Hey, good evening everyone. It is an absolute honor to stand before you at Com 42 Mission Learning 2025 conference. My name is thi I'm leading engineer from Credit Union. Today we embark into the fraud of financial security, a place where. Artificial intelligence and mission learning and risk management matches to redefine our digital economy. Imagine in our modern financial ecosystem as a vast network of countless transactions harming every second in this vibrant landscape. Trust is the foundation of every interaction, and even the smallest irregularity can spark a major risk before risk management. Coming through records, spotting errors and reacting only after incident had occurred. That method, although one sufficient now falls short against the sophisticated methods of modern frauders. Today, AI acts as a tireless guardian that learns continuously from realtime data by predicting unusual behavior long before it evolves into a full blown threat. With advanced neural networking and machine learning, AI not only detects anomalous patterns, but also stops them in the tracks picture and intelligent systems that monitors thousands of transactions simultaneously with each transaction. It. Compared current behavior with historical pattern learning what normally happens and flagging any D deviations. This proactive approach means that potential threats or neutralize it before any harm is done. It's like having a vigilant security team that never sleeps, always adapting to new challenges and ensuring the. Today will reveal how AI reshapes risk management from increasing detections accuracy to reduce false alarms and up responses to threat. I'll share real time examples by illustrating how financial institutes are saving millions simply by being mu up ahead of. As we dive in, think of AI not as a cold mission, but as a dedicated partner that supports you every time you make a digital transaction. Coming to next slide, oh. AI power fraud detection. Fraud detection in today's financial world is no longer a reactive process. It is a dynamic AI power strategy that works around the clock. Traditional systems rely on pixel tools on historical data, but the static models often fall short when facing rapidly evolving fraudulent techniques. Today, air fraud detection elevates our defense. By continuously learning from vast amount of data, uncovering hidden patterns, once responding to threats with unprecedented speed and accuracy, imagine a system that increased detection accuracy by 87%. This means that rather than relying on simplistic rules that might miss signs of. Understands the complex behaviors behind transactions. By analyzing hundreds of variables in real time. AI identifies even the slightest deviation from normal patterns. For example, if your customer who normally make small purchases should initiate multiple high value transaction, the AI system instantly for further investigations. Is only part of the story. One of the biggest challenge in fraud detection is the issue of false positive. When a system triggers too many alert, legitimate transactions can be unnecessarily. Customers and clogging up financial channels. AI addresses this by reducing false positive by 63% by ensuring that only through suspicious activities are escalated. These kind of this refined approach saves valuable time and resources enabling institutions to focus on real threats without causing in to genuine customers. Another remarkable benefit is accelerated response time, which is up to 95% faster than traditional systems. In today's digital age, every millisecond counts when an anomaly is detected. The AI Systems act immediately by haling a transaction before it completes. If it deems two. This near instantaneous reactions is vital for preventing fraud as delays can allow activities to succeed. Coming to next slide pattern, recognization breakthrough. In this fight against fraud, pattern recognition is the secret weapon that sets a part. Fraud detection often hinges on the ability to see the unseen. A web of SubT patterns that humans might miss. AI is breakthrough in pattern recognition. Enormous dataset. Identify normal behavioral trends and pinpoint anomalies that signs fraud before any visible damage occurs. Let's begin with process of data collection. In today's faults, financial data use sourced from different channels like bank transactions, payment gateways, mobile apps, and even social media interactions. These AI systems aggregate this diverse data stream to build a comprehensive picture of financial behavior. Every transaction service has a puzzle piece contributing to an overall pattern that defines what is normal. Once this massive collection of data is like harmonized. It is analyzed using advanced neural networking techniques. These networks learn from historical transactions, establishing baseline patterns for individuals and collective behavior. Furthermore, AI leverages anomaly detection algorithms that flags activities even when. When viewed in isolation, considered multiple low value transactions spread across different accounts. Each transaction individually might not seem suspicious, but together they form a pattern that suggest coordinated fraudulent activities. Transaction systems might overlook such kind of behavior, but AI pattern recognizes brings this hidden connections to light. Coming to next slide, real time financial transactions, monitoring and interactions. Imagine a system that never sleeps, addit Sentinel, that monitors every financial transaction as it happens, scrutinizing hundreds of risk factors in microseconds. That is