Articles

  • Decision Engineer: role, skills, and impact in automated decision making

    A decision engineer designs and builds decision models that automate decision logic. These models keep outcomes transparent, explainable, and aligned with business goals. This article covers the technical skills, including data analytics, programming, and machine learning, and the soft skills, including domain awareness, clear communication, and problem solving, needed to design and deploy reliable automated decision-making.

    Published on:: 2024-08-10 18:36:47

  • Using Champion x Challenger in decision strategies

    Champion x Challenger is a practical way to test a new decision model against the one already in production. This article explains how it works, when to use it, and how to roll out a challenger safely before replacing the champion.

    Published on:: 2024-08-10 18:36:47

  • 5 Important Components of Rule Engine Architecture

    This article explains the five components of rule engine architecture that shape decision logic and operational control. It covers rule repositories, rule management and deployment interfaces, execution, and historical versioning for traceability and compliance.

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

  • 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