Box 1: Fraud management tools and initiatives of
potential interest to the Department
Australias central and extensive control process,
developed by the central agency handling social benefits,
Centrelink. Centrelinks accountability to the Australian Government
is underpinned by Business Partnership Agreements, which detail
joint outcomes and Key Performance Indicators. Centrelink
manages a national database for customer records, the Income
Security Integrated System (ISIS). This system centrally holds 23
million customer records (6.2 million records support a current
benefit determination). In terms of indicators, measures and
indicators of fraud control in the Department of Family and
Community services include: a targeted multimedia education
campaign to reduce the number of people who fail to inform
Centrelink about changes to personal circumstances (the performance
indicators [per year, from 2004-2008] for the campaign are for
100,000 customers to notify Centrelink and for Centrelink to
receive 15,000 tip-offs); setting of targets for the number of
overall compliance reviews (147,000 reviews per year); and the
determination of the number of data-matching reviews (25,000 per
year). Each measure is associated with targets for expected savings
(the reduction of overpayment multiplied by the potential period of
benefit overpayment), which are indicated for a four-year period
(2004-2008). These programmes are continuously reviewed for
cost-effectiveness (savings made against the cost of
administration) and on this basis can be cancelled. The general
trends have been the expansion of data-matching reviews and the
emphasis on joint targets and outcomes.
Canadas focus on prevention, which has achieved savings
through risk-based Claimants Information Sessions (Integrity
Information Sessions). These sessions provide high-risk
clients with information on benefit programmes, their rights and
obligations, and control measures. The result of the sessions has
been that to some extent the occurrence of fraud and error in
certain high-risk groups has been pre-empted and reduced. Since
1999, 225,000 sessions have been held. Through this process, CA$800
million (390 million) in actual savings was identified.
Frances recently launched range of measures to improve
data management, including the planned development of a national
database of customer records and use of national reference numbers
to identify claimants across different benefit schemes and
insurance funds. This national reference number would also
allow more comprehensive data-matching and targeted compliance
reviews.
Irelands comparative risk assessment through the Fraud
and Error Surveys, which establish baseline rates of error and
fraud per benefit type (e.g. recorded fraud and error rates per
programme). These surveys have taken place in 2003 and
2004 for specific benefit schemes and will become more
comprehensive and regular in the coming years. They allow for a
more thorough analysis of the change in the baseline rates over
time and the main causes of this change. The analysis of the
drivers for change tries to find out the characteristics of fraud,
such as for instance the profiles (marital status, age) of
claimants involved in fraud. Control instruments (such as
data-matching and compliance reviews) are directed accordingly.
Fraud and error is measured on the basis of randomly selected
cases, which are then reviewed (taking into account the size of the
sample and the duration of the review). The monetary value of any
changes as a result of the review activity together with the
monetary value of the sample are captured in order to extrapolate
the estimated value of the loss and the baseline rate of fraud and
error.
The Netherlands use of random response surveys, the use
of cost-benefit analyses to determine the cost-effectiveness of
control measures, the presence of unique fiscal identifiers, and
the emphasis in the Dutch social security system on the rights and
obligations of the claimant. The Netherlands conducted
random response surveys (POROSZ) of claimants in 2000, 2002, and
2004 to establish the motivations behind fraudulent behaviour and
types of fraudulent behaviour associated with specific benefit
types. The analysis of the survey outcomes over time allows the
Dutch government to better understand fraudulent behaviour, to
establish risk profiles, and to direct control measures to specific
types of fraud. The Netherlands also performs cost-benefit analyses
of new control programmes, in which savings in benefit payments are
set against administrative costs. These analyses are set out in the
annual report of the Ministry and serve to determine whether
programmes are or remain cost-effective. The use of a unique fiscal
identifier allows the Dutch government to track individuals through
the system and more comprehensively data-match specific cases. The
Dutch benefit system places much emphasis on the rights and
obligations of the claimant to inform the authorities of new
circumstances. For instance, benefit claim forms contain a
contract, which stipulates the rights and obligations of the
claimant. Claimant profiles are also updated once every two
years.
New Zealands Accuracy Reporting Programme (ARP), the
setting of targets for control measures, the requirement for
claimants to re-establish core eligibility for benefits after a
fixed period, and the introduction of single core benefit.
ARP aims to estimate the accuracy of the total population of
benefits based on a relatively small sample. It reports on the
overall accuracy of benefits rather than the size of incorrect
payments. New Zealand has an extensive target regime for control
activities ranging from the number of reviews (e.g. 35,000 for
data-matching; 80,000 over number of reviews) to the outcome of the
reviews. The New Zealand Government also has targets for the return
on investment per control measure (e.g. NZ$2.5 [reduction of
overpayment] for every NZ$ spent on data-matching). The core
eligibility for benefits is re-assessed after 26 to 52 weeks
depending on the benefit. The government agreed in principle to
introduce a single core benefit from 200708. The single core
benefit will involve one set of rates and one set of eligibility
criteria and add-ons for people with high housing, childcare or
disability costs.
Swedens integration of measures of fraud in the overall
quality reporting system (Qben II). Qben II requires
social security agencies to measure and report the correct
proportion of decisions to pay benefits and the proportion of
correct payments. The initial results of a new IT system to
cross-check data showed some impressive reductions in the number of
cases of overpayment. However, in keeping with Swedens
customer-oriented approach, Qben II is primarily a quality control
system to check whether claimants receive payments accurately and
on time, rather than a system to aggregate fraud and error.
The USAs use of neural networking. Neural
networking (developed to study how the brain processes information)
is a technique for processing and analysing large volumes of data.
In social security systems, neural networking analyses associations
and patterns among data elements, which allows it to find
relationships that can result in new reviews. The more data a
neural network processes, the better it performs (i.e. the better
it identifies the characteristics of potentially fraudulent
payments). In Texas, a neural networking programme was commissioned
to look at fraud and abuse in the States Medicaid programme. In
2000, the programme managed to recover $3.4 million (about 2
million) in payments. Such initiatives complement the wider use of
data mining (the analysis of the characteristics of fraudulent
behaviour based on data patterns) and data-matching programmes
throughout the USA.
NOTE: For more extensive information on these initiatives, see
the sections on examples of actions being taken to combat fraud and
error in the respective country studies.