Our goal for this project was to attempt to model systemic risk by implementing multiple variations of a coupled stochastic differential equation, as described in ‘Systemic Risk Illustrated’. After implementing the model, we analyzed the effects of changes to the parameters, to better understand which lead to greater chances of systemic risk events. Our initial implementation showed that increasing borrowing and lending between banks increases general stability, but also increases the risk of a systemic event. Next we implemented an extension to the model designed by an undergraduate group at UCSB, that incorporated graph theory into the network. Lastly, after analyzing the effects of novel network designs on stability, we designed and implemented an extension to the model that accounts for the initial capital distribution of the banks, and the difference between the capital of individual banks. All models demonstrated systemic failure, as hoped and expected. We found that adjustment of almost all parameters lead to a tradeoff between decreasing the mean number of failures, the likelihood and severity of a systemic failure. We ultimately decided that the UCSB model provided the best results, but with some adjustments or extension would likely be the most useful.