Articles

  • 6 reasons to use a rule engine instead of Python code

    Keeping decision logic in Python can work at the start. This article outlines 6 reasons to use a rule engine instead of Python code, including maintenance, consistency, adaptability, collaboration, and integration.

    Published on:: 2024-08-10 18:36:09

  • Difference Between Batch Processing Decision Engine and Real-Time Decision Engine

    Batch processing and real-time decision engines solve different problems. This article explains how each works, where each fits, and how to choose based on data volume, latency, and how often your decision logic changes.

    Published on:: 2024-08-10 18:36:09

  • Decision Engine 101: How and Why to Choose the Right One

    Choosing a decision engine means comparing decision logic, deployment model, and the level of control you need. This article breaks down the main types of engines, the trade-offs between cloud and on-premise, and the core features that matter when you evaluate one for your stack.

    Published on:: 2024-08-10 18:36:09

  • A Nested Rule Set Runs. Manageability Takes Work.

    A policy can execute correctly and still be hard to change, inspect, and explain. For credit risk teams, the difference is the structure around each decision: a model that makes ownership, coverage, and the effect of a revision visible gives reviewers a clearer basis for action.

    Published on:: 2024-08-10 18:36:09

  • The benefits of using a SaaS decision engine over a spreadsheet

    A spreadsheet has no place to attach the full record of a credit decision: the rule revision, resolved inputs and person linked to the change. A decision engine stores that record with the result, preserves releases, and checks whether decision-table rows overlap or leave inputs uncaught.

    Published on:: 2024-08-10 18:36:09

  • Decision tables: practical guide

    Decision tables are compact matrices that express decision logic more clearly than large decision trees. They make rules easier to read, test, and audit. This guide explains how to design solid tables, make conditions and outcomes mutually exclusive and collectively exhaustive, order them by precedence, use a catch-all row, and test boundary, standard, and error paths.

    Published on:: 2024-08-10 18:36:09

  • Evaluating the ROI of Decision Engines in Financial Companies

    This article explains how to evaluate the ROI of a decision engine in financial underwriting, using a Southeast Asia loan example. It compares manual and automated decision logic, estimates monthly and annual savings, and covers integration costs, error reduction, and faster decisioning.

    Published on:: 2024-08-10 18:36:09

  • Evaluating the ROI of Moving to a Decision Engine for Risk Management

    This article breaks down the ROI of moving risk management from hard-coded logic to a decision engine. It uses a Latin America-based lending company to compare current development costs, deployment speed, and explainability against a more flexible decision flow.

    Published on:: 2024-08-10 18:36:09

  • How to test a decision table properly

    Start by defining the table’s purpose. Then iterate. Draft test cases from each condition and action, run them, verify the outcomes, and refine the decision logic until it behaves predictably. Use synthetic datasets and valid value ranges when missing results are acceptable, or use exhaustive, edge-case testing, manually or with a tool, when the table must always return a result.

    Published on:: 2024-08-10 18:36:09

  • The process of implementing a decision engine

    Implementing a decision engine lets companies automate and scale decision-making to reduce costs, accelerate processes, and improve efficiency. This article walks through the practical steps, from identifying candidate business processes and mapping existing decision logic and data, to formalizing a decision model, selecting the right engine, and deciding whether to use internal expertise or external consultants.

    Published on:: 2024-08-10 18:36:09