Banque de France feedback on fraud prevention for EEA payments Open Banking & Instant Payments Summit - 24-25-26|MARCH|2021 OB & IP Forum 2021 ...
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Banque de France feedback on fraud prevention for
EEA payments
Open Banking & Instant Payments Summit
24-25-26|MARCH|2021
OB & IP Forum 2021 www.kinfos.events/ob-ips/
Context
Starting point
The April 2016 Bangladesh CB fraud has leaded BDF to launch new services to secure customer transactions against fraud.
To cover all the scope of payments, set of 3 services :
Ø CORE Flows(1): Banque de France has subscribed to the SCFP service (SEPA Core Fraud Prevention) provided by
STET and based on Artificial Intelligence
Ø SWIFT Flows(2): Based on the SWIFT Solution (Payment Control Services) coupled with SAA Rules (White lists,
Thresholds)
Ø STEP2 Flows(3): No fraud detection system provided by EBA Clearing => Need to develop our own solution,
referred as OASIS (Current proof of concept and project coming soon; most challenging issues to deal with
were :
1. Real time/ near real time analysis of payments
2. Focus business analysis on most risky payments
3. Strengthen fraud detection with Artificial Intelligence
(1) CORE (Compensation REtail) is an automated clearing house, managed by STET. CORE processes the clearing of retail payments (transfers, withdrawals, card
transactions,…) between banks located in France. It also allows to exchange SEPA transfers in France and Europe
(3) SWIFT (Society for Worldwide Interbank Financial Telecommunication) provides a network that enables financial institutions worldwide to send and receive
information about financial transactions
(3) STEP2 is a Pan-European Automated Clearing House, provided by EBA Clearing
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March 25 2021 Banque de France – Fight against Fraud 2
Scoring tool description
Technical informations
o Based on Python (GUI and Back End)
o Daily extracts of all cross borders STEP2 or neobanks transactions + data injections into OASIS
o Risk score computation per transaction
o Alerting through GUI for user analysis
Transactions scoring
o Each transaction is processed and scored according to 2 statistical indicators:
Ø Recency – half life: has this type of transfer ever occured in the past?
Ø Normal distribution: is the amount usual? Is the daily number of transactions usual?
Data Scientists have tuned the detection pattern according to real fraud attempts Banque de
France coped with
o Then each score is multiplied by the amount of the transaction(s) in order to enlighten the most
unsafe ones (Risk + Amount)
o To avoid transactions slicing, all daily transactions between an ordering IBAN and a beneficiary are
aggregated, and scored as one
Business analysis
o End users can access the resulting scores from a GUI; this scoring scheme helps them to decide
whether a deeper analysis is need or not
o In some cases, account managers have to contact their customer to make sure transfer is clean
o Depending on the outcome of the analysis, it is possible to include the beneficiary IBAN in a whitelist
or blacklist
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March 25 2021 Banque de France – Fight against Fraud 3
Artificial Intelligency
Kohonen maps
Set of 9 variables, representatives of the beneficiary accounts
§ Benficiary accounts with same features are located in the boxes of a
100x100 sacalable grid
§ The brighter the squares are, the more fraudulous acconts they contain
§ Nearly all suspicious accounts are concentrated in a few set of squares
§ An account located in one of these boxes has a higher probability to be
risky
§ Payments located in the high risk boxes of the Kohonen maps are
identified with a special icon on the GUI => Further information for the
user
OB & IP Forum 2021 www.kinfos.events/ob-ips/
March 25 2021 Banque de France – Fight against Fraud 4
Results
Priority to the highest amounts
Cross border payments
§ The system tuning provides good results: unsafe transfers are given the
highest risk scores. As consequence, by focusing only on few transactions,
account manager can efficiently identify fraudulous ones
§ 60% of the fraudulous transactions are detected; but they represent nearly all
the volume of fraudulous funds
Neobanks payments
§ Compare to cross border payments, the tool is not as efficient for settlements
sent to neobanks accounts
§ 2 reasons:
1. The beneficiary country an not be used as a risk pattern
2. Amounts are much flatter and normal distributions are less efficient
Results
§ More than 3 M€ of fraud detection
§ Many pending trials to get back the funds
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March 25 2021 Banque de France – Fight against Fraud 5
Step forward
Real time scoring
§ Scoring transactions occurs the day when the transactions are sent to Clearing and Settlement Mechanisms (EBA Clearing)
§ Time range to stop fraudulous settlements is very short
§ Feeding the fraud detection engine as soon as transactions reach the Banque de France system is a priority
Operating other AI systems
§ Kohonen map is not the only AI process adapted to this kind of analysis
§ Fraudulous settlements detection process can be enhanced by using other AI mechanisms
§ Identifying a fraudulous payment is not a binary decision… the tool can only give a risk score that can be the result of clues provided
by various patterns
Random Forest XG Boost
Based on previous fraud attempts, build of a Slincing settlement space to segregate
decision tree previous fraudulous payments. If a new
A nex settlement is analysed regarding to this payment is located in a bad area, it is
tree supposed to be fraudulous also
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March 25 2021 Réunion de Direction DSB 6
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