Paper Title
A Comprehensive Framework for Detecting Bot-Driven Fraudulent Transactions in Fintech and Ecommerce Companies
Abstract
As bot attacks increasingly compromise the cybersecurity of fintech and eCommerce companies, there's an urgent
need to develop more effective defense strategies. These attacks lead to significant financial losses through credit write-offs
from fraudulent transactions and risk compromising sensitive user data. This paper proposes a comprehensive framework to
counter such threats by detecting bot-driven fraudulent transactions. The framework incorporates an automated bot detection
system leveraging machine learning (ML), which enhances detection speed and response time, providing cleaner login data
for informed decision-making. This paper further discusses the limitations of existing statistical anomaly detection
techniques, emphasizing the need for more complex solutions. In response, we propose the integration of causal inferencebased
methods, utilizing ML models for generating counterfactuals and segment-specific bot activity flagging. The
framework also layers human knowledge onto the ML approach, prioritizing segments driving the majority of transactions,
and considering the triggering of risk models as ground truth for bot activity detection. Finally, we address the scalability
and generalizability of the framework across different products and metrics, underscoring its potential for wide adoption in
enhancing cybersecurity measures in fintech and eCommerce companies.
Keywords - Automated