| October 1, 2024
Project overview
The Lusis Chair on AI and Finance focuses on two main research areas:
- Fraud detection in credit card payments
- Algorithmic trading
Both fields require scalable, interpretable, and high-speed classification and prediction methods, with a particular emphasis on large-scale fraud detection. While deep learning offers advantages in terms of model complexity and expressive power, it also presents challenges, particularly in anomaly detection on tabular data, where traditional approaches often outperform neural networks.
Decision trees are widely used for tabular data classification, typically relying on simple node conditions, where a variable is compared to a threshold. This project explores enhanced decision trees with more complex conditions, such as:
- Oblique conditions [1], which involve linear combinations of multiple features rather than single-variable comparisons.
- Multi-variable conditions, where several features are evaluated simultaneously against multiple threshold values, allowing for richer and more flexible decision rules.
The objective is to design and implement a decision tree model capable of handling multi-variable conditions and to compare its performance with standard single-variable and oblique-condition trees. The study will be applied to both fraud detection and algorithmic trading datasets to assess the effectiveness of these enhanced decision trees across different financial domains.
Deliverables include:
- Development and testing of a decision tree model incorporating multi-variable conditions.
- Comparative analysis with conventional and oblique decision trees.
- Application to fraud detection and trading datasets to evaluate real-world effectiveness.
- A final report summarizing the modeling process, experimental results, and performance evaluation.
Reference:
[1] Hothorn, T., Hornik, K., & Zeileis, A. – ctree: Conditional Inference Trees.