National Fraud Initiative using IDEA

National Fraud Initiative

Kevin Boon

IT Performance Specialist

Audit Commission

IDEA User Group – November 27 2008

Agenda

• Background to NFI

• Successes NFI 2006/07

• New Areas

• Statutory Powers & Data Protection

• NFI Process

• NFI Process and IDEA

Background to NFI

What is it?

• Data Matching Exercise

When is it carried out?

• Every two years

• but moving to more frequently

What does it do?

• Prevents and detects fraud

Background to NFI

Who is involved?

• The public sector

– Local councils

– NHS bodies

– Emergency services

– Government departments

• Plus the private sector (voluntary)

– Financial Institutions

– Social housing bodies

Background to NFI

Background to NFI

What data is involved?

• Student loans (absconders/Benefits/visas etc)

• Payroll (multiple employment/H & S/visas)

• Benefits (undeclared income)

• Pensions (deads)

• Rents/arrears (absconders)

• Right to buys (undeclared capital/ money laundering)

• Visas/asylum seekers (deportation/working)

• Licences (undeclared income, visas etc)

• Care homes (deads)

• Concessionary fares/parking (abuse)

• Council Tax (Single Persons discount)

• Creditors (duplicate payments)

• Companies House (conflict of Interest)

Background to NFI - 2006/07 Outcomes

Background to NFI - National Report

• Overpayments/fraud of £140 million detected (overall

figure since 1996 nearly £450 million)

• 157 public sector staff ineligible to work

• 69 council properties were recovered.

• 2,819 cases where pension continued to be paid after

the death of the pensioner

• 16,102 deceased persons’ blue badges were cancelled

• duplicate payments of £1.75 million detected

• £24 million+ housing benefit overpayments detected

• £4 million Income Support (IS) and Job Seeker

Allowance (JSA) fraud and overpayments

• 4,310 cases IS/JSA benefits reduced/ceased

• 31 prosecutions, 22 administrative penalties and 76

cautions issued by JCP/TPS

New areas 1 Expansion of existing work

• Introduce government departments and agencies

– HB, Payroll, Procurement etc

• Council of Mortgage Lenders

• Cross border matching

• Housing Associations

New areas 2 Non fraud

• Council Tax arrears

• Rent arrears

• Other debts

New areas 3 Public safety

• Child protection

– List 99

– Sex offenders register

• Absconders from justice

• Identity fraud

• Counter terrorism

Data protection and Statutory powers

• Audit Commission Act

• Data Protection Act

– Information Commissioner

– Code of Data Matching Practice

– Parliamentary approval

– Fair processing

• Serious Crime Act

NFI Process

Specification

Extraction

HousekeepingSubmissionChecking

Manipulation

Results

Investigation

Prioritisation

Reporting

Matching

Cleansing

NFI Process and IDEA

Specification

Extraction

HousekeepingSubmissionChecking

Manipulation

Results

Investigation

Prioritisation

Reporting

Matching

Cleansing


Manipulation/Pre-filtering Example Specification

• Print reports

• Joining databases

• Aligning/Appending databases

• Virtual fields

– Padding out

– Combining/Separating

– Changing formats e.g. dates

– Adding indicators

• Discard unwanted records/fields

• Reordering fields


Checking/Validation

• Date sorting for completeness

• Control totals for reasonableness

• Alpha sorting for completeness

• Format checking for data quality

• Record count – truncation

• Gap detection for quality


Prioritisation

Mostly available online (filtering, sorting)

not suitable for Trade Creditors duplicate payments

WHY?

• Periodic dataset used

• Multi-level data required

• Non trade creditors included

• Lack of pre-filtering

• Duplicate or regular payment?

• LARGE VOLUME OF MATCHES

But

Leading to:-


Prioritisation

Trade Creditor duplicate paymentsExample - On basis of same creditor and invoice amount

View matches in IDEAUse IDEA functions:-

– Extraction by equations

– Summarisation

– Extraction by range

– Joining

– Sampling


Prioritisation – using IDEA

Suggested process:-

• Extraction by equations (to discard unwanted records) - view

• Summarise by Cred Ref/Amount (to show number of records in each match)

• Extract on number of records (to exclude regular payments and singletons) - view

• Extract on Amount range (to exclude outliers) - view

• Random sample (to select for checking) - view

• Joining (to reconstruct matches) – view

Next stage


Housekeeping• Producing subsets for email lists (exception reports v

contacts list)

• Updating contacts lists from external sources


Other possible uses• Benford’s law

• Statistical analysis

• Piloting new matches

Questions

Payroll Specification Back

Field name Data format

Employee reference number Character

Employee post number Character

Department Character

Title or Sex Character

Surname Character

Forename(s) Character

Address line 1 Character




Post code Character

Date of birth Date

Date started Date

Date left Date

Leaver indicator Character

National insurance number Character

Gross pay to date Numeric

Date last paid Date

Sort code Character

Bank account Character

Building society roll number Character

Print Report Back Import

Print Report importBack

Trade Creditors – payments history Back


Prioritisation – using IDEA

Equations:-

• Non numeric invoice numbers – view

• Include only paid invoices - view

• Exclude specific payment types - view

• Exclude general types - view

Next stage


ExampleOne of the last two characters of the invoice number is numeric:-

@right(@strip(supplier_invoice_number) ,1) <= “9” .OR @mid(@right(@strip(supplier_invoice_number) ,2),1,1) <= “9”

Trade Creditors – payments historyBack

ExampleDefault date imported as „Error‟:-

@IsFieldDataValid ("PDATE") = 1


ExampleAdvances included in dataset:-

.not. @isini(„advance‟ , supplier_invoice_number)


ExampleHousing benefit payments included in error, the invoice number refers to the HB number e.g. HB123456:-

.not. @regexpr(Supplier_invoice_number , “HB[0-9]”)


ExamplePotential quarterly and monthly payments excluded. Singletons excluded where other sides of the match no longer there.

.not. (no_of_recs = 1 .or. no_of_recs = 4 .or. no_of_recs = 12)


ExampleRisk areas seen to be between £100 and £5,000.

Total_Invoice_Amount > 100 .and. Total_Invoice_Amount < 5000


Biased or random sampling technique could be used to select matches for checking


New target population selected, the other side of the matches need to be rejoined.

Joining requiredPrimary database = original fileSecondary database = sample fileKey = Credref/AmountAll match records

National Fraud Initiative using IDEA

Software

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