Financial Risk Management of Life Insurers by CAMEL-S Rating

Financial Risk Management of Life Insurers by CAMEL-S Rating Shu-Hua Hsiao, Leader University, Taiwan ABSTRACT It is important for regulators to take action early to prevent insolvency or financial distress of life insurers. This study summarizes the financial risk and CAMEL-S rating of firms. The purpose of this study is first to review the financial landscape and to monitor the solvency of life insurers by using the CAMEL-S model. Further, to evaluate the financial soundness of life insurers. Finally, it s important to explore if there is no statistically significant mean difference between the domestic insurers and foreign branches of insurers. Results show there were significant differences between the domestic and foreign branches of life insurers for TFI. According to the results, the risk management implication is proposed as the reference of early warning system. Keywords: Financial Risk Management; CAMEL-S Rating; Early warning system. INTRODUCTION It is important for regulators to take action early to prevent insolvency or financial distress of life insurers. To do otherwise would incur high social costs and impact the financial button line. For instance, the Guo Guang Life Insurance Company (Guo Guang Life Ins. Co.) in Taiwan went bankrupt because its improper investment in April 1970. Recently, Hong Fu Life Ins. Co. in Taiwan experienced a financial crisis due to a decline in the investment environment resulting in interest spread loss and poor performance of capital investment after the 1997 financial crisis in Southeast Asia. Hong Fu and Ging Feng were subsequently taken over by the Hontai Group and the Prudential Life Ins. Co. In addition, the Chung Shing Life Ins. Co., and Ging Feng Life Ins. Co. were recently transferred to another group because of their financial insolvency (Ku, 2001). These phenomena also provide the motivation for this study and have touched off a heated debate regarding the performance of financial rating systems. In addition, the financial rating system can improve and regulate the solvency of life insurers, the CAMEL-style model that has been used successfully by bankers for credit union evaluation will be adopted in this study to construct a financial rating system. The CAMEL-S model was formed after a sixth component S was added in 1997 -Sensitivity to market risk. As Gasbarro, Sadguna, and Zumwalt (2002) said, Bank Indonesia s non-public CAMEL rating data allow the use of a continuous bank soundness measure rather than ordinal measure. Thus, the objective of the CAMEL-S was to help the Department of Insurance to supervise companies financial soundness. In addition to the CAMEL-S model, the risk-based capital (RBC) was adopted to make an insolvency prediction. There is a disparity of assumptions between the CAMEL-S and the RBC model, in that the CAMEL-S model discloses the overall financial situation. As Lopez (1999) and Hall, King, Meyer, and Vaughan (2002) found, the CAMEL-S ratings reflects a bank s overall financial conditions and can offer summary measures of the private supervisory information. However, the RBC model standards show only the minimum capital requirement. The risk coefficient of RBC needs to be re-examined and revised every two to five years Thus, the CAMEL-S was selected in this study. The importance of this study is: first, results can provide important financial information to new policyholders before they sign contracts. Second, the results also may also be used as a reference for supervisors to monitor and predict the solvency of life insurance companies. Third, to limit the social cost and promote efficiency of supervision, supervisions could list the sequence of the financial states and decide the sequence of financial examination based on the CAMEL-S ratings. The main purposes of this study including: 1. an evaluation of financial rating models by using the CAMEL-S; 2. reducing the classified cost, this study establishes an examination sequence based on the results of CAMEL-S; 3. to explore if there is no statistically significant mean difference between the domestic insurers and foreign insurers.

