Insurance Pricing Automation – Streamlining Rates in Insurance with Business Rules Engines

MARCIN NOWAK
February 27, 2024
Blog

By 2004 a system leveraging Artificial Intelligence (AI) techniques, including a fuzzy logic rules engine and an evolutionary algorithm completely automated 19% underwriting processes for Long Term Care (LTC) and Life Insurance applications in Genworth Finance. This system has significantly automated the underwriting process, increasing efficiency, consistency, and accuracy in application processing while maintaining transparency and adherence to legal requirements.

According to a study by Accenture, average underwriter spends only 30% of his time actually underwriting, meaning that 70% of his time he's doing something else:

  • 40% – administrative tasks,
  • 30% – negotiations and sales support.

It doesn't take into account the fact that some of the changes need to be implemented by your overloaded, fighting legacy systems IT department.

And during rapid events like natural catastrophes or geopolitical changes, you need to act fast, or else your competition will have a head start.

What if there was a tool to do the heavy lifting automatically and free up the capacity of both your IT department and underwriters? A tool to automate rate management? What if we told you there is one?

The Need for Agile Rate Management in Insurance

According to PwC, companies that applied agile methodology are:

  • Tailoring products and services to customer needs is a consistent practice.
  • Operating in a rapidly changing market with quick time-to-market cycles.
  • Providing seamless and top-notch services across all communication channels.
  • Enjoying high customer satisfaction levels, leading to regular recommendations.
  • Embracing agile principles to realign organizational strategies.

Understanding Business Rules Engines and Insurance Rate Management

Business Rules Engines (BREs) automate and streamline rate management in insurance by applying specific "if/then" rulesets to decision-making processes. When a predefined condition is met (If A happens), the BRE executes a corresponding action (do B). In the context of insurance rate management, this means that when certain risk factors or market conditions change (A), the BRE automatically adjusts insurance premiums or policies accordingly (B). 

This automation facilitates real-time adjustments to rates based on up-to-date risk assessments, customer behavior, and external market influences. BREs efficiently process vast amounts of data, enabling insurers to apply complex pricing strategies without manual intervention. This capability ensures that insurance products are priced accurately and competitively, enhancing both risk management and customer satisfaction.

Possible, profit-increasing benefits of using BREs in rate management are:

  • Enhanced Efficiency: Automates routine decision-making processes, reducing manual effort and speeding up operations.
  • Improved Accuracy: Minimizes human errors in rate setting and policy adjustments, leading to more reliable outcomes.
  • Dynamic Pricing Capability: Enables real-time pricing adjustments based on changing risk factors, market conditions, and customer behavior.
  • Increased Agility: Facilitates quick adaptation to market trends and regulatory changes, maintaining competitiveness and compliance.
  • Cost Reduction: Lowers operational costs by automating tasks that would otherwise require significant manual labor and IT development.
  • Better Risk Management: Allows for more sophisticated risk assessment and management through detailed, data-driven rules.
  • Personalized Customer Experience: Supports the creation of tailored insurance products and rates, enhancing customer satisfaction and loyalty.
  • Streamlined Compliance: Automatically updates rates and policies to comply with new regulations, reducing the risk of non-compliance penalties.
  • Data-Driven Decisions: Leverages large volumes of data for informed decision-making, improving strategic planning and product development.
  • Scalability: Easily accommodates business growth and changes without the need for extensive additional resources or restructuring.

The Impact of Automated Rate Adjustments on Operational Efficiency

Automated rate adjustments significantly enhance the operational efficiency of insurance companies by streamlining processes, optimizing pricing strategies, and reducing the likelihood of errors. This technology accelerates the time to market for new products and supports dynamic pricing models while minimizing human error, even under tight deadlines or high-pressure situations.

Time to Market

The integration of automated rate adjustments drastically shortens the development and deployment cycles for new insurance products. Cutting down time to market has been a Holy Grail for lots of companies. By automating complex calculations and eliminating manual steps in the rate-setting process, insurers can respond more swiftly to market opportunities or emerging risks. This agility enables companies to launch competitive products faster than ever before, capturing market share and meeting consumer demands promptly. We could see it in the early 2000s, when banks lost market share to fintech startups, when mobile banking became a thing.

Dynamic Pricing

Automated systems empower insurers with the ability to implement dynamic pricing models. These models adjust premiums in real-time based on evolving risk factors, customer behavior, or market conditions. By harnessing data analytics and machine learning, insurers can refine their pricing strategies to reflect the current risk landscape accurately, offering more personalized and competitive rates to policyholders.

Reducing Errors

Manual rate adjustments are prone to errors, especially under the pressure of tight deadlines or complex requirements. According to the data from University of Texas, human employees make at least 3 mistakes, increasing to 11-15 per hour under stress or fatigue. Machines are more efficient, less error-prone, and stress as well as fatigue has no effect on them.

Automated rate adjustments reduce this risk significantly. By relying on predefined rules and algorithms, the process becomes more accurate, consistent, and less susceptible to the stress-induced errors that can occur when employees are under pressure. This ensures more reliable pricing and helps in preventing employee burnout by alleviating the workload and stress associated with manual adjustments.

