Event 5

Summary Report: “KICK-OFF VPBANK TECHNOLOGY HACKATHON 2025”

Event Objectives

  • Officially launch the VPBank Technology Hackathon 2025 with technical sponsorship from AWS
  • Provide contestants with essential foundational knowledge on Cloud, AI/ML, and Data Engineering
  • Equip participants with AWS best practices for account management, security, and cost optimization
  • Offer early guidance on key competition topics such as Fraud Detection, Generative AI, and ML model development
  • Connect contestants with mentors, organizers, and AWS experts prior to the main hackathon

Speakers

  • VPBank CIO & CHRO – Opening & orientation
  • AWS Experts – Cloud Foundations, AI Services, SageMaker practice
  • Hackathon Mentors – Panel discussion & challenge orientation
  • Tung Cao – AWS Speaker for AI Services Workshop

Key Highlights

Hackathon Kick-off & Challenge Orientation

As a contestant, this workshop served as the official technical warm-up before entering the main competition. The organizers provided an overview of the hackathon’s objectives, judging criteria, and expectations — especially focusing on innovation, security, and real-world applicability in the banking domain.

A significant emphasis was placed on:

  • AI/ML applications in banking
  • Fraud Detection & anomaly detection models
  • Data pipelines and real-time processing
  • Responsible AI & security considerations for financial use cases

The challenge briefing helped define problem boundaries and aligned contestants on the difficulty and technical depth expected.

AWS Cloud Foundations Workshop

This hands-on workshop delivered essential cloud practices for contestants to prepare their environment correctly before building solutions.

The AWS team covered:

  • Overview of AWS global infrastructure
  • Best practices for AWS account setup, IAM permission boundaries, and secure access
  • Security fundamentals applicable to financial systems
  • Cost management strategies to avoid unnecessary usage during the hackathon
  • Common architectural patterns recommended for rapid prototyping

The Q&A session was particularly useful for contestants who needed clarification on resource quotas, dataset access, and service limitations.

AI Services & SageMaker Workshop

The second workshop focused on AWS AI services and practical demonstrations using SageMaker — an important toolset for teams working on ML models or fraud detection.

Topics included:

  • Introduction to AWS AI services (Amazon Rekognition, Comprehend, Textract, Bedrock, etc.)
  • Practical ML workflow with AWS SageMaker
  • Model training, evaluation, deployment, and cost-optimized experimentation
  • Q&A and troubleshooting guidance

This session provided a clear roadmap for teams intending to utilize generative AI or machine learning as a core element of their hackathon solution.

Event Agenda Overview

2:00 PM – 2:30 PM | Guest Check-in 2:30 PM – 2:35 PM | Welcoming speech by VPBank CIO & CHRO 2:35 PM – 2:40 PM | Hackathon Journey 2:40 PM – 3:10 PM | Challenge Briefing 3:10 PM – 4:10 PM | Panel Discussion with Mentors 4:10 PM – 4:25 PM | Tea Break 4:25 PM – 6:00 PM | Workshop: AWS Cloud Foundations

  • AWS overview introduction
  • Account preparation & security best practices
  • AWS cost management best practices
  • Q&A 6:00 PM – 6:15 PM | Closing

Key Takeaways

Hackathon Strategy & Technical Direction

  • Banking-related use cases require high security, data reliability, and explainability

  • Fraud Detection systems benefit from ML models such as:

    • Anomaly detection
    • Graph-based detection
    • Behavioral profiling

  • AWS offers many managed services suitable for rapid development under time pressure (SageMaker, Lambda, S3, DynamoDB, API Gateway)

Cloud & Security Best Practices

  • Proper IAM configuration and MFA are mandatory for safe development
  • Using budgets and cost alerts prevents overspending during experimentation
  • Competitors should follow the principle of least privilege when creating roles or service accounts

Using AWS AI Tools Efficiently

  • SageMaker accelerates model training and experimentation with built-in algorithms
  • AI services can speed up prototyping by providing pre-trained capabilities
  • Combining Bedrock (GenAI) with ML detection models can create more powerful hybrid solutions

Applying to Work

  • Prepare AWS account early and configure IAM roles properly
  • Use S3 for dataset storage and ensure versioning where possible
  • Train baseline models quickly in SageMaker before optimizing
  • Implement CI/CD for model iteration if time allows
  • Document architecture choices to support hackathon presentation
  • Apply fraud detection best practices such as feature engineering and real-time inference patterns

Event Experience

As a contestant, attending the Kick-off Workshop gave me a clearer view of the hackathon’s direction and expectations. The technical content from AWS helped me understand how to approach the challenge with both security and efficiency in mind.

What I Learned from the Event

  • How to prepare and manage an AWS environment effectively during a high-pressure competition
  • The importance of secure and scalable architecture when building financial applications
  • How AI/ML workflows can be constructed efficiently using SageMaker
  • Key considerations when designing fraud detection solutions

Networking & Community

  • Connected with VPBank mentors and organizers
  • Met other contestants and formed early collaboration channels
  • Had valuable discussions with AWS experts regarding ML workflows and fraud detection

The Kick-off Workshop not only launched the competition but also equipped me with the technical confidence needed to build a strong, secure, and innovative solution for VPBank Technology Hackathon 2025.