White paper on fraud detection with acl (send afterwards)~9
Fraud and Error in Government
1. IBM Smarter Analytics:
Fraud, error and analytics in UK public sector
Fraud
& Error.
Fraud and error costs
the UK government
an estimated £30bn
every year1
. Fraud
alone amounts
to more than £20bn.
Nearly £10bn goes
missing as a result of
administrative errors.
This adds up to more
than £1,000 for every
British taxpayer2
.
What can be done
to stem the tide?
2. Fraud and error is a massive drain on public finances. It combines money
owed to the government and funds that have been dishonestly or mistakenly
removed from the public purse. The amount of money lost every year through
fraud and error is equivalent to the UK’s entire defence budget4
.
Fraud accounts for the bulk of government losses and currently exceeds
£20bn. Tax fraud accounts for more than two-thirds of this, with a loss of
£14bn in the year to 2012. This figure is the sum of tax evasion, “hidden
economy” fraud (untaxed income from undeclared economic activity, including
moonlighting) and criminal attacks, which include the use of false identities to
obtain tax repayments5
.
The complex and multifaceted nature of the public sector makes it a prime
target for fraudsters. Money appears to be leaking from many parts of
government. In addition to unpaid tax, the UK’s National Fraud Authority
enumerates losses from a total of 18 different areas.
At central government level, fraud losses (excluding tax and benefits) totalled
£2.5bn in 2011. Among these were losses from frauds related to procurement,
grants, TV licence fee, payroll, NHS patient charges, student finance, pensions
and National Savings and Investments (NS&I). Procurement fraud represented
the greatest single area of loss (£1.4bn), NS&I the smallest (£460,000)6
.
Local government losses are similar in magnitude. In 2011, these amounted
to £2.2bn7
. Among them were frauds associated with housing tenancy,
procurement, payroll, council tax, Blue Badge parking, grants and pensions.
The breadth of these frauds indicates that fraudsters are not only
opportunistic, but also creative and determined. In addition to this, the
prevalence of Blue Badge fraud (the abuse of parking privileges for disabled
motorists), underlines the extent to which fraudsters are prepared to operate
outside normal social constraints. If an opportunity for dishonest personal gain
exists, financial or otherwise, it seems fraudsters will always attempt to exploit it.
Fraud is not the only challenge. Government errors result in annual losses
that are estimated to be nearly £10bn8
. These include honest mistakes made
by members of the public and administrative oversights by government
officials. Errors in favour of welfare recipients mean that state benefits and tax
credits are overpaid to the tune of £3.6bn every year9
. A further £6bn is lost as a
result of tax errors.
However, it is likely that the true level of error-related loss is significantly greater
than £10bn. Current figures only reflect losses identified by the Department for
Work and Pensions (DWP) and HM Revenue and Customs (HMRC). They do not
include potential losses associated with health, education or defence.
IBM Smarter Analytics: Fraud, error and analytics in UK public sector
In our experience, efforts to combat and
prevent fraudulent activities are often
fragmented, inefficient and outdated.
As a consequence, the government writes
off up to £8bn of debt every year3
. The
application of information and insights
obtained through advanced data analytic
techniques could help the government to
stem these losses. To do so, departments
and agencies should adopt a more
joined-up approach and share more data.
3. How often have you made
major decisions with
incomplete information or
information you don’t trust?
Personal
experience
and intuition
To a
great
extent
To a
little
extent
Analytically
derived
Collective
Experience
To what extent do you make business decisions
based on the following factors?
To what extent do Lack of information forces decision makers to be most reliant on their
intuition:make business decisions based on the following factors?
1 in 3
often do
Always
Frequently
Sometimes
Rarely
Never
25%
19%
9%
43%
35%
14%
43%
28%
9%
54%
15%
5%
Lack of information forces decision makers to rely on intuition
Source: GigaOM, Software Group, IBM Institute for Business Value
Fraud and error losses in the UK are equivalent to more than five per cent of
government receipts10
. This is a very heavy burden at a time when public
finances are under unprecedented pressure.
