How to Keep Your Pricing Engine Running Smooth

on October 2, 2023
Implementing a Pricing Engine

Pricing engines (or rating engines) such as a mortgage pricing engine or an insurance rating engine calculate optimal prices, rates, and other values in real-time. Different technologies have developed over the years to implement pricing engines within an organization. However, not all implementations will yield optimal results, especially when it comes to maintenance.

To Use a Rules Engine or Not To Use?

My answer is that it depends. If you can express your logic as a formula, then you’re in a good position to use business rules, even if there are a few exceptions to that formula. In fact, business rules are great with handling exceptions. For example, in insurance, you could implement a pricing engine for calculating compensation or benefits with a modest number of business rules.

But what about calculating premiums? Assuming you operate in all 50 states, you’re dealing with just under 42,000 zip codes, not to mention several risk score bins based on age, health, etc. The combinations of scenarios start to seem endless. Translating all these scenarios into business rules is a daunting task. If your rules engine has rule import capabilities, that can make things easier the first time around. However, unless the actuarial team sends out updated rate tables that explicitly highlight what changes have been made, rule import is basically useless moving forward.

Lookup Models as the Pricing Engine Block

This is where having a lookup model capability come in handy. Think VLOOKUPS (or INDEX and MATCH when you’re dealing with multiple criteria) in spreadsheets. Lookup models essentially do the same thing. Let’s say you receive a rate table in spreadsheet form that lists every scenario and the corresponding premium price (ex. Column A = State, Column B = Zip Code, Column C = Age … Column Z = Premium). The lookup model will retrieve that premium that matches all the previous columns.

You can also use the same spreadsheet in multiple lookup models. For example, since that rate table contains both state and zip code, you could implement a lookup model to autofill or validate addresses in the online application form. In addition to their versatility, I prefer lookup models because I don’t have to manually translate the spreadsheet into an executable lookup model. At least this is true in our decision management platform SMARTS™. I simply import the spreadsheet and define how I want the data to be treated. The platform takes care of the rest. Because lookup models are indexed, they can return prices and other rates very fast, regardless of the size of your spreadsheet.

Have Your Cake and Eat it Too with Decision Management

Who says you can’t do both? Depending on the situation, it might make sense to use both lookup models and business rules in your pricing engine. You may start with a lookup model and later apply business rules for fine-tuning. This is why I recommend leveraging a decision management platform like SMARTS™ to implement your pricing engine. SMARTS™ combines multiple logic representations and execution engines in one. In addition to business rules and lookup models, SMARTS™ also supports scorecard models and predictive models. Plus, SMARTS™ is applying generative AI to make implementation of pricing engines and other automated decisions even more intuitive.

Learn more about Sparkling Logic’s SMARTS™ Decision Management Platform.

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Sparkling Logic Inc. is a Silicon Valley-based company dedicated to helping organizations automate and optimize key decisions in daily business operations and customer interactions in a low-code, no-code environment. Our core product, SMARTS™ Data-Powered Decision Manager, is an all-in-one decision management platform designed for business analysts to quickly automate and continuously optimize complex operational decisions. Learn more by requesting a live demo or free trial today.