“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Building Deep Learning-Based Anti-Fraud Applications
Focuses on building anti-fraud applications using deep-learning techniques for financial and transaction-oriented scenarios.
Duration
8 hours
Level
Advanced
Format
Virtual, On-site, or Hybrid
Language
English
NVIDIA
Deep LearningBuilding Deep Learning-Based Anti-Fraud Applications
Deep Learning Frameworks
On this page
Ideal for
Audience Profile
Built for these roles
Built for teams building AI-led fraud-detection systems.
Overview
Executive overview
Official NVIDIA DLI workshop for anti-fraud application development using deep learning methods.
Readiness
Prerequisites
- Experience with Python and machine learning workflows.
Program Outcomes
Capabilities your teams will gain
Understand fraud-detection modeling patterns
Apply deep learning to anti-fraud use cases
Curriculum
Curriculum roadmap
Fraud signal modeling
Deep-learning anti-fraud workflows
1Module 1
Create anti-fraud AI solutions
+
Module 1
Create anti-fraud AI solutions
Explore deep-learning approaches for transaction risk and fraud detection.
- Fraud signal modeling
- Deep-learning anti-fraud workflows
Delivery Models
Delivery models
Engagement Fit
Engagement fit
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Strengthens fraud-detection capability using deep-learning approaches for higher-risk transaction scenarios.
- •Use your fraud or transaction-monitoring use case
- •Add deployment and alerting logic
Credentials
Certification & official source
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
