Application of machine learning and data visualization techniques for decision support in the insurance sector

Rawat, Seema; Rawat, Aakankshu; Kumar, Deepak; Sabitha, A. Sai

doi:10.1016/j.jjimei.2021.100012

Cited by 71 publications

(22 citation statements)

References 33 publications

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“…“Risk” is another such relevant construct that can be made auditable through this matrix, as operationalized in automated risk scoring systems that are used in the criminal justice system, 48 the public sector, 17 the healthcare sectors, 49 or the insurance industry. 50 , 51 For example, person -based risk scores that are used in predictive policing 48 should remain stable if, for example, the ZIP code associated with individual persons changes.…”

Section: Cues For Auditabilitymentioning

confidence: 99%

A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability

2022

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Section: Cues For Auditabilitymentioning

confidence: 99%

A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability

2022

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“…AI technologies enable the mining of hidden, innovative patterns and notable information from massive datasets with no need for prior knowledge of the data. Models such as ANNs, BNNs, DL, and SVRs have been employed for predicting the relationship between financial variables (Chen et al, [2021]; Cogoljević et al, [2018]; Wang et al, [2020]; Li et al, [2019]; Rybinski, [2020]; Wu et al, [2020]; Aggarwal et al, [2021]; Rawat et al, [2021]; and Nasir et al, [2021]).…”

Section: Literature Reviewmentioning

confidence: 99%

Can Oil Price Predict Exchange Rate? Empirical Evidence from Deep Learning

Safi

Aliyu

Ibrahim

et al. 2022

IJEEP

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This paper critically analyses the predictability of exchange rates using oil prices. Extant literature that investigates the significance of oil prices in forecasting exchange rates remains largely inconclusive due to limitations arising from methodological issues. As such, this study uses deep learning approaches such as Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) to predict exchange rates. In addition, the Empirical Mode Decomposition (EMD) of time series dataset was utilized to ascertain its effect on the quality of prediction. To examine the efficacy of using oil prices in forecasting exchange rates, bivariate models were also built. Of the three bivariate models developed, the EMD-CNN model has the best predictive performance. Results obtained show that oil price information has a strong influence on forecasting exchange rates.

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“…The data sets were derived from Web APIs and web services collected in data spreadsheets. The data collected is partitioned into three parts; training data, validation of the learning outcomes model, and test data, namely the data used for prediction [24]. Training data produces features used as indicators and selected according to modeling purposes.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Prediction measuring local coffee production and marketing relationships coffee with big data analysis support

Sinaga

Putra²,

Girsang³

2022

Bulletin EEI

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Following the increasing enthusiasm of the coffee market in Indonesia, a machine learning model is developed to study the relationship between coffee producers, consumers, production, and the market. Machine learning work flow is constructed in various stages; explore, develop, and validate the models. In this research, the building model predicts the production and market of late departure coffee based on labeled and unlabeled variables. The best predictions from the trained type of model algorithms of machine learning like tree accuracy of 85.7%, support vector machine (SVM) accuracy of 82.9%, and k-nearest neighbors, the accuracy of 82.9%, which produce three categories, namely, high production of 2 provinces, medium production of 21 provinces, and low production of 11 provinces. The accuracy classification is supported by the AUC value obtained for a high class, a medium class, and a low class. In addition, local coffee marketing modeling used in logistic regression was found with an accuracy of 88.9%, aiming to classify coffee interests between Arabica coffee and Robusta coffee. We found that the AUC value logistic regression for arabica coffee is about 0.94, while for Robusta is 0.92. The analysis of the classification modeling results shows a high level of accuracy of 93.0%.

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Application of machine learning and data visualization techniques for decision support in the insurance sector

Cited by 71 publications

References 33 publications

A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability

A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability

Can Oil Price Predict Exchange Rate? Empirical Evidence from Deep Learning

Prediction measuring local coffee production and marketing relationships coffee with big data analysis support

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