Paycap, a payment system

Zsolt MolnarZsolt Molnar | | Freelance
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As a developer (contractor) in the dynamic world of blockchain technology, I had the incredible opportunity to participate in the development of the Ionic frontend for Paycap, a cutting-edge blockchain-based payment system. Paycap aimed to revolutionize the payment landscape by simplifying transactions and enhancing security through the power of blockchain.

Before diving into the details of Paycap, I must mention my collaboration with SmartX Solutions, one of the best Hungarian consulting companies in fintech solutions. As a subcontractor of SmartX, I had the privilege of being part of a highly skilled team that consistently delivered excellence. SmartX Solutions' commitment to innovation and client satisfaction has earned them a stellar reputation in the market.

The Technology Stack

Paycap leveraged a powerful and efficient technology stack to achieve its objectives. We utilized AWS services on the backend to ensure seamless scalability and top-notch performance. Some of the key AWS services we employed included:

AWS Lambda: AWS Lambda allowed us to execute code in response to various events, ensuring the backend remains lightweight and scalable.

DynamoDB: We employed Amazon DynamoDB, a fully managed NoSQL database, to securely store and manage vast amounts of transactional data.

Node.js: Node.js served as the backend runtime environment, providing an efficient and event-driven architecture for the Paycap system.

Ionic Frontend for a User-friendly Experience: On the frontend, we used the Ionic framework, a popular open-source toolset for building cross-platform mobile and web applications. Ionic allowed us to create a seamless and intuitive user interface for Paycap, ensuring that users can effortlessly manage their payments and transactions.

Simplifying Payments with Blockchain Technology

Paycap's main goal was to simplify payments and enhance security by adopting blockchain technology. By leveraging the immutable nature of blockchain, Paycap ensured that all transactions were transparent, traceable, and tamper-proof. Users could experience faster, cheaper, and more secure payments, eliminating the need for traditional intermediaries and reducing the risk of fraud.

Adding AWS Recognition for Automated KYC

To simplify payments and enhance security, Paycap also fully automated the Know Your Customer (KYC) process using Amazon Rekognition, a fully managed AI service provided by AWS. This integration with Amazon Rekognition enabled us to streamline the identity verification process, reducing user friction and ensuring a seamless onboarding experience.

Why Amazon Rekognition Identity Verification?

Traditional in-person user identity verification processes can be slow, costly, and cumbersome for businesses and users. However, by leveraging machine learning-powered facial recognition capabilities offered by Amazon Rekognition, Paycap was able to transform the KYC process into an efficient and automated workflow. The benefits of using Amazon Rekognition for identity verification in the Paycap platform included:

Quick Onboarding: With Amazon Rekognition, we can verify user identities online within seconds, allowing rapid user onboarding. This expedited process could help Paycap to convert more visitors into customers, fostering growth and user acquisition.

Fraud Reduction: Online visual identity verification complemented password-based authentication, enhancing fraud prevention capabilities. By comparing a user's selfie picture with their identity document picture or existing user images, Paycap could detect and prevent fraudulent or duplicate account creation attempts.

Cost-Effectiveness: Leveraging Amazon Rekognition's pre-trained and customizable APIs reduced the time and cost of in-person identity verification. This allowed Paycap to focus on providing a seamless user experience without the need to build and manage complex ML infrastructure.

How It Works: Key Features of Amazon Rekognition Identity Verification

Face Liveness Detection: Amazon Rekognition Face Liveness helps verify that only real users, not bad actors using spoofs, can access Paycap's services. This feature detects various spoofing attempts, such as printed photos, digital photos, digital videos, 3D masks, or pre-recorded/deepfake videos.

Selfie Picture Validation: Amazon Rekognition Face Detection was used to validate that the user's selfie picture was captured correctly. It detected the presence of a face in the picture. It assessed attributes like bounding box size, pose, brightness, sharpness, eyes open, mouth open, and eyeglasses worn to ensure picture quality.

Face Comparison: Amazon Rekognition Face Comparison helped measure the similarity between a user's selfie picture and identity document pictures in near real-time. This allowed Paycap to verify the authenticity of the user's identity.

Detecting Duplicate Users: Amazon Rekognition Face Index and Search allowed Paycap to create a face collection of existing users and search for new user selfie pictures against all faces in the collection. This process efficiently detected duplicate or fraudulent account creation attempts.

Enhancing User Trust and Safety

By implementing Amazon Rekognition's robust identity verification capabilities, Paycap was able to mitigate fraudulent attacks and provide a secure onboarding process for legitimate customers. As a result, users could gain trust in the platform, experiencing increased customer satisfaction and loyalty.

The collaboration with Amazon Rekognition perfectly complemented Paycap's blockchain-based payment system, making it a powerful and secure solution for seamless payments, reduced fraud, and lower user verification costs.

Working as a developer on the Paycap project was an enlightening experience. The collaborative efforts of SmartX Solutions, Paycap, and the development team resulted in a convincing prototype app. I hope that Paycap's journey toward revolutionizing the payment landscape continues, and I am proud to have been a part of this innovative endeavor.

  • ionic
  • angular
  • AWS
  • typescript
  • NodeJS
  • next.js
  • github