Prashanthi
Matam
Ex- Senior Software/MLops Engineer at Discover Financial Services
"AI for Financial Security: Smart Fraud Detection and Credit Loss Reduction"
"AI for Financial Security: Smart Fraud Detection and Credit Loss Reduction"
Wed May 21 - 1:30 PM EDT/New York (See in local time)
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05/21/2025 1:30 PM
05/21/2025 01:50 PM
America/New_York
#WTGC2025
"AI for Financial Security: Smart Fraud Detection and Credit Loss Reduction"
#WTGC2025
"AI for Financial Security: Smart Fraud Detection and Credit Loss Reduction"
https://www.womentech.net/ringcentral
https://www.womentech.net/ringcentral
"AI for Financial Security: Smart Fraud Detection and Credit Loss Reduction"
"AI for Financial Security: Smart Fraud Detection and Credit Loss Reduction"
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Session: AI for Financial Security: Smart Fraud Detection and Credit Loss Reduction
This talk explores how Artificial Intelligence (AI) can be leveraged to intelligently detect and prevent both third-party and first-party credit card fraud with greater accuracy and speed. Using real-world datasets such as Stripe Radar's risk score predictions for payments and fraud probability data from Card Transaction Fraud records, we demonstrate how AI models can be trained to identify suspicious patterns, flag high-risk transactions, and adapt to evolving fraud tactics.Our approach combines supervised learning with advanced feature engineering to enhance fraud detection systems. We will also highlight the unique challenges in distinguishing between third-party fraud (unauthorized use of a card) and first-party fraud (intentional chargebacks or misuse by the legitimate cardholder), and how AI can be tuned to address both. The session concludes with insights into model evaluation metrics, deployment considerations, and ethical concerns surrounding AI in financial security.
Attendees will gain a deeper understanding of how data-driven models can transform fraud prevention systems, improving accuracy while reducing false positives and manual review overhead.
Key Takeaways
- AI Enhances Fraud Detection Accuracy
- Real-World Data Drives Results
- Supervised Learning + Feature Engineering is Key
- Understanding Fraud Types is Crucial
- Ethics and Deployment Matter
Bio
I have about 7 years of experience as a Senior Software Engineer and MLOps Lead, having worked at Goldman Sachs, Discover, Capgemini, and Enel X. Throughout my career, I’ve had the opportunity to lead and contribute to impactful projects involving machine learning models, artificial intelligence, cloud infrastructure (AWS), and modern programming frameworks. I’ve worked extensively with technologies such as JavaScript, Java, Python, React JS, and Node JS, and have also gained proficiency in C++.I hold a Master’s degree in Computer Systems Engineering from Northeastern University, Boston, where I strengthened my academic foundation in system design and scalable architectures. In addition to my technical roles, I’ve actively embraced leadership and advocacy opportunities—serving as Vice-Chairman of the ACM (a globally recognized computing organization) and as a Global Ambassador for Red Hat and Mozilla Firefox. These roles allowed me to foster community engagement, mentor peers, and develop strong organizational and communication skills on an international platform.
