> For the complete documentation index, see [llms.txt](https://block-dog.gitbook.io/blockdog/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://block-dog.gitbook.io/blockdog/blockdog-and-crypto-lending/liquidation-predictions-via-machine-learning.md).

# Liquidation Predictions via Machine Learning

**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

<figure><img src="/files/9wWpfTQ869VniTC0RzV8" alt=""><figcaption><p><strong>Tree based model predicts most reliably.</strong></p></figcaption></figure>

* For the same false positive rate in the graph, the *tree based models have the best true positive rate.*&#x20;
* 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.

<figure><img src="/files/lyHxNP2bkSKvFtzn2Q3s" alt=""><figcaption><p><strong>Overall accuracy for the models</strong></p></figcaption></figure>

**INTEGRATION**

* Lenders can immediately begin integrating our models into their lending strategy.&#x20;
* We have exposed APIs for our logistic regression and tree based models.&#x20;
* 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](https://calendly.com/blockdog) for more details.