In order to explore these research questions, this study will test the following null hypotheses: Ho1: there is no statistically significant mean difference between domestic insurers and foreign insurers using the total financial index (TFI) of CAMEL-S. Ho2: there are no significant differences in rank for domestic insurers using TFI of CAMEL-S between years 1998 to 2002. Ho3: there are no statistically significant differences in rank for foreign insurers using TFI of CAMELS between years 1998 to 2002. The CAMEL Rating The CAMEL rating system, developed from the Uniform Financial Institutions Rating System (UFIRS), was adopted on November 13th, 1979 by the Federal Financial Institution (FFICE). The objective is to evaluate five different components of an institution s operations including: capital adequacy, asset quality, management, earnings, and liquidity. A sixth component was added in 1997 sensitivity to market risk. Each of the factors is scored from one to five, with one being the strongest rating (Barr, Killgo, Siems, & Zimmel, 2002). A researcher may adopt many independent variables; however, fewer than ten variables are selected to construct a model, and these variables are grouped into CAMEL, CAMEL-S, or CAMELO models. As Swindle (1995) pointed out, the original purpose of the CAMEL model was to improve the inadequately capitalized banks in the U.S. in the 1980s. In addition, with regard to the application of CAMEL-S, Riekers (2003) writes, Deposit insurance premium levels generally correlate with the CAMEL-S ratings regulators assign to banks (p. 1). If banks are rated as a one or two, then they pay nothing for coverage. But if banks are rated as three, four, or five, then they pay increasingly large premiums. Related studies of deposit insurance include: Federal Deposit Insurance Corporation (FDIC), Hoffman (1989), and Guerrero (2000). Many prior studies by bank examiners and regulators have used the CAMEL-S rating system to detect financial efficiency and performance. However, few researchers study the insurers financial rating systems, let alone use the CAMEL-S model and RBC model simultaneously to evaluate the financial rating systems of life insurers, as in this study. For example, some researchers, such as Guerrero (2000), Rosenstein (1987), Milligan (2002), Scott, Spudeck, and Jens (1991), Phillips (1996), Paden (2002), and Rieker (2003) have studied the application of the CAMEL or CAMEL-S model but only Burton, Adams, and Hardwick (2003) have applied the CAMEL model to the insurance industry. CAMEL-S ratings, or CAMEL-S scores, provide a letter grade or numerical ranking to indicate the safety or soundness of the institution as assigned by supervisors. Table 1 shows implications of CAMEL-S rating, based on Barr, Killgo, Siems, and Zimmel (2002). This study assumes the financial insolvency of life insurers when their CAMEL-S rating is four or five. Table 1: The CAMEL Rating and Indication Rating Financial indication 1 It s basically sound in every respect. 2 It s fundamentally sound but has moderate weaknesses. 3 It s an institution with financial, operational, or compliance weaknesses. 4 It s an institution with serious financial weaknesses that could impair future viability. 5 It s an institution with critical financial weaknesses that render the probability of failure extremely high. Source: Barr, Killgo, Siems, and Zimmel (2002) METHODOLOGY Participants and Data Source The participants of this study, based on an annual report of life insurers in Taiwan, were companies classified as either domestic or foreign insurers. There are 15 domestic companies, including Department of CTC in Taiwan, such as Prudential and Cathay. In contrast, ten branches of foreign insurers are included: Aetna, Georgia, Metropolitan, Pruco, Connecticut General, American, Manufacturers, Transamerica Occidental, New York, Republic-Vanguard, and

National Mutual. The Kuo Hua Life Insurance Companies were eliminated because of missing data or incompleteness in their annual financial report. The annual report of life insurers was published by the Republic of China in conjunction with the Life Insurance Association of the Republic of China. This database contains records obtained from insurers statutory annual statements. The analysis period of this study covers the years from 1998 to 2002. Thirty-two independent variables are shown in Appendix A. Factor Analysis and TFI* The factor pattern matrix takes the following form: Xi = a i1 F1+ a i2 F2+ +a ik Fk + ε i (i=1,2,, n) (1) where X is an independent variable; F is the unobservable common factor; ε i is the residual error term and a ik are loadings. In order to simplify the problem, this study considers the following theoretical conditions or assumptions: 1. F and ε are mutually independent. 2. E (F) = 0, Cov (F) = 1, where F is composed of the common factor a from (n*k). 