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Fraud Investigation-Latest trends and novel Methods

Explore types of fraud, detection methods, and categories within housing loan fraud, along with prevention techniques and real-life case examples.

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Fraud Investigation-Latest trends and novel Methods

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  1. Fraud Investigation-Latest trends and novel Methods National Housing Bank March 17 2010

  2. Discussion in this session • Business scenario :home loan fraud • Types and categories of fraud and common Causes and illustrations • Novel and uncharted methods for fraud detection and prevention

  3. Growth of housing loans • Housing loans grew from a level of Rs 16,000 crore in 2001 to a level of Rs 1,86,000 crore in 2006

  4. What is more alarming is • Brazenly • Big numbers: In a nationalised bank in Wadala Mumbai- there were 102 fraud HL between 2004 and 2006 from out of just 150 accounts • Detected late – more than 4 – 5 years

  5. Types and Categories of fraud • Fraud purely from forces outside the entity • Fraud with collusion from within

  6. Causative factors for purely external fraud • Derelection of duty or lackadaisical approach • Intellectual capabilities of fraudsters and awareness of loopholes • Systems have been evolving and dynamic, giving an atmosphere or uncertainty and inconsistency of policy and procedures

  7. Causative Factors for fraud with internal collusion • Awareness of gaps in procedures etc • Non accountability • Existence of Organised Crime

  8. Standard Procedure for Housing Loans • POTENTIAL BORROWER APPROACHES BANKS / INSTITUTIONS- BASIC ELIGIBILITY • SUBMISSION OF APPLICATION • SUBMISSION OF TITLE DOCUMENT • INSPECTION OF SITE/PLACE • VALUATION • SUBMISSION OF FINANCIAL DOCUMENTS • APPRAISAL OF FINANCIAL DOCUMENTS • APPRAISAL OF TITLE DOCUMENT • VALUATION OF PROPERTY AND ASSETS. • OBTAINING INSURANCE POLICY OF BORROWERS 11. GETTING NOC FROM BUILDER/SOCIETY AS RELEVANT AND BANK’S CHARGE CONFIRMATION FROM THE SCOIETY • GETTING ORIGINAL SHARE CERTIFICATE • POST DATED CHEQUES FOR MONTHLY EMIS

  9. Instances of lakadaisical approach

  10. Common Causes- home loans • Borrowers credentials- not examined to identify those who cannot pay • Guarantors merely interviewed telephonically, if at all • No structured procedure in place to spot fake OR modified documents, title deeds, etc • Unreliable, inflated or even fictitious valuation reports, if there is collusion from within the financing bank • Field checks- Laxity in field inspections of site, property, flat and other verifiable submissions before sanctioning a loan have contributed to a rise in frauds, • Absence of system in place which can detect whether the same property has been mortgaged more than once • Perfunctory examination of financial statements, tax returns. No application of mind • Title checks through legal advisors- very unreliable

  11. Major Challenges- home loans through collusion • Staff Collusion with borrowers- individual level • Organised Fraud : Builder-borrower-staff-advocate-chartered accountant nexus is believed to be the root cause of banks falling prey to home loan frauds • Agents are available for forging and creation of fictitious documents and rubber stamps

  12. Staff Collusion helps in fraud being detected years later • HL not classified as HL but outsider fraud • Teeming and lading of cheque collections • EMIs not presented

  13. Case We have seen which encompassed every level of collusion • Property did not exist • Borrower was a sweeper staying on the road • Borrower was not interviewed • Guarantor did not exist • Agreement was fictitious • Registration receipt fictitious • Valuer backed out stating that he had not issued any report • Paneladvocate report was not taken • No recovery suit was filed • Police report conveniently stated borrower was absconding

  14. Novel Audit Tests for detecting frauds • Other Mathematical tools: Benford’s Law, RSF, Statistical methods such as regression • Tiger Team Tests, test packs • Sting Operations- decoy purchases/sales, sting interviews • Placebo effect • Nanoscience approach-data mining • Barium Test • Repetitive verifications

  15. Measures of prevention at early stages- technology based • Data mining for duplicate names, registration numbers, telephone nos etc • Time dates- registration on Sundays/ Holidays • Advanced data mining techniques Benford • law etc

  16. General methods • Verification of Registration receipt • Development of forensic cell • Training of staff for better awareness and techniques • Use of investigation software • Use repetitive valuers • Data base of tainted borrowers, builders, consultants

  17. Benford’s Law

  18. Benford’s Law

  19. Tiger Team Tests Can be used in Housing Loans

  20. Thank you Chetan Dalal

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