Liquidation Predictions via Machine Learning
Leveraging machine learning for default predictions.
MOTIVATION
Liquidation for a collateralized debt position (CDP) can happen due to various factors. It is an important factor for lenders to check a wallet’s credit-worthiness.
Higher probability of liquidation often leads to a higher lending risk.
Our credit scoring systems are built on top of ML models predicting probability of liquidation.
BLOCKDOG'S ML MODELS
Our ML models are built using millions of transactions in blockchain.
They look at historical transactions in lending protocols to predict if a CDP for a wallet will be liquidated.
We trained the models based on various factors that eventually lead to liquidations and achieved promising results.
PERFORMANCE
For benchmarking the models we evaluated them on Aave lending dataset.
The goal of the model was to predict if a user will be liquidated in the future based on their historical activity in the protocol.
We tested the following models:
Random (baseline)
Logistic Regression model
Tree model
For the same false positive rate in the graph, the tree based models have the best true positive rate.
Tree based models are most accurate in predicting if a user will liquidate in future or not. Using them to predict defaults is the safest lending strategy.
INTEGRATION
Lenders can immediately begin integrating our models into their lending strategy.
We have exposed APIs for our logistic regression and tree based models.
All our models are completely customizable. We understand that different lenders have different risk tolerance for liquidations and hence, we support model customization - allowing lenders to fine-tune the credit scores depending upon their risk appetite.
Contact us for more details.
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