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  • Mortgage Bankers Association 08/19/10 Mortgage Bankers Association
  • Mortgage Bankers Association 08/19/10 Mortgage Bankers Association
  • Mortgage Bankers Association 08/19/10 Mortgage Bankers Association
  • Mortgage Bankers Association 08/19/10 Mortgage Bankers Association
  • Mortgage Bankers Association 08/19/10 Mortgage Bankers Association
  • Mortgage Bankers Association 08/19/10 Mortgage Bankers Association
  • Mortgage Bankers Association 08/19/10 Mortgage Bankers Association
  • Transcript

    • 1. Using Lender Data to Pursue Legal Remedies for Fraud Richard Wohl Fortace LLC
    • 2.
      • Lenders are “Sharing” Huge Fraud Losses but not Data
      • Common Fraud Schemes, Actors & Basis for Recourse
      • Legal Pursuit is Required as Part of Loss Mitigation
      • Fraud Identification, Analysis and Pursuit --Program Template
      • Using Fraud Data to Prevent New Losses
      • The Future: Sharing Data to Fight Fraud
    • 3. Lenders are “Sharing” Huge Fraud Losses but not Data Source: US Department of Treasury’s Financial Crimes Enforcement Network (FinCEN), FBI, TowerGroup. 40,000 60,000 50,000 80,000 10,000 20,000 30,000 70,000 1996 1998 2000 2002 2004 2006 2008P 2010P 1,318 1,720 2,269 2,934 3,515 75,122 18,391 9,539 5,387 4,696 71,554 65,049 50,038 37,313 25,989 SARs Filed
      • Fraud Trends:
      • Losses in 2008 will be between $15.0 & $25.0 BB
      • Growth of SARs will continue at high levels
      • Data sharing and vigorous pursuit will be critical
      Total losses skyrocketing 20.0 30.0 25.0 5.0 10.0 15.0 Total Yearly Losses, $BB
    • 4. Variety of Fraud Schemes are Proliferating
      • Common Fraud Schemes
      • “ Bad Actors”
      Breach of Legal Duty
      • Inside Collusion
      • Silent Seconds
      • Closing Protection Letter
      • Closing Agent
      • Property Flipping
      • Value Fraud
      • Identity Theft
      • Phantom Assets
      • Churning
      • Foreclosure Bailout
      • Quit Claim Fraud
      • Adding False Owner
      • Short Sale’s Fraud
      • Straw Buyer
      • Builder Bailout
      • Appraiser
      • USPAP / E&O Claim
      • Loan Officer
      • Mortgage Broker
      • Correspondent Lender
      • Fidelity Policy Claim
      • Broker Agreement
      • Seller Agreement
      • Realtor
      • Home Builder
      • Title Company
      • Tort Claim
      • Fraudulent Underwriting
    • 5. Legal Pursuit and Recovery is a Vital Component of a Robust Anti-Fraud Program
      • Fraud Losses Destroy both Lender and Investor Returns
        • Fraud destroys bond and portfolio ROIs
        • Secondary Market Performance Models can account for statistical losses, but not for fraud
        • Lenders and Servicers have historically not pursued fraud recovery beyond the originator
      • Servicers Have a Contractual Duty to Pursue Fraud Recovery
        • Servicers are pre-occupied with more politically pressing loss mitigation efforts like loan modifications
        • Lenders and servicers owe fiduciary duties to Investors to provide loss mitigation programs
        • The “Servicing Standard” now includes aggressive action to recoup fraud losses
      • Investors and Regulators are Demanding Aggressive Action
        • HUD and the GSEs have dramatically increased the pressure on Lenders to repurchase fraudulent loans
        • Loan modifications and bankruptcy “cramdowns” will exacerbate losses
        • Congress and Regulators are pressing Servicers for accountability
    • 6. Credible Fraud Recovery Requires a Thorough, Proven Process Portfolio Scrubbing Loan Portfolio or Pool Bad Actor Database Search Fraud Marker Algorithm Search No Further Work File Audit Review Field Investigation Claims Pursuit File Level Audit Review by trained Forensic Auditors Field Auditor validates findings and determines collectability Funds remitted to Lender or Bond Investors via Trustee No Further Work No Further Work Management QC Review No Funds Collected Yes Yes No No No Yes Claim Developed & Pursuit Initiated Settlements & Judgments Yes Yes
    • 7. Fraud Detection Tools Maximize Recovery Pool
      • “ Bad Actor” Proprietary Data
      • Lender/ Servicer Shared “Bad Actor” Data
      • Business Partner “Bad Actor” Data
      BACKTEST AND REFINE Database and Algorithms with Known Fraud Loans
      • DQ Loans
      • Repurchased Loans
      • QC Findings
      • M/I Claim Denials
