Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata

Severino, Matheus Kempa; Peng, Yaohao

doi:10.1016/j.mlwa.2021.100074

Cited by 45 publications

(22 citation statements)

References 33 publications

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“…By using the method, we have used in this paper, Automobile Insurance companies can imply this method to monitor the leakage of claims where scammers sometimes overstate or fabricate incidents in order to support fraudulent claims [14,15].…”

Section: Discussionmentioning

confidence: 99%

Vehicle Damaged Detection Using Deep Learning

2022

IRJMETS

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Object Detection based Vehicle Damage Detection system can potentially save insurance companies Million. In this paper we propose a Vehicle Damage Detection System based on YOLO v4. We created a dataset of damaged vehicles and annotated regions consisting of Dents, Shattered Glass, damaged tail lights and scratches. We then pre-processed the images inline with the requirements of YOLO v4 and trained the model to achieve an mAP@50 of 81.20. The system proposed in this paper surpasses all previously proposed methods and gives promising results.

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Section: Discussionmentioning

confidence: 99%

Vehicle Damaged Detection Using Deep Learning

2022

IRJMETS

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“…Recent examples of gradient boosting machine applications for time series prediction can be seen in Johnson et al (2017) and Lopez‐Martin et al (2019); specifically for COVID‐19 data, this method was applied in Haimovich et al (2020) and Malki et al (2020). Specifically for financial problems, recent works include Gumelar et al (2020), which compared XGBoost with Long Short‐Term Memory (LSTM) neural networks for stock price prediction using data from 25 companies listed in the Indonesia Stock Exchange; Jabeur et al (2021), which compared two GBM variants (XGBoost and CatBoost) to forecast the time series of gold price data; and Severino and Peng (2021), which compared GBM with eight other machine learning models for the fraud detection task using real‐world microdata using eXplainable Artificial Intelligence (XAI) methods to evaluate the most relevant features at both global and local levels.…”

Section: Boosting Modelsmentioning

confidence: 99%

Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

Albuquerque

Peng

Silva

2022

Journal of Forecasting

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This paper discusses the application of ensemble techniques for the prediction of time series, presenting an in‐depth review of the main techniques and algorithms used by the recent literature, with emphasis on the bootstrap aggregation (bagging) and boosting approaches. We also discuss the theoretical foundations of the ensemble‐based models, presenting measures of model stability and the main aggregation methods to combine the forecasts of the individual models, as well as recommendations for future developments for related research agendas.

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“…It was thanks to the contract on sharable elements that designers and engineers from industry and academia across the data processing pile were able to innovate at both system level (TPU, convolutional core GPU on a microprocessor), as well as at the system level (a pervasiveness of architectures constructed on common libraries, such as PyTorch, Machine learning, Horovod and others). Identifying the correct abstract concepts for a particular domain is challenging, but the potential reward is enormous because they can activate advancement along multiple integrations [10].…”

Section: Domain-specific Designsmentioning

confidence: 99%

Critical Analysis of Parallel and Distributed Computing and Future Research Direction of Cloud Computing

Padovano

2021

JCNS

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"Cloud computing" refers to large-scale parallel and distributed systems, which are essentially collections of autonomous. As a result, the “cloud organization” is made up on a wide range of ideas and experiences collected since the first digital computer was used to solve algorithmically complicated problems. Due to the complexity of established parallel and distributed computing ontologies, it is necessary for developers to have a high level of expertise to get the most out of the consolidated computer resources. The directions for future research for parallel and distributed computing are critically presented in this research: technology and application and cross-cutting concerns.

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Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata

Cited by 45 publications

References 33 publications

Vehicle Damaged Detection Using Deep Learning

Vehicle Damaged Detection Using Deep Learning

Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

Critical Analysis of Parallel and Distributed Computing and Future Research Direction of Cloud Computing

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