On-Demand Webinar

Understanding Fraud Prediction for Banking & Finance Sector through Iguazio

The global banking industry is becoming increasingly vulnerable under powerful, multi-faceted fraud attack. Organizations and banking institutions are losing nearly 5% of their yearly revenues to fraudulent activities, according to the Association of Certified Fraud Examiners (ACFE). And credit card fraud topped over $32 billion in 2020.

In this webinar, the Royal Cyber’s MLOps experts will join hands with the Iguazio team in a detailed Q&A session on how MLOps can solve the fraud prediction problems in the banking and financial space.

The webinar will focus on how banking and financial institutions can use cutting-edge machine learning and AI tools and technologies to accelerate their results, acquire creditworthiness, detect and prevent financial crimes, generate customer experience innovations, and answer the following questions.

Takeaways from the Q&A session!

  • How can ML help in detecting and preventing credit card fraud?
  • How do machine learning operations help improve traditional ML practices?
  • What are model options available to detect fraud in banking?
  • What are the different evaluation practices to incorporate value in your model?
  • What are the essential features for fraud prediction in Iguazio?
  • How class imbalance affects model performance?
  • Is the accuracy metric enough for case evaluation?
  • Which other evaluation metrics should you be looking for?
  • Is it possible to reach better accuracy even with extreme class imbalance?
  • How MLOps help deploy models in a real-time environment?

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Iguazio Speaker
Yaron Haviv

Yaron Haviv is the Founder of Iguazio, has served in multiple VP and CTO roles, and initiated and led numerous products, features, and open-source projects from concept to production with top to bottom and usability first approach.

Host Speaker
Umer Qaiser

Umer Qaisar is working as a Senior Data Scientist at Royal Cyber Inc., he is experienced in delivering data-driven solutions on enterprise scale in the financial industry. He has been involved in all the phases of data science lifecycle, from requirements understanding with business to development and deployment of use cases.

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