      STEP 1 Identify Target Loan Pool STEP 2 Prepare and Transmit Data for Analysis STEP 3 Find “Bad Actor” and Detection Algorithm Matches STEP 4 Determine Loans with High Fraud Markers for Audit Proprietary Detection Algorithms Database Partner Detection Algorithms MARI/ Lexis-Nexis
    • 8. Use Forensic Audits to Confirm “Collectible Fraud”
      • “ Bad Actor” matches
      • Fraud detection algorithm correlations
      • Utilize specially trained“ fraud finder” auditors
      • Specialist Audit firms can assist (Clayton)
      • Fraud must be clear and convincing
      • Fraudulent acts must be attributable to a professional party to the transaction
      • Find significant available assets from sponsoring company or insurance policy
      • Develop and prepare claim for legal pursuit
      • Liaison with federal, state and local law enforcement to enhance asset discovery and recovery
      STEP 5 Audit High Fraud Marker Loans STEP 6 Use Specially Trained Auditors STEP 7 Find Material Fraud, Culpable Parties and Recovery Resources STEP 8 Prepare Case for Litigation
    • 9. Use Qualified Counsel to Pursue and Litigate Claims
      • Standardized & proven pleadings and motions cover common claims types by jurisdiction
      • Refine agreements, pleadings and motions based on case experience and results
      • Use Counsel with specific experience in pursuing mortgage fraud
      • Use established metrics to ensure timely case resolution
      • Identify and manage outside claims attorneys in key jurisdictions
      • Utilize uniform performance agreements, quarterly accountability reports and standard fee schedules
      • Develop parameters for approval of settlements and judgments
      • Settlement and judgment proceeds will mitigate lender and investor losses
      STEP 9 Use Proven Templates for Claims, Demands and Litigation STEP 10 Develop Specialized and Capable Case Management STEP 11 Qualify and Manage Strong Network of Outside Attorneys STEP 12 Closely supervise negotiations and settlements
    • 10. “ Those Who Ignore History are Condemned to Repeat it”
      • 2004-2005…
      • Hawthorne, CA Home
      • Civil Fraud Judgment: LA County Superior Ct, 2005
      • Title Agent forged signature on Deed and recorded it to convey title to new owner
      • New Owner Borrowed: $470,000
      • Prior Lien Payoff: $203,000
      • Net Fraud Proceeds/ $267,000
      • Bank Losses
      • 2007-2008
      • Rancho Cucamonga, CA Home #1 Rancho Cucamonga, CA Home #2
      • Criminal Conviction : US Attorney, Central Dist CA
      • Straw Buyers found to purchase properties using fraudulent title/escrow company
      • Straw Buyer Borrowed: $4,000,000
      • Prior Lien Payoff: $2,000,000
      • Net Fraud Proceeds/ $2,000,000
      • Bank Losses
      Angela Cotton Victory Title & Escrow
    • 11. Using Fraud Data to Prevent New Losses
      • Lexis-Nexis/Choice Point
      • Fraud Guard (SS#, Employment History, Address History
      • Credit Report Fraud Alerts – contact borrower directly to confirm identity and legitimacy of proposed transaction
      • Internet
        • Social Security Verification
        • State, County & City Licensing Verification
        • Tax Preparer Authentication
        • Reverse Phone Number & Address Lookups (free sites)
        • People/Business Searches (zabasearch.com – free & fee sites)
        • Salary Approximation on Stated Income (salary.com – free site)
      • Utilize 4506-T with the IRS to validate Tax Returns & Filing Status (stated income loans)
      • Property searches & aerial photos (Sitex.com AVM’s – fee; RealQuest.com – fee; Zillow.com – free; Realtor.com – free; Googleearth.com – free & fee)
      • County Assessor Data
    • 12. The Future: Shared Lender Database Key Elements Key Legal Risks Key Mitigants
      • Data Contributed by Major Lenders and GSE’s
      • Fair Credit Reporting Act Claims
      • FCRA consumer defamation claims prohibited
      • Providing FCRA appeals process
      • Covers all major categories of “Bad Actors” (closing agents, realtors, brokers, loan officers, appraisers)
      • GLB privacy claims
      • GLB safe harbors for preventing fraud and for reporting to Credit Agencies
      • Contains origination and servicing “incident” information
      • “ Interference with Contract Claims”
      • Qualified privilege for lenders to share information under “common interest” exemption
      • Develop for Mortgage Industry (MERS Model)
      • State UDAP Claims
      • MARI Case Experience

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