Fraud The Environment of Fraud Preventing Internal Fraud External Fraud.
Card fraud costs to banks increase to $40bn · 2017-01-05 · Card fraud costs to banks increase to...
Transcript of Card fraud costs to banks increase to $40bn · 2017-01-05 · Card fraud costs to banks increase to...
Authors
Jonathan Crossfield +44 20 3393 0633
Andrew Griffin [email protected]
Card fraud costs to banks increase
to $40bn
Revisiting the benefits of advanced fraud
risk management systems January 2017
source: Featurespace
Advanced fraud management systems offer $15.8bn of savings for card issuers
In 2015, the global card industry lost approximately $22bn (2014 $16bn) due to fraud, and we estimate
it loses a further $18bn ($15bn) in managing fraud and in revenue lost to competitors driven by false
positives – good transactions that are mistakenly blocked by the industry’s existing fraud management
systems. Adaptive, machine learning fraud management systems, such as Featurespace’s ARIC Fraud
Hub, can lower the incidence of undetected fraud. However, the main advantages are the savings on
fraud management and missed revenue.
Learning from ‘good’ behaviour rather than pattern matching ‘bad’ behaviour
Most fraud management systems on the market today work by looking for ‘bad’ behaviour; in other
words, they rely on pattern-matching against recognised past fraud types. In contrast, advanced fraud
detection and management systems that utilise deep machine learning and behavioural analysis are
able to better understand the normal, ‘good’ behaviour of each individual customer, and therefore
recognise the importance of the subtle anomalies that indicate a card user is acting out of character.
70% reduction in false positives, 25% reduction in fraud
Over the past few months, Featurespace has been challenged by a number of major card issuers to
demonstrate the benefits of ARIC Fraud Hub. Using real historical customer data provided by the card
issuers, Featurespace demonstrated a 25% reduction in the incidence of undetected fraud and,
simultaneously, a 70% reduction in false positives.i Oakhall applied these results to card industry data
to estimate the implied savings for the industry at $16bn, comprising $6bn reduced fraud and $10bn
reduced fraud management costs and lost revenue.
This is an update to our original analysis of 2014 data, published June 2016
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ARIC Fraud Hub
Featurespace’s ARIC Fraud Hub is a real-time, machine learning fraud management software system
for organisations in financial services, including retail banks, payment providers and card issuers. The
ARIC (Adaptive, Real-time, Individual, Change Identification) platform enables card issuers to monitor
each individual customer’s behaviour in real-time.
Instead of starting from point-in-time data assuming what fraud looks like, the ARIC Fraud Hub uses
times-series data to learn what normal behaviour is for each individual card user. This enables ARIC to
spot and block new fraud types in real-time. It also means ARIC understands the context of normal
behaviour, reducing the number of transactions that get unnecessarily blocked in an attempt to stop
fraud.
ARIC uses adaptive behavioural analytics to detect anomalies in individual behaviour to spot new
fraud attacks as they occur. ARIC’s scalable models self-learn as fraud evolves, significantly reducing the
need for manual intervention in the detection and management of fraud.
In this report we show how field tests using real historical card-issuer transaction data reduce the
incidence of undetected fraud by 25%, but more importantly that the much better performance in
avoiding the rejection of good transactions, known as false positives, yields twice that value in cost of
fraud management and avoidance of lost revenue, which we refer to in Fig. 1 as fraud-related costs.
Fig. 1: Potential benefits of the Featurespace ARIC Fraud Hub to the card industry
source: Nilson, Oakhall estimates based on Featurespace ARIC Fraud Hub performance data
The cost of fraud and fraud management
Using industry data, we estimate the total cost of card fraud to credit and debit card issuers at
approximately $40.1bn (previously $31.0bn, an increase of 29.4%). $21.8bn (+33.9%) reflects costs
associated with incidents of card fraud; $18.3bn (+24.5%) are fraud-related costs associated with false
positives.
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Costs associated with incidents of card fraud - $21.8bn (+33.9%)
The Nilson Report recently estimated that global card fraud losses totalled approximately $21.8bn in
2015, +20.6% from an upwardly revised 2014 estimate of $18.1bn. When compared to total credit and
debit card transactions of $31.3tn (+8.5% y/y), this figure is equivalent to 7.0c for every $100 spent
on credit and debit cards (2014: 6.3c). The Nilson Report projects that worldwide card fraud losses will
reach $31.7bn by 2020 (CAGR of 7.7%). In the UK alone, banks saw 1.5m cases of attempted card fraud
in 2015, with 80% occurring in card not present transactions (CNP) such as internet, mail order or
telephone purchases.
