Scholarlink

With the advancement of internet technology, electronic financial transactions have equally been experienced in the same style, where they give the user a high level of convenience but at the same time exposure to fraudulent individuals or groups. This paper proposes a new approach to address the challenge posed by online finance scams through a model that employs technology, regulation, community, and consumer participation as core strategies. With an impressive accuracy of 92%, it was designed to use state-of-art AI and machine learning to detect new scams in real-time basis and adapt to changes in scams’ patterns. Alphabetically, the efficiency of the model and how it can used to lower the rate of financial fraudulence is illustrated in this study using a online dataset with randomized payment URLs. A novel model designed to confront the challenge of online finance scams with a multifaceted approach that integrates technology, regulatory compliance, community engagement, and consumer empowerment. Achieving a notable accuracy rate of 92% in identifying fraudulent transactions, the model leverages advanced artificial intelligence (AI) and machine learning algorithms to provide real-time detection and adaptation to new scam patterns. Through a online dataset that includes randomized payment URLs, the study demonstrates the model’s effectiveness and its potential to significantly reduce the incidence of financial fraud. Promoting the concept of stakeholder cooperation and consumers’ engagement supported by educational tools and resources, the model reflects an extensive strategy for strengthening digital financial safety. The possibilities for the further development of this research are the development of finer-grained anti-scam approaches, the use of other data sources, and the optimization of the algorithms employed in the analysis. This work is useful in enhancing the efforts of creating a safer internet based financial environment to prevent prospective instances of financial fraud by advocating for technology advancement and cooperation.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Confronting Online Finance Scams with Vigilance, Regulations and Action

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • V. Adithya,
  • M. R. Dileep,
  • S. D. Vidya Sagar,
  • Sreekanth Rallapalli,
  • Jaime Lloret,
  • Lavanya Addepalli

摘要

With the advancement of internet technology, electronic financial transactions have equally been experienced in the same style, where they give the user a high level of convenience but at the same time exposure to fraudulent individuals or groups. This paper proposes a new approach to address the challenge posed by online finance scams through a model that employs technology, regulation, community, and consumer participation as core strategies. With an impressive accuracy of 92%, it was designed to use state-of-art AI and machine learning to detect new scams in real-time basis and adapt to changes in scams’ patterns. Alphabetically, the efficiency of the model and how it can used to lower the rate of financial fraudulence is illustrated in this study using a online dataset with randomized payment URLs. A novel model designed to confront the challenge of online finance scams with a multifaceted approach that integrates technology, regulatory compliance, community engagement, and consumer empowerment. Achieving a notable accuracy rate of 92% in identifying fraudulent transactions, the model leverages advanced artificial intelligence (AI) and machine learning algorithms to provide real-time detection and adaptation to new scam patterns. Through a online dataset that includes randomized payment URLs, the study demonstrates the model’s effectiveness and its potential to significantly reduce the incidence of financial fraud. Promoting the concept of stakeholder cooperation and consumers’ engagement supported by educational tools and resources, the model reflects an extensive strategy for strengthening digital financial safety. The possibilities for the further development of this research are the development of finer-grained anti-scam approaches, the use of other data sources, and the optimization of the algorithms employed in the analysis. This work is useful in enhancing the efforts of creating a safer internet based financial environment to prevent prospective instances of financial fraud by advocating for technology advancement and cooperation.