3. E (ε) = 0, Cov (ε) = Φ, Φ is a diagonal matrix. The basic model of factor analysis uses matrix symbols and a vector method, represented as Z= [Z1, Z2,, Zn], which belong to (n*1) transfer matrix, the dependent variable of this research. Furthermore, Φ is defined as a matrix of factor loading. Other factor descriptions state the following: F= [a1, a2,, ap] are unobservable factors, and ε=[ε1, ε2,,εn] is a residual error term that follows the normal distribution. This research adopts 32 financial ratios (X1 ~ X 32, see Appendix A) and analyzes the financial evaluation from 1998 to 2002. Deleting outliers and conducting tests for normality for all variables is the first step. The null hypothesis requires the random variables follow a normal distribution. The significance level is set at p 0.05. Some variables may be transformed into logarithm, radical, reciprocal expressions if violations of the normal distribution occur. The TFI of CAMELS is calculated by factor analysis and the following formulas: Yi= (Xi- Xmin)*100/ (Xmax - Xmin) (2) Here, i is an index of variable. The CAMEL scores will be assigned for each company based on the total financial index (TFI) that follows the normal distribution. The calculation of total financial index is shown as followed. Total financial index is (TFI k ) = TFI = ΣΣW ij * Y ijk (3) W ij = (H ij2 / ΣH ij2 ) * ((G j / ΣG j ) * 100) (4) Where, H is loading of j factors, and G is an eigenvalue. In order to explore life insurers comparative financial standing more clearly, the TFI* represents a financial indicator that was revised from the TFI based on the range method. The formula is expressed as follows: TFI* = (TFI- min of TFI) *100/ (max of TFI- min of TFI) (5) RESULTS The Outcomes of CAMEL-S Fourteen variables had been extracted from the original thirty-two variables after factor analysis. These variables were assigned to six components of the CAMEL-S model. The principal components analysis was adopted in factor analysis of this study. In the outcome of factor analysis, the Kaiser-Meyer-Olkin Measure of sampling adequacy is 0.627, the Bartlett s Test of Sphericity, the Chi-Square statistic, is 3,080.378, and the P-value is 0.00. Table 2 also displays the component matrix and an overall outcome of the CAMEL-S model. The communalities for all selected variables are equal to one. The reliability analysis for all factors is 0.822, 0.96, 0.8006, 0.9761, 0.8967, and 0.9785, which are all respectable values (all > 0.80). In addition, the eigenvalue in each component is shown in Table 3 and the cumulative percentage achieves 90.386%. Obviously, it is sufficient to be embraced for using factor analysis (this is not clear to me). The outcomes of TFI* are shown in Table 4.

Table 2: CAMEL-S Component, KMO and Bartlett s Test Components Variables loading Communalities Reliability Capital adequacy X20 0.990 1 0.8220 X1 0.989 1 Assets X19-0.978 1 0.9600 X18 0.969 1 Management X13 0.940 1 0.8006 X12 0.811 1 X24 0.785 1 X14 0.685 1 Earning X10 0.975 1 0.9761 X31 0.974 1 Liquidity X3 0.962 1 0.8967 X30 0.965 1 Sensitivity of market X27 0.962 1 0.9785 X26 0.959 1 Kaiser-Meyer-Olkin value= 0.627, Barlett s Test of Sphericity, Chi-Square = 3080.378, and P-value=0.000. Table 3: Eigenvalue and Cumulative Percentage Components / Item Eigenvalue Weight of Eigenvalue % of variance % of cumulative Management 2.651 0.210 18.930 28.935 Earning 2.106 0.166 15.042 33.977 Assets 1.990 0.157 14.217 48.194 Capital assets 1.984 0.157 14.174 62.368 Sensitivity of market 1.973 0.156 14.095 76.463 Liquidity 1.949 0.154 13.922 90.386 Table 4: TFI* Value Insurer 1998 1999 2000 2001 2002 1 28.35 20.02 13.12 14.11 4.92 2 45.88 64.68 47.10 49.56 50.75 3 35.24 28.36 26.95 42.36 39.26 4 100.0 92.77 94.26 90.57 88.73 5 66.60 86.06 68.74 80.49 84.61 6 60.35 57.50 47.86 52.32 40.82 7 79.82 73.93 65.01 75.81 42.09 8 36.50 66.00 27.90 40.31 28.14 9 58.39 57.15 42.18 33.84 16.29 10 62.72 61.10 51.96 47.03 38.78 11 26.76 26.08 27.31 29.59 22.84 12 16.88 18.98 4.68 39.99 24.02 13 16.92 21.04 26.38 11.87 14.19 14 50.92 0.00 27.19 30.93 50.91 15 9.81 14.85 3.78 33.90 56.28 16 23.25 22.42 20.64 23.27 21.90 17 0.68 33.46 13.21 19.37 51.44 18 32.98 40.93 37.68 43.25 48.00 19 49.40 73.24 48.90 52.89 31.64 20 18.13 21.91 15.67 22.71 24.66 21 34.51 39.41 27.39 21.19 24.87 22 22.69 17.09 10.60 35.89 38.01

23 20.32 7.45 17.38 18.51 34.79 24 26.70 21.73 9.09 15.25 24.58 25 47.59 13.59 26.93 53.57 47.04 Mean 38.86 39.19 32.08 39.14 37.98 Test of Hypothesis Ho1 were created to test whether there are financially significant differences with respect to the CAMEL between the domestic and foreign branches of life insurers, based on the different groups from the period 1998 until 2002. An independent samples t-test was used to determine if there was any significant difference between the means of the domestic insurers and foreign insurers. The F-ratio of 14.13 yielded a significance value of 0.000, a t value of 4.161 and p value of 0.000 if equal variances were not assumed. Hence, the null hypothesis is rejected at the 0.05 significance level in Table. There are financially significant differences with respect to the CAMEL between the domestic and foreign branches of life insurers. The Wilcoxon Signed-Rank Test was used to test the hypothesis about the location of a population distribution. Hypotheses