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

Tree based model predicts most reliably.

• 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.

Overall accuracy for the models

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.