To combat these losses, government needs to develop two capabilities. First,
it needs to be able to identify where fraud and error losses are occurring, or are
likely to occur. Second, it needs solutions that meet day-to-day operational
needs, with front-line tools and embedded processes that reduce the amount of
fraud and error that happens in the first place.
The implementation of advanced analytic techniques and the creation of a
single customer view are vital if these needs are to be met. However, moves in
this direction are hampered by a number of factors.
BARRIERS TO PROGRESS
The government doesn’t always know what it knows. It may hold vital data but be
unaware of its importance or relevance – and it may not even know it holds that
data, the often ignored “unknown knowns”. Technology is not the only problem but
data remains a stumbling block. Combating fraud means eliminating silos and
making intelligent use of big data resources that already exist across government.
Current modes of operation may not help government employees visualise,
understand and engage with customers. Tools and incentives are needed to take
ownership of problems. Yet it is people, rather than government departments,
that are responsible for collecting information and acting on it. The trouble is,
those same individuals often find it more difficult to track down information and
documents held on their own internal systems (compared to the internet). This
problem is not confined to the public sector – for 72 per cent of companies, it is
more difficult to find information they own, compared with information they do
not11
. Research from analysts such as Fulcrum, Gartner and Xerox indicates that:
•80 per cent of business activities are supported by unstructured data;
•80 per cent of unstructured data supports revenue-producing processes;
•40 per cent of employee time is spent searching for content;
•70 per cent of all content is recreated;
•60-80 per cent of the time, employees cannot find content they need.
4. There is therefore a strong requirement to have content that can be
found across departmental boundaries using a system that promotes
consistency in access control, content life-cycles, indexing and retention
on a global scale.
In tandem with this, departmental isolation can create a fertile breeding
ground for fraud and error. Poor integration and a lack of data sharing between
departments means that identification of fraud and error – and debt recovery –
can be haphazard, particularly in complex cases.
This is compounded by ambiguities surrounding data use and concerns
about data loss. The Data Protection Act imposes restrictions on data being
used for purposes other than those for which it was collected. The existence
of both perceived and real legal boundaries leads to identical systems being
duplicated across government and an increasingly entrenched silo mentality.
The picture is complicated by a high degree of inter-departmental overlap:
nearly 10 per cent of the money due to government is owed by individuals or
households with more than one debt to the HMRC, DWP or HMCTS – the Courts
& Tribunals Service. In a significant proportion of cases, a government agency
will pursue a single individual for repayment, unaware that other departments
are doing exactly the same thing.
Duplication of effort is not only extremely wasteful, but also sends out
a signal to potential fraudsters that the system is fragmented and therefore open
to abuse. Evidence from the government’s fraud, error and debt taskforce
suggests that fraudsters operate across organisational and sector boundaries12
.
A significant proportion of fraud and error could be intercepted at the
application phase. But at present, front-line staff have only limited access to
relevant information. The inability to cross-reference applications has led to
the development of a potentially wasteful and risky “pay first, check later” culture
around welfare payments and services.
In cases where relevant data is available, access is impeded by a
plethora of different gateways. For example, the Cabinet Office identified
86 different legal gateways between eight major debt departments and local
authorities13
. This adds to the complexity and time it takes to manage fraud
and error cases.
The cost and complexity of conventional investigations deters both central
and local government agencies from pursuing every case, so a level of
unrecovered loss is inevitable. In part, this is because current systems try to
automate existing manual procedures. In doing so, they replicate and even
amplify deficiencies inherent in the original paper-based processes.
However, it is equally clear that central and local government are
increasingly risk-averse. Concerns about job security and increasing
operational pressures mean civil servants can be reluctant to think outside
the box. This stifles innovation and reduces the willingness to challenge
conventional approaches.
COMBATING FRAUD AND ERROR
Rooting out fraud and error hinges on the ability of government to manage
and explore data in new ways. One of these is the use of mining and advanced
analytic techniques to uncover patterns hidden in huge amounts of data.
