Tezislər / Theses



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Figure 3.
The comparison of different ML based models 
Insurance-demographic risk can arise from many sources, each requires 
accurate prediction modelling. For example, before providing a car insurance 
product to an individual, the insurer needs to accurately predict the number 
of claims the driver might make in the future.
Additionally, life-insurers need 
to have an accurate estimate of the expected lifespan of demography before 
providing life-insurance products to the individuals belonging to the 
demography.
Claims modelling
refers to the prediction of all future costs 
associated with insurance claims made by the policyholders. Bi-variate 
regression models can be used to model the dependency between the 
frequency and severity of claims

Operational risk is the risk of direct or indirect loss resulting from 
inadequate or failed interval processes, people, and systems or from external 
events. Fraudulent Activities is one of the major sources of operational risk 
for companies, particularly those in the finance sector. It can take on many 
forms: for example, bank-activities related fraud (fraudulent credit card 
transactions money laundering activities), and insurance-related fraud 
(fraudulent insurance claims), securities and commodities fraud or other 
frauds such as mass-marketing fraud or corporate fraud. Many studies in the 
literature address fraud detection as a binary classification problem. Binary 
classifiers such as logistic regression, neural networks, k-nearest neighbor, 
decision trees and support vector machines are widely used in this section 
of the literature.


THE 3
rd
 INTERNATIONAL SCIENTIFIC CONFERENCES OF STUDENTS AND YOUNG RESEARCHERS 
dedicated to the 99
th
anniversary of the National Leader of Azerbaijan Heydar Aliyev
126

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