Applied Machine Learning
Classification, regression, model evaluation and feature engineering on real-world datasets.
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Software Development
Machine Learning Models
We solve business problems with machine learning models that deliver measurable results. We bring many years of experience in banking and finance in particular: credit risk, scoring, collections and fraud detection models.
What we offer
- Credit risk, application and behavioural scoring models
- Fraud and misuse detection
- NPL, collections forecasting and portfolio valuation
- Customer segmentation, churn and recommendation models
- Demand, consumption and price forecasting
When you need it
- You want to use data to make faster, more accurate decisions
- Your existing rule-based systems are no longer enough
- Your current models need to be rebuilt or validated
How we work
Problem framing
We define the business goal and success metric together.
Data preparation
We analyse the data and engineer features.
Modelling
We build, compare and validate models.
Deployment
We integrate the model into your systems and monitor it.
What you get
- A validated model and performance report
- Model documentation and explainability analysis
- A scoring service or batch scoring process
- Monitoring and retraining plan
Related courses
Data Science with Python
Data preparation, exploratory analysis and visualisation: a solid foundation in data science.
Learn more →MLOps: Taking Models to Production
CI/CD, model versioning, monitoring and data drift, plus LLMOps for LLM applications: keeping models reliable in production.
Learn more →Frequently Asked Questions
What data is needed?
It depends on the problem; usually historical data including the target variable. We run a short data readiness check before starting.
Are the model's decisions explainable?
Yes. Explainability is provided at model and individual-decision level; in areas like credit it's a requirement.
Can you rebuild our existing models?
Yes. We can assess and rebuild existing models or validate them independently.
What happens after the model goes live?
A monitoring and retraining plan is part of the deliverables and can be automated with MLOps infrastructure.
Related articles
Machine Learning for Fraud Detection: From Rules to Models
Rule-based fraud systems can't keep up with new schemes. How machine learning improves fraud detection: approaches, imbalanced data, the right metrics and hybrid architectures.
Read more →How to Validate Credit Scoring Models: A Practical Guide
Validating a credit scoring model is more than checking the Gini coefficient. Conceptual soundness, discriminatory power, calibration, stability, explainability and monitoring.
Read more →Let's talk about your project
Tell us about your training, consulting or software needs and we will get back to you within 2 business days.