About this Research Topic
In this Research Topic, we explore the opportunities for artificial intelligence (AI) in insurance and finance. AI is becoming a core component of the two industries, motivated by the increasing availability of massive structured and unstructured data, scalable cloud computing, exponential growth in computing power, new modeling tools, and the ongoing drive for operational efficiency as well as cost reduction.
These two fields that have been traditionally led by predictive analytics and risk assessment are moving to a whole new level of accuracy and customer experience through applications of AI. Some of the applications of AI in insurance revolve around claims management, retention analysis, enhancement of customer experience, patient case management, record linkage, risk analysis, fraud mitigation, policy pricing, and customer acquisition. Likewise, in finance, automated trading strategies and financial services are often designed and improved with machine learning algorithms.
With big data and new data sources, such as social media, Internet of Things (IoT), telematics, web logs, images, videos, and click streams, the opportunity to apply artificial intelligence and computer vision has never been greater across areas of insurance and finance operations. For example, underwriters do not need to rely exclusively on established variables like vehicle make and model, age and driving history to calculate the risks of the insured and the corresponding premiums because they can also acquire information from vehicle monitoring devices that track variables such as speed patterns, hard braking, location, environment, and weather. Artificial intelligence algorithms can efficiently use these new sources of information to classify drivers into different risk groups automatically and efficiently. If successfully coupled with advanced cryptographic tools (e.g., multi-party computation and homomorphic encryption) or emerging technology frameworks such as blockchain, then AI could be expected to make accurate, trustworthy learning and predictions pertaining to individuals while preserving the privacy of data.
We aim to present in this collection an array of in-depth case studies that are shaping the changing landscapes in insurance and finance as driven by emerging AI theories, practices, and technology.
This Research Topic is based on the outputs of the First SDM Workshop on Artificial Intelligence in Insurance.
------------------------
Luisa Fernanda Polania Cabrera is an Experienced Professional at Target Corporation (United States).
Victor Wu is a Product Manager at GitLab Inc, San Francisco, United States.
Sou-Cheng Choi is a Consulting Principle Data Scientist at Allstate Corporation.
Lawrence Kwan Ho Ma is the Founder, Director and Chief Scientist of Valigo Limited and Founder, CEO and Chief Scientist of EMALI.IO Limited.
Glenn M. Fung is the Chief Research Scientist at American Family Insurance.
These two fields that have been traditionally led by predictive analytics and risk assessment are moving to a whole new level of accuracy and customer experience through applications of AI. Some of the applications of AI in insurance revolve around claims management, retention analysis, enhancement of customer experience, patient case management, record linkage, risk analysis, fraud mitigation, policy pricing, and customer acquisition. Likewise, in finance, automated trading strategies and financial services are often designed and improved with machine learning algorithms.
With big data and new data sources, such as social media, Internet of Things (IoT), telematics, web logs, images, videos, and click streams, the opportunity to apply artificial intelligence and computer vision has never been greater across areas of insurance and finance operations. For example, underwriters do not need to rely exclusively on established variables like vehicle make and model, age and driving history to calculate the risks of the insured and the corresponding premiums because they can also acquire information from vehicle monitoring devices that track variables such as speed patterns, hard braking, location, environment, and weather. Artificial intelligence algorithms can efficiently use these new sources of information to classify drivers into different risk groups automatically and efficiently. If successfully coupled with advanced cryptographic tools (e.g., multi-party computation and homomorphic encryption) or emerging technology frameworks such as blockchain, then AI could be expected to make accurate, trustworthy learning and predictions pertaining to individuals while preserving the privacy of data.
We aim to present in this collection an array of in-depth case studies that are shaping the changing landscapes in insurance and finance as driven by emerging AI theories, practices, and technology.
This Research Topic is based on the outputs of the First SDM Workshop on Artificial Intelligence in Insurance.
------------------------
Luisa Fernanda Polania Cabrera is an Experienced Professional at Target Corporation (United States).
Victor Wu is a Product Manager at GitLab Inc, San Francisco, United States.
Sou-Cheng Choi is a Consulting Principle Data Scientist at Allstate Corporation.
Lawrence Kwan Ho Ma is the Founder, Director and Chief Scientist of Valigo Limited and Founder, CEO and Chief Scientist of EMALI.IO Limited.
Glenn M. Fung is the Chief Research Scientist at American Family Insurance.
Keywords: Big Data, Machine Learning, InsurTech, FinTech
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
