Predicting Automobile Insurance Losses Using Artificial Neural Networks

2002 ◽  
pp. 167-187
Author(s):  
Fred L. Kitchens ◽  
John D. Johnson ◽  
Jatinder N.D. Gupta
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Business Process ◽  
Financial Risk ◽  
Automobile Insurance ◽  
The Core ◽  
Insurance Business ◽  
Artificial Neural ◽  
Lloyd's Of London

The core of the insurance business is the underwriting function. As a business process, underwriting has remained essentially unchanged since the early 1600’s in London, England. Ship owners, seeking to protect themselves from financial ruin in the event their ships were to be lost at sea, would seek out men of wealth to share in their financial risk. Wealthy men, upon accepting the risk, would write their name under (at the bottom of) the ship’s manifest, hence the name “underwriters.” The underwriters would then share in the profits of the voyage, or reimburse the ship’s captain for his losses if the ship were lost at sea. This practice lead to the founding of Lloyd’s of London, the most recognized name in the insurance business today (Gibb, 1972; Golding & King-Page, 1952).

Artificial Neural Networks Used in Automobile Insurance Underwriting

2005 ◽  
pp. 168-172
Author(s):  
Fred L. Kitchens
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Financial Risk ◽  
Automobile Insurance ◽  
Insurance Business ◽  
Artificial Neural ◽  
Lloyd's Of London

As the heart of the insurance business, the underwriting function has remained mostly unchanged for nearly 400 years when Lloyd’s of London was a place where ship owners would seek out men of wealth. The two would contractually agree to share the financial risk, in the unlucky event that the ship would be lost at sea (Gibb, 1972; Golding & King-Page, 1952).

Underwriting Automobile Insurance Using Artificial Neural Networks

2011 ◽  
pp. 3865-3870
Author(s):  
Fred Kitchens
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Automobile Insurance ◽  
Long Time ◽  
Artificial Neural ◽  
Pencil And Paper

For hundreds of years, actuaries used pencil and paper to perform their statistical analysis It was a long time before they had the help of a mechanical adding machine. Only recently have they had the benefit of computers. As recently as 1981, computers were not considered important to the process of insurance underwriting. Leading experts in insurance underwriting believed that the judgment factor involved in the underwriting process was too complex for any computer to handle as effectively as a human underwriter (Holtom, 1981). Recent research in the application of technology to the underwriting process has shown that Holtom’s statement may no longer hold true (Gaunt, 1972; Kitchens, 2000; Rose, 1986). The time for computers to take on an important role in the insurance underwriting process may be upon us. The author intends to illustrate the applicability of artificial neural networks to the insurance underwriting process.

scholarly journals A Sustainable Facility Management Outsourcing Relationships System: Artificial Neural Networks

2021 ◽  
Vol 13 (9) ◽  
pp. 4740
Author(s):  
Ka Leung Lok ◽  
Albert So ◽  
Alex Opoku ◽  
Charles Chen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Service Provider ◽  
Service Providers ◽  
Facility Management ◽  
Core Model ◽  
Quantitative Methodology ◽  
Information Technology Industry ◽  
The Core ◽  
Artificial Neural

The Contingency Outsourcing Relationship (CORE) model originated from the Four Outsourcing Relationship Types (FORT) model; the CORE model is used in the globalized Facility Management (FM) industry, while the FORT model is originally used in the global information technology industry. The purpose of this paper is to thoroughly analyse the simulated case studies of the four different categories (i.e., in-house, technical expertise, commitment and common goals) of the CORE model from the perspective of the various clients. This study builds on the previous work on the outsourcing relationships between a client and a globalized FM service provider. It further explores the application of this model with the aid of artificial neural networks (ANNs) towards a sustainable future. A quantitative methodology through a survey is used to analyse eight outsourcing strategies for the four outsourcing relationships. A set of revised rules of the CORE is introduced and discussed regarding the approaches to investigate the four simulated outsourcing relationship systems. The study further reveals that an interesting understanding of the four outsourcing categories can be systematically and efficiently implemented into the FM outsourcing relationships through the methodology of scientific Artificial Intelligence (AI). It is concluded that FM outsourcing categorization may help to define the appropriate relationships. This further detailed outcome generated from the ANN can be clearly considered a strong and solid reference to define and explain the existing outsourcing relationships between the stakeholders and the service providers with the aim to assign an outsourcing category to the FM relationship between the client and service provider based on the learnt rules.

scholarly journals An Efficient Model for Financial Risks Assessment Based on Artificial Neural Networks

2020 ◽  
Vol 55 (3) ◽  
Author(s):  
Hayder M. Kareem Al_Duhaidahawi ◽  
Jing Z S. Abdulreza ◽  
Meriem Sebai ◽  
Sinan Abdullah Harjan
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Risk Assessment ◽  
Risk Management ◽  
Artificial Neural Networks ◽  
Financial Risk ◽  
Banking Sector ◽  
Financial Risks ◽  
Proposed Model ◽  
Artificial Neural

According to the developments in financial liberalization and banking innovation, the bank risks have been changed in their nature which leads to use new financial instruments. Thus banks increasingly adopt risk assessment to avoid it. Therefore, this article describes a new model to assist financial risk management based on artificial intelligence. This entails using artificial neural networks to forecast financial risks and support the decision-makers and the consumers in making better risk management decisions. A real-world case study based on the Iraqi banking sector is presented to guarantee the applicability, accuracy, and efficiency of our proposed model. The sample was selected from a data of 16 banks for the period (2004-2018), taken from Iraq Securities Commission, regular market (https://www.isc.gov.iq/). The data were examined with an initial analysis and then converted to the formula compatible with neural networks. The authors describe the results obtained and compare them with previous studies. It confirmed the effectiveness of the proposed model for risk assessment by the results obtained from the approved form on artificial intelligence.

Sign in / Sign up

Export Citation Format

Share Document