Insurance News: Implications of Big Data on Pricing StrategiesInsurance News: Implications of Big Data on Pricing Strategies

Traditionally, the insurance industry has been an actuarial science-driven business that used historical data to underwrite and price risk. Yet the boom in digital data capacity, or so-called “big data,” is fundamentally changing the way that insurers foster pricing strategies. To complement the previous article on big data and insurance pricing, here are some implications that could be identified for how insurers price using big data.

Big Data: A story of (basically) infinite data

Big data Generally speaking, it is a large and complex dataset that cannot be effectively processed using traditional software. This data can originate from many sources in the context of insurance, such as:

⦁ Public Records: Age, seismic score, lava flow hazard area zone, locations of nearby retail claims
⦁ Connected devices: telematics feed from the vehicle; wearable tracking of health and activity levels
⦁ Social media: sentiment analysis and online behavior patterns (with proper privacy)
⦁ Track customer transactions: payment history, insurance utilization trends

Big Data Pricing Advantages:

Improved Risk Assessment: When insurers have more types of data to analyze, they can make a much better estimate of which risk profile their policyholders fall into. This means a paradigm shift from generic risk ratings to individualized pricing, under which the premiums more accurately represent an individual’s endangerment.
Finding New Risk Factors: Big data can be used to reveal hidden patterns and correlations that traditional methods may overlook, helping insurers find new risk factors and develop better pricing models.
Price Accuracy: With a wider and finer-grained set of data points, an insurer can price premiums based more closely on the likelihood—in real terms—that each policyholder will claim. curr  This will translate into better pricing for all the customers.
Dynamic Pricing Models: Real-time data can help insurers develop dynamic pricing models that adapt premiums according to the very latest information. Car insurance premiums, for instance, might change depending on the types of roads a driver is using or weather conditions.

Issues and Tradeoffs:

⦁ Data privacy concerns: Big Data will use more personal and sensitive information to help businesses. However, for the essence of such applications, the usage of personal details can be troublesome. Similarly, insurers must ensure that they are compliant with all applicable legislation regarding the gathering, retention, and use of data.
⦁ Algorithmic Bias: There is a risk associated with big data algorithms learning from training for biases that were only present in the original data. If not watched and corrected, this could easily lead to discriminatory pricing practices.
⦁ So, they pose data security risks (insurers collect personal data on a colossal scale, so it makes them more likely targets for cyberattacks). It is pertinent that customer data security measures are established.
⦁ Explainable Model: Big data models tend to be more complex and, hence, can be extremely difficult to understand. Ensuring that pricing decisions are fair and can be justified by customers is very important.

Recent News and Developments:

One of the UK’s leading insurers introduced a novel pay-as-you-drive car insurance based on telematics technology. -Source Needed
A regulator issued guidelines on data privacy and algorithmic fairness in AI-powered pricing by insurers (source needed).
Zero Evidence: A related research paper by a team of data scientists showing how big data can better flood risk assessment and enable more accurate pricing on the cost to insure against floods.

Where Big Data in Insurance Pricing is Going Next

How Insurtechs Drive Big Data leads to transformation in insurance companies: optimization, monetization, and pricing. Here are a few trends we can look forward to in advanced data collection and analysis:
⦁ Pricing and Underwriting: Insurers will have more data to create even more detailed risk profiles for each customer.
⦁ Risk Mitigation: Pricing models that encourage good behavior to reduce risk, e.g., installing smart home devices or participating in wellness programs.
⦁ More Transparent and Explanatory Diligence: Insurers are going to have to make transparent how big data contributes to pricing in order not to lose the credibility of their customers.


Big Data in Insurance Pricing: Opportunities and Dangers Now is the time to take on big data responsibly and ethically to create much-needed price differentiation that rewards well-managed risk while still making sure customers are being charged a fair rate—something good for both them and our industry. Nonetheless, individuals will have data privacy issues that need to be addressed, and there are algorithmic biases in the design of ML algorithms. It is important for transparency on how things got priced this way so future sustainability can work out well in big data-driven insurance pricing territory!