘analytics’ are often used interchangeably when referring to how risk is assessed in the insurance industry. In fact, these two factors are distinct in that insights are typically derived from or based on analytics. Both insights and analytics play a crucial role in how insurance risks are rated and assessed both during the underwriting process and when constructing product pricing models.

Analytics as the basis for data-based decisions

Traditionally, analytics in the insurance sector refers to the collecting, processing and presenting of data and the creation of reports or dynamic dashboards. This typically involves examining large sets of data to uncover patterns, correlations, and other valuable information. In insurance, this might include analysing historical claims data, customer demographics and other relevant factors to predict future risks and set prices accordingly.

The insurance industry has used analytics primarily for risk assessment and underwriting, using techniques such as data aggregation, descriptive statistics and testing. The process of collating this analytical data is aimed at providing underwriters and insurers with a clear picture of historical performance.

The insurance industry also uses advanced analytical methods such as predictive modelling and data mining to formulate forward-looking models and views to better understand present and future risk. The real value of analytics therefore lies in being able to create a summarised, data-based snapshot of past performance, which can be used to understand present performance and make important predictions on the future.

With the advent of artificial intelligence and other parallel developments, the ability to analyse data and produce high quality analytics has changed dramatically. With these tools and technological innovations at their disposal, insurers now can bring more metrics and variables into the analytics mix. This in turn, can allow insurers and their stakeholders to develop more accurate predictive models and risk assessment tools.

Insights as action points

Simply put, insights are the actionable conclusions that are drawn from analytics by identifying patterns, trends, causes and anomalies within the data. Insights therefore go beyond the raw data and statistical analysis to provide meaningful interpretations and recommendations.

Ultimately, the ‘extraction’ of these insights aids in moving towards more optimized performance in the following areas: risk assessment, underwriting, pricing optimization, customer experience and engagement, product development, market efficiency and sustainability.

A practical example

The relationship between analytics and insights is best explained by way of an example in which an insurance company uses analytics to assess customer data, including demographics, past claims history, and behaviour patterns. They could then apply machine learning algorithms to predict the likelihood of future claims based on these factors.

Through these analytics, the insurer could identify specific types of risk (manufacturers of specific products or certain types of buildings) that consistently shows higher-than-average claim frequencies or certain root causes of said claims. They could also discover that certain types of products or industries tend to have more frequent and costly claims or that these claims typically fluctuate seasonally.

Armed with these insights, the insurer can make informed decisions around how to adjust premium rates for high-risk industries, areas or products to better reflect the actual risk. They could also, for example, improve underwriting guidelines to assess risk factors and adjust coverage terms accordingly more accurately.

In the long run, by making these data-informed decisions, the insurer could potentially reduce overall claim costs, improve profitability, create new products and enhance customer satisfaction by offering fairer pricing and tailored coverage options.

The intricate relationship between analytics and the insights that are derived from them, is therefore an imperative part of how insurers can maintain viable and sustainable operations. The key to harnessing this value lies in understanding this relationship and leaning on data to improve accuracy and relevancy.