the promise of real transaction. Access both the vigilant and watchman under pro and scepter in today's high speed distal economy. Timing is everything ideally of even f fraction of a second can turn a minor anomaly. Full-blown security breach. Realtime financial transactions monitors begins the moment a transaction is initiated. Data flow instantly through intelligent algorithms that compare each new transaction against millions of historical records, checking for any deactive, any deviations from expected behavior. These systems evaluate details like transaction amount. The time, the geographical location, and even the user's typical behavior. When any unusual activity is detected, perhaps a SHA done change in spinning pattern or a new device used for access, the system is immediately locked. This instantaneous detection allows the AI not only to flag the as suspicious, but also to take. For instance, when the system recognizes a potential threat, it can auto inter by using the transaction or routing it for additional verifications. The response is nearly instantaneous, up to 95% faster than traditional methods reducing the window in which fraud can occur. Coming to next slide. This is the slide where we discuss about transforming loan collections. Loan collection has traditional libin, challenging lab in process, manual follow ups and standardized reminders of and field sort of addressing the unique circumstances of. Revolutionizing loan collections by transforming it for a rigid one size fits all approach into a personalized data-driven strategy that improves recovery rates and strengthens customer relationships. At the heart of this transformation is the ability of AI to analyze and historical data points by evaluating. Borrowing history, payment behavior, seasonal spending, and even extra economic factors. AI can predict each borrower's likelihood of repayment with remarkable procession. Imagine in receiving personalized outreach that does more than just remind you about your next payment. It speaks to your individual financial situations offering. Flexible solutions that suits your needs. For financial institutions, the benefits are very immense. AI driven strategies have boosted recovery rates significantly in many cases. Personal roles, personal loans, received recovery has seen improvements up to 27% points. In the business loans, traditionally the most difficult to recover have jumped from 41 to 76%. These improvements are not just numbers that represent real challenges for borrowers and lenders. When each interaction is tailored to the individual collections become less about. Confrontation and more about clear empathy communication. Consider the concept of personalizing timing. AI systems, analyze patterns to detect undetermined, many borrowers most to communications in the month. When. Later when a regular payment cycle is able to begin by finding the optimal moment, the system can send a friendly and timely reminder that resonates and results in positive responses rather than a generic message that might be ignored or cause frustrations is also building relationship. Borrow. Understood. They're most likely to engage with the institution and work toward resolving their financial challenges. The system can suggest alternative payment plans by offering counts on managing finances and adopted approach as a situation change in real time, continuous feedback from these interactive. Helps define the AI models by ensuring that each subsequent intervention is even more effective. Coming to next slide payment processing revolution in our rapid evolving digital error. Payment processing is undergoing a dramatic transformation. In the time are increasingly unable to meet today's demands for speed accuracy on security. Enter the payment processing revolution propel by a, which is rapidly redefining how every transaction is executed, conventional payments systems. Being the backbone of financial transactions. However, these systems can be inefficient and slow. The rely on manual interventions for exceptions, process transactions in batches, and often like the ability required in today's high speed digital economy. In contrast, AI enhance systems offer a fluid adoptive approach by payment processing the dramatically improved performance. Imagine every payment being processed as if it'll on fast track once your transaction is initiated. AI systems immediately This. Using a set of adapt multifactor authentication checks, and comparing current details with historical data and patterns. This means that transaction no longer follow a singular predetermined route. Instead, they are intellectually routed through the network, along the path that maximizes both speed and security, in instance, where problem arises. These systems can automatically resolve issues by self-managing exceptions that would have previously required manual interventions. The result is like transactions are crossed almost instantaneously with minimal risk of errors or delays for businesses. This translates to improved customer satisfaction. Free payment process for financial institutions. The reduction in processing times means lower operational cost and enhanced overall efficiency. Every second. Saving in processing helps build more dynamic contrast once financial ecosystem alongside speed and efficiency. The revolutions in payment processing also introduces a new level of. Coming to next slide, ethical AI framework. Coming to ethical AI framework as we integrate AI into every fact of financial security, tackling issues of fairness, transparency, privacy, and