In machine learning, insurance fraud detection approach is an important application that safeguards various insurance companies billions of dollars every year. Machine learning framework is used to identify illegitimate activities and assists insurers to autonomously indicate illegal claims by carried out the validation process. So, scholars will be allocated a professional team to understand their Insurance Fraud Detection Machine Learning project work. We let you to communicate directly so you can track your work in progress. All areas of machine learning will be covered by us by our high-quality resource team we provide 100% plagiarism free paper while we work from scratch and conduct multiple revisions to avoid errors for your project.
We discuss the procedural flow of the creation of insurance fraud detection framework through the use of machine learning below:
Describe the major objective: To build machine learning based insurance fraud detection model to effectively detect the illegitimate insurance claims.
We conclude that, machine learning based insurance fraud detection approach is a huge effective application that aids to crucial cost savings and functional efficiencies. Make sure the moral considerations such as preventing unfairness that may suspect the genuine claimants inappropriately.
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Insurance Fraud Detection Machine Learning Thesis Ideas
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Insurance fraud detection, machine learning, supervised learning, unsupervised learning
Our paper estimates the supervised and unsupervised learning by utilizing proprietary insurance claim data. To investigate the performance of each method in term of finding new fraudulent claims. Unsupervised machine learning specially isolation forest can effectively detect insurance fraud. Supervised learning can also achieve strongly, Inspite of few named fraud cases. We used both supervised and unsupervised learning detect new fraudulent claims based on various input information.
Fraud; insurance claims; artificial neural networks (ANN); logistic regression (LR); random forest (RF); Saudi Arabia
We used to detect health care fraud by utilizing the methods supervised machine and deep learning analytics namely RF, LR and ANN. Our paper aims to enhance the health model that automatically detects fraud from health insurance in Saudi Arabia. We have to label the imbalanced dataset by utilizing three supervised deep and machine learning techniques. To balance the dataset, we used the SMOT method. To eliminate the insignificant features Boruta object feature selection method was utilized.
Insurance fraud detection, machine learning algorithms, risk assessment, automated fraud detection, data analysis techniques.
We present an innovative method to address the insignificant financial burden enforced by insurance fraud. To generate an automated system for detecting fraudulent activities our paper uses sophisticated ML methods like DT, SVM, KNN, RF and Gradient Boosting. We concentrate on improving fraud detection by including geographic, customer and sales process parameters to decrease reliance on subjective judgement.
Fraud detection, Classification
Our paper discovered six ML methods to define the best method for detecting insurance fraud the methods we used are XGBoost, LR, RF, DT, SVM and NB. The result shows our random forest method gives best accuracy. We compare the two methods Analysis of variance (ANOVA) and RF classifier to determine the better feature selection method and our RF classifier will be more beneficial for this method.
SMOTE, Sequential forward selection, K-nearest neighbor, Adaboost, Linear discriminant analysis, Gradient boosting machine, Bagging, Stacking
We propose a Sequential Forward Selection (SFS) and SMOTE oversampling technique to healthcare insurance fraud detection. For classification purpose we used the methods like KNN, ANN, LDA, GBM, bagging classifier and stacking meta-estimator. We also preferred stack aggregator to others in fusion with SFS as there is slight difference among them.
Insurance, Financial decision making, Predictive models, Boruta algorithm
Our paper proposes fraud detection in auto insurance industry by utilizing predictive methods. We can utilize a publicly available dataset to perform feature selection and uncover most important feature through Boruta method. We applied three predictive methods to improve the fraud detection mechanism namely LR, SVM and NB. Six metrics are calculated from confusion matrix to evaluate the performance of predictive model. Our SVM gives the best accuracy rate.
Decision support systems, Health insurance, Interactive machine learning, Machine intelligence
The purpose of our paper is to execute and calculate a novel architecture to detect fraud. Interactive ML can be used to allow incorporative expert knowledge in unsupervised setting that can be used to detect fraud and abusive case in health care. We used analytic hierarchical processing (AHP) for weighting the actors and attributes and expectation maximization (EM) for clustering similar actors. The eFAD system effectively handles the fragmented nature of abnormal activities.
Traditional Process, XGBoost Algorithm Automated Framework, Fraud Insurance Claims
We paper uses machine learning and XGBoost method to build an automated fraud detection application structure in our paper. To accurately find the fraud claims in a smaller amount of time is the aim of our paper. We can analyse the data by using the process to validate, sanitize, and to retrieve the pertinent data. The insurance firm can retain the reputation outside with this structure and can have a consistent relation with client.
Anomaly Detection, Medical Insurance, Data Mining, Regression.
Our paper proposes a machine learning-based method to predict the cost that has been claimed based on other patient history and to predict that is fraud or abnormal cost in claim that can vary from other claims. A new data sampling method can be proposed to lead the machine learning methods. Our proposed method can reduce the absolute error detection rate.
Federated Learning, Insurance claim fraud detection, Genetic Algorithm, particle swarm optimization
Our paper offers a novel hybrid technique and we suggest an automated way to regulate the process of vehicle insurance claim fraud detection in insurance industry. We can combine the methods like Federated Learning (FL), Genetic Algorithm and Particle Swarm Optimization (PSO) to incorporate the benefit of every technology. Our optimized feature will fed into FL with PSO model.
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