Benchmarking Indian general insurance firms

Abstract

PurposeThe purpose of this paper is to benchmark the solvency status of Indian general insurance firms.Design/methodology/approachThe paper collects, compiles and analyses the key financial, operational and business data of eight Indian insurance firms. The authors first decide on initial firm‐specific economic variables and use data of last five years from IRDA Reports and Company Annual Reports. The NAIC IRIS ratios method was used to obtain an initial risk classification. This was used as a proxy of insolvency risk. Linear regression and logit techniques were thereafter applied to estimate the significant factors (direction‐wise and magnitude‐wise) which influence insurer solvency.FindingsThe results suggest that the factors that most significantly influence Indian non‐life insurers are lines of business, the firm's market share, the premium growth rate, the underwriting performance and the claims incurred. Further, the factors which have the strongest effect are market share, change in inflation rate, firm size, lines of business and claims incurred.Research limitations/implicationsThe sample of Indian general insurers used is limited with regard to the time span. No holdout sample was used and the entire data set was subjected to statistical analysis. These somewhat limit the findings and implications.Practical implicationsThe paper provides insurers with easy‐to‐use operational and marketing indicators to benchmark their solvency risk. It will lead to competitive goal setting for continuous improvement. Estimation of appropriate market/economic parameters can be a useful input for regulators. A few suggested indicators are new.Originality/valuePrevious studies of insurance companies have focused on developed economies (USA, Europe) or the Asian Markets (China and Japan). This paper determines a set of marketing, financial and operational variables to predict benchmark financial strength of general insurance firms in India. It incorporates qualitative inputs from practising managers and industry experts before carrying out quantitative modeling and analysis.