two and three show there were no statistically significant differences in rank for domestic/foreign life insurers using TFI of CAMEL-S in Table 6. Table 7 show the Frequency of the CAMEL-S scores each year. Table 5: Outcomes of Ho1 Indicators TFI State Domestic Foreign Samples 79 46 Mean 34.1066 28.1369 Std. error Dev. 1.1156 0.8649 Levene s Test F= 14.13 P-value=0.000 T-statistics T=4.161 P-value=0.000 Decision Reject Ho Table 6: Outcomes of Ho2 and Ho3 (α=0.05) Hypothesis Year Z P-value Ho2 (domestic) 1998-0.118 0.906 1999-0.039 0.969 2000-0.472 0.637 2001-0.190 0.849 2002 0.000 1.000 Ho3 (foreign) 1998-0.060 0.952 1999-0.070 0.944 2000-0.510 0.959 2001-0.103 0.918 2002-0.141 0.888 Table 7: The Frequency of the CAMEL-S scores CAMEL-S 1998 1999 2000 2001 2002 1 2 4 1 3 2 2 6 5 4 3 4 3 9 4 11 12 10 4 7 11 8 7 9 5 1 1 1 0 0 Sum of Insolvency 8 12 9 7 9 Sum of Solvency 17 13 16 18 16

CONCLUSION Under the impact of internationality, liberalization, disaster, more competitors and the changing environment, insurers must maintain financial solvency. The two most important responsibilities of insurer supervision are monitoring the solvency of the life insurance industry and protecting consumers. As Grace, Klein, and Phillips (2003) emphasize, minimizing the social costs of insolvency is a regulatory objective that includes action to prevent a troubled insurer from becoming insolvent and action against an insurer for the purpose of conserving, rehabilitating, reorganizing or liquidating (p. 7). As Feaver (1994) indicated, the purpose of standards is to regulate a minimum level, in order to indicate a floorlevel security of life insurers solvency. Hence, financial rating systems such as the CAMEL-S model for life insurers are inevitable. In order to maintain social order, protect consumers, and prepare for future market liberalization, as well as strengthening management abilities, more attention will need to be paid to capital structure and capital management. Under the impact of internationality, liberalization, disaster, more competitors and the changing environment, insurers must maintain financial solvency. The two most important responsibilities of insurer supervision are monitoring the solvency of the life insurance industry and protecting consumers. As Grace, Klein, and Phillips (2003) emphasize, minimizing the social costs of insolvency is a regulatory objective that includes action to prevent a troubled insurer from becoming insolvent and action against an insurer for the purpose of conserving, rehabilitating, reorganizing or liquidating. Based on the CAMEL-S model, 45 samples were estimated to have a risk of insolvency with CAMEL-S scores of four or five. If the firms don t deal properly with revised strategies, bankruptcy could occur, particularly if signs of potential financial insolvency are present. Financial distress begins when a corporation is unable to meet its scheduled payments or are unable to maintain cash flow (Drapeau, n.d., p. 1). In order to maintain social order, more attention will need to be paid to capital structure and capital management. Thus, assessment of pure risk management, therefore, does not meet the needs of assessing insurers solvency. The following recommendations are based on the findings of this study: 1. Incomplete archive databases resulted in research limitations. Earlier financial annual reports in Taiwan were in written forms which limited the study s accuracy. Hence, another recommendation to improve supervision is the construction of complete databases for further researchers. 2. The findings of Browne, Carson, and Hoyt (1999) suggest that economic and market variables are significant predictors for the failure of life insurers. Hence, it is suggested that further research should include economic and market factors. REFERENCES Barr, R. S., Killgo, K. A., Siems, T. F., & Zimmel, S. (2002). Evaluating the productive efficiency and performance of U. S. commercial banks. Managerial Finance. 28(8), 3-25. Browne, M. J., Carson, J. M., & Hoyt, R. E. (1999). Economic and market predictors of insolvencies in the life-health insurance industry. The Journal of Risk and Insurance, 66(4), 643-659. Burton, Adams, & Hardwick (2003). The determinants of credit ratings in the United Kingdom Insurance Industry. Journal of Business Finance & Accounting, 30(3-4). Department of Insurance. (2002). Insurance in Taiwan liberalization for a brighter future. Ministry of Finance Republic of China. Retrieve from http://www.insurance.gov.tw Drapeau, R. (n.d.). Bankruptcy prediction model using discriminant analysis on financial ratio derived from corporate balance sheets. Retrieved from http://dept.lamar.edu/lustudentjnl/current%20edition/bankruptcy%20prediction%20model.pdf Feaver, C. (1994). Risk-based capital ratios bring changes to life insurance industry. Indianapolis Business Journal, 44(52), 15. Gasbarro, D., Sadguna, I., & Zumwalt, J. K. (2002). The changing relationship between CAMEL ratings and bank soundness during the Indonesian banking crisis. Review of Quantitative Finance and Accounting, 19(3), 247-260.

