Hello everyone. Thank you for joining me today. My name is Nas. I'm thrilled to discuss how AI power relationship data analysis, transforming the fight against the financial fraud and financial fraud is a global crisis costing market over 3 trillion annually. This isn't just a number, it's a threat to economic stability, investor confidence, and the trust that underpin our financial system. Traditional fraud detection matter while. Often falls short when it comes to identifying sophisticated criminal networks. Why? Because they miss the hidden relationship that enabled the complex fraud schema. Today I'll show you how AI driven relationship analysis powered by machine learning and graph based algorithm is revolutionizing the space. We'll explore how these technologies. Enable financial institution to uncover intricate connections and interview before significant damage occur. Let's start by understanding the scale of the problem. Fraudulent activities drain over 3 trillion from the global economy every year. To put that into perspective, the more than the GDP of many countries combine. This isn't just a problem for bank or cooperation. It affect consumers, small businesses, and the entire economies. But here are the good news, the AI power systems, and are making a real difference. For example, these systems can reduce false positive by 35%, and it means less time wasted investigating legitimate transaction and more focus on the actual threat. Even more impressive AI enhanced fraud detections rates by 60%. This mean we are now able to identify sophisticated fraud network and high risk individual with far greater accuracy. These aren't just incremental improvement. They are game changing advancement power by machine learning model, like random forest gradient boosting and deep neural network of traditional detection methods. Now, let's talk about why the traditional methods often fail First. Traditional system focus on the individual transactions. In isolations, they look at one transaction at a time, but fraudster don't operate that way. They use multiple accounts, entities, and time period to hide their activities. Traditional systems means these patterns entirely. Second, these systems rely too heavily on the historical fraud patterns. While history is important, criminals are constantly innovating. By the time traditional system catch up, the damage is already done. Third, traditional system only work with the structured data. Things like transition, record and account is, but fraudster leaves clues in unstructured data. Social media, post news article, and other external sources, traditional system can't process this information. Leaving critical intelligence on the table. These limitations create gap, that criminal exploit, and that's where the AI and machine learning comes in. This is where the AI relationship analysis, Heinz, let me break it down for you. First, AI deliver an 80% reduction in false positives compared to traditional rule-based system that a massive improvement in efficiency. Second AI uncovered complex multi-layer fraud schema by identifying non-obvious relationship. For example, it can connect the dot between seamlessly unrelated accounts and entities using a graph based machine learning models like Graph Sage, graph Conversional network, and the third AI seamlessly integrate structure and unstructured data. It can analyze the transition record. Alongside social media, post news article, and more using natural language processing the techniques like bird or transformer models. Finally, AI enabled the proactive risk identification. Instead of waiting for fraud to happen, AI identifies suspicious pattern and emerging their threat before financial losses occur. In short, AI gives us a powerful tool to stay ahead of fraudster. To achieve this AI system, integrate data from multiple sources. Let's look at some example. Public record, like government registries, corporate filing, property records, real complex ownership hierarchies, and potential shell companies. I. These are often used in fraud schema to hide the true beneficiaries. Transition histories are another critical source. By analyzing financial flow across the multiple accounts and time period, AI can expose sophisticated money movement pattern and coordinate fraudulent activities using time, citizen analysis, anomaly detection algorithm like isolation, forest, and. Auto encoder. The social connections are also key. Digital footprints across social platform and professional network can illuminate hidden relationship, affiliation, and potential conclusions between signaling unrelated parties using social network analysis. We also call SNA. Finally, the real time news and the media monitoring provide critical context for evaluating suspicious behavior and emerging fraud trend Using NLP and sentiment analysis by combining these sources, AI paint a comprehensive picture of fraud, potential fraud. At the core of this technology, a graph based machine learning model, let me explain how they work. First, the system ingest and pre-process multi-source data. This involve occurring, validating and standardizing data from various sources Using EDL extract, transform load pipeline. Next it perform entity resolution. Unifying deduplicating identify using fuzzy matching algorithm clustering techniques like DB scan or kmi. For example, it might recognize that John Smith or J Smith are the same person I. Then it mapped the relationship generating a comprehensive network graph that despite how entities are connected, using the graph database like Neo 4G or Amazon Neptune. Finally, it applies algorithm to detect and melos pattern. For example, it might flag a network of account that are transferring