The Modeller Platform

Modeller is a powerful credit risk modelling platform designed to help financial institutions develop, validate and deploy scorecards with full transparency and control.

Combining proven statistical techniques with modern machine learning, Modeller enables teams to build robust, high-performing models while maintaining explainability and regulatory compliance.

Key Features

End-to-End Model Development

From data import and transformation through to model build, validation and reporting - all in one platform.

Transparent & Explainable Models

Full visibility of model drivers, with clear audit trails and explainability embedded at every stage.

Advanced Modelling Techniques

Supports traditional scorecards (WoE, logistic regression) alongside machine learning methods such as decision trees, random forests and gradient boosting.

Intelligent Data Preparation

Automated grouping and variable transformation tools accelerate development while retaining expert control.

Automated Field Reduction

Automatically identifies and selects the most predictive variables by removing low-value and redundant fields - reducing noise, improving model performance and significantly accelerating development.

Integrated Reporting & Documentation

Generate comprehensive, regulator-ready documentation with minimal effort.

Seamless Deployment

Export models to SQL, Python, PMML and more for straightforward integration into existing systems.

AI-Enhanced Modelling

Modeller bridges traditional scorecard development and modern machine learning techniques.

Find out more about our Credit Risk Modelling Software

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AI-Enhanced Modelling (Explainable & Controlled

Modeller bridges traditional scorecard development and modern machine learning techniques - enabling organisations to adopt advanced analytics in a controlled, transparent and production-ready way.

  • Machine Learning Support
    Develop and benchmark models using a range of statistical and machine learning techniques to enhance predictive performance, alongside traditional scorecard approaches. Paragon Classification: General 
  • Explainability by Design
    Maintain clear visibility of model drivers, assumptions and outputs – supporting regulatory expectations and internal governance requirements. 
  • Human-in-the-Loop
    Combine analytical capability with expert judgement, ensuring appropriate oversight, challenge and control throughout the model development lifecycle. 
  • Controlled Adoption of Advanced Techniques
    Incorporate more advanced methods where appropriate, within a structured and governed framework aligned to model risk management standards. 
  • Open & Extensible Architecture
    Integrates with Python and broader data science ecosystems, enabling teams to incorporate external models, tools and evolving analytical approaches. 
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Why Modeller?

  • Proven, industry-tested methodologies
  • Designed for credit risk teams, not just data scientists 
  • Balances performance with governance and interpretability 
  • Enables adoption of advanced analytics without sacrificing control 
  • Supports internal model ownership and regulatory expectations

Typical Use Cases

  • Credit application scorecards 
  • Behavioural scoring models 
  • IFRS 9 / impairment modelling 
  • Model redevelopment and challenger builds
  • Model risk management and validation suppor

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Contact us for more details

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