Krishan K. Pandey

A study on factors influencing claims in general insurance business in India

Purpose - The fact that complaints regarding general insurance claims are three times as numerous as those of life insurance claims suggests that claims behaviour of general insurers be investigated to minimize operating losses and ensure operational excellence. This paper seeks to address this issue. Design/methodology/approach - Study of variance and factor analysis has been undertaken to achieve the objective of identifying factors which govern claims in general insurance business. In order to understand the dependency of claims over the sectors and segments, statistical hypothesis testing along with cross tab analysis has been conducted. The study also evaluates the relationship of these factors over the sectors and segments by running a multiple regression. Findings - An empirical result of the study proves that there exists an association between type of sectors, i.e. public and private and segments of insurance namely fire, marine and miscellaneous. The study also suggests a claim projection model for the general insurance players. Research limitations/implications - Exclusion of specialized players due to the reason being new entrants and in order to maintain common parlance of sectors may be a limitation to this study. Originality/value - The study recommends that insurance players should not treat the claims settlement strategies in isolation of segments. The claims projection model as suggested in the study may prove to be extremely helpful in projecting the claims and in turn reduce the increasing underwriting losses.

Aftermath of 2008 financial crisis on oil prices

Geopolitical and economic events had strong impact on crude oil markets for over 40 years. Oil prices steadily rose for several years and in July 2008 stood at a record high of

A Review on Harvesting, Oil Extraction and Biofuels Production Technologies from Microalgae

145 per barrel. Further, it plunged to

Waste to energy status in India: A short review

43 per barrel by end of 2008. There is need to identify appropriate features (factors) explaining the characteristics of oil markets during booming and downturn period. Feature selection can help in identifying the most informative and influential input variables before and after financial crisis. The study used an extended version of MI3 algorithm i.e. I2MI2 algorithm together with general regression neural network as forecasting engine to examine the explanatory power of selected features and their contribution in driving oil prices. The study used features selected from proposed methodology for one-month ahead and twelve-month ahead forecast horizon. The forecast from the proposed methodology outperformed in comparison to EIAs STEO estimates. Results shows that reserves and speculations were main players before the crisis and the overall mechanism was broken due to 2008 global financial crisis. The contribution of emerging economy (China) emerged as important variable in explaining the directions of oil prices. EPPI and CPI remain the building blocks before and after crisis while influence of Non-OECD consumption rises after the crisis.