EARLY WARNING INDICATOR FOR TURKISH NON-LIFE INSURANCE COMPANIES

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1 EARLY WARNING INDICATOR FOR TURKISH NON-LIFE INSURANCE COMPANIES Dr. A. Sevtap Kestel joint work with Dr. Ahmet Genç (Undersecretary Treasury) Gizem Ocak (Ray Sigorta)

2 Motivation Main concern in all corporations maintain the business control the system against risk of failures keep the financial robustness Risk indicators financial ratios random variables giving signals on the financial status

3 Motivation Determine the impact of financial ratios on insolvency risk of a company Predict a year ahead insolvency risk with a model based on historical occurrences Determine a scaling on the company s financial performance as an early warning indicator

4 Solvency Indicators Solvency: capability of a firm to maintain its business in long-term while meeting all obligations Insurance: having enough equity to carry on operations and meting the liabilities Solvency II regulations

5 Solvency II To maintain the operations and survivability during financially distress periods Main considerations are risk of Underwriting Market Operational Liquidity

6 Solvency II Pillars Pillar I Quantitative measurements Model and its validation SCR and MCR Pillar II Internal audit Risk Management Pillar III Reporting Transparency

7 Solvency Indicators in Turkey Undersecretariat Treasury Financial Ratios Solvency Ratios Premiums Received/Equity Equity/Total Assets Equity/Technical Provision Profitability Ratios Loss Ratio (Loss/Premiums Earned) Expense Ratio (Sales&Service Expense/Premiums Earned Technical Profit/Premiums Earned Profit/Premium Earned Operational Ratios Retention Ratio Compensation Ratio Liquidity Ratios Liquidity Ratio (Liquid Assets/Total Assets) Current Ratio Premium&Reinsurance Receivables/Total Assets Agency Receivables/Equity

8 Why Early Warning? Determine the strong and weak transactions and years Take precautions to avoid insolvency

9 Who did What? Beaver (1966) Introducing 6 groups financial ratios by applying dichotomous classification test on failed and non-failed firms. 14 ratios are found to be significant 78% accuracy to predict 5-year before bankruptcy Altman (1968) employed Multivariate discriminant analysis (MDA) capturing the interaction between indicators. 22 indicators are selected 95% accuracy to predict bankruptcy one year ahead. Deakin (1972) illustrated the effectiveness of MDA

10 Who did What? Ohlson (1980) : conditional logit improved predictions for 1-3 years ranging 93%-96% Zmijewski (1984): Probit model Shumway (2001): Hazard model controlling the timevarying covariates Heijden (2011) emphasized on normalization of data compared to other type of transformations such as lognormal, square root

11 Turkish Case Genc (2002) Financial analysis of non-life insurance companies Early warning model Developing a rating score Time period selected is financial ratios are chosen. Multivariate linear regression is applied. Dependent variable indicates insolvency as 0, solvency as 1. 5 significant ratios yield 71% accuracy on predicting the insolvency.

12 Turkish Case Isseveroglu (2005) and Isseveroglu & Gucenme (2010) Non-life companies Determined indicators in failure Comparison of logit, multiple linear regression and MDA for the term Logit results predicting % one three years bankruptcy

13 AIM Improve the existing studies done for Turkish insurance sector Expanding the time horizon Taking into account new variables Introducing a new scaling having the impact of the time

14 Models Data Structure 1 ith firm is insolvent Yi 0 ith firm is solvent X ij jth financial ratio of ith firm X real numbers ij j 1,..., k

15 Models Linear Regression Model Y i i ~ k j X j 0 N(0, 2 ij ) i Normality assumption. Multicollinearity, Heteroscedasticity due to binary response variable

16 Models Multivariate Discriminant Analysis determines which variables are the best predictors of response variable k Z i X j 0 j ij Discriminant function, with discriminant coefficients Needs normality assumption as well determines variables with respect to a score which weights the variables in the combination

17 Logistic Regression p P( Y 1 X ) and 1- p P(Y 0 X) p ln 1 p k j 0 X j ij P( Y 1 1 X ) 1 exp j k 0 X j p is the probability of being insolvent under independent variables ij

18 Bayesian Regression Coefficients of Linear model are random Prior distribution Prior estimates are taken from the linear model Gibbs sampling method is employed to generate the coefficients Y i j k j j 0 ~ f( ) X ij i

19 Turkish Insurance Sector Number of Companies: in Non-life, 17 Composite, 22 Life total 63 in Non-life, 6 Life, 18 Pension total 58 Premium Production 22% increase in premium production; non-life share around 83% Loss Ratio decrease from 55% to 43% in between 2008 to 2013 Crises impact 1994, 2001, 2008 economical crises 1999 Marmara-Düzce earthquake

20 ANALYSES Non-life insurance companies operating between Data set Descriptives Transformation Models Comparison of the models and model selection Assessment of Solvency Indicators

