Machine Learning Scoring Models Program
Build practical skills in credit scoring and risk assessment through hands-on training
Starting September 2025, we're running an intensive program focused on real-world applications of machine learning in financial scoring. This isn't theoretical coursework—you'll work with actual datasets from Thai financial institutions and build models that address genuine business challenges.
The program runs for 16 weeks with evening sessions twice weekly. We designed it specifically for professionals who want to transition into quantitative finance or strengthen their analytical capabilities in credit risk.
Common Challenges We Address
Real obstacles our students typically face—and how we help resolve them
Limited Dataset Access
Most online courses use cleaned, unrealistic data that doesn't prepare you for actual financial datasets with missing values, inconsistencies, and regulatory constraints.
Our Approach
We provide anonymized datasets from partner institutions in Thailand. You'll learn data cleaning protocols that comply with local regulations and handle real-world messiness.
Theory-Practice Gap
Understanding algorithms conceptually is different from implementing them in production environments where model explainability and regulatory compliance matter.
Our Approach
Every module includes implementation exercises that mirror actual deployment scenarios. You'll document model decisions and create explanation frameworks that satisfy audit requirements.
Business Context Missing
Technical skills alone aren't enough—you need to understand how scoring models fit into approval workflows, portfolio management, and strategic decisions.
Our Approach
Guest sessions with risk managers and credit officers show how models integrate into actual business processes. You'll learn to communicate technical findings to non-technical stakeholders.
Learn from Practitioners
Our instructors work in quantitative roles at financial institutions. They bring current industry practices and real case studies to every session.
Siriporn Chalermwat
Lead Instructor - Model Development
Siriporn spent seven years building scoring models for consumer lending at a major Thai bank. She specializes in feature engineering and model validation, particularly for thin-file borrowers common in Southeast Asian markets.
Anong Praiwan
Technical Instructor - Implementation
Anong manages the machine learning infrastructure at a fintech company. She focuses on model deployment, monitoring, and the practical challenges of maintaining scoring systems in production environments.
Program Investment
Standard Track
฿85,000
16-week program with evening sessions, dataset access, and project review. Payment plans available with 30% deposit.
Professional Track
฿115,000
Includes everything in Standard Track plus individual mentorship sessions, portfolio project support, and extended dataset library access for six months post-program.
What You'll Build
Module 1-4: Foundation and Feature Engineering
Credit scoring fundamentals, exploratory data analysis, feature creation from transaction data, handling categorical variables, dealing with class imbalance
Weeks 1-4
Module 5-8: Model Development
Logistic regression baseline, tree-based methods, ensemble techniques, hyperparameter tuning, cross-validation strategies specific to time-series financial data
Weeks 5-8
Module 9-12: Validation and Interpretation
Model evaluation metrics beyond accuracy, population stability index, characteristic analysis, SHAP values for model explanation, documentation for regulatory review
Weeks 9-12
Module 13-16: Deployment and Monitoring
Score integration into decision systems, A/B testing frameworks, model monitoring and drift detection, portfolio performance analysis, final project presentation
Weeks 13-16