money in a statically come in probably way using community detection algorithm like, label propagation. This end-to-end process allow us to identify fraud with unprecedented. I could see one of the most powerful feature of AI relationship analysis is this ability to detect hidden relationship. Let's look at some example. First, alias detection. Frauds are often operate under multiple identities. AI can identify these aliases by analyzing linguistic pattern, behavioral fingerprints, and contextual relationship using an LP models like world to work vertical org glowy. For example, it might notice that two accounts using similar language are having overlapping IP addresses. Second layer ownership. Fraudster often use nested shell companies and complex copyright hierarchy to hide their control. AI can penetrate the structure to expose the ultimate beneficial owners using graph traversal algorithm like breath first, search depth first search. Indirect connections. Fraud often use intermediate issue to hide the relationship. AI can uncover these hidden connections by mapping share assets. Common associate and other indirect links using links prediction algorithm. Finally, a temporal pattern. AI can detect suspicious timing, correlation across transaction, using time series analysis and statistical model like A-R-I-M-A of profit. These capabilities make it. Incredibly difficult for fraud to hide. Let's look at some real world result. AI and relationship analysis achieves 95% detection rate compared to just 40% with traditional system. That's more than double the effectiveness. It also reduces false positive from 60% to just 25%. This means fewer unnecessary investigation and more focus on actual threat in terms of processing time. AI solution can process suspicious transition in just 15 hours compared to 72 hours with conventional methods. This enabled a faster responses to potential credit. And when it comes to complex fraud, schema AI detect 85% of cases versus only 30% with tradition system. This number speaks for themselves. AI is a game changing in fraud detection. I. To successfully implement AI relationship analysis, there are several best practice to follow for establish a robust data governance framework. This means having a comprehensive policies for secure data acquisition, ethical usage, second integrated AI system seamlessly with existing technology. This might involve deploying the advanced graph database like Neo 4G Tiger Graph. The an analytics platform that communicate flawlessly with your current infrastructure. The third deploying phase start with a high impact business area. To validate ROI refine the process before rolling out organization-wide. Finally, create a dynamic learning ecosystem. This means implementing structure feedback loops with fraud analyst to continuously enhance model accuracy and adapt the emerging fraud pattern using reinforcement learning and online learning techniques. By following this best practice, you can maximize the effectiveness of AI in your organization. Financial, regulatory Now mandate transparent AI decision making process. This means generate. This means system must generate comprehensive audit trial and clear justification when flagging suspicious relationship pattern using explainable AI technique like SHAP, sharply Adaptive Explanation or LIME, local interpretable model agnostic explanation. Privacy compliance is also essential. The data acquisition and processing must strictly adapt to G-T-P-R-C-C-P-A and other financial. A November organization must implement rigorous concept mechanism and practice data minimization throughout analysis workflow. Continuous testing and third party validation of AI model is also essential for regulatory compliance, regular independent audit insurer. I will call them fairness, statistical accuracy, and freedom from discriminatory outcome. Finally, across border consideration are important. International financial investigation of complex jurisdiction requirements organization must establish robust framework for compliant data sharing across regulatory boundaries with varying legal standard, the future of AI in financial compliance. Looking ahead, the future of AI in financial compliancies, incredibly exciting. First, we'll see real time fraud detection. Instead of investigating fraud after it happened, system will identify as it happened using streaming data processing framework like Apache Kafka. Apache Flink Prevention will replace the investigation. Second, the cross industry collaboration will become more, more common. Finance institution will Moore and patent using federated learning technique, creating a collective defense that strengthen all participants. That quantum computing will take patent detection to UDE by, by enabling analysis of vastly large geo dataset, quantum computing will require patent that are currently invisible. Finally, predictive risk model will anticipate new fraud type before the emerge. Use in generative adverse serial network and simulation based on approach says this mean. Adaptation will out face the criminal innovation. In short, AI will continue to revolutionize the fight against financial fraud. To wrap up AI power, relationship data analysis is transforming the fight against financial fraud. By uncovering hidden connections, reducing false positive and enabling proactive is identification. It come a powerful solution to a global problem. The numbers speak for themself. AI is more than doubling detection rates, reducing the positive, enabling faster response time. But beyond the number, AI is giving us a tool to stay ahead of fraud starts in an increasingly complex world. Thank you for your time. I look forward to your questions. Thank you.