Grace, M. Harrington, S. & Klein, R. (1998). Identifying troubled life insurers- an analysis of the NAIC FAST system. Journal of Insurance Regulation, 249-290. Guerrero, K. (2000). FDIC will seek legislation to raise lid on premiums. American Banker, 165(99), 6. Hall, J. R., King, T. B., Meyer, A. P., Vaughan, M. D. (2002). What can bank supervisors learn from equity markets? A comparison of the factors affecting market-based risk measures and BOPEC scores. Working paper of The Federal Reserve System. Hoffman, D. G. (1989, April). Comment: Why the fuss over risk-based premiums? American Banker, 4. Ku, J. (2001). Ownership structure, compensation and efficiency the evidence of local life insurer. Master s Thesis, Feng Chia University, Taiwan. Lopez, J. A. (1999). Using CAMEL-S ratings to monitor bank conditions. FRBSF Economic Letter, 99(19), 1-3. Milligan, J. (2002). Guess who s rating your bank? American Banker Association. ABA Banking Journal, New York, 94(10), 68-72. Paden, R. (2002). The focal point for examiners. Texas Banking, 9(2), 2-12. Phillips, S. (1996). The Federal Reserve s approach to risk management. The Journal of Lending and Credit Risk Management, 78(6), 30-36. Rieker, M. (2003). In focus: A sharp rebuke for fifth third s controls. American Banker, New York, 168(60), 1. Rosenstein, J. (1987). Credit unions to get new rating system CAMEL rating method will be implemented. American Banker, New York, 151(11), 1. Scott, D. F., Spudeck, R. E. and Jens, W. G. (1991). The secrecy of CAMEL-S. The Bankers Magazine, Boston, 174(5), 47-51. Swindle, C. S. (1995). Using CAMEL rating to evaluate regulator effectiveness at commercial bank. Journal of Commercial Services Research, 9(2), 123-141. Variable X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26 X27 X28 Appendix A: Financial Variables Name Net premiums written (NPW) to equity Equity to total assets Liquid assets/total assets Return on equity (ROE) Net operating expenses to operating revenues Net operating expenses to net premiums written Acquisition expenses and compensation to agents/ net premiums written Benefit payment to net premiums written Reinsurance commission received to reinsurance premium expense Pre-tax profit or loss for the year to income Business and administrative expenses to net premiums written Percentage change of first year premium receipts Percentage change of total premium receipts Percentage change of renewal ordinary premium Percentage change of reserves Turnover rate of total assets Turnover rate of fixed assets Fixed assets to total assets Fixed assets to long-term debts Reserves to equity Running assets to operating revenues Working capital to total assets Percentage change of total assets Percentage change of operating revenues Percentage change of profit or loss for the year Possesses percentage of first year premium receipts Possesses percentage of total premium receipts Net operating revenues to total assets

X29 Return on assets X30 Acid test ratio X31 Profit or loss for the year to total premium revenues X32 Fixed assets to equity Note: 1. Net Premiums Written (NPW) = Insurance Premium Received + Reinsurance Premium Received - Reinsurance Premium Expenses 2. Percentage change of item = (this year last year)*100%/ last year 3. Possesses rate of item = item of insurers * 100% / total sum of industry 4. Turn over rate = operating revenues * 100% / item