Financial SMS and call fraud, often called smishing and phone phishing or fishing, encompasses fraudulent practices where individuals receive fraudulent text messages or phone calls designed to get private information (e.g., One Time Password (OTP)) to gain unauthorized access to the victim’s account. These scams are engineered to deceive and exploit unsuspecting individuals for monetary motives. In Qatar, there has been a substantial surge in financial crime over recent years, resulting in significant financial losses. The severity and complexity of these attacks are escalating as attackers leverage technology and artificial intelligence and evolve their methods to deceive victims. Hence, there exists an urgent national imperative to confront this threat. To combat the financial SMS and call fraud in Qatar, and based on our discussion with relevant stakeholders, we propose three primary technological AI-based research solutions to limit the attack beginning from the Service Providers (SPs) side where the attack originates and extending to the bank side, where fraudsters cash out the stolen funds. First, we seek to detect spam SMS messages through advanced AI methodologies, such as cutting-edge Large Language Models (LLMs). Second, we aim to detect suspicious spam calls using call graphs. Both suspicious SMS and call detection will occur on the ISP side and in real- time to warn users as they receive the SMS or call. Finally, we utilize collaborative AI learning techniques to detect mules and compromised accounts within the banking sector. These techniques aim to leverage global knowledge while ensuring the privacy of relevant participants and stakeholders at a local level. In this proposal, we address the challenges associated with each research direction and explain our strategies to overcome them. We anticipate that the implementation of our solutions by stakeholders will lead to a significant decrease in both attacks and financial losses, thanks to the effectiveness of our defense mechanisms and models.