There are lot of hype about Decision Engine. Many vendors that provide a decision engine (or decision management of some sort) come from business rules management system segments of the market. So, what is exactly the case of decision engine vs rule engine?

Why, all of a sudden, do rules seem to be an outdated term and everyone is talking about decisions?  There are fundamental differences between rules and decisions.

At a very high level, a decision is a business question to be answered.

  • Do we want to engage with this client from a creditworthiness point of view?
  • What treatment should we provide to this patient?
  • How much is the health insurance premium of this customer?
  • … and so on …

Decision Model

A Decision Engine must be able to execute a Decision Model. As we discussed in the top 6 critical requirements of a decision platform, you should be able to model a decision model explicitly.

Insurance pricing model - Decision Engine vs Rule Engine

A decision model is not just a beautiful graphical representation of a decision-making process, it must be an executable artifact! Therefore, a decision engine must be able to execute a decision model. And clearly, a rule engine will not be able to execute a decision model.

Once a decision is modeled, there are many ways a decision can be realized. And all depends on the nature of the decision itself.

Rule-Based Decision

In many scenarios, a decision may be based on business rules. Here are some scenarios:

  • [Fraud] Should we process this transaction?
  • [Insurance] How much is the premium of this car?
  • [Retail] How much is the price of this order?

As we clearly see from these examples, they are all deterministic in nature.

Decision model that combines predictive and Machine Learning model as part of execution - Decision Engine vs Rule EngineAlthough business rules can be implemented in many ways, a decision engine must be able to execute rule-driven models. A rule engine for sure can execute one or more types of rules.

Data-Driven Decision

Everyone talks about data-driven decisions, but what does that really mean? A data-driven decision is a method of looking at data and concluding an answer. Here are some examples of these types of decisions:

  • [Health Insurance] How much this person should pay for the health premium base price?
  • [Fraud Prevention] What is the likelihood of this transaction being fraudulent?
  • [Health] How likely is this patient to have cancer?

As you can see from the nature of these questions, there are no definite calculations neither clear yes and no answers to them. These are probabilistic, meaning with a certain confidence factor you can find an answer.

Machine Learning Builder using AutoML and Permutation Feature Importance

There are many methods that can be used to build a model to answer these types of questions one of which is Machine Learning or ML in short. A decision engine for sure should be able to execute these models e.g. Machine Learning directly and also they should allow the data scientists to bring their own algorithm as part of the decision execution. Clearly, a rule engine has no relevance to these types of decisions.

Process-Driven Decision

Many decisions are neither rule-based nor data-driven. Yes, some of the decisions are process-driven. Many business questions they don’t have a clear path of resolving and finding the answers for them. They need to go through multiple hoops, checks, and balances as needed, for example:

  • [Finance] Should we give a certain amount of loan to this individual?
  • [Law] How do we complete the list of tasks associated with our firm’s partners?
  • [Education] How to help students to complete their education journey and receive a certificate?
Decision-making process on the customer creditworthiness

Loan Approval Workflow – A decision-making process for a customer creditworthiness checks.

As you can see from these examples, there are one or more processes that need to work together to accomplish an outcome. In some cases, these processes are automated, therefore a decision engine should be able to execute workflow and orchestration models. Clearly, a rule engine neither can execute a workflow nor orchestration models.

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A decision is a business question to be answered. Therefore, business decisions can come in many shapes and forms that are different in nature. They might be probabilistic or deterministic in nature. As a result, they might be rule-based, data, or process-driven.

As we compared head-to-head, Decision Engine vs Rule Engine, we can clearly see that decision engine definitely can execute business rules, however, a business rule engine lacks lots of good capabilities when it comes to modern requirements of a digital business.

In the modern age, you cannot limit your requirements and you need to have a way to automate as many decisions in any shape and form to ensure your digital business can excel. Therefore, a decision engine that allows you to execute all types of decisions is an essential component of your business. A decision engine generally is a part of a complete decision automation platform, but you can integrate an engine separately into your SaaS application business if needed using its SDK.

Last updated October 19th, 2023 at 01:58 pm Published October 21st, 2021 at 11:08 am