Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| author | Dahiphale, Devendra Madiraju, Naveen Lin, Justin Karve, Rutvik Agrawal, Monu Modwal, Anant Balakrishnan, Ramanan Shah, Shanay Kaushal, Govind Mandawat, Priya Hariramani, Prakash Merchant, Arif |
| author_facet | Dahiphale, Devendra Madiraju, Naveen Lin, Justin Karve, Rutvik Agrawal, Monu Modwal, Anant Balakrishnan, Ramanan Shah, Shanay Kaushal, Govind Mandawat, Priya Hariramani, Prakash Merchant, Arif |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19845 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach Dahiphale, Devendra Madiraju, Naveen Lin, Justin Karve, Rutvik Agrawal, Monu Modwal, Anant Balakrishnan, Ramanan Shah, Shanay Kaushal, Govind Mandawat, Priya Hariramani, Prakash Merchant, Arif Cryptography and Security Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning 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. |
| title | Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach |
| topic | Cryptography and Security Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2410.19845 |