[09-17]idv identity verification-bank apply customer due diligence

Shifting shape of banking biometrics

Date:2021-09-17

cdd process

Author:client onboarding process kyc    pillars of aml program

Keywords:account takeover scenarios,    red flag aml,    kyc compliance meaning,    idv verification,    anti money laundering checklist,  trulioo pass

Description:

Method and apparatus for verifying financial account information
Minority Report CommercialsBiometrics: The process of using acharacteristic of the body to determine anindividuals identityDifferent types of biometricsFingerprintEye ScanFace or HandprintVoice Pattern RecognitionFingerprint:Person places finger on scannerImage is taken as templateTemplate is stored as series of numbersNumbers are recognized as system*Most commonly usedEye Scan:Low intensity light source scans the retinaSearches for unique patternsFace and Handprint:Scan face or hand with measuring technologyMeasuring distances between key features that cantbe changedVoice pattern recognitionUses the unique characteristics of a person's voiceThe rhythm of their vocal chords and the shape oftheir mouth to create a "voice print"BIOMETRICS IN BUSINESSTYPES THAT HAVE A BUSINESS APPLICATION:SIGNATUREKEYSTROKE DYNAMICS ( HOW A PERSON TYPES)HAND GEOMETRYFINGERPRINTAND MANY OTHERS BIOMETRICS IN BUSINESSMOST CURRENT USES IN BUSINESSARE FOR SECURITY PURPOSES ANDFOR PAY-BY-TOUCHBIOMETRICS IN BUSINESSWHAT BIOMETRICS CAN SECURE :DOCUMENTSDEPARTMENTSCONFIDENTIAL DATACLASSIFIED INFORMATIONCUSTOMER RECORDSAND MUCH MOREBIOMETRICS IN BUSINESSGrocery stores and othersuse a Pay-by-Touch systemFingerprint linked to a debit orchecking accountFunds transferred electronicallyNo need to carry your debit cardBIOMETRICS IN BUSINESSComparative Biometrics Market Share: 2001BIOMETRICS IN BUSINESSTotal Biometric Revenues: 1999C2005BIOMETRICS IN BUSINESSTHE FUTURE OF BIOMETRICS IN BUSINESS :SOME COMPANIES AND ORGANIZATIONS LOOKINGINTO IMPLEMENTING BIOMETRICS :LewishamPrimary Care Trust wants to install fingerprint scanning technologyto protect patient recordsThe Government has proposed a national ID card that incorporatesBiometricsSupermarket chain CO-OP is in the process of using a fingerprint identificationsystem to pay at the registerBIOMETRICS IN BUSINESSTHE FUTURE OF BIOMETRICS IN BUSINESS :"When the technology does start to filter into the private sector,banks and financial services organizations are likely to be earlyadopters. Financial security will be at the forefront, but I thinkpilots and trials will start to crop up all over the place."-Nick KalisperasDirector of MarketsTrade Group IntellectBIOMETRICS IN BUSINESSVIDEO ON BIOMETRICS :BIOMETRIC TECHNOLOGYSOURCES USEDhttp://www.eff.org/wp/biometrics-whos-watching-youSLIDES 9-12SLIDES 13-14http://www.findbiometrics.com/Pages/feature%20articles/anatomy.htmlSLIDES 15-16http://www.computing.co.uk/computing/analysis/2162073/businesses-cool-biometricsSlide 3http://www.emory.edu/BUSINESS/et/biometric/Index.htmSlide 4-8http://www.identitytheft911.org/articles/article.ext?sp=71
Liveness detection is a fundamental element for all biometric systems that have to be safe against spoofing attacks at sensor level. In particular, for an attacker it is relatively easy to build a fake replica of a legitimate finger and apply it directly to the sensor, thereby fooling the system by declaring its corresponding identity. In order to ensure that the declared identity is genuine and it corresponds to the individual present at the time of capture, the task is usually formulated as a binary classification problem, where a classifier is trained to detect whether the fingerprint at hand is real or an artificial replica. In this chapter, unlike the binary classification model, a metric learning approach based on triplet convolutional networks is proposed. A representation of the fingerprint images is generated, where the distance between feature points reflects how much the real fingerprints are dissimilar from the ones generated artificially. A variant of the triplet objective function is employed, that considers patches taken from two real fingerprint and a replica (or two replicas and a real fingerprint), and gives a high penalty if the distance between the matching couple is greater than the mismatched one. Given a test fingerprint image, its liveness is established by matching its patches against a set of reference genuine and fake patches taken from the training set. The use of small networks along with a patch-based representation allows the system to perform the acquisitions in real time and provide state-of-the-art performances. Experiments are presented on several benchmark datasets for liveness detection including different sensors and fake fingerprint materials. The approach is validated on the cross-sensor and cross-material scenarios, to understand how well it generalizes to new acquisition devices, and how robust it is to new presentation attacks.

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