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ICDIM 2018 Author Index

<span title="">2018</span> <i title="IEEE"> 2018 Thirteenth International Conference on Digital Information Management (ICDIM) </i> &nbsp;
of Machine Learning and Feature Extraction Methods for Sentiment Analysis Hadi, Raad Ahmed 320-326 An Automated Ear Detection Method based on Sobel Edge Detection and Image Subtraction Techniques  ...  -289 Blockchain as an enabler for the Social Health Insurance Program in the kingdom of Bahrain 290-295 Distributed Ledger Transformations for Fintech Industry, Challenges, and Opportunities 296-301  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdim.2018.8846994">doi:10.1109/icdim.2018.8846994</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f5l5uufqcrbrdb3xdl5id7kabe">fatcat:f5l5uufqcrbrdb3xdl5id7kabe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428210301/https://ieeexplore.ieee.org/ielx7/8843511/8846966/08846994.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ad/bc/adbcd8ab1d7f438555d55aac45b18d48e20b23ae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdim.2018.8846994"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Page 125 of The Journal of Business Vol. 58, Issue 1 [page]

<span title="">1985</span> <i title="University of Chicago, acting through its Press"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_the-journal-of-business" style="color: black;">The Journal of Business </a> </i> &nbsp;
‘‘Knowledge Representation for Intelligent Ac- counting Machines.” Donald L. Burkhard. Georgia. ‘‘An Examination of Decision Support Sys- tems’ Effectiveness: An Empirical Investigation.”  ...  ‘‘Cyclical Underwriting Profits in the Property-Liability Insurance Industry: An Equilibrium Approach.”  ... 
<span class="external-identifiers"> </span>
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Deep Learning in Business Analytics: A Clash of Expectations and Reality [article]

Marc Andreas Schmitt
<span title="2022-05-19">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The benefits of deep learning (DL) are manifold, but it comes with limitations that have - so far - interfered with widespread industry adoption.  ...  It is shown - by a mixture of content analysis and empirical study - that the adoption of deep learning is not only affected by computational complexity, lacking big data architecture, lack of transparency  ...  Methods and materials Machine learning This part gives an overview of predictive analytics and the ML models used in the experiment.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.09337v1">arXiv:2205.09337v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/trk3yggn7rcfhkzs3rebidj554">fatcat:trk3yggn7rcfhkzs3rebidj554</a> </span>
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Digital Business Model Innovation: Empirical insights into the drivers and value of Artificial Intelligence

Johannes Winter
<span title="2021-05-27">2021</span> <i title="CIRWOLRD"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jh2yw2w72rbdreebtgswzt7fkq" style="color: black;">INTERNATIONAL JOURNAL OF COMPUTERS &amp; TECHNOLOGY</a> </i> &nbsp;
The aim of this article is to intensify the debate on digital business model innovation in industry and the service sector and to enrich it with practical examples of the successful implementation of artificial  ...  The business activities of traditional industrial companies have commonly focused on products and product-related services.  ...  Based on the empirical findings obtained, five trends can be derived that represent an increase in the importance of digital offerings in the industry and strengthen the transformation of this key industry  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24297/ijct.v21i.9035">doi:10.24297/ijct.v21i.9035</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xz2dm5of3vh3joyixyqriu7usm">fatcat:xz2dm5of3vh3joyixyqriu7usm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210601173233/https://rajpub.com/index.php/ijct/article/download/9035/8218" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/01/b7/01b798289488747f216015dba4b513c70ffaf6a3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24297/ijct.v21i.9035"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Page 148 of The Journal of Business Vol. 62, Issue 1 [page]

<span title="">1989</span> <i title="University of Chicago, acting through its Press"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_the-journal-of-business" style="color: black;">The Journal of Business </a> </i> &nbsp;
‘‘Strategic Decision-making Processes: Compre- hensiveness and Performance in the Insurance Industry.”’  ...  ‘‘A Machine Learning Approach to Flexible Manufacturing System Scheduling.”’ Ajay Sukumar Mookerjee. Harvard.  ... 
<span class="external-identifiers"> </span>
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Using Decision Tree Learner to Classify Solvency Position for Thai Non-life Insurance Companies [article]

