scholarly journals APPLICATION OF MACHINE LEARNING FOR ESTIMATING KENYAN MOTOR VEHICLE INSURANCE PREMIUM

2019 ◽  
Author(s):  
Fidelia Pamba ◽  
Lucy Waruguru
Keyword(s):  
Machine Learning ◽  
Motor Vehicle ◽  
Insurance Premium ◽  
Motor Vehicle Insurance

scholarly journals IMPLEMENTASI METODE BAYES PADA PENGHITUNGAN PREMI ASURANSI KENDARAAN BERMOTOR

2020 ◽  
Vol 3 (2) ◽  
pp. 112-123
Author(s):  
Rika Fitriani ◽  
Gunardi Gunardi
Keyword(s):  
Historical Data ◽  
Motor Vehicle ◽  
Insurance Premium ◽  
Insurance Companies ◽  
Bayes Method ◽  
General Insurance ◽  
Pure Premium ◽  
Historical Data Analysis ◽  
Claim Frequency ◽  
Motor Vehicle Insurance

One type of general insurance is motor vehicle insurance. Premium pricing of general insurance can be calculated by some methods. In this study, Bayes method will be used. The distribution of claim frequency is Poisson distribution and the distribution of claim severity is Exponential distribution. The premium is calculated by multiplying the expectation of claim frequency and the expectation of claim severity. Based on the historical data analysis using the Bayes method, the highest pure premium of motor vehicle insurance in Indonesia is Hino brand and the lowest pure premium is Honda brand. The result of this premium pricing can be used as a reference for the insurance companies to manage their motor vehicle insurance reserves.

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

scholarly journals Reserves of Motor-Vehicle Insurance in Finland

1962 ◽  
Vol 2 (1) ◽  
pp. 161-173 ◽  
Author(s):  
Teivo Pentikäinen
Keyword(s):  
Motor Vehicle ◽  
Insurance Companies ◽  
Accurate Calculation ◽  
Short Term ◽  
The Subject ◽  
Image Position ◽  
Premium Income ◽  
The Cost ◽  
Motor Vehicle Insurance ◽  
Cluster Points

The Ministry of Social Affairs, which acts i.a. as the supervising office in Finland, has given instructions regarding the normal reserves of insurance companies. A summary of these and some comments are given here as far as they concern motor-vehicle insurance. The instructions as far as they concern the subject referred to in the following in the items 2-6, 9 and 10, were compiled by a committee, presided over by Mr. I. Ketola, M. Sc, which availed itself of the experience of several Finnish insurance companies.In order to give a review of the system as a whole many items, which are mathematically trivial and well-known, are briefly explained.The conventional principle of “pro rata parte temporis” is followed, which leads to the well-known reserve where P is the premium income of the company. This provides that the days when the premiums fall due are approximately equally distributed over the year (which can be checked from the premium sums of the different months in the book-keeping) or at least have no cluster points in the second half of the year and that the cost of the collecting of premiums is not less than 0.2 P. A more accurate calculation takes into account i.a. temporary short term policies etc.In casu-reserve. All unpaid claims (except those mentioned later) due to accidents which occured before the end of the account year, are listed and rated one by one. Doubtful cases, e.g. where the cause of the accident is still under litigation, are calculated in accordance with the “worst” alternative.

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.

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