Reduce IT Development

Automating the rate adjustment process significantly lessens the demand on IT resources. Traditional rate changes often require extensive IT development work, including coding, testing, and deployment, which can be time-consuming and costly. With automated systems, the need for custom software development is reduced, as these systems are designed to apply complex rules and adjustments without manual intervention. This streamlines the rate adjustment process, allowing IT departments to focus on other critical projects and innovations, further enhancing operational efficiency.

Saving Time

The implementation of automated rate adjustments results in substantial time savings across various departments within an insurance company. By eliminating the need for manual calculations, data entry, and prolonged validation processes, employees can redirect their efforts towards more strategic tasks, such as product development, customer service, and risk assessment. As we said before, 70% of Underwriters time is wasted on non-essential tasks not related to actual underwriting.

This efficiency speeds up the rate-setting process and accelerates decision-making and policy issuance, improving overall productivity and customer satisfaction.

Adapting to Market and Regulatory Changes Swiftly

In a rapidly changing insurance landscape, the ability to quickly adapt to new market conditions and regulatory requirements is crucial. Automated rate adjustments enable insurers to respond to these changes almost instantly. Whether it's a sudden shift in the market, emerging risks, or new legal regulations, automated systems can be updated to reflect these changes with minimal delay. This agility ensures that insurers remain compliant with regulations and competitive in the market, all while minimizing the risk of errors and the operational lag associated with manual adjustments.

Higson use cases in Streamlining Insurance Rates

The Trigger

Triggers are the cornerstone of any rules engine, serving as the initiating events that activate predefined rules within the system. In the context of insurance, triggers can range from IoT device data, such as sensors detecting smoke or water leakage, to specific information updates, like changes in a policyholder's status or environmental conditions. 

For instance, a spike in humidity detected by sensors could trigger a review of flood insurance policies in the area. These triggers enable insurance companies to automate responses, adjust policies, and manage risks more efficiently by ensuring that the rules engine reacts to real-time data and relevant information, thereby optimizing operational efficiency and customer service.

Furthermore, leveraging triggers within a rules engine can extend beyond risk management to proactive customer engagement. For example, the system could be configured to automatically send notifications, emails, or SMS messages to clients who, according to the database, lack property insurance. This could occur when data analysis identifies increased risk factors, such as a client moving to a flood-prone area without corresponding flood insurance coverage. 

Such personalized, data-driven communication enhances customer service by offering timely advice and protection options but also opens new opportunities for insurance companies to address coverage gaps, thereby strengthening their relationship with clients and promoting a more secure and informed policyholder base.

Time bound Dynamic pricing 

Say that a country has a law that requires drivers to change their tires on a specific date. For example, in Austria you are bound to have winter tires by November 1st, and keep them until April 15th.

You can set a rule that an auto insurance premium to adjust to it by date.

Region based pricing

By utilizing GPS data from policyholders' vehicles or mobile apps, the system can accurately assess whether a driver primarily uses their vehicle in urban or rural areas. This assessment allows for premium adjustments based on the different risk profiles of these locations, with urban areas generally posing a higher risk due to factors like increased traffic density and theft rates. Conversely, rural areas might have lower accident frequencies but potentially more severe consequences when they do occur. When the system detects a change in the driving environment of a policyholder, it automatically updates their premium rates to reflect this new risk level. 

Data based pricing

It's feasible to integrate your actuarial systems with weather forecasting to adjust the pricing of new property and casualty (P&C) insurance policies based on rainfall levels or impending hurricanes. This approach could mitigate underwriting losses by proactively adjusting premiums in anticipation of increased claims related to weather events. For example, an uptick in rainfall or an imminent hurricane could signal a higher likelihood of property damage, prompting an adjustment in policy prices to reflect this increased risk.

Similarly, in the case of wildfires, like those experienced in Hawaii, your system could automatically increase premiums if a specified number of sensors are damaged by fire. This ensures that the insurance pricing remains aligned with the level of risk posed by ongoing or potential natural disasters.

Changes made within a Rule Engine are instantaneous and do not require the intervention of the IT department for implementation. This swift and autonomous adjustment capability enhances the agility and responsiveness of your insurance offerings, ensuring that your risk exposure is managed effectively in real-time.

Contact us with your use case

If you're exploring ways to enhance your organization's operational efficiency, risk management, and customer engagement, Higson's rule engine might be the solution you've been seeking.

Our rule engine is designed to automate and streamline complex decision-making processes, enabling your business to adapt swiftly to market changes, regulatory requirements, and evolving customer needs. Whether you're interested in integrating real-time data for dynamic pricing adjustments, automating notifications for policy updates, or exploring other innovative applications, we're here to tailor a use case specifically for your unique challenges and opportunities.

We invite you to reach out to our team to discuss your specific needs. Together, we can explore how Higson's rule engine can transform your operations, enhance your service offerings, and drive your business forward. Contact us today to request a customized use case prepared just for you. Let's unlock the full potential of your operations together.

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