International Carriers combatting by-pass fraud with Orange expertise and services Pierre Paufique December 2014 Anti-Fraud Interconnect Roaming & Supervision of Transactions 2014
why we should fight by-pass fraud by-pass fraud is a major threat, damaging operator revenues and margins by-pass fraud is among the top 5 threats to Operators and MVNOs worldwide (source : CFCA) 2013 Survey: what do you view as the top 5 fraud types GLOBALLY? 2 in 2011, the top 5 fraud types were: PBX/VM fraud, IRSF, subscription fraud, by-pass
direct financial impact costs the industry over USD 2 billion / year (1) telecom industry analysts size the loss for 5-15% of each operator's interconnect termination revenues (Orange experience: up to 50%) why we should fight by-pass fraud by-pass fraud impacts operator-revenues both directly, and through collateral effects normal call 3 indirect financial impact call quality highly affected (VoIP + cells overloaded) lack of origin CLI no call-back possibility inbound roamers are not reachable overall brand impact by-pass call (1) CFCA report 2013
the by-pass fraud market dynamic, market sensitive to many drivers the Orange IC experience 2 nd wholesaler + 1000 customers silver/ gold routing analysis @first customers and trials by-pass fraud is intense in several areas 4 and it is dynamic: strongly linked to market drivers regulation international vs. national interconnection rate control on SIM distribution + activation competition on retail market volume of international incoming traffic cooperation between operators
by-pass fraud market the market is evolving strongly in some areas, like West- and Central Africa by-pass fraud increased significantly in the past few months in Central-and West-Africa MITR is high in all this area ~20c countries along the coast and with high volume potential are the most infected national by-pass rate evolution 5
fighting against by-pass fraud Orange expertise and @first services are sized depending on fraud intensity by-pass fraud intensity low high by-pass detection solutions + measures call tracking solution (CTS) profiling solution TBS-D customer actions advantages quick to deploy fast detections off-net no false-positive (FP) exhaustive: all calls, all customers less sensible to fraudster detection more detections F&RA distribution marketing legal IT & N limitations sample method sensible to fraudster detection (B-numbers) offnet more difficult higher risk on FP Orange managed services: teams, skills 6
Orange @first/ TBS-P 2014 key figures and facts (1) detection of 315k national SIMBOX 170k on-net SB, 145k off-net 1 st external customers contracts with Groups revenue > 1M 1 st contract with IGW operator TBS-P revenue growth > 500% delivery of a webinterface ~25 customers > 300 probes 15 trials > 5 external customers new: trials and contracts in Asia and South-America 7 (1) forecast based on realized 17 th of October 2014
Orange @first/ TBS-D an advanced by-pass protection service based on adaptive profiling profiling most advanced technology expertise by-pass fraud patents data mining auto learning understanding fraudster behaviour real-time CDR integration, detection and alerts easy to use no need for customer expertise flexibility automatic or manual cut-off. VIP protection privacy protection deployed locally web interface TBS-D service management 8
adaptive profiling is key fraudsters behaviour is evolving FMS detection threshold-based rules fraudster IMEI forecasts lack of incoming calls incoming calls from the same numbers frequency of outgoing calls... fraudsters make efforts to avoid them spoofing the IMEI of SIM box device hosting equipment where calls can reach multiple cells sites simulating human behaviour accepting a few/ random incoming calls performing intra Simbox calls/ fake calls or SMS using a high volume of SIM, low volume of traffic using moving vehicles for broadband connections Orange TBS-D profiling service prevents fraudsters staying under the radar fed by other detection services auto learns fraudster behaviors and creates fraudster profiles based on +100 criteria 9
@first/ TBS-P + TBS-D an integrated suite for the highest quality end-to-end by-pass protection TBS-P or any other by-pass detection solution complementary services fraudster detection used as a sample to generate and update fraudster profiles SIM list datamining model update TBS-D CDR IN user profile update user ranking score filtering, VIP protection send results SIM list 10
Orange best practices ad-hoc analysis for optimisation of Simbox detection and deactivation impact of virtual numbers change on national Simbox detections B-numbers changed: 1 st part 17.09, 2 nd part 22.09 offnet by-pass hit rate during WD and WE Average: WD: 2.6 WE: 5.2 11
Orange best practices ad-hoc analysis on by-pass activity during Aïd el Fitr 1 selection was made of 4 TBS-P customers in Middle-East and North Africa region 3 conclusion analysis 2 Aïd el Fitr average IC volumes 1 by-pass infection rate and fraudulent SIMs detection clear impact of Aïd el Fitr on by-pass activity actions to anticipate with TBS support team when known traffic peak (CAN...): routes, smartphones, B-numbers 12 (1) aggregated volumes -25/7 volumes taken as reference
Orange best practices Orange experts to help customers to eradicate by-pass fraud number of fraudulent SIMs and usage extrapolated from practice break-even when the average number of minutes of usage (MoU) per fraudulent SIM decreases, fraudsters increase the number of SIMs below a MoU threshold depending on local context, fraudster BP is at break-even (eg. 15 min) 13
Orange best practices Orange experts to help customers to eradicate by-pass fraud Orange and the customer have to take the right actions to decrease the fraudulent SIMs MoU decrease detection time using and optimizing the right tools optimise the delay for SIMs barring/ blocking control SIMs distribution adapt retail marketing offers work with regulators fraudster business plan (BP) (1) 1 SIM acquisition cost w average recharged amount lost per SIM r price / min inter. entering mobile traffic x price/ min local mobile traffic y minutes of usage / fraudulent SIM number of fraudulent SIM Indirect costs (manpower, SIMBOX,...) d fraudster margin: N*[ z*( x y) w r ] -d Z N 14 (1) to be duplicated for on-net and off-net
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