Phaiboon Jhongpita, Sukree Sinthupinyo, Thitivadee Chaiyawat
<span title="2012-03-14">2012</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper introduces a Decision Tree Learner as an early warning system for classification of the non-life insurance companies according to their financial solid as strong, moderate, weak, or insolvency  ...  The results show that the method is effective and can accurately classify the solvency position.  ...  of non-life insurance in 2009 to evaluate the capital adequacy or financial solid for the non-life insurers (Seetable 1).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1203.3031v1">arXiv:1203.3031v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tucqmreppzgg7dmtk5pbevah44">fatcat:tucqmreppzgg7dmtk5pbevah44</a> </span>
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An Approach for Variable Selection and Prediction Model for Estimating the Risk-Based Capital (RBC) Based on Machine Learning Algorithms

Jaewon Park, Minsoo Shin
<span title="2022-01-04">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jgmncbgvj5ffna5sozuvnv2jr4" style="color: black;">Risks</a> </i> &nbsp;
The risk-based capital (RBC) ratio, an insurance company's financial soundness system, evaluates the capital adequacy needed to withstand unexpected losses.  ...  This study employs a combination of Machine learning techniques: Random Forest algorithms and the Bayesian Regulatory Neural Network (BRNN).  ...  The RBC ratio method is an evaluation method for insurance companies that measures the amount of risk inherent in the corresponding capital held by an insurance company.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/risks10010013">doi:10.3390/risks10010013</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tdcm5xw7ozhunaa63lovwskfei">fatcat:tdcm5xw7ozhunaa63lovwskfei</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220505032326/https://mdpi-res.com/d_attachment/risks/risks-10-00013/article_deploy/risks-10-00013-v2.pdf?version=1641381355" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b2/79/b27981241bb9682ae9d8c94180a8b3869dd7cc7b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/risks10010013"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Automated machine learning: AI-driven decision making in business analytics [article]

Marc Schmitt
<span title="2022-05-21">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providing fully automated off-the-shelf solutions for model choice and hyperparameter tuning.  ...  This paper analyzed the potential of AutoML for applications within business analytics, which could help to increase the adoption rate of ML across all industries.  ...  Methods and Materials AutoML Automated Machine Learning or AutoML is a method for automating the predictive analytics workflow.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.10538v1">arXiv:2205.10538v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jbvmn5bfgfailm7wy7edonccy4">fatcat:jbvmn5bfgfailm7wy7edonccy4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220525121050/https://arxiv.org/ftp/arxiv/papers/2205/2205.10538.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ed/e5/ede53cd87532acbc8b947e2f311d0510ccef7f04.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.10538v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

V. Selvakumar, Dipak Kumar Satpathi, P. T. V. Praveen Kumar, V. V. Haragopal
<span title="">2021</span> <i title="Horizon Research Publishing Co., Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/u3adk35oqng4xhuvfiydhg45tu" style="color: black;">Universal Journal of Accounting and Finance</a> </i> &nbsp;
Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement  ...  We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively.  ...  Insurance Claims Using Machine Learning Approach for Different Types of Motor VehiclesFigure 2.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.13189/ujaf.2021.090101">doi:10.13189/ujaf.2021.090101</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s2ofsox4dncqhh27nylf2beaay">fatcat:s2ofsox4dncqhh27nylf2beaay</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210211020259/https://www.hrpub.org/download/20210130/UJAF1-12217675.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/39/19/3919f24efc089207288558e1dd2289b08d3c5d82.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.13189/ujaf.2021.090101"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Analyzing The Strength Between Mission And Vision Statements And Industry Via Machine Learning

Faleh Alshameri, Nathan Green Green
<span title="2020-05-01">2020</span> <i title="Clute Institute"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/75sdhkp6lje5blrerrn2awzxvm" style="color: black;">Journal of Applied Business Research</a> </i> &nbsp;
We show high predictive power via machine learning to determine an industry by looking only at the mission and vision statements  ...  We show the distinctiveness and connectiveness of each industry via text processing and machine learning techniques.  ...  In order to predict an industry based on mission and vision statements, and to discover the relationship between industries, we empirically test 6 different machine learning classifiers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.19030/jabr.v36i3.10348">doi:10.19030/jabr.v36i3.10348</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b4lipksq5rcnvdmhlzwntqgdpi">fatcat:b4lipksq5rcnvdmhlzwntqgdpi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200928134316/https://clutejournals.com/index.php/JABR/article/download/10348/10392" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ff/61/ff618b4cab7f0c2e63b907803bc87ab51b6a4918.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.19030/jabr.v36i3.10348"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Transforming Underwriting in the Life Insurance Industry