accountability is paramount. Our ethical AI framework. Is the bedrock upon which responsible innovations is built. It ensures that while we leverage sophisticated technology, we remain steadfast in our commitment to human values. Fairness in AI means that every decision made by our systems is free from our. Audited to prevent discrimination across any demographic. By designing systems that adopt and learn without reinforcing historical data. We build trust not only within institutions, but also with our customers. Transparency is equally crucial. Every action. An AI system must be explainable when transaction is flagged, or a loan application is deviated. The reason is clearly documented. This transparency benefits regulatory and instills confidence among. Customers. Privacy is another pillar of our framework. Financial data is highly sensitive, and our approach guarantees that personal information is never exposed to anyone. High level encryption on Strat an normalization protocols ensure that data is used only to. Accountability is maintained through a robust governance structure. Regular review and designated oversight committees and comprehensive audit trials all contribute to a system where every decision is. Trackable and all stakeholders are responsible for maintaining ethical standards. In our session today, we'll explore how an ethical AI framework is an obstacle to innovation. It is the guide focus that ensures technology serves humanity. I will share example of institutions adopting standard showing that. Effective governance can coexist with rapid technological advancements. When people see that decisions are made transparently, unfairly, the trust grows. Trust is the currency of the digital economy under our ethical framework. Guarantees that AI works for all of us. Just on responsible manner. Coming to next slide, which is regulatory navigation. In this slide, navigating the regulatory landscape in today's financial voltage request, both foresight and adaptability with evolving legislations and diverse regulations across. Financial institutions must be enough to compel with new requirements while continuing to innovate. A is proving to be an invaluable asset in this regard by offering tools that. Not only ensure compliance, but also enhance transparency and risk management. Modern AI systems can continuously scan changes in regulatory standards across regions and sectors. They flag potential contradictions and compliance gaps in real time. Imagine a system that updates itself with every new regulation published. Immediately adjust its risk assessment models, ensuring all operations remain within legal boundaries. This realtime monitoring is not only proactive, but also incredibly efficient by reducing the risk of non-compliance, which could otherwise lead to costly ities and reputational damages. Another significant benefit is. Every decision made by ai, whether it involves flagging, suspicious transaction or processing loans is recorded with clear documentation of reasoning behind it. The documentation is invaluable during regulatory reviews as it shows a clear path of decision making that regulators can follow. Transparent documentation, builds trust with. Demonstrates a commitment to responsible ethical financial practices. Coming to next slide, implementation strategy assessment. Pilot program. Scalable development and continuous evolution are like the four strategies we follow as aspect of implementing artificial intelligence in financial industries. Implementing AI driven risk management is yet transformational journey that requires careful planning, testing, and scaling. The process begins with assessment of. During the pilot program, the focus is on gathering data and refining the algorithms through real world feedback. The system is closely monitored by enabling adjustments in the real time to maximize efficiency and accuracy. Coming to. Oh, sorry. There are two more strategies that need to be discussed as part of this slide. That is skill development and continuous evolution. We'll also examine strategies for maintaining continuous evolution without within. Your AI system. This means establishing feedback loops that regularly incorporate new data, updating the models and ensuring the system remains robust in the face of evolving threats. The journey does not stop it. The initial rollout. Ongoing monitorings and regular audits and interactive improvements ensure that AI. Effective and secure. Lastly, our decision will include practical examples from institutions that have successfully implemented AI driven strategies. These courses, studies highlights the challenges they encountered. The benefits realized and the best practice serve. Coming to next, last slide. In conclusion today we have journey to the transformation to role of artificial intelligence and machine learning and financial risk management. We begin at Financial Frontiers exploring how AI redefine security by predicting and preventing threats before. Current security landscape learned how AI powered fraud detection and pattern recognization on real time monitoring creates proactive defenses and witnessed how these technologies transform process from payments to loan collections. We have also seen that responsible innovations is built upon. That emphasizes fairness, transparency, privacy and accountability. Navigating the complex regulatory, implementing AI solutions and continuously evolving these systems is not just a technical challenge, but also cultural journey. Together, these breakthrough makes our financial systems. And thank you everyone.