Insurance Fraud Detection Machine Learning Project

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:

  1. Objective Description:

Describe the major objective: To build machine learning based insurance fraud detection model to effectively detect the illegitimate insurance claims.

  1. Gathering of Data:
  • We gather the previous claim data that is labeled as illegitimate or legitimate claim.
  • In this, some important characteristics specifies the claimed money, user’s claim history, kind of policy, witness testimonies and information of loss or fraudulent activities.
  1. Preprocessing of Data:
  • Data Cleaning: Our approach focuses on data cleaning procedure including managing missing values, outliers and data standardization.
  • Feature Engineering: We retrieve new significant features that denote the illegitimate activities. For instance: Quick access of claim from an individual may indicate the red flag.
  • Categorical Features: Methods such as ordinal encoding or one-hot encoding are utilized by us to transform categorical data into number pattern.
  • Feature Scaling: We carry out the feature scaling process specifically working with techniques such as K-NN or SVM.
  1. Model Chosen & Development:
  • Decision Trees and Random Forest: we consider these algorithms because of their ability and understandability to manage an integration of categorical and number pattern data.
  • Neural Networks: When dealing with enormous amount of data, our work makes use of this method.
  • Unsupervised Techniques (for example: Autoencoders): By using these techniques, we identify abnormalities in the data and that is examined as possible illegitimate data.
  • Gradient Boosting Machines (for example: LightGBM & XGBoost): Our project achieves greatest accuracy by using these methods.
  1. Model Training:
  • Data Split: We split the dataset into three sets like training, validation and test datasets.
  • Handling Imbalance: To manage the class imbalance issue, various methods are utilized by us such as SMOTE, ADASYN or considering several performance metrics such as F1-Score because illegitimate claims are mostly lesser than genuine claims.
  • Training: By utilizing training data, we train the model and validate the model on the validation dataset.
  1. Evaluation of Model:
  • Accuracy will not be an appropriate metrics because of the data imbalance problem. Therefore, in terms of several metrics like recall, F1-score, precision and ROC-AUC, we evaluate the model.
  • Cost-Sensitive Evaluation: We specify distinct values to false positives and false negatives. A false positive (i.e a genuine claim is indicated as fake) is always less costly than a false negative (i.e illegitimate claim is identified as genuine)
  1. Optimization and Hyperparameter Tuning:
  • By considering the type of data and values of incorrect categorizations, we tune the framework’s hyperparameters.
  1. Deployment:
  • After we fulfill with the efficiency of our framework, implement it in a claim related platforms of insurance company.
  • Our framework also automatically indicates the fraud claims for additional human feedbacks.
  1. User Interface (if applicable):
  • To analyze the classified claims, examine the framework’s forecasting and offer reviews, we develop an interface for claim examiners or associations.
  • Retrain our framework periodically by the use of feedback loops.
  1. Conclusion and Future Improvement:
  • We document the end results of our approach, possible effects and limitations.
  • Our work may include the following ideas like considering additional data sources, elaborating the range of other kinds of insurance fraud and actual time fraud identification for future enhancements.


  • Collaborate with Domain Experts: To interpret common illegitimate formats and patterns, we deal our framework by associate with insurance claim examiners.
  • Continuous Learning: Often, we must reconstruct our framework by considering new data related to the evolving illegitimate formats due to the periodical evolution of fake claimers.

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.

 Dissertation proposal ideas will be shared by our professional team. We share topic ideas from trending and current journals. We offer innovative and flexible dissertation services as per your needs by providing complete explanation.

Insurance Fraud Detection Machine Learning Topics

Insurance Fraud Detection Machine Learning Thesis Ideas

Writing a thesis is the crucial step in the research work, we are well known for our thesis writing service because of the caliber of researchers we have in our concern. Your thesis will be mastered carefully and serve as a winning paper for you as we do it original. You can be at ease as your work will be in good hands. Some of our works are given below.

  1. Detecting insurance fraud using supervised and unsupervised machine learning


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.  

  1. Fraud Detection in Healthcare Insurance Claims Using Machine Learning


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.

  1. Enhancing Insurance Fraud Detection -A Machine Learning Approach for Timely and Accurate Risk


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.

  1. Auto-Insurance Fraud Detection Using Machine Learning Classification Models


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.

  1. Health Insurance Fraud Detection Using Feature Selection and Ensemble Machine Learning Techniques


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.

  1. Insurance fraud detection: Evidence from artificial intelligence and machine learning


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.  

  1. An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance


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.

  1. Insurance Fraud Detection using Machine Learning


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.

  1. Semi-Supervised Medical Insurance Fraud Detection by Predicting Indirect Reductions Rate using Machine Learning Generalization Capability


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.

  1. A Hybrid Federated Learning Model for Insurance Fraud Detection


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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