Decision Engine Core Components - Visual Rules Editor

Rules engines (a.k.a decision engine) play a crucial role in decision-making processes within various applications, providing a systematic way to define and execute business rules. In this blog post, we'll explore the concept of rules engines, the role of programmers in traditional engines like Drools, and how a visual rules editor simplifies the rule creation process.

Rules engineers are developing their own rulesets daily on their own without any help or guidance from engineering teams. Decision engine should supply an entire variable library for our clients to work with. Banks can build their own custom variables as they please. These variables can be used in the decisioning process as well as displayed during the application process.

Understanding Rules Engines

A rules engine is a software component designed to automate decision-making processes by enforcing business rules in a systematic and efficient manner. These rules are essentially logic statements that define the conditions under which a specific action or set of actions should be taken. Rules engines play a crucial role in various domains, ranging from finance and healthcare to logistics and beyond.

As a software system evolves in complexity and usage, the process of writing and deploying new code for every change to its logic or behavior can become cumbersome and expensive. The objective of a rules engine is to offer a straightforward interface that enables business analysts to articulate and capture new rules and logic, shaping the system's behavior without the programmers’ attention. Subsequently, the engine provides a backend mechanism to process these rules, streamlining the adaptation of the system without the necessity of extensive code modifications for each update.

Key Components of a Rules Engine:

• Rules Repository:

  • A repository or storage mechanism that holds a collection of rules.

  • Rules can be expressed in various formats, such as natural language, decision tables, or specific rule languages.

• Inference Engine:

  • The core of the rules engine responsible for interpreting and executing rules.

  • It evaluates conditions defined in rules and triggers associated actions based on the outcome.

• Rule Editor:

  • A tool or interface that allows users to create, modify, and manage rules.

  • In modern rule engines, visual rule editors provide a user-friendly environment, enabling both technical and non-technical users to participate in rule creation.

• Variable Library:

  • Variable library is connected to a variety of third party data providers. 

  • Decision engine should connect to all data providers and make their variables available through third party vendors to verify identity, credit worthiness as well income verification.

• Rule Outcomes:

  • Decision engines should developed an Rule Outcome engine which gives directions to the entire LendAPI workflow system to generate Emails and SMS based on a specific outcome of a ruleset or node.

  • These outcomes drives a variety of actions such as additional information requested, email instructions the end-clients on performing further actions in their customer portal or simply a welcome message.

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Characteristics of Rules Engines:

• Declarative Nature:

  • Rules engines operate in a declarative manner, focusing on "what to do" rather than "how to do it."

  • Users define rules without specifying the procedural steps; the engine handles the execution details.

• Agility and Adaptability:

  • Rules engines enhance the agility of applications by allowing dynamic changes to business rules without requiring code modifications.

  • Business users can modify rules to reflect changes in policies, regulations, or business strategies.

• Transparency:

  • Rules engines provide transparency into decision-making processes.

  • Rules are typically expressed in a human-readable format, making it easier to understand and audit the logic applied.

• Real time responses and speed:

  • Rule engine must be run in real time and provide sub-section responses. Often, these rulesets are fired during the application process and rule engines should act quickly to retrieve data and render a result as fast as possible.