Dynamic Policy Pricing for Insurers

The challenge of pricing for insurers 

The landscape of insurance policy pricing is changing rapidly. This change is powered by the growing abundance of customer data and the market demand for personalized pricing policies. No customer wants to pay for what they don’t use and every insurer is looking to mitigate risk. Enter Dynamic Policy Pricing (DPP), a pricing strategy that tailors rates based on customer and business variables.

DPP is powered by machine learning (ML) algorithms that consume vast amounts of customer data, market data, and business data and parameters to provide fast and individualized pricing recommendations.

The AI & Analytics Engine can help insurers adopt ML for dynamic pricing, a win-win for insurers and customers. Customer policies are tailored to their needs and insurers can better and more efficiently evaluate risk and generate profitable pricing strategies.

Traditional policy pricing strategy used by insurers has leaned towards being inflexible, unwavering, and manual. Customer-specific variables are not considered in their one-size-fits-all approach. The strategy has seen success in the past. However, insurers today face a new generation of customers with new expectations of timely service, transparency, and individualized offers - The Gen Y and Z customers. This new wave of customers demands more from insurers. And, newer technology-based insurers are quickly filling the dynamic policy pricing gap in the industry.

A study by PwC indicated 44% of Gen Z respondents have an app to track their health, wellness, or fitness¹. This number is only set to grow. Pair this with the willingness of Gen Z to provide their data for better service, offers, and personalized pricing, and the full value add of insights extracted from data in smart connected technologies becomes evident. Insurers have the resources and the size to pivot their pricing strategy to meet market demands. However, they need some assistance in connecting to and developing the right algorithms for the data that is available to them.

This is where the AI & Analytics Engine can help. For other relevant use cases, check out our article on the Top 18 essential AI Use Cases in Leading Industries!

The machine learning solution for dynamic pricing

An Accenture report has found that most insurance companies process only 10–15%² of the data they have access to. The combination of automation, scalable analytics, and machine learning capabilities makes the highly laborious and time-consuming process of analyzing data and extracting predictive insights accessible, and fast, allowing businesses to tap into the hidden value of unprocessed data.

Input data for dynamic pricing policies 

Other additional data sources with more personal customer details come in the form of IoT devices like activity trackers and smart health sensors, and social media. Open data also serves its purpose of building and gathering intelligence on customer demographic groups. Being privy to insights extracted from such data, insurers can form highly precise patterns on a customer’s profile, enabling them to price policies to fit the individual and decrease risk, all delivered by more optimized dynamic pricing.

ML can automate labor-intensive data analysis tasks and modeling to detect accurate patterns of customer behavior and recommend optimal policy pricing. Enabling policies to be priced quickly, accurately, and fairly. When customers have more transparency over their policy prices, their satisfaction improves, and higher retention rates follow.

Machine learning made accessible with the AI & Analytics Engine

Step 1: Data Ingestion

Easily connect to relevant data sources that affect pricing, like:

• Health sensors
• Social media
• Online customer behavior 
• Customer activity or input 
• Business parameters 

Once ingested the Engine can be used to prepare the data for ML. 

Step 2: Define your goal

Define the goal desired for the policy pricing strategy. For example, pricing recommendations for each customer are based on individual variables identified as relevant to the goal.

Step 3: Model training

The Engine can use the prepared dataset and provide recommended models given the defined goal. The Engine will also provide indications of each model’s performance prior to training to allow the selection of the best model before investing time into training the model.

Step 4: Deploy & use ML

The final step is to quickly and easily deploy the ML model into production. For example, connected to an insurer’s website where the user (customer) inputs lifestyle variables and gets as output the optimal policy price.

Benefits of an ML approach to dynamic pricing with the AI & Analytics Engine

• Higher customer satisfaction from having more transparency over their policy pricing configurations
• Increased retention rates that come from being priced fairly according to their needs
• Maximize profits with more accurate data-driven pricing, like pricing higher-risk individuals appropriately
• Move from a one-size-fits-all pricing approach to an individualized dynamic pricing one.

1. https://www.pwc.de/de/handel-und-konsumguter/gen-z-is-talking-are-you-listening.pdf
2. https://www.accenture.com/_acnmedia/pdf-84/accenture-machine-leaning-insurance.pdf