Pavlović, Branko

Email: pavlovic.branko@gmail.com

Branko Pavlović was born on June 19, 1972. He graduated in 1997 from the Faculty of Electrical Engineering, University of Belgrade, and specialized in actuarial science in 2009, at the Faculty of Economics, University of Belgrade.
<br He is a member of the Executive Board of Globos Insurance. For over 17 years he was a member of the top management of Delta osiguranje, Delta Generali osiguranje and Generali osiguranje. In the period from 2010 to 2011 he was the President of the Management Board of the Health Center Jedro in Belgrade, and subsequently, a member of the Management Board and Executive Director of the Voluntary Pension Fund Management Company Delta Generali (from 2011 to 2016).

He is the President of the Association of Actuaries of Serbia, a member of the Actuarial Commission of the National Bank of Serbia, a member of the Council of the International Association of Actuaries, and a court expert in actuarial science. He was the President of the Actuarial Commission of the Association of Serbian Insurers in the period from 2013 to 2018, and in 2018, the President of the Electronic Business Committee of the Council of Foreign Investors (FIC).

In 2008, he was awarded the prestigious license of the National Bank of Serbia for a certified actuary. He has been the appointed actuary at Delta Generali osiguranje a.d.o. Beograd, Delta Generali reosiguranje a.d.o. Beograd, Generali osiguranje Montenegro a.d. Podgorica, Delta Generali životna osiguranja a.d. Podgorica, and Globos osiguranje a.d.o. Beograd.

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ARTICLES

PARTIAL INTERNAL MODEL UNDER THE SOLVENCY II FOR THE LIFE INSURANCE LAPSE RISK

Neizvesnost u pogledu realizacije očekivane stope prekida ugovora životnog osiguranja utiče na rizik od nepreciznog određivanja kapitalnog zahteva za solventnost, iznos minimalnog kapitalnog zahteva i performanse poslovanja osiguravajuće kompanije. Zbog toga precizno projektovanje rizika od prekida ugovora životnog osiguranja ima velik značaj. Brojni faktori utiču na stopu prekida. Kapitalni zahtev za solventnošću rizika od prekida ugovora životnog osiguranja u režimu Solventnosti II može se odrediti primenom standardne formule ili parcijalnog internog modela. Na primeru podataka sa tržišta osiguranja Srbije, korišćenjem softverskog paketa R, u radu će biti detaljno prikazan izbor faktora za modeliranje zavisnosti stope prekida, postupak formiranja GLM modela prekida ugovora i provera ispunjenosti pretpostavki modela. Razvijeni parcijalni interni model može biti primenjen za određivanje očekivane stope prekida ugovora osiguravajuće kompanije koja posluje na domaćem tržištu. Ključne reči: parcijalni interni model, rizik od prekida ugovora

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ARTICLES

CHALLENGES IN APPLICATION OF MACHINE LEARNING IN INSURANCE INDUSTRY

The Internet of Intelligent Things, Blockchain technology2 , software robots3 and various aspects of artificial intelligence such as machine learning are often referenced in modern literature as having great potential to improve the processes in insurance business. This paper will focus on machine learning. Machine learning is a subset of artificial intelligence intended for study and recognition of behaviour patterns by using statistical methods for available data processing. In other words, machine learning is a software able to make its own decisions based on previous experience. The key benefit companies can draw from machine learning is prediction of future trends, where software independently discovers patterns in the available data. Insurance companies may use machine learning to optimize their tariffs, settle claims more efficiently, and improve the quality of loss reserving, and as a powerful tool in combating frauds.

Key words: machine learning, insurance