Fig. 2: Card Not Present incidents dominate card fraud case volume in the UK
source: Financial Fraud Action UK, 2016
Costs associated with genuine transactions declined - $18.3bn (+24.5%)
Using industry data, we estimate the industry’s annual card fraud losses are about equal to the
combined costs associated with legitimate transactions that have been incorrectly blocked by
existing fraud prevention systems. Such incidents are referred to as genuine transactions declined or
false positives.
False positives can occur when a customer attempts a transaction that falls outside certain parameters
that the bank considers normal. A transaction may be flagged as an anomaly if, for example, it occurs
at an unusual time, or is an uncommon size; or perhaps the location or the nature of the merchant is
considered questionable.
Based on historical customer data provided by several global retail banks, Featurespace estimates that
incumbent fraud detection and management systems prevent 10 legitimate transactions for
every fraudulent purchase identified. In some instances, this figure can be much higher (20+ legitimate
purchases stopped per fraudulent transaction identified), particularly where cards are used for online
payments (CNP transactions).
When a genuine transaction is rejected by a bank’s fraud management system, the response from the
bank’s customer is typically twofold:
o The customer uses an alternative method of payment – often provided by a rival card issuer –
to complete the transaction.
o The customer calls the card issuer to resolve the issue, and/or files a complaint.
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In this way, each false positive is potentially both a lost revenue opportunity and an additional cost to
the card issuer.
Loss of market share - $6.0bn revenue lost to competitors
Following a false positive event, Featurespace estimates that spending on the affected card typically
falls between 4% and 10% as customers switch to alternative methods to pay merchants.ii As a result,
the issuing bank suffers a loss of income from card fees it would otherwise have earned if the card was
operational. Applied to the Nilson Report’s global card spend data this equates to $6.0bn of fee revenue
(previously $4.0bn, with the increase driven primarily by higher numbers of genuine transactions
declined due to higher incidence of fraud and overall growth in card transaction volumes).
Fraud management industry cost - $12.2bn
Banks employ teams of analysts who study fraud events and implement rules that enable the card issuer
to block potentially fraudulent transactions. The level of staffing has to match potential spikes in calls
around fraud or false positive events, as illustrated below:
Fig. 3: Fraud losses and call centre activity
source: Oakhall
Third party fraud detection providers may share data to enable their customers to benefit from fraud
detection across the network; it still takes time, however, for new incidents of fraud to be factored into
the rules of these systems.
Based on industry data, we estimate the cost today of managing fraud is approximately $12.2bn each
year (previously $10.7bn, with the increase driven by higher total card transaction volumes and
incidence of fraud and good transactions declined).
Quantifying the benefits of the Featurespace ARIC
Fraud Hub
Reduced fraud costs
In a proof-of-concept trial using historical customer data from a multinational retail bank, the
Featurespace ARIC Fraud Hub reduced undetected incidents of fraud by approximately 25% compared
to the bank’s existing fraud management system.
Card fraud losses totalled nearly $22bn globally in 2015. Had card issuers employed the Featurespace
ARIC Fraud Hub, this would imply a reduction in undetected fraud of $5.5bn.
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Internal cost savings through reduced false positives
ARIC also provides a unique technical approach to false positives. When anomalous changes occur,
instead of assuming the customer is ‘guilty until proven innocent’, the ARIC platform understands the
wider context of positive and negative behaviours. It is therefore able to accept more genuine
transactions, minimising inconvenience to legitimate customers.
Reduced card fee income losses due to false positive incidents
A leading credit card issuer challenged Featurespace to compare the ARIC Fraud Hub to its existing
fraud management system by using real historical customer data. In this proof-of-concept trial, the ARIC
engine lowered the false positive ratio from ten false positives per fraud, to three, a reduction in
false positive incidents of over 70%.
We estimate that card fee income losses, due to false positive incidents, cost card issuers $6.0bn
annually. A reduction in false positive incidents of 70% would therefore save the industry approximately
$4.2bn per year.
