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RMA SPRING CONFERENCE LOAN REVIEW AND CREDIT QUALITY FROM THE AUDITORS POINT OF VIEW

RMA SPRING CONFERENCE LOAN REVIEW AND CREDIT QUALITY FROM THE AUDITORS POINT OF VIEW. Presenting Panel Cynthia A. Dopjera, Shareholder Brian P. Holle, Advisory Manager Kevin C. Horton, Assurance Principal. Topics for Discussion:. Underwriting Observations in 2013

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RMA SPRING CONFERENCE LOAN REVIEW AND CREDIT QUALITY FROM THE AUDITORS POINT OF VIEW

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  1. RMASPRING CONFERENCELOAN REVIEW AND CREDIT QUALITY FROM THE AUDITORS POINT OF VIEW

  2. Presenting PanelCynthia A. Dopjera, ShareholderBrian P. Holle, Advisory ManagerKevin C. Horton, Assurance Principal

  3. Topics for Discussion: • Underwriting Observations in 2013 • Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements

  4. Underwriting Observations in 2013 Community Banks $100MM - $2.5B Houston & Southeast Texas Most Frequent Loan Policy Exceptions from 2013 Reviews (regardless of loan origination/vintage): • High Loan to Value (LTV) • Financial Statements (FS) below standard • Waive personal guarantees • Extended amortization schedule

  5. Underwriting Observations in 2013 New Loans Underwritten since 4Q2012: • 43% are CRE • 19% are CRE owner occupied • 28% are C&I loans

  6. Underwriting Observations in 2013 CRE Lending Underwriting: • Sponsor/guarantor Global Cash Flow (GCF) Analysis • 40% of CRE subject to GCF Analysis • 33% of CRE did not need GCF Analysis • 27% of CRE needed GCF, but was not performed • GCF Analysis • Consistency • Complete documentation challenges • Sponsor guarantor GCF trends

  7. Underwriting Observations in 2013 CRE and C&I Underwriting – Common Technical Challenges: • Not documenting/verifying guarantor personal liquidity • Excessive reliance on pro-forma future CF estimates for DSCR • Failing to consider borrower’s debt service requirements from other lenders in DSCR • Relying on non-recurring cash flows to support DSCR • Not accounting for AR exceptions/carve-outs in borrowing base agreements • Not establishing any financial ratio covenants

  8. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements • What is Fraudulent Financial Reporting? • Common Types of Schemes • Indicators of Financial Statement Fraud • Methods to Assist in Analyzing Financial Statements

  9. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements What is Fraudulent Financial Reporting? • Manipulation, falsification, or alteration of accounting records or supporting documentation from which financial statements are prepared • Misrepresentation in, or intentional omissions from, the financial statements of events, transactions, or other significant information • Intentional misapplication of accounting principles relating to amounts, classification, manner of presentation or disclosure within financial statements

  10. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements Common Types of Schemes: • Improper Revenue Recognition • Overstatement of Assets • Understatement of Liabilities (including contingent liabilities)

  11. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements Indicators of Financial Statement Fraud: • Complex business arrangements or structures; including related party activity • Frequent changes in auditors • History of inability to generate cash flows from operations while reporting earnings and growth • Unusual rapid revenue and/or profit growth

  12. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements Methods to Assist in Analyzing Financial Statements: • Beneish M-Score Model • Ratio Analysis • Vertical and Horizontal Analysis • Altman Z-Score

  13. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements • Beneish M-Score Model: • Professor Messod Beneish published an empirical equation using eight ratios to detect earnings manipulation • M stands for manipulation • M-Score model is based on eight values determined from the financial statements of the company • DSRI – Days’ Sales Receivables Index • GMI – Gross Margin Index • AQI – Asset Quality Index • SGI – Sales Growth Index • DEPI – Depreciation Index • SGAI – Sales, General and Administrative Expense Index • LVI – Leverage Index • TATA – Total Accruals to Total Assets

  14. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements • Beneish M-Score Model – (continued): • An M Score> -2.22 indicates a strong likelihood of the company being a manipulator

  15. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements

  16. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements Vertical & Horizontal Analysis - Balance Sheet

  17. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements Vertical & Horizontal Analysis - Income Statement

  18. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements • Ratios:

  19. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements • Ratios – (continued):

  20. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements • Altman Z-Score: • Developed by Professor Edward Altman • Indicator of a company’s financial solvency • Z-Score is based on five ratios; each ratio is weighted • Return on total assets • Sales to total assets • Equity to debt • Working capital to total assets • Retained earnings to total assets • A Z-Score of 1.81 or below indicates possible future insolvency

  21. Borrower Financial Statements – Fraud Indicators and Methods to Assist in Analyzing Financial Statements

  22. www.harperpearson.com

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