CeFPro Connect

Article
The future of fraud detection and prevention
Exploring the evolving role of machine learning and large language models in fraud prevention, highlighting their immediate impact on real-time detection and the need for ethical considerations in successfully adopting these technologies.
Sep 04, 2023
Sudharshan Narva
Sudharshan Narva, Director, Data Analytics Internal Audit, TIAA
The future of fraud detection and prevention
The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization

  • Exploring the current state of AI in fraud detection, highlighting the rapid adoption of Generative AI and machine learning algorithms in businesses.

  • How swift analysis of structured and unstructured data for real-time risk mitigation will shape the future potential of machine learning for fraud protection.

  • Achieving efficient customer protection with AI technologies to reduce false positives, accelerate detection, and enhance customer experience

  • Enforcing AI ethics, advanced authentication, and regular model validation to mitigate risks of fraudulent AI usage:

  • Identifying the hurdles for successful adoption of AI in fraud teams through training, better data handling, improved interpretability, managing cultural shift and change resistance, and ensuring model robustness, fairness, and ongoing maintenance.

  • Tackling cost-efficiency concerns in AI investment.

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