Hi, this is Sandeep Dhargula. I'm a senior software engineer and welcoming you all who have joined in CONF42, CloneA2 2025 conference on my talk. And here I'm going to present my topic on AI driven fraud detection. And I'm going to explain how AI is transferring the way financial institutions prevent fraud and financial crime by leveraging predictive analytics and behavior based model. And AI is set to revolutionizing financial fraud. detection. So this fraud detection market is projected to surplus dollar 190 billion by 2030. It's a big, huge number. And by the end of this section, everyone will get all the info how AI is driving significant reduction in financial losses and inefficiencies. Let's dive into predictive analytics. in predictive analytics, AI algorithm analyze billions of transactions daily to identify fraud patterns with unparalleled accuracy. These advanced models can achieve detection rates as high as 90%. The processing transactions in milliseconds. So this improvement drastically reduces false alerts and enhances operation efficiency compared to traditional systems. And real time insights, AI continuously adapts by learning from new fraud patterns. So keeping financial institutions based ahead of emerging threats. This dynamic learning capabilities allows organizations to remain compliant with regulatory standards. by affecting combating evolving fraud tactics and reduced false positives. Unlike traditional system, which can generate up to 50 percent false positives, AI powered fraud detection reduces false positives to under 10%. So this precision enables security teams to focus on genuine threats, streamlining investigations and enhancing customers satisfactions. Let's drive into behavior based models. Here in the behavior based model, AI tracks and analyzes user behavior in real time, processing thousands of transactions per second. By comparing current activity against historical profiles, AI can quickly flag anomalies like unusual international wide transfer, changes in transaction location, or abnormal purchasing pattern across multiple platforms. So here, the product to fraud prevention. So AI systems are designed to identify and address suspicious behavior pattern before fraud escalates. Since over 60 percent of fraud originates from irregular behavior, early detection is crucial in reducing financial losses and maintaining customer trust. So in significant loss reduction, so financial institutions that implements AI driven behavior monitoring have seen up to 40 percent reduction in fraud reduction losses. So in sectors like such as Banking and e commerce these transactions in into millions of dollars saved revenue So while improving the customer experience by minimizing false alert And financial gains like using AI, financial institutions like JP Morgan, MasterCard and Goldman Sachs are deployed sophisticated AI system to revolutionizing their fraud detection capabilities. These organizations are investing millions of dollars on AI artificial intelligence to protect the trillions of transactions across the global market and improve their detection using AI. And demonstrate 30 to 50 percent increase in the fraud detection accuracy while processing the transaction 60 percent faster than the traditional methods. And real time, real world results. So early adoption of AI fraud detection has reported up to 60 percent reduction in false positives and 40 percent decrease in fraud related losses. Let's go into machine learning. And machine learning and data analysis is a key backbone of AI fraud. Detection systems. So it processing vast data sets and evolving to handle new fraud tactics. So these system analyze intricate patterns and correlations that human might miss analyst might miss so it accuracy rate is more than 95 And enhance the security. So AI iterates, leveraging capabilities significantly strengthen fraud prevention, ensuring that systems adaptable to increase complex threats by continually refining detection methods. And AI offers more robust security compared to traditional approaches. And here, the real world impact was AI powered fraud detection has demonstrated up to 50 percent improvement in detection accuracy. And while processing transactions 60 percent faster than conventional methods. So financial institutions adopting AI prevention billions of dollars in potential fraud, which some banks reported like over 2 billion in fraud prevention in a single year. That's a big, huge number. So let's continue with the anomaly detection and anomaly detection. He's AI advanced anomaly detection system continuously monitoring transactions and instantly flag suspicious behaviors that deviate from established patterns. So this real time monitoring allows for immediate risk assessment, enabling quick responses to potential threats and strengthening fraud prevention. Here are the three categories of anomaly detection. The one is baseline establishment and real time monitoring and alert generations. Let's drive into real time risk assessment. In the real time risk assessment, AI system assess risk in real time. offering immediate insights and allowing financial institutions to take shift. protective actions when suspicious activities are detected. this protective approach ensures that fraud is mitigated before it cause significantly harm. three type of detections. in risk management, they are showcasing here in three categories. One is transaction analysis and risk scoring and automated action. And building a robust strategies, building robust fraud prevention strategies are By using AI transformation, by using raw data into actionable insights, enabling organizations to prevent fraud, detect threats early, and respond swiftly. By implementing multi layer defense approach, institutions can reduce financial losses, increase operational efficiencies, and maintain a secure environment for legitimate transactions. And the key Importance of three type of categories have the strategies, DOPA strategies. One is data governance and second one is model validation and third one is continuous monitoring. Let's dive into actionable insights here in the actionable insights that transform raw data into powerful action steps through AI powered fraud analysis. Our comprehensive approach helps organizations not only prevent potential threats, but also reduce the financial losses. So these are the three actionable insights, strategies, prevention, detection and response. And the project growth as I mentioned that the detection fraud market is growing to 190 billion dollars by 2030. It shows this graph shows indicates from 2024 to 2030 it's gradually increasing the detection fraud market. And let's go into key intakes, key takeaways. AI driven fraud detection system can reduce financial losses by up to 60 percent through early detection of threats. with behavior based models, machine learning, and real time monitoring, Yeah, offers unmatched accuracy in fraud detection, ensuring that organizations are better prepared to combat increasing sophisticated fraud items. strengthening the security, AI ability to continuously adopt and learn from data enhanced security methods, ensuring robust production against emerging threats by providing real time monitoring and instant responses. And AI helps financial institutions maintain both high security and smooth customer experience. And thank you all who have joined the call.