Fraud Investigation-Latest
trends and novel Methods
National Housing Bank
March 17 2010
Discussion in this session
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Business scenario :home loan fraud
Types and categories of fraud and common
Causes and illustrations
Novel and uncharted methods for fraud
detection and prevention
Growth of housing loans
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Housing loans grew from a level of Rs
16,000 crore in 2001 to a level of Rs
1,86,000 crore in 2006
What is more alarming is
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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
Types and Categories of fraud
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Fraud purely from forces outside the entity
Fraud with collusion from within
Causative factors for purely
external fraud
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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
Causative Factors for fraud with
internal collusion
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Awareness of gaps in procedures etc
Non accountability
Existence of Organised Crime
Standard Procedure for Housing
Loans
POTENTIAL BORROWER APPROACHES BANKS /
INSTITUTIONS- BASIC ELIGIBILITY
2. SUBMISSION OF APPLICATION
3. SUBMISSION OF TITLE DOCUMENT
4. INSPECTION OF SITE/PLACE
5. VALUATION
6. SUBMISSION OF FINANCIAL DOCUMENTS
7. APPRAISAL OF FINANCIAL DOCUMENTS
8. APPRAISAL OF TITLE DOCUMENT
9. VALUATION OF PROPERTY AND ASSETS.
10. OBTAINING INSURANCE POLICY OF BORROWERS
11. GETTING NOC FROM BUILDER/SOCIETY AS RELEVANT AND
BANK’S CHARGE CONFIRMATION FROM THE SCOIETY
12. GETTING ORIGINAL SHARE CERTIFICATE
13. POST DATED CHEQUES FOR MONTHLY EMIS
1.
Instances of lakadaisical
approach
Common Causes- home loans
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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
Major Challenges- home loans
through collusion
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Staff Collusion with borrowers- individual level
Organised Fraud : Builder-borrower-staff-advocatechartered 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
Staff Collusion helps in fraud
being detected years later
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HL not classified as HL but outsider fraud
Teeming and lading of cheque collections
EMIs not presented
Case We have seen which
encompassed every level of
collusion
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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
Novel Audit Tests for detecting frauds
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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
Measures of prevention at early
stages- technology based
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Data mining for duplicate names,
registration numbers, telephone nos etc
Time dates- registration on Sundays/
Holidays
Advanced data mining techniques Benford
law etc
General methods
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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
Benford’s Law
Benford’s Law
Digit
1
2
3
4
5
6
7
8
9
7
30.10 %
17.60 %
12.50 %
9.70 %
7.90 %
6.70 %
5.80 %
5.10 %
4.60 %
Tiger Team Tests
Can be used in Housing Loans
Thank you
Chetan Dalal