Decision Engine vs. Rules Engine


Today we delve into the conversation comparing and contrasting Decision Engines (also referred to as a Credit Decision Engine) to Rules Engines (sometimes referred to as a Business Rules Engine).

From a software architecture standpoint, a Decision Engine and a Rules Engine are both the same. Both operate on basic logical and mathematical principles, and both use if-then-else logic to process data and deliver a decision.

This article attempts to clear up the confusion between the two types of decisioning systems and examines the differences and the benefits of both approaches.

Please note that for this article the Decision Engine is assumed to be a  Credit Decision Engine.

What is a Decision Engine?

A Decision Engine is a subset of a Rules Engine. In the lending industry context, it  takes application information and other information sources, such as from a credit bureau or alternative data, and makes a financial decision to approve/decline/refer the application, calculate affordability, assign a credit limit and also other terms and conditions.

In the account management environment, a Decision Engine can be used to make decisions regarding collections actions, ongoing credit limit management, approve/decline transactions etc.

A Decision Engine can also be used when customers are searching for products online and provide recommendations and ‘next best product’ suggestions.

Another example of a specialist type of Decision Engine is a Fraud Detection Decision Engine which makes a decision within milliseconds whether to approve/decline/refer a transaction based on its fraud risk.

Simplistically, a Decision Engine handles business questions that need to be answered:

  • Do we approve/decline/refer this applicant for credit?
  • If we approve this customer’s loan, what limit do we set?
  • If this customer goes delinquent, how will we handle them?
  • What other types of products should we offer to this customer?

From a technology perspective, a Decision Engine represents the logic, often in the form of rules and a decision tree, which can then be operationalised to automate a business decision. Most business decisions tend to be complex and can be made up of a series of smaller decisions.

For example, if a customer applies for a credit card, their information is captured into the Decision Engine, which then pulls in all of the necessary data (application information, credit bureau data, identity verification, KYC, income verification, fraud), and makes an approve/decline/refer decision based on the criteria and strategy set by the lender.

A decision tree is an integral component of a Decision Engine and is a way to standardise the language of business decisions based on defined rules, often in a table. With a decision tree, decisions can be modelled and executed using the same language. The decisions and rules are modelled within easy-to-read tables, which make them easily executable by the Decision Engine.

The simplicity of decision trees enables easy edits and quick fixes, as well eliminating the need for translation between business users and developers. In a decision tree, the user defines a specific input and based on that input, they will receive a specific output. The output is referred to as the decision.

A Decision Engine and a Decision Model are closely connected, as a Decision Engine must be able to implement or execute a Decision Model in order to make decisions. Machine Learning or ML for short is one of the most popular decision modelling techniques and a Decision Engine will have the ability to execute a trained ML model which is part of the decision execution.

A Rules Engine has no capability for these types of decisions. To summarise, a Decision Engine is software that automates decision making processes based on predefined rules, data, and logic. It evaluates the available information and applies rules to determine the best course of action or outcome.

What is a Rules Engine?

A Rules Engine describes a type of decisioning system which follows a prescribed set of rules or directions in order to render a logical or mathematical conclusion.

A Rules Engine is a software program that stores and executes specific rules, such as decision tree rules to a business process environment. In most cases, these rules are core to the business process.

Rules Engines are ubiquitous and are often running unnoticed in the background and embedded in all kinds of technology to assist and make decisions for us. Examples of this include:

Fast food computer screens that are used to order food from a menu, take your payment and generate a receipt and order number. These fast food kiosks follow a set of workflows or rules to help the customer arrive at their final decision point which is to pay for the order and produce an order number as a receipt to eventually receive the food.

Another example of a simple Rules Engine is traffic lights. The traffic lights system is organised and directed by a Rules Engine. Sometimes the traffic lights system gives traffic direction based on time, sometimes it is based on actual traffic patterns. Either way, the automated decision is based on certain input from sensors embedded in the road or from a camera detection system.

Examples of the types of rules that are found within a Rules Engine include:

  • Statement Rules
  • Truth Tables
  • Matrix Rules
  • Rule Chains
  • Rule Sets
  • Expression Rules
  • Sequential Rules
  • Rule Extensions

What is the Difference?

The main difference between a Decision Engine and a Rules Engine lies in their approach to decision making processes. While both aim to automate business decisions, they have distinct characteristics:

  • Operational Scale: Decision Engines can work with outcomes of multiple rule flows, simplifying complex decisions by focusing on the desired outcome rather than the steps to achieve it. In contrast, traditional Rules Engines typically handle individual rules without considering broader decision models.
  • Autonomy: Decision Engines separate decision logic from applications, allowing for direct configuration by business users. This autonomy enables quick adjustments to decision flows without the need for IT support. In contrast, Rules Engines often require manual scripting and detailed step-by-step configuration.
  • Data Processing Capacity: Decision Engines, especially those driven by Machine Learning models are more productive and flexible in handling large volumes of data and sudden changes. They can adapt quickly to increases in delinquencies, regulatory modifications, or other events that may overload decision making systems. Traditional Rules Engines may struggle to cope with such dynamic scenarios.

In summary, Decision Engines provide a more autonomous, outcome-focused, and scalable approach to decision making compared to traditional Rules Engines.


Hopefully, this article explains the main differences between a Decision Engine and a Rules Engine and has cleared up the confusion over the two forms of decisioning.

A Rules Engine can render a single decision, for example, switching traffic lights colours. A Decision Engine may have a variety of outcomes which trigger other interactions and require human intervention (such as an underwriting referral) and be linked to other decision points.

From a context perspective, when somebody refers to a Rules Engine as a Decision Engine, it is usually regarding the banking and payments industries.

About the Author

Stephen John Leonard is the founder of ADEPT Decisions and has held a wide range of roles in the banking and credit risk industry since 1985.

About ADEPT Decisions

We disrupt the status quo in the lending industry by providing lenders with customer decisioning, credit risk consulting and advanced analytics to level the playing field, promote financial inclusion and support a new generation of financial products.