Devendra Dahiphale

Devendra Dahiphale

Devendra D. Dahiphale (Senior Member, IEEE) holds the B.E. degree and the M.Sc degree in computer science. He is a Research/Software Engineer with Google, USA; and has worked for Cisco Systems, USA; Amazon.com, USA; and Imagination Technologies, India. Devendra has more than a decade of professional experience in the field of Computer Science research and engineering.
Authored Publications
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    Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach
    Anant Modwal
    Govind Kaushal
    Ramanan Balakrishnan
    Shanay Shah
    Monu Agrawal
    Justin Lin
    Prakash Hariramani
    Priya Mandawat
    Rutvik Karve
    Naveen Madiraju
    Preview abstract Digital payment systems have revolutionized financial transactions, offering unparalleled convenience and accessibility to users worldwide. However, the increasing popularity of these platforms has also attracted malicious actors seeking to exploit their vulnerabilities for financial gain. To address this challenge, robust and adaptable scam detection mechanisms are crucial for maintaining the trust and safety of digital payment ecosystems. This paper presents a comprehensive approach to scam detection, focusing on the Unified Payments Interface (UPI) in India, Google Pay (GPay) as a specific use case. The approach leverages Large Language Models (LLMs) to enhance scam classification accuracy and designs a digital assistant to aid human reviewers in identifying and mitigating fraudulent activities. The results demonstrate the potential of LLMs in augmenting existing machine learning models and improving the efficiency, accuracy, quality, and consistency of scam reviews, ultimately contributing to a safer and more secure digital payment landscape. Our evaluation of the Gemini Ultra model on curated transaction data showed a 93.33% accuracy in scam classification. Furthermore, the model demonstrated 89% accuracy in generating reasoning for these classifications. A promising fact, the model identified 32% new accurate reasons for suspected scams that human reviewers had not included in the review notes. View details