This paper seeks to leverage deep neural networks for predicting insurance claims by the automobile customers based on their characteristics and past behavior. With increasing power of storage and computation technologies, insurance companies worldwide are looking forward to improving management of claims. Consequently, such an effort will enhance companies’ profits and increase customer satisfaction. A key challenge for insurance companies is to accurately predict the cost of insurance for automobile drivers. Drivers who are cautious in their driving are to be charged with fair insurance amount, conversely risky drivers are to be penalized. This paper provides an application of deep learning in predicting the likelihood of raising an auto claim for insurance by the customers. This paper used the data of Porto Seguro, one of the largest auto insurance companies in Brazil which is made available on the Kaggle website. Several factors that determine a file for auto claim for the insurance are considered for the task of prediction. Exploration of the data is carried out to understand the characteristics of auto claims and a model is trained for the task of predicting auto claims. Furthermore, the model’s hyperparameters are fine-tuned to alleviate the problem of over-fitting. Experimental results show that the deep learning model has achieved better accuracies over baseline models.