Identity Verification using Amazon Rekognition

Verify user identity online using machine learning.

In-person user identity verification is slow to scale, costly, and high friction for users. Machine learning powered facial biometrics can enable online user identity verification. Amazon Rekognition offers pre-trained facial recognition and analysis capabilities that you can quickly add to your user onboarding and authentication workflows to verify opted-in users' identity online. No machine learning expertise is required. With Amazon Rekognition, you can onboard and authenticate users in seconds while detecting fraudulent or duplicate accounts. As a result, you can grow users faster, reduce fraud, and lower user verification costs.

What's Amazon Rekognition Identity Verification (1:22)

Benefits

Grow users faster

Convert more visitors into customers by decreasing onboarding time and increasing user convenience. With Amazon Rekognition, you can verify users in seconds online anywhere in the world, and scale from hundreds to millions of identity verifications per hour. Users can now access your services online without having to visit in-person.

Reduce fraud

Strengthen your fraud prevention capabilities by complementing passwords-based authentication with online visual identity verification. Guard against fraudulent account openings or transactions by comparing user’s selfie picture with identity document picture or your collection of existing users’ pictures.

Lower costs and overheads

Reduce the time and cost of in-person identity verification by using Amazon Rekognition pre-trained and customizable APIs. With Amazon Rekognition, you can onboard and authenticate users online without building and managing your own ML infrastructure.

How it works

How Redshift data sharing works

Features

Validate selfie picture

Amazon Rekognition Face Detection helps you detect that the user’s selfie picture is captured correctly. You can detect if a face is present in the picture. You can also use predicted attributes such as bounding box size, pose, brightness, sharpness, eyes open, mouth open, and eyeglasses worn to determine picture quality.

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Compare selfie picture with user ID

Amazon Rekognition Face Comparison helps you measure the similarity of two faces to help you determine if they are the same person. You can receive a similarity score prediction for a user’s selfie picture against their identity document picture in near real time.

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Detect duplicate users

Amazon Rekognition Face Index and Search enables you to create a face collection of existing users and search new user selfie picture against all faces in your collection to detect duplicate or fraudulent account creation attempts.

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Classify ID document

Amazon Rekognition Object Detection helps you determine the type of user identity document such as driver’s license or passport. You can also use Amazon Rekognition Custom Labels to detect an identity document type unique to your region by training a custom machine learning model with a few annotated images.

Extract user data

Amazon Rekognition Text Detection helps you extract key pieces of text on an identification card, such as name, date of issue, age, and identification number. You can compare this information with the user application form data.

Customers

Aella Credit provides instant loans to individuals with a verifiable source of income in emerging markets using biometric, employer, and mobile phone data.

“Identity verification and validation have been a major challenge in emerging markets. The ability to properly identify users is a key hindrance in building credit for billions of people in emerging markets. Using Amazon Rekognition for identity verification on our mobile application has reduced verification errors significantly and given us the ability to scale. We can now detect and verify an individual’s identity in real time without any human intervention, thereby allowing faster access to our products. We tried various well-advertised solutions, but none of the popular alternatives could accurately map out various skin tones. Amazon Rekognition helped us effectively recognize faces of our customers in our markets. It also helped us with KYC in discovering overlapping profiles and duplicate datasets."

Wale Akanbi, CTO & Co-Founder - Aella Credit

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Banco de Bogotá

With 150 years of experience in operation, Banco de Bogotá is part of the Aval Group and ranks second in the Colombian banking system for its assets, a position that places it as a major player in the country.

"With AWS, we have become the first bank in Colombia to launch loan and credit products, which can be purchased in less than 5 minutes from our digital channels."

Gabriel Morris, Technology Leader, Directorate of Digital Strategy and Data - Bank of Bogotá

Banco Inter

Banco Inter SA

Banco Inter SA offers complete services in banking, investments, credit and insurance, in addition to having a mall that brings together the best retailers in Brazil. With 11 million customers, the company has an expanded credit portfolio of R$9.4 billion, shareholders' equity of R$3.3 billion and R$19.8 billion in total assets.

"Three years ago, we opened 200 accounts a day. Today there are 29,000 accounts opened daily and, without Amazon Rekognition, we would not have the agility to do this.”

Bruno Picchioni, Machine Learning Engineer - Banco Inter

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CreditVidya

CreditVidya is a startup headquartered in India whose underwriting technology is opening the country’s loans market to over 250 million financially excluded citizens. CreditVidya plans to go live soon with Amazon Rekognition, which adds facial recognition to applications.

"We plan to use Amazon Rekognition to complete our electronic “know your customer” processes. We will compare users’ uploaded identity cards and selfies to ensure that applicants are uploading their own identity cards.”

Srikanth Gaddam, VP of IT & Security - CreditVidya

Carbon is a digital financial service platform powered by OneFi that provides services to underbanked individuals in West Africa through an Android mobile app, which has over 900,000 downloads.

"In May 2016, Carbon launched its mobile app for its loan application process. With the mobile app, images are constantly being generated and consumed at faster rates than before. Carbon needed to meet its growing need for image analysis for fraud detection and risk analysis. We wanted to be able to identify if a human face was truly detected in an uploaded image and identify other labels such as gender and identity. We chose Amazon Rekognition because of its ease in adding image analysis to our mobile app and the accuracy of its facial analysis."

Olawale Olaleye, Head of IT Infrastructure Engineering - OneFi

K-STAR Group is an entertainment company that provides concert ticketing and payment services. 

“As an entertainment company, we provide concert ticketing and payment services for our customers. A recurring pain point at concerts is the long line that attendees wait in to provide proof of their purchased paper ticket and then have it validated at the entrance. To solve this issue, we developed a ‘Face Ticket’ service using Amazon Rekognition. Now, attendees can quickly verify their purchase rather than waiting in line to get tickets or scan their paper ticket upon entrance. The concerts we support no longer have lines and the attendees have enjoyed the convenience and fun experience of using our new ‘Face Ticket’ system. When we were developing this service we compared Rekognition with other local facial analysis services, and we ultimately decided to use Rekognition due to its scalability with S3 and the seamless integration with other AWS services.”

Hyojin Kim, Chairman - K-STAR Group

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