21 Data Set Data set contains 14 financial ratios, their age in the system and a robustness indicator based on premium production 43 insurance companies Source: Annual Financial Reports Determination of insolvency with indicators Loss Ratio>1. Premium collection ratio< years average (Liability/Liquid Asset)>2

22 22 Data Set Years Company Failed Company Failed

23 Descriptives X1: Liquid Asset/Total Asset X6: Premium Production/Coverage X11: Technical Profit/Premium X2: Premium Collection Ratio X7: Payables on Reinsurance Operations/Equity X12: Total Income/Total Asset X3: Net Premium Receivables/Total Asset X8: Liability (Short term)/liquid Asset X13: Total Payables/Equity X14: Reinsurance Share/Gross X9: Total Reserve/Net Premium X4: Loss/Premium Premium X5: Profit/Paid Capital X10: Total Reserve/Liquid Asset D: Age C1 C2 C3 C7 C8 Std. Std. Std. Std. Mean Mean Mean Mean Std. Dev. Mean Dev. Dev. Dev. Dev. x x x x x x x x x x x x x x

24 Descriptives X1: Liquid Asset/Total Asset X6: Premium Production/Coverage X11: Technical Profit/Premium X2: Premium Collection Ratio X7: Payables on Reinsurance Operations/Equity X12: Total Income/Total Asset X3: Net Premium Receivables/Total Asset X8: Liability (Short term)/liquid Asset X13: Total Payables/Equity X14: Reinsurance Share/Gross X9: Total Reserve/Net Premium X4: Loss/Premium Premium X5: Profit/Paid Capital X10: Total Reserve/Liquid Asset D: Age C27 C28 C29 C30 C31 C32 Mean Std. Std. Std. Std. Std. Mean Mean Mean Mean Dev. Dev. Dev. Dev. Dev. Mean Std. Dev. x x x x x x x x x x x x x x

25 Descriptives X1: Liquid Asset/Total Asset X6: Premium Production/Coverage X11: Technical Profit/Premium X2: Premium Collection Ratio X7: Payables on Reinsurance Operations/Equity X12: Total Income/Total Asset X3: Net Premium Receivables/Total Asset X8: Liability (Short term)/liquid Asset X13: Total Payables/Equity X14: Reinsurance Share/Gross X9: Total Reserve/Net Premium X4: Loss/Premium Premium X5: Profit/Paid Capital X10: Total Reserve/Liquid Asset D: Age CORRELATION MATRIX OF C21 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 X * X * X X * * X **.619 * *.665 ** X X ** ** **.934 **.528 * 0.0 X ** *.938 **.529 * -0.1 X ** * ** 0.3 X X *.549 * 0.2 X X X *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

26 2,5 2 1,5 1 0,5 0-0,5 X1: Liquid Asset/Total Asset X6: Premium Production/Coverage X11: Technical Profit/Premium X2: Premium Collection Ratio X7: Payables on Reinsurance Operations/Equity X12: Total Income/Total Asset X3: Net Premium Receivables/Total Asset X8: Liability (Short term)/liquid Asset X13: Total Payables/Equity X14: Reinsurance Share/Gross X9: Total Reserve/Net Premium X4: Loss/Premium Premium X5: Profit/Paid Capital X10: Total Reserve/Liquid Asset D: Age x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 Original and transformed data set for C x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14

27 Early Warning Indicator Predicted Y Mean Standard Deviation I Q>0.8 Q>0.84 Q>0.88 Q>0.82 Q>0.85 Q>0.72 Q>0.81 Q>0.68 W S Q<0.6 Q<0.54 Q<0.51 Q<0.45 Q<0.5 Q<0.5 Q<0.41 Q<0.34

28 C1 I I C11 I W S S S W C15 S W S S S I I I C16 W S S S C17 S S S S S S S S C18 S S S S S S S S C19 S I I I W S S S C20 S S W S S S S W C21 S S W S S I S S C22 W I W W W I W W C23 S S W W S S S S C24 S S S S S S S S C25 S I S S S S S S C26 W S W W W I W W C27 W S W S S S S S C28 S S W S S S S S C29 S S S S S S S S C30 W S W I S S S S C31 S S S S W I W W C32 S S S S S S S S C33 S S S S S S S W C33 I I S S S S W C34 W W W W W W S S C35 W I I I W W W S C36 S S S S S W S S C37 S W S S S S S S C38 I I S S S W W S C40 W I I S C41 S S S C42 I W I S S W W S C5 I S W W W I W I C7 S W C8 S I C9 I I

29 Comments Updated study on an extended time period Implementation of Bayesian regression improved the R- square Logistic regression did not yield agreeable coefficients. The prediction power of the model is around 67% Semi-nonparametric non-linear models as future work.

30

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