Driving Trust and E-commerce Security Through AI Solutions

The company recognizes that in an increasingly digital environment, trust and security in transactions are key to ensuring a smooth and safe shopping experience. This understanding has led them to prioritize the implementation of advanced technological solutions to protect both the brand and its consumers.

About Carestino

Imagen de Carestino

Driving trust and e-commerce security through AI solutions

Carestino is a baby clothing brand that has established itself as a leader in the children’s apparel industry by focusing on the quality of its products and creating unique experiences for every family.

As the company has grown, it has expanded its reach across the Latin American market, with online sales becoming one of its primary distribution channels.

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The Challenge

Carestino’s expansion across Latin America brought a growing challenge: the rise of fraud in online transactions. This issue jeopardized both the company’s financial security and its customers’ trust in the platform. Carestino needed a robust solution to ensure transaction security, so they turned to DinoCloud for a technological response that would mitigate fraud risks while improving the security and reliability of its operations.

Our Approach

Stages, technologies, and services implemented

DinoCloud proposed an advanced AI-based solution to tackle these challenges.
The project involved implementing a fraud detection model using XGBoost on Amazon SageMaker, with a pipeline that included three stages:

  1. Feature Engineering: Conducted an exploratory data analysis (EDA) to identify fraud patterns, clean data, and select the most relevant features. Extracted and transformed features from an Amazon S3 bucket, utilizing tools like boto3 for efficient dataset management.
  2. Model Training: Trained the model using Amazon SageMaker with a script that fine-tuned the XGBoost algorithm to the prepared data. Evaluated the model using metrics such as precision, recall, and F1-score to ensure high effectiveness in fraud detection.
  3. Real-time Inference: Deployed the trained model using AWS Lambda functions, allowing real-time inference on new transactions. This enabled automatic classification of transactions as legitimate or fraudulent before processing.

Automated Infrastructure Deployment

To maintain the system efficiently, the infrastructure and serverless resources were deployed using:

  • AWS CDK: Automated the core infrastructure—including S3 buckets and security roles—with the AWS Cloud Development Kit (CDK), facilitating integration and scalability.
  • Serverless Framework: Implemented Lambda functions to ensure a swift and scalable response for real-time fraud data inference.

At DinoCloud, we specialize in transforming a company’s current infrastructure into a modern, scalable, high-performance, and cost-effective system designed to meet business objectives. If you’re interested in optimizing your data management, enhancing analytics, and reducing costs, feel free to contact us here.

The Results

The implementation of these solutions had an immediate and positive impact on Carestino’s operations:

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