Insights obtained from both historic and real-time data could provide new
ways to identify and tackle everything from tax dodging and benefit fraud to
TV licence fee evasion.
In tandem with this, government needs the ability to access a single
customer view for every citizen interaction. This means pulling together data
from multiple sources to provide a clearer picture of each individual customer.
As well as displaying basic biographical data – name, age, address and
National Insurance number – a single customer view could include information
about relevant interactions across a number of government entities. To protect
privacy, the picture provided could be tailored, so only information relevant
to the situation in hand need be included.
Many types of data could be integrated into a single customer view.
As well as internally available data, it is also possible to incorporate external
data sources both structured and unstructured – including data gleaned from
social networks.
The application of advanced analytic techniques and a single customer view
would help the government to move towards achieving the following objectives:
Detect more fraud and error before it becomes debt. Smarter use of
existing data is the key to early detection. By leveraging the power of data
analytics, more potential fraud could be identified and prevented at the point
where an application is made for benefits, services or government grants.
IBM Smarter Analytics: Fraud, error and analytics in UK public sector
Smarter tax
collection
IBM data analytics is already transforming the effectiveness
of tax debt recovery in the United States.
The New York State Department of Taxation and Finance
is a case in point. IBM’s Tax Collections Optimizer, deployed
in 2010, uses a combination of data analytics and advanced
modelling techniques to create effective action plans for each
tax case.
Intelligent case management maximises the total
amount of debts collected while taking into consideration
the workload, personnel resources and the anticipated
effectiveness of suggested actions.
In its first year of operation, the solution helped tax
authorities to recover $83 million in outstanding taxes –
an increase of eight per cent on 2009 and double the
average increase of earlier years before the new technology
was introduced.
5. Get the most out of new data streams. Government is awash with
data, with channel shifts continuing to generate huge volumes of web data
as traditional face-to-face and paper-based models are replaced. Analytics
could make sense of the growing volume, velocity and variety of available data,
allowing government to get more out of its investment in new channels.
Make true zero-tolerance strategies a reality. Analytics should reduce
the cost and complexity of investigating abuse. The ability to pursue more cases,
more effectively, not only stems long-term losses but also serves as a deterrent.
Respond proportionately to debtors. By providing a clearer picture of
individual debtors, a single customer view could make it easier to distinguish
between people who can’t pay and people who won’t. This would allow
government to make fairer, better informed decisions about debt collection.
Deliver an enhanced user experience. Citizens expect government to
have a single customer view of them. Closer integration of data would pave the
way for a single point of contact with government, improving efficiency in the
delivery of services and providing enhanced levels of customer satisfaction.
Understand where risks really lie. Data analytics could allow
government to ask better questions and build better models. For example,
it could make it possible to develop a matrix of types of fraud and compare
these to different types of response, intervention and investigation. It also
makes it possible to associate customer profiles with fraud propensity, so that
risk scoring can be improved.
Explore innovative approaches to debt collection. For example, debt
recovery can be enhanced by “nudge” methods, such as sending personalised
text messages to systematic defaulters. Better data would help government to
beta test and evaluate new approaches in a fraction of the time, and for a
fraction of the cost, associated with conventional methods.
Tap into new sources of knowledge. Shared know-how and best
practice across government represents a great, untapped resource. The scope
for collaboration across the UK’s 5.9 million-strong public sector is enormous.
And unlike the private sector, there are no commercial restrictions that might
limit mutually beneficial collaboration.
Knowledge
Discovery – Social
Network Analytics
Fraud
Discovery – Social
Scoring Rules
External/ Third
party sources
Internal/ legacy
sources
Single view
of a claimant
Fraud Risk
Scores
Social Network
Visualisation
Case Packs
Search
Monitoring and
reporting (BI)
Unsupervised
Techniques
Supervised
Techniques
Generation
of rules
Unstructured
data
Structured
data
Business data
e.g. Organisation,
Claimant details
Transactional data
e.g. Claim payment,
Tax filings
Claimant
A counter fraud capability using a single customer view to assess each transaction.
