Fighting Tenancy Fraud

Transcription

1 CIH Fighting Tenancy Fraud Jon Rayfield Experian Public Sector November 2013 Experian and the marks used herein are service marks or registered trademarks of Experian Limited. Other products and company names mentioned may be the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without prior written permission of Experian Limited.

2 Agenda Title: Improving data sharing to tackle tenancy fraud Is it necessary or desirable to share data when tackling tenancy fraud? What data is available to share Guide lines and legislation around data sharing Data sharing agreements and privacy notices Data matching between housing associations and local authorities What needs to improve in data sharing and what housing providers to do to improve their own practices and procedures. 2

3 Identity fraud and how it links to SHTV Most of us are just who we say we are, but there are many other people who want to be us too Armed with a few easily obtained pieces of paper, anyone determined enough can manufacture or steal identities Two main types of identity fraud: False identity Stolen identity (impersonation and account take-over ) In parallel with identity fraud, we use the notion of eligibility fraud: When your circumstances, or eligibility is not in line with the requirements for a service Crimes relating to the misuse of personal data, such as identity fraud, including impersonation and the takeover of accounts, constituted over half of all frauds in Nearly two thirds of all frauds (65%) are directly related to the misuse of personal details, according to CIFAS, the UK s Fraud Prevention Service 3

4 What data can be used to tackle SHTV? Data which addresses the concerns from the previous slide Name Address Postcode Date of birth Phone number land, mobile Other mortgage details? Children s school details? Experian CAIS data (credit account information sharing) Mortality files Clearly there is a need to make clear to tenants what data is required and how it will be used Proportionality is legally required there has to be a valid reason for requesting and using data The DPA does not deny or outlaw data sharing and the ICO has recently and publicly stated data sharing as something the DPA has no view about, but that people hide behind the DPA as reasons for inaction! 4

5 Making it clear to the tenant at the point of application Channel Shift Identity Verification Verification of Circumstances Fraud / Error Intelligence Sharing New Applications One off runs Point of Application Service Fulfilment Changes Periodic rechecks SAFO 5

6 Eliminate Genuine Error Data used to validate and verify an identity Validate Name DoB Current Address Previous Address Time at Address Bank Account Credit card Identity Document Ask questions that only the genuine person is likely to know the answer to. Verify 2011 Experian Limited. All rights reserved. 6

7 Best practice periodic verification of circumstance (eligibility) Individuals not resident who should be More people resident than there should be Individuals resident who should not be Claimant not found at address Claimant found at an alternative address Financial Associations Aliases Income Discrepancy Deceased 2011 Experian Limited. All rights reserved. 7

8 Intelligence Sharing mimicking the private sector Controlled Data Sharing Person ID Number Authority A Product Type Last Name First Name Income Liabilities Date of Birth Authority B Matching Rules Occupation Address (lines) Postal Code Employer Employer Address Phone Number Fax Number Only data agreed to be shared Mobile Phone Number is disclosed Bank Account Number Credit Card Number Address Only records involved Terms of Product in Notes rules on Case are shared 2011 Experian Limited. All rights reserved. 8

9 A specific fraud solution in practice (social housing) End to end process Data Cleanse Data Matching Ongoing Consultancy Value Added Services Flagging incomplete records, enhancing where possible - Key - Core Social Housing Tenancy Verification Client engagement on Very High Risk cases - singular focus on revocation without court proceedings 192 Business Trace (no sanction required) Citizenview Plus (Section 29/35) Inclusion in the Experian Hunter Confirmed Social Housing Fraud database Optional 9

10 The key role of investigation Data Matching 110 revoked properties Social Housing Tenancy Verification 0 revoked properties 10

11 Data sharing results 2012 pilot London bias 105,000 records 7 organisations but 10 input files local authorities and HA s Experian data involved, including known National and Insurance fraudsters Matching covered all possible permutations: same phone number different address same name and DOB different address.. As well as the more typical data matching process FIVE people had tenancies with different providers FOUR people had applied for additional housing from their current housing provider Evidence that the same mobile and land phone number used by different people Some data quality issues FIFTEEN deceased tenants with tenancy still occupied 470 matches to National and Insurance fraud files (SAFO) 11

12 Data sharing results 2012 pilot But despite which the vast majority of fraud uncovered from this process, was uncovered through the standard data matching programme. It is because of this fact that data sharing for SHTV does not seem to add that much BUT Experian does see real value in adding a SHTV fraud file within the process as an option for our clients. 12

13 Local Government Fraud Investigator feedback (LBFIG) 13

14 Local Government Fraud Investigator feedback (LBFIG) LBFIG reported (17 responses): 100% of responders tackle SHTV 82% tackle Housing Benefit fraud 82% tackle SPD 70% tackle procurement fraud (wide ranging including internal fraud) 52% tackle grant fraud In answer to data sharing 8 do, 9 don t so about 50% do, BUT there is a seemingly overwhelming desire to share data to tackle FRAUD (not just SHTV) with 82% scoring 4 or 5 good/very good idea In answer to the suggestion of using SAFO fraud files: 82% (again) scored 4 or 5 good/very good idea And 56% of responders thought working with (say) DWP was a good idea 14

15 CASE STUDIES Amy was traced and found to be living in Peru She has 12 (!) council properties in the Greater London area She was grossing over lots per annum through sub letting Each property was applied for using a new, false identity The identities were created using forged European ID Cards/Driver Licenses, accompanied by forged bank statements and utility bills IDENTITY FRAUD 15

16 CASE STUDIES A couple (husband & wife) and their two daughters each have two bed council properties in West London They live in the council properties (3 in total) As a family, they have three buy-to-let mortgages on 300k houses outside of London The mortgages are paid by private tenants, with any profits going back to the family ELIGIBILITY FRAUD 16

17 CASE STUDIES Charlie is living in a 2 bed council flat in South London He applied for the council flat under an assumed Alias, which he uses frequently alongside his genuine date of birth and various addresses He owns a property in Guildford, which he rents out He lives with his partner in the council flat the partner is not declared and has substantial income IDENTITY & ELIGIBILITY FRAUD 17

18 CASE STUDIES Lawful tenant had a live tenancy with Circle 33, whilst living in another Registered Providers property. Data matching highlighted this, and the lawful tenant was confronted. Had been a victim of an impersonation scam for the last 13 years and that there was another Spanish person of similar age pertaining to be a Circle tenant. The fraudster had obtained a Spanish ID Card and had opened lots of financial accounts using our genuine tenant s details IDENTITY IMPERSONATION FRAUD 18

19 Best practice Governance (Local Government) Fighting Fraud Locally (2012) 19

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