Predicting Fraudulent Claims in Automobile Insurance

Kowshalya, G.; Nandhini, M.

doi:10.1109/icicct.2018.8473034

Cited by 40 publications

(13 citation statements)

References 6 publications

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“…The results showed that XGboost produces high performance as compared to other existing learning algorithms. An online learning solution is proposed in [6] that can automatically handle real-time updates of the insurance network. Machine learning-based techniques are applied to predict fake claims in automobile insurance and simplifies the calculation of premium amounts based on previous financial details for different customers.…”

Section: Related Workmentioning

confidence: 99%

Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

Matloob¹,

Khan²,

Hussain³

et al. 2021

Applied Sciences

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The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee’s needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees’ changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees’ insurance records. The designed technique is validated on real employees’ insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees.

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Section: Related Workmentioning

confidence: 99%

Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

Matloob¹,

Khan²,

Hussain³

et al. 2021

Applied Sciences

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“…According to this study, Random Forest outperforms all other algorithms in terms of fraud prediction. And [11] predicts fraudulent claims and estimates insurance premium amounts for a range of customers depending on their personal and financial data. The results showed that the Random Forest outperforms the other two algorithms on the Insurance claim dataset.…”

Section: Related Workmentioning

confidence: 99%

Comparing SMOTE Family Techniques in Predicting Insurance Premium Defaulting using Machine Learning Models

Kotb¹,

Ming²

2021

IJACSA

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Default in premium payments impacts significantly on the profitability of the insurance company. Therefore, predicting defaults in advance is very important for insurance companies. Predicting in the insurance sector is one of the most beneficial and important study areas in today's world, thanks to technological advancements. But because of the imbalanced datasets in this industry, predicting insurance premium defaulting becomes a difficult task. Moreover, there is no study that applies and compares different SMOTE family approaches to address the issue of imbalanced data. So, this study aims to compare different SMOTE family approaches. Such as Synthetic Minority Oversampling Technique (MOTE), Safe-level SMOTE (SLS), Relocating Safe-level SMOTE (RSLS), Density-based SMOTE (DBSMOTE), Borderline-SMOTE(BLSMOTE), Adaptive Synthetic Sampling (ADSYN), and Adaptive Neighbor Synthetic (ASN), SMOTE-Tomek, and SMOTE-ENN, to solve the problem of unbalanced data. This study applied a variety of machine learning (ML)classifiers to assess the performance of the SMOTE family in addressing the imbalanced problem. These classifiers including Logistic Regression (LR), CART, C4.5, C5.0, Support Vector Machine (SVM), Random Forest (RF), Bagged CART(BC), AdaBoost (ADA), Stochastic Gradient Boosting, (SGB), XGBOOST(XGB), NAÏVE BAYES, (NB), k-Nearest Neighbors (K-NN), and Neural Networks (NN). Additionally, model validation strategies include Random hold-out. The findings obtained using various assessment measures show that ML algorithms do not perform well with imbalanced data, indicating that the problem of imbalanced data must be addressed. On the other hand, using balanced datasets created by SMOTE family techniques improves the performance of classifiers. Moreover, the Friedman test, a statistical significance test, further confirms that the hybrid SMOTE family methods are better than others, especially the SMOTE -TOMEK, which performs better than other resampling approaches. Moreover, among ML algorithms, the SVM model has produced the best results with the SMOTE-TOMEK.

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“…Artificial Intelligence (AI) and machine learning systems have the capability to be integrated into the claims processing, customer service, and fraud detection sub-sectors of the insurance sector. A case study of fraud and premium prediction in automobile insurance was presented in [8]. A data mining-based method was applied to calculate the premium percentage and predict suspicious claims.…”

Section: Related Workmentioning

confidence: 99%

“…For fraud detection, a dataset with more than 64 thousand claims are used to train, validate, and test the classifier. Eight classes are considered and detailed in Table 3 where T (0) refers to non-fraud claims, T (1), T (2), and T The XGBoost performances are compared to those of three other classifiers used in literature for fraud detection in insurance applications, namely the decision tree [38], the naive bayes [8], and the nearest neighbor [10] algorithms. To tune our machine learning models, we use different hyperparameters.…”

Section: A Fraud Detection and Risk Measurementmentioning

confidence: 99%

“…As an online learning method, we propose the use of a Very Fast Decision Tree (VFDT) algorithm to dynamically train the fraud detection and classification model for insurance companies. Although using batch machine learning algorithms for auto insurance fraud detection have been investigated in literature [8]- [10], there is no previous study used XGBoost for this purpose. Moreover, classifying claims into different fraud types and predicting risky customers have not been investigated using machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement

et al. 2020

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The private insurance sector is recognized as one of the fastest-growing industries. This rapid growth has fueled incredible transformations over the past decade. Nowadays, there exist insurance products for most high-value assets such as vehicles, jewellery, health/life, and homes. Insurance companies are at the forefront in adopting cutting-edge operations, processes, and mathematical models to maximize profit whilst servicing their customers claims. Traditional methods that are exclusively based on human-in-theloop models are very time-consuming and inaccurate. In this paper, we develop a secure and automated insurance system framework that reduces human interaction, secures the insurance activities, alerts and informs about risky customers, detects fraudulent claims, and reduces monetary loss for the insurance sector. After presenting the blockchain-based framework to enable secure transactions and data sharing among different interacting agents within the insurance network, we propose to employ the extreme gradient boosting (XGBoost) machine learning algorithm for the aforementioned insurance services and compare its performances with those of other state-of-the-art algorithms. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7% higher accuracy compared to decision tree models when detecting fraudulent claims. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7% higher accuracy compared to decision tree models when detecting fraudulent claims. Furthermore, we propose an online learning solution to automatically deal with real-time updates of the insurance network and we show that it outperforms another online state-of-the-art algorithm. Finally, we combine the developed machine learning modules with the hyperledger fabric composer to implement and emulate the artificial intelligence and blockchain-based framework.

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Predicting Fraudulent Claims in Automobile Insurance

Cited by 40 publications

References 6 publications

Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

Comparing SMOTE Family Techniques in Predicting Insurance Premium Defaulting using Machine Learning Models

A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement

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