Insurtech Fostering Automated Insurance Process using Deep Learning Approach

Agarwal, Sumit; Bhardwaj, Garima; Saraswat, Ekta; Singh, Namrata; Aggarwal, Rashmi; Bansal, Ajit

doi:10.1109/iciptm54933.2022.9753891

Cited by 28 publications

(8 citation statements)

References 4 publications

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“…Most applications in agriculture tend to use supervised learning, especially for classification and prediction tasks, as seen in studies like leaf disease detection [49,68] and corn plant disease classification [67]. Deep learning applications in agriculture face several challenges, including the need for large labeled datasets, high computational costs, and the complexity of interpreting DL models.…”

Section: Challenges and Best Practices In Applying ML To Agriculturementioning

confidence: 99%

“…Challenges in model interpretability and the need for significant computational resources to train and deploy models are notable issues [14]. In other applications, accurately classifying various rice leaf diseases and achieving high validation accuracy in models is difficult [68]. Inadequate pre-processing steps, lack of accurate feature identification, and suboptimal classification algorithms hinder accurate disease grade measurement.…”

Section: Challenges and Best Practices In Applying ML To Agriculturementioning

confidence: 99%

“…A critical observation in reviewing recent literature on ML and DL applications in agriculture is the frequent omission of detailed descriptions regarding data processing architectures and specifics of model construction [18,68]. This gap presents significant challenges in replicating studies, understanding the nuances of model performance, and applying these models in varied agricultural contexts.…”

Section: Transparency Gaps In Data Processing For Agricultural MLmentioning

confidence: 99%

“…Moreover, for some papers that did provide certain details, the information concerning the model was often insufficient for result reproduction [19,26,30,32,68]. A substantial inconsistency was observed regarding metric selection, with many papers comparing their results to randomly chosen algorithms.…”

Section: Challenges In Model Architecture and Training Transparencymentioning

confidence: 99%

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A Review of Machine Learning Techniques in Agroclimatic Studies

Tamayo-Vera,

Wang,

Mesbah

2024

Agriculture

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The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL in agricultural research, with a pronounced emphasis on agroclimatic impacts and adaptation strategies. Our investigation reveals a dominant reliance on conventional ML models and uncovers a critical gap in the documentation of methodologies. This constrains the replicability, scalability, and adaptability of these technologies in agroclimatic research. In response to these challenges, we advocate for a strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies and standardizes the model development process but also democratizes ML expertise, thereby catalyzing the advancement in agroclimatic research. The incorporation of AutoML stands to significantly enhance research scalability, adaptability, and overall performance, ushering in a new era of innovation in agricultural practices tailored to mitigate and adapt to climate change. This paper underscores the untapped potential of AutoML in revolutionizing agroclimatic research, propelling forward the development of sustainable and efficient agricultural solutions that are responsive to the evolving climate dynamics.

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Section: Challenges and Best Practices In Applying ML To Agriculturementioning

confidence: 99%

Section: Challenges and Best Practices In Applying ML To Agriculturementioning

confidence: 99%

Section: Transparency Gaps In Data Processing For Agricultural MLmentioning

confidence: 99%

Section: Challenges In Model Architecture and Training Transparencymentioning

confidence: 99%

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A Review of Machine Learning Techniques in Agroclimatic Studies

Tamayo-Vera,

Wang,

Mesbah

2024

Agriculture

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“…This topic is highlighted in our research in terms of technology, for example in the relevance of AI that is indicated for leveraging IA by combining the strengths of RPA/IPA, AI and human intelligence [63,65,68,105,161,169,[218][219][220][221][222][223][224][225]. Now that organizations are beginning to implement technology that has gone beyond the proof-of-concept phases into live systems, the demand for a structured framework to ensure competitiveness, while also guaranteeing the ability to meet the demands of security, regulatory compliance, change management, rapid response to disruptive events and integration with current systems, has become the order of the day [3,10,42,65,90,91,96,129,141,170,178,179,[195][196][197][198][199][200].…”

Section: Emerging Technologiesmentioning

confidence: 99%

Intelligent Process Automation and Business Continuity: Areas for Future Research

2023

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Robotic process automation and intelligent process automation have gained a foothold in the automation of business processes, using blocks of software (bots). These agents interact with systems through interfaces, replacing human intervention with the aim of improving efficiency, reducing costs and mitigating risks by ensuring and enforcing compliance measures. However, there are aspects of the incorporation of this new reality within the business continuity lifecycle that are still unclear, and which need to be evaluated. This study provides a multivocal literature review of robotic process automation and intelligent process automation correlated with business continuity, to identify the level of awareness of these two emerging forms of automation within the business continuity management lifecycle. Based on the reviewed literature, the study develops a discussion of the main research areas for investigation, identifying what is attracting the attention of practitioners and researchers and which areas they highlight as promising for future research. Numerous sources from relevant backgrounds reveal an interest in these interrelated topics but there as yet is little or no information available on the direct connection between them.

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Not with the bot! The relevance of trust to explain the acceptance of chatbots by insurance customers

de Andrés-Sánchez,

Gené-Albesa

2024

Humanit Soc Sci Commun

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Industry 4.0 profoundly impacts the insurance sector, as evidenced by the significant growth of insurtech. One of these technologies is chatbots, which enable policyholders to seamlessly manage their active insurance policies. This paper analyses policyholders’ attitude toward conversational bots in this context. To achieve this objective, we employed a structured survey involving policyholders. The survey aimed to determine the average degree of acceptance of chatbots for contacting the insurer to take action such as claim reporting. We also assessed the role of variables of the technology acceptance model, perceived usefulness, and perceived ease of use, as well as trust, in explaining attitude and behavioral intention. We have observed a low acceptance of insureds to implement insurance procedures with the assistance of a chatbot. The theoretical model proposed to explain chatbot acceptance provides good adjustment and prediction capability. Even though the three assessed factors are relevant for explaining attitude toward interactions with conversational robots and behavioral intention to use them, the variable trust exhibited the greatest impact. The findings of this paper have fair potential theoretical and practical implications. They outline the special relevance of trust in explaining customers’ acceptance of chatbots since this construct impacts directly on attitude but also perceived usefulness and perceived ease of use. Likewise, improvements in the utility and ease of use of robots are also needed to prevent customers’ reluctance toward their services.

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Insurtech Fostering Automated Insurance Process using Deep Learning Approach

Cited by 28 publications

References 4 publications

A Review of Machine Learning Techniques in Agroclimatic Studies

A Review of Machine Learning Techniques in Agroclimatic Studies

Intelligent Process Automation and Business Continuity: Areas for Future Research

Not with the bot! The relevance of trust to explain the acceptance of chatbots by insurance customers

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