Our consultants help clients automate and improve their operational business decisions by applying decisioning modelling and artificial intelligence. Over the past five years, it has focused on machine learning-enhanced decisions, specializing in model development/deployment, data visualization, client training/mentoring and improving the integration of predictive analytics and machine learning within financial operational decisions (e.g., Liquidity Risk Management, PEPs/Sanctions, Customer Lifetime Value and Legal Classification). We provide three graduated DMN courses, which are firmly based on practical experience of modelling rather than dry PowerPoint presentations. In 2020, Lux Magi was one of the first companies to apply large language models to build interactive Q&A systems, analyze corporate documents for greenwashing and check legal contracts.

Why DMN?

Lux Magi is an ardent supporter of the Decision Model and Notation, believing it to be the most effective means of integrating machine learning and generative AI into operational decision-making.

DMN provides a standardized, accessible framework for defining, analyzing, and managing operation business decisions. Its primary benefits are transparency, agility, and alignment with key business objectives, enabling decision-makers and analysts to effectively capture and communicate business requirements. DMN acts as a “prime record,” which ensures that business requirements are both understandable to stakeholders and deployable in real-time systems, minimizing translation errors between business and IT teams. This format empowers subject matter experts to define and adjust decision logic directly, aligning decisions with KPIs and reducing dependency on individual experts.

DMN improves operational transparency by documenting each decision’s logic and hierarchical structure, which supports accountability and traceability—both crucial in regulatory contexts and for post-hoc outcome explanations. It also allows for integrating generative AI and machine learning into decision-making, as well as decision tables and deterministic calculations.

Promoting adaptability, DMN simplifies change management by allowing subject matter experts to swiftly assess and implement adjustments without recoding. This practice fosters cost-effective automation, shortens delivery times, and enables quick adaptation to new data or regulatory shifts.

The standardization of DMN is widely supported across industries, with tools from over 20 vendors, ensuring model interoperability and vendor flexibility. Industries like finance and healthcare have adopted DMN, further supporting its utility in regulated sectors. Additionally, DMN’s compatibility with BPMN enhances process and decision integration, making it ideal for enterprises with complex decision dependencies.

In summary, Lux Magi endorses and recommends DMN for the delivery of a consistent, agile, and scalable decision framework, providing clear communication, reduced complexity, and robust support for integrating machine learning, business analytics, and regulatory standards across an organization. DMN will continue to be pivotal to Lux Magi’s decision projects.