Predicting automobile insurance fraud using classical and machine learning models

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases

Predicting tensile strength of spliced and non-spliced steel bars using machine learning- and regression-based methods

Mechanical properties of steel reinforcement bars, which have a critical effect in the overall performance of reinforced concrete (RC) structures, should be reported and assessed before being used in structural elements. Determining bars’ properties could be time-consuming and expensive specifically in the case of incorporating splices. Therefore, this study aims to predict tensile strength of bars using machine learning-based methods including nonlinear regression, ridge regression and artificial neural network. To this end, a comprehensive database including over 200 tests on non-spliced and spliced steel bars by mechanical couplers was collected from the available peer-reviewed international publications. Bar size, splice method, steel grade, temperature and splice characteristics (length and outer diameter of couplers) were the input parameters considered for predicting tensile strength. The efficiency of the models was evaluated through Taylor diagram and common performance metrics namely coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results demonstrated that the predicted values agreed well with the actual values reported in the experimental studies used for collecting the database. A parametric study was also conducted in order to examine the influence of coupler length, coupler outer diameter and temperature on the tensile strength of spliced bars. Based on the parametric study results, three different equations were suggested for calculating tensile strength of spliced bars using the mentioned parameters. The outcomes of this study can assist practitioners to effectively and accurately estimate tensile strength of spliced and non-spliced steel bars in reinforced concrete structures without the need to carry out expensive and timely physical tests