Citation:

Venkataswamy Naidu Gangineni, Sriram Pabbineedi, Mitra Penmetsa, Jayakeshav Reddy Bhumireddy, Rajiv Chalasani, Mukund Sai Vikram Tyagadurgam. "Efficient Framework for Forecasting Auto Insurance Claims Utilizing Machine Learning Based Data-Driven Methodologies" International Research Journal of Economics and Management Studies, Vol. 1, No. 2, pp. 45-53, 2022.

Abstract:

The industry has changed a lot because of digitization and big data and now it’s crucial to forecast claims for efficiency, less fraud and better customer experiences. Traditional models are not well-suited to process complex and varied data that come from features like driver information, insurance plans and vehicle tracking. To overcome this, machine learning (ML) can use large amounts of information, adapt to any situation and spot complex patterns to enhance the decision-making process. This work presents a framework that uses InceptionV3, a deep learning (DL) model that was tailored for efficient feature learning and classification accuracy. The data went through several treatments before the training step, including filling in the missing data, encoding the labels and removing unnecessary information. The InceptionV3 model demonstrated superior performance with 97% accuracy, 96% precision, 95% recall, and 95% F1-score, outperforming both Logistic Regression (LR) and Extreme Gradient Boosting (XGBoost). This proves that the model can help insurers predict claims with better accuracy, swiftly decide on matters based on data, prevent more losses and offer more relevant services to clients.

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Keywords:

Auto Insurance, Machine Learning, Deep Learning, Inceptionv3, Claim Prediction, Data-Driven, Insurance.