Predicting Fraudulent Motor Vehicle Insurance Claims Using Data Mining Model

2021 ◽  
pp. 1-11
Author(s):  
Jacob Muchuchuti ◽  
Stewart Muchuchuti
Keyword(s):  
Data Mining ◽  
Motor Vehicle ◽  
Insurance Claims ◽  
Using Data ◽  
Motor Vehicle Insurance ◽  
Mining Model

scholarly journals PREVENTATIVE MEASURES OF INSURERS AGAINST INSURANCE FRAUD WITH PARTICULAR REGARD TO MOTOR INSURANCE

Obiter ◽  
2017 ◽  
Vol 38 (1) ◽  
Author(s):  
Samantha Huneberg
Keyword(s):  
Motor Vehicle ◽  
Insurance Industry ◽  
Insurance Contract ◽  
Insurance Fraud ◽  
Insurance Claims ◽  
Preventative Measures ◽  
Motor Insurance ◽  
Major Increase ◽  
Motor Vehicle Insurance

Insurance fraud is prevalent in all spheres of the insurance industry; however, motor vehicle insurance sees a major increase in fraudulent insurance claims. It is for this reason that insurers need mechanisms in place to protect themselves from fraudulent claims by an insured. One of the more common preventative measures that insurers are using to protect themselves is by inserting forfeiture clauses in the insurance contract itself. These clauses aim to protect the insurer against any type of fraudulent claim by the insured. These clauses do, however, also bring a host of issues to the fore; including the fairness of these clauses as against the insured. These clauses do tend to be one-sided and therefore, a proper evaluation of these clauses is necessary to understand the application and effect these clauses can have on both the parties to an insurance contract.

Customer Relationship Management Using Data Mining Model

2019 ◽  
Vol 118 (7) ◽  
pp. 95-100
Author(s):  
S. Balamurugan ◽  
Dr.M. Selvalakshmi
Keyword(s):  
Data Mining ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Customer Profitability ◽  
Different Types ◽  
Classification Prediction ◽  
Using Data ◽  
The Right ◽  
Mining Model ◽  
Common User

The paper describes marketing insights from Data Mining about new promotions to create, focus on profitability and emphasis on the most profitable promotion that could be sent. The paper shows about the development of predictive modeling, from data mining which provides insights into future customer behavior and customer profitability. Data Mining provides a blueprint and how to define and use customer profile. It shows how to acquire new customers in the most profitable way possible and retain profitable customers. Data mining is an effective method to target at risk-customers with the right marketing promotion and services to keep them loyal. The paper discusses the number of data mining techniques with reference to customer retention for mobile phones (CART, Rule inductions, Ann etc) with a common user interface that the tool can support, an ability to support a number of different types of analysis including classification, prediction, and association detection.

Structured Scheme of Data Mining Based on SQL Server 2005

2011 ◽  
Vol 179-180 ◽  
pp. 646-650
Author(s):  
Xiao Hong Han ◽  
Lei Wang ◽  
Pei Jun Zhang
Keyword(s):  
Data Mining ◽  
Sql Server ◽  
Data Mining Techniques ◽  
Application Program ◽  
Development Platform ◽  
Data Mining Algorithms ◽  
Sql Server 2005 ◽  
Using Data ◽  
Mining Model ◽  
Mining Algorithms

This paper highlights the data mining components of SQL Server 2005 and the building of data mining process, completes the creation, training, and the corresponding predictions of data mining model, implements the operation of data mining using data mining algorithms, so the application program, relationship database and data mining are seamless integrated. SQL Server 2005 provides data mining solution with a powerful design and development platform, without too much acquaintance with data mining techniques and data mining algorithms.

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