Good morning. Good afternoon. Good evening, everyone. Thank you for being here. My name is Vinodupaturi. I do have 11 years of experience in the financial industry. Currently, I am working as a senior software engineer. Today, I am thrilled to talk about the subject that's not just critical for financial institutions, but for everyone. AI powered fraud. detection. Did you know the financial fraud cost the global economy over 5. 4 trillion dollars annually? This is a staggering number emphasize the urgent need of innovation. Today I'll be discussing how artificial intelligence and machine learning are transforming the fraud detection into high efficient, proactive, and customer friendly process. Let's dive into the fundamentals of AI powered fraud detection and understand why it's such a revolutionary force in financial security. Billions of transactions are processed every day. Within these transactions, fraudsters are hiding, adopting, and exploiting the loopholes. How do we oversmart them? Using artificial intelligence. There are three major approaches for fraud detection. Supervised learning, unsupervised learning, and deep learning. Supervised learning. AI will always thrives on learning from the past. Using billions of label transactions, supervised learning algorithms refine themselves over time to achieve a staggering 99. 9 percent accuracy in the fraud detection. For an instance, think of how your email filters spam. AI applies a similar concept to transactions, but with precision sharp enough to spot even the most complex patterns of fraud in milliseconds. Unsupervised learning. Fraud isn't always static. It evolves. Unsupervised learning steps in where the pattern isn't clear. It identifies hidden threats, the anomalies we don't know exist, and improves early fraud detection by 42%, 42%. Imagine a AI system spot unusual behavior in the transaction from a new system or a new location. Even if it doesn't match previous fraud patterns, AI flags it for a deeper analysis. Deep learning. This is the powerhouse for of modern fraud detection. Deep learning network process thousands of data points for transaction in real time. This means they don't just identify a fraud. They stop it as it doesn't, as it's happening. Deep learning has slashed cardinal present fraud by 76%, a major achievement in the fraud against, fight against digital fraud, real time fraud prevention. Let's move from detection to the prevention in the real world, area where AI truly shines. Imagine a fraudster attempt to use a stolen credit card online. Before the transactions can be processed, the system flags it as suspicious, blocks it, and notifies the rightful account holder. All this is happening in a fraction of a second. Prevention can be done in three major ways. Real time transaction analysis, behavioral biometrics, synthetic identity detection. Real time transaction analysis. AI system analyzes 100k transactions per second. Scanning for anomalies within a few seconds. is 50 milliseconds response time. For an instance, consider a user, in New York whose card is currently used for purchases in London. EA immediately flags this as a potential threat based upon the patterns like unusual location and transaction size. This level of speed ensures the fraud in, fraud is intercepted before it can be used. can cause damage, protecting customers and saving billions. Behavioral biometrics. AI studies how users interact with their devices. Their typing rhythms, swipe gestures, even the pressure they apply on a touch screen. This creates a digital fingerprint unique to each user, which is incredibly hard for a fraudster to replicate. Behavioral biometrics have reduced the account overtake attempt by impressive of 89 percent accuracy. Synthetic identity detection. Fraudsters create fake identities by combining the stolen data from multiple individuals. These synthetic identities often go unnoticed in the transaction. Traditional systems. AI maps and analyzes fabricated identities with 94 percent precision, intercepting fraudulent activities before it can occur. Use cases. We have explored the power of AI, how it prevents fraud in real time. Now let's zoom in on real world applications. Real time transaction monitoring. AI analyzes thousands of data points for transactions like location, time, device, and spending patterns within milliseconds. Suspicious transactions are flagged and blocked immediately, preventing the fraud before it cause damage. Let's imagine a customer card is suddenly used to buy high value items from unfamiliar locations. AI detects this anomaly and stops the transaction instantly, saving the customer and institution from significant loss. Account takeover protection. Fraudsters often gain unauthorized access to accounts using the stolen credit cards and stolen credentials or by brute force attacks. Behavioral biometrics and machine learning 5. Deviations in the user behavior such as login patterns, device usage, and to identify and stop these unauthorized access. AI neutralizes account takeover attempts and protects both customers data and institutional trust. Synthetic identity fraud mitigation. Synthetic, synthetic fraud is one of the fastest growing threat where fraudsters create fake identities using the mix of the real and fabricated data. AI cross reference the data patterns across multiple systems, identifying inconsistencies that human investigators might miss. AI prevents 87 percent of synthetic fraud attempts saving institutions billions of dollars annually. For an instance, if the loan application is flagged as a suspicious due to the mismatched identity details, AI can block the process before any funds are dispersed. Phishing detection and prevention. Phishing is one of the most common and, common entry point for the fraud. Natural language processing scans communications like emails, messages, and calls, for phishing keywords and patterns and the links blocking them in real time. Impact. Institutions intercept phishing attacks before they