Marc Maier, Hayley Carlotto, Freddie Sanchez, Sherriff Balogun, Sears Merritt
<span title="2019-07-17">2019</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
The existence of large historical data sets provides an unprecedented opportunity for artificial intelligence and machine learning to transform underwriting in the life insurance industry.  ...  Life insurance provides trillions of dollars of financial security for hundreds of millions of individuals and families worldwide.  ...  Acknowledgments The authors are grateful for contributions made by Paul Shearer, Martha Miller, John Karlen, Debora Sujono, and Sara Saperstein.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v33i01.33019373">doi:10.1609/aaai.v33i01.33019373</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rcxlgdv56vh45jxpfzdsc7e6aa">fatcat:rcxlgdv56vh45jxpfzdsc7e6aa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200808154355/https://www.aaai.org/ojs/index.php/AAAI/article/download/4985/4858" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/12/6e/126e381bff2cf630e2076d90c339ae3d818b158e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v33i01.33019373"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Identification of Potential Valid Clients for a Sustainable Insurance Policy Using an Advanced Mixed Classification Model

You-Shyang Chen, Chien-Ku Lin, Yu-Sheng Lin, Su-Fen Chen, Huei-Hua Tsao
<span title="2022-03-28">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oglosmy3gbhuzobyjit4qalakq" style="color: black;">Sustainability</a> </i> &nbsp;
as insurance dealers and insurance salespeople as a reference for looking for valid clients in the future, and is conducive to the rapid expansion of insurance business.  ...  The performance of the insurance industry is highly competitive; thus, in order to develop new and old business from existing clients, information on the renewal of client premiums, purchase of new policies  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/su14073964">doi:10.3390/su14073964</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ydo6w4fw6nbmjkvdqfxevsvowe">fatcat:ydo6w4fw6nbmjkvdqfxevsvowe</a> </span>
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Introductory Chapter: Machine Learning in Finance-Emerging Trends and Challenges [chapter]

Jaydip Sen, Rajdeep Sen, Abhishek Dutta
<span title="2021-12-22">2021</span> <i title="IntechOpen"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/me5lkx7ag5bkhjds3xfcg4wn24" style="color: black;">Artificial Intelligence</a> </i> &nbsp;
Loan and Insurance Underwritings: Loans, credit, and insurance underwriting is also an area where the large-scale deployment of machine learning models can be made by financial institutions for achieving  ...  Financial Chatbots: Automation in the finance industry is also an outcome of the deployment of machine learning and artificial intelligence.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/intechopen.101120">doi:10.5772/intechopen.101120</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r274n77acfdqdaei4rsngxzw44">fatcat:r274n77acfdqdaei4rsngxzw44</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220123181502/https://api.intechopen.com/chapter/pdf-download/79388.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/42/22/422288211094366a35cdf260ec43729aa98a171d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/intechopen.101120"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Sequence embeddings help to identify fraudulent cases in healthcare insurance [article]

I. Fursov, A. Zaytsev, R. Khasyanov, M. Spindler, E. Burnaev
<span title="2019-10-07">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose architectures for text embeddings via deep learning, which help to improve the detection of fraudulent claims compared to other machine learning methods.  ...  The empirical results show that our approach outperforms other state-of-the-art methods and can help make the claims management process more efficient.  ...  Machine learning for healthcare and insurance While a number of machine learning methods have been applied to problems in healthcare and insurance in recent years, deep learning and embeddings for fraud  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.03072v1">arXiv:1910.03072v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zw4gmd3hdfck7o6aoy6gt7w4hi">fatcat:zw4gmd3hdfck7o6aoy6gt7w4hi</a> </span>
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Big Data and Actuarial Science

Hossein Hassani, Stephan Unger, Christina Beneki
<span title="2020-12-19">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tdvcddxjjzfavkwy267ww7wn5m" style="color: black;">Big Data and Cognitive Computing</a> </i> &nbsp;
, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.  ...  We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance  ...  Figure 4 . 4 Flowchart for minimizing human losses with machine learning methods. Figure 5 . 5 Flowchart of predicting and evaluating flood hazard.  ... 
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