Machine Learning for Trade Classification

Netting is a core process in post-trade operations where multiple financial obligations are consolidated into net settlement positions. This helps reduce settlement exposure, collateral requirements, and operational risk. However, identifying which trades are nettable is often still handled through manual review or rule-based systems, which can become difficult to scale in high-volume clearing environments. This research explores how supervised machine learning can automate the classification of settlement records into netting categories and support faster, more reliable trade routing.

Why Netting Trade Classification Matters

Netting trade classification is important because incorrect routing can create settlement failures, margin issues, and compliance concerns. At the same time, unnecessary manual review of clearly non-nettable trades increases analyst workload. By using machine learning, clearing operations can identify non-nettable trades earlier and route eligible trades into the right netting pathway with greater consistency.

Machine Learning Approach and Key Results

A domain-informed synthetic dataset of 5,000 settlement records was created for this study. Each record included features such as Notional Ratio, Settlement Date Distance, CCP Membership Overlap Score, Currency Match, Same Counterparty, Trade Size, and Partial Fill flag.

Two supervised machine learning models were evaluated: Random Forest and Gradient Boosted Trees. The models were tested using 5-fold stratified cross-validation for both binary classification and multiclass classification.

The Random Forest model achieved strong results, with binary PR-AUC of 1.000, ROC-AUC of 1.000, and multiclass macro F1 score of 0.950. The most important features were CCP Membership Overlap Score and Notional Ratio, which align closely with real netting decision logic.

Deployment Value and Future Scope

The study proposes a two-stage deployment approach. In the first stage, a binary model separates Non-Nettable trades from potentially Nettable trades. Since Non-Nettable records represented about 57% of the dataset, this stage could reduce analyst workload by routing a large portion of records directly to straight-through processing.

In the second stage, a multiclass model routes the remaining trades into the correct netting pathway, such as Bilateral Net, Multilateral Net, Payment Net, or Cross-Currency Net.

The main limitation is that the study used synthetic data, so validation on real clearing platform data is needed before production deployment. Future work can include richer trade attributes, real analyst decisions, and explainability methods to help users understand why a trade was assigned to a specific category.