6. WHAT CAN BE DONE ?
The government can take action by working to integrate the data, collect the right
debts, reduce the amount of fraud and ultimately direct public money to where it
is needed most:
Front office transformation through analytics. Tackling fraud and error
should start with a transformation of the way government interacts with
individuals. This is achieved primarily through the creation of a single customer
view and by embedding analytics in front office processes.
Manage who sees what. Sophisticated data governance is required to
manage access to information on shared systems. This would make it possible
for rules-based solutions that allow staff to access the right information when
they require it, without the need to create a permanent single file.
Big data analysis as a service. Design once, build once: a single secure
hosted platform with shared data and processes would help to optimise
outcomes and life cycle costs.
Take actionable steps following analysis. Working the data asset will
trigger opportunities to realign processes and workflows. The greatest long-term
benefits could be captured through the adoption of agile approaches and the
recognition that requirements will evolve as new streams of data become available.
TACKLE FRAUD AND ERROR: A FIVE-STEP PLAN
1 Review your organisation’s strategic approach in using analytics to
counter fraud for improved business outcomes.
•What are you trying to achieve, what outcomes do you require?
•What data sets are available to you?
•What additional processes or tools do you need to achieve these goals?
•Where is the balance point between preventative measures and controls
and dynamic and responsive business processes?
2Deploy smarter analytical and predictive processes to unearth
previously unidentified fraud behaviours and overlooked
anomalies, using data from multiple data sources.
•Have you considered all of the data currently available to you?
•Are your counter fraud measures purely rule based?
•What advanced and predictive analytical tools do you use?
•How do you monitor transactions?
•How do you define anomalous transactions?
•Is your compliance regime robust?
3Clarify your data strategy to identify which additional data is required
and how existing and new data stores can be effectively employed
to achieve your counter fraud outcomes.
•What data is freely available to you?
•What data could you buy?
•What data do you really need?
•Can you create new data?
•What does the future hold and are you ready for it?
IBM Smarter Analytics: Fraud, error and analytics in UK public sector
4Optimise the application of prevention and detection focussed fraud
rules to ensure effective use of data stores in your organisation.
•Is your data governance and management effective?
•Are your systems and processes able to use available data to run
your rules effectively?
•What rules do you currently employ to counter fraud?
•How effective are these rules in terms of your desired business outcomes?
•What new rules would improve your outcomes?
•What do other organisations do?
5Deploy effective business processes to feedback intelligence from
your initiatives to continuously learn, and enhance your counter
fraud outcomes.
•How do you adapt your business rules?
•Do your systems actively learn from past behaviours?
•Do the processes align to your business risk appetite?
•What market and business intelligence do you have for counter fraud
and who reviews this?
The benefits
of good data
Faced with its own growing data challenge, the French social
services agency, CNAF, responded by focusing on a “single
version of the truth”. The agency distributes €70bn in benefits
each year to 18 million beneficiaries through 123 delivery
branches, each of which held and managed its data differently
– including citizens, case workers and providers across
multiple programmes. This meant that every time someone
applied for a different benefit CNAF had to ask for information
already buried somewhere in the system. Systems were
siloed, data wasn’t being shared or matched (sometimes due
to privacy restrictions), updates could be problematic and,
worst of all, they risked leaving CNAF open to fraud.
The agency’s leadership decided it needed a system to
bring all of this disparate data together, while adding greater
analytical understanding of citizen information. Such a
system would help determine benefit eligibility by integrating
identity and relationship resolution functionality, thereby
helping to deter fraud.
As a result, services at CNAF improved along with its
understanding of citizens, applicants and providers across
multiple programmes, cases and locations. The number of
improper payments was reduced, identification of improper
cases after audit was improved and CNAF experienced a
35 per cent productivity gain through the use of analytics.