Internal cost savings due to reduced false positive incidents
ARIC allows card issuers to improve operational efficiencies. ARIC is self-learning so the models do not
degrade, which means card issuers can cut manual labour costs by reducing manual review tasks. The
platform is currently trialling in the financial sector; in the gaming sector, however, Featurespace’s clients
have already seen a reduction in analyst headcount of up to 50%.
We estimate that fraud-related call centres cost card issuers $12.2bn annually. Assuming a 50% cost
saving would imply global cost reductions for card issuers of $6.1bn.
Featurespace has trialled its ARIC Fraud Hub with historical customer data provided by companies in
the gaming and financial sectors and is being used in a live environment in the gaming sector with
Betfair and other customers. Across both sectors, the ARIC Fraud Hub has performed similarly well,
generating consistent results: 70% reduction in false positives and 50% reduction in call centre
costs.
Reducing customer and regulator friction
False positives increase reputational and regulatory risk
Banking regulators maintain a close watch on the level of complaints received by banks and especially
those which are escalated to the regulators own ombudsman services. In the UK, the Financial Conduct
Authority collects complaints data from the firms it regulates. The most recent data for banking and
credit card products shows there were 634,000 complaints in the first half of 2016, each of which will
have taken time to resolve.
High levels of complaints can be a significant drain on management time and may cause issues with
regulators. While the absolute cost is hard to estimate, keeping the number of fraud and false positive
events under control will help reduce potential complaints and strengthen relations with regulators.
Card fee income is under pressure due to changes in regulation
Another issue here is pressure on card fee rates resulting from regulatory caps (e.g. the EU caps on
interchange fees for consumer debit and credit card transactions of 0.2% and 0.3%). While this may
lower the overall cost of false positives, we would expect it to increase the emphasis on reducing
customer friction and market share gains within banks. In addition, the cost of fraud-related call centres
as a percentage of card fee income will increase leading to greater emphasis on cost control.
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Appendices
Appendix I: Calculating lost card fee income due to false positive incidents
iii
iii
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Appendix II: Calculating call centre costs due to false positives
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Published by
Oakhall Ltd
33 Cannon St
London
EC4M 5ST
London, UK based Oakhall was established to provide smart financial analysis and articulation to private
and public companies and has particular experience in the FinTech sector. Oakhall worked with
Featurespace to verify and analyse the savings that Adaptive Behavioural Analytics could achieve in the
card payments sector.
Disclaimers and disclosures
Copyright 2016 Oakhall Ltd. All rights reserved. This report has been commissioned by Featurespace Ltd and prepared and issued
by Oakhall Ltd. Oakhall Ltd will not accept any liability to any third party who for any reason or by any means obtains access or
otherwise relies on this report. Oakhall Ltd has itself relied on information provided to it by third parties or which is publicly
available in preparing this report. While Oakhall Ltd has used reasonable care and skill in preparing this report, Oakhall Ltd does
not guarantee the completeness or accuracy of the information contained in it and the report solely reflects the opinions of
Oakhall Ltd.
The information provided by Oakhall Ltd should not be regarded as an offer to buy or sell securities and should not be regarded
as an offer or solicitation to conduct investment business as defined by The Financial Services and Markets Act 2000 (“the Act”)
nor does it constitute a recommendation. Opinions expressed do not constitute investment advice. Any information on the past
performance of an investment is not necessarily a guide to future performance. Oakhall Ltd operates outside the scope of any
regulated activities defined by the Act. If you require investment advice, we recommend that you contact an independent adviser
who is authorised by the Act to conduct such services. Oakhall Ltd does not have any direct investments in any companies
contained in the report.
i Featurespace’s ARIC Fraud Hub has been demonstrated to detect up to 40% of transaction fraud. To do this, however,
inevitably requires an increase in the number of false positive alerts. Detecting 25% of transaction fraud optimises the ARIC
Fraud Hub platform to simultaneously detect 70% of false positive incidents, and therefore these are the numbers we use in this
report.
ii Featurespace estimates that when a card is used in a fraudulent transaction, spending on that card typically falls between 4%
and 10% as customers switch to alternative methods to pay merchants. This range is based on anonymised data from
Featurespace clients.
iii We have used fraud data from the UK to build our fraud rate assumptions. This is because the UK has demonstrated
comparatively lower rates of fraud, ensuring our assumptions to remain conservative.