Benchmarking the Performance of US Banks

1. Benchmarking the Performance of US Banks R. Barr, SMU T. Siems, Federal Reserve Bank of Dallas S. Zimmel, SMU Financial Industry Studies, Dec. 1998: www.dallasfed.org

2. Motivations and Goals • Motivations • Safety and soundness of banking system • Protection of FDIC insurance fund • Best allocation of examiner resources • Goals • Prioritization of on-site examinations • Early-warning indicators of troubled banks

3. Objectives of the Research • Benchmark the U.S. banking system over the last decade • Assess performance with DEA-based model • Isolate best- and worst-practice banks • Support bank auditors by predicting trouble • Evaluate DEA in large-scale benchmarking role

4. Previous Work • Measuring bank management quality with DEA • Barr, Seiford, Siems, 1993 • Bank Failure Prediction Model • DEA score as input to logit forecasting model • Barr and Siems, 1996 • Technical report versions available at: • www.smu.edu/~barr

5. Data Envelopment Analysis • A methodology for integrating and analyzing benchmarking data that: • Performs a multi-dimensional “gap analysis” • Considers interactions, tradeoffs, substitutions • Integrates all performance measures • Gives an overall performance rating • Suggests credible organizational goals, benchmarking partners, ….

6. Bank Performance Model Inputs (Resources, Xs) Outputs (Desired outcomes, Ys) • Earning assets • Interest income • Noninterest income • Salary expense • Premises & fixed assets • Other noninterest expense • Interest expense • Purchased funds

7. Defining Efficiency • Efficiency = ratio of weighted sums of the inputs and outputs (>0) • Defines best practice in a DEA model

8. How DEA Works • Instead of using fixed weights for all units under evaluation, • DEA computes a separate set of weights for each bank • Weights optimized to make that bank’s score the best possible • Constraints: no bank’s efficiency exceeds 1 when using the same weights

9. Formulating a DEA Model • There are many DEA models • The basic idea in each is to choose a set of weights for DMU k that:

10. Measuring Distance Efficient frontier of best practice f1 z f Inefficient bank

11. Introducing Expert Judgment • Classic models may result in unreasonable weight assignments for inputs & outputs • e » 0 weights on unflattering dimensions • Can overemphasize secondary factors • We added weight multipliers to the DEA • Based on survey of 12 FRB bank examiners • Used response ranges to set UB/LBs on weights

12. Survey-Derived Constraints Analytic Hierarchy Survey range Survey average process weights Inputs Salary Expense 15.8% - 35.9% 23.10% 25.20% Premises/Fixed Assets 3.1% - 15.7% 9.60% 11.40% Other Noninterest Expense 15.8% - 35.9% 22.70% 19.80% Interest Expense 17.2% - 42.8% 25.90% 23.50% Purchased Funds 12.1% - 34.0% 18.80% 20.20% Outputs Earning Assets 40.9% - 69.5% 51.30% 52.50% Interest Income 25.7% - 46.9% 34.30% 33.80% Noninterest Income 10.2% - 20.2% 14.40% 13.70%

13. Banking Industry Test Data • End of year data for: • 1991 11,397 banks • 1994 10,224 banks • 1997 8,628 banks • Used constrained CCR-I model • Run with large-scale specialized DEA software

14. 1991 Profiles by DEA E-Quartile 1991 data DEA Efficiency Quartile most to 1 2 3 4 least efficient most efficient least efficient difference INPUTS -0.40% * Salary Expense / Total Assets 1.43% 1.54% 1.65% 1.83% -1.22% * Premises and Fixed Assets / Total Assets 1.00% 1.48% 1.76% 2.22% -0.87% * Other Noninterest Expense / Total Assets 1.53% 1.62% 1.84% 2.41% 0.08% * Interest Expense / Total Assets 4.71% 4.70% 4.66% 4.62% -9.78% * Purchased Funds / Total Assets 6.29% 8.17% 11.12% 16.07% OUTPUTS 4.44% * Earning Assets / Total Assets 92.68% 91.67% 90.59% 88.24% 0.13% * Interest Income / Total Assets 8.68% 8.71% 8.67% 8.55% Noninterest Income / Total Assets 0.95% 0.79% 0.89% 1.00% -0.05% N 2,850 2,848 2,849 2,850 0.2728 * average efficiency score 0.7340 0.5982 0.5387 0.4611 lower boundary 0.6334 0.5665 0.5092 0.0000 upper boundary 1.0000 0.6334 0.5664 0.5091 * Significant at 0.01 (Values expressed as a percent of total bank assets)

15. 1997 Profiles by DEA E-Quartile 1997 data DEA Efficiency Quartile most to 1 2 3 4 least efficient most efficient least efficient difference INPUTS Salary Expense / Total Assets 1.67% 1.60% 1.64% 1.75% -0.08% -1.45% * Premises and Fixed Assets / Total Assets 0.98% 1.55% 1.94% 2.44% Other Noninterest Expense / Total Assets 1.85% 1.31% 1.50% 1.92% -0.07% 0.14% * Interest Expense / Total Assets 3.29% 3.30% 3.27% 3.15% -4.85% * Purchased Funds / Total Assets 10.46% 12.33% 13.63% 15.32% OUTPUTS 2.33% * Earning Assets / Total Assets 92.99% 92.60% 91.83% 90.65% Interest Income / Total Assets 7.45% 7.41% 7.37% 7.33% 0.13% ~ 0.90% * Noninterest Income / Total Assets 1.80% 0.77% 0.84% 0.90% N 2,157 2,157 2,157 2,157 0.3617 * average efficiency score 0.6685 0.4313 0.3717 0.3067 lower boundary 0.4722 0.3982 0.3451 0.0000 upper boundary 1.0000 0.4721 0.3981 0.3450

16. 1991 significant differences, Q1-Q4: All inputs, and most outputs DEA scores Changed by 1997: Inputs: Salary, other non-interest (not sig.) Outputs: non-interest income now signif. Noninterest income a new focus for banks Fee income Off-balance sheet activities Analysis of Results

17. Other Bank Performance Metrics

18. Efficient banks: Greater return on assets Higher equity capital Fewer risky assets 1991 vs. 1997 Not comparable scores But underlying trends of variables’ importance help explain banking industry changes Relationship with Other Metrics

19. FRB Bank Examination Criteria • Capital adequacy • Asset quality • Management quality • Earnings • Liquidity

20. Confidential scores from on-site visits On each CAMEL factor and overall Values from 1 to 5 1 = sound in every respect 2 = sound, modest weaknesses 3 = weaknesses that give cause for concern 4 = serious weaknesses 5 = critical weaknesses, failure probable Bank Examiner Ratings

21. Compared CAMEL ratings and DEA efficiency scores Included banks examined recently: 1991: 7,487 banks 1994: 7,679 banks 1997: 4,494 banks CAMEL rating groups Strong: 1 or 2 rating Weak: 3-5 rating DEA-score groups Quintile, by efficiency If no relationship, each group should contain 20% of each of the other metric’s groups CAMEL Ratings & DEA Scores

22. Efficiency vs. CAMEL Ratings

23. “Strong” vs. “Weak” CAMELs

24. In Summary • DEA useful in benchmarking in service industry • Can provide information for examiners, but not perfect predictor • Large-scale efficiency analyses can give insight into industry dynamics and structure changes