reach customers and ensuring their safety. AI protects every aspect of the financial ecosystem, from individual transactions to large scale operations. This solution doesn't just respond to fraud, they anticipate and prevent it. By safeguarding accounts and transactions, AI strengthens the bond between institutions and their customers. Implementation roadmap. To realize the future, organizations need a clear and actionable roadmap. Let's discuss the steps to simplify the implementation and AI powered fraud detection. Building a robust AI fraud detection system isn't just about deploying algorithms. It's about the strategy, alignment, and continuous improvement. Define objectives. Conduct a comprehensive internal assessment to map out a plan. fraud detection goals and align them with overall risk assessment and risk management strategies. For an instance, if your goal is to reduce a fraud by 50 percent and ensure the customer's trust, define it clearly to guide your approach. Data assessment. Data Audit your existing data infrastructure to ensure quality diversity. Poor quality data leads to biased and ineffective AI models. For an instance, if an organization may discover gaps in the data collection, like missing behavioral insights and address them before training their AI systems. Model selection and training. Experiments, experiment with multiple models leveraging advanced machine learning techniques like supervised, unsupervised, and deep learning. Choosing the model that adapts to evolve the threads and provide explainability. Deployment and monitoring. Rolling out the AI in phases. with continuous performance tracking and the feedback loops to refine defect detection mechanism. Fraud detection is not like a fight and forgot effort. It requires a real time adaptability. For an instance, if an organization deploys AI for online payment first, refine it accurately before expanding to the other regions or demographics or maybe other type of transactions. Benefits of AI powered fraud detection. We have seen how AI detects and prevents fraud while enhancing customer experience. Now let's focus on the game changing benefits that makes AI must have for financial institutions. Fraud isn't just a cost, it's a threat. to trust, efficiency, and customer loyalty. With AI, we are not just fighting fraud, we are transforming the way business operates, protects, and grows. Let's break this down for you. Reduce fraud loss. Every fraudulent transaction costs not only money but reputation. By intercepting the fraud at a source, AI prevents the loss before they occur. Enhance security. Traditional security systems rely on static rules. Can't keep up with evolving threats. Adapt to algorithm. Continuously learn and adjust to the new threat. New fraud tactics create creating an intelligent and evolving shield. Customers and business. Feel confident knowing their data and transactions are safeguarded. 24. 24 by seven. Improved efficiency. Manual fraud investigators are labor intensive and slow. By automating fraud detection workflow, AI reduces the investigation times by 73%. For an instance, if a finance, for a financial institutions receiving hundreds of fraud alerts daily, AI prioritize the most critical cases and provide investigators with actionable insights to reduce them in minutes rather than days. Enhance the customer experience. Customers want security that doesn't disrupt their experience. Frictionless fraud Prevention operates behind the scenes, allowing the customers to use the transactions confidently without interruption. This approach has driven 90 percent customer satisfaction rate, turning the security into competitive advantage. Data driven insights. Fraud detection isn't just about stopping threats, it's also about learning from them. Sophisticated analytics decodes, complex, fraud patterns, providing actionable intelligence to provide strategies. AI identifies a recurring fraud pattern tied to a specific geography, allowing institutions to proactively strengthen defenses in that area. Challenges and considerations. We have explored the incredible power and benefits of AI in the fraud detection. However, No transformative technology comes without its challenges. To maximize its potential, we must recognize and address these hurdles. Data quality and bias. AI systems are only as good as the data they are trained on. Poor quality or biased data can lead to inaccurate predictions. A biased model might incorrectly flag legitimate transactions from certain demographics, creating frustration and loss of trust. Model explainability. AI systems are often seen as a black box, making it difficult to explain how decisions are made. Transparency is essential for a stakeholder's trust, from regulators to customers. For an example, if a customer asks why their transaction was flagged as fraudulent, provide a clear explanation building confidence in the system. Moreover, incorporate explainable AI techniques that provide clear and understandable insights into how decisions are made. Regular Rhetoric Compliance As AI adoption grows, so does the regulatory scrutiny. Laws surrounding adoption Data analytics, data privacy, ethics, and algorithm fairness are evolving rapidly. Noncompliance can lead to hefty fines, reputational damage, and operational setbacks. Continuous learning and adaptability. Fraud. Fraud detection evolves constantly. Static models quickly become absolute in the face of emerging threats. A system trained on past fraud patterns may miss a new type of phishing, scam, or synthetic detection attacks. By leveraging continuous learning, AI models stay agile, updating themselves with real time data to identify new fraud techniques. Institutions must invest in ongoing training and monitoring to ensure AI remains effective and future proof. Future of AI powered fraud detection It looks, let's look ahead and explore how AI will evolve to meet the growing challenges of financial security. The future is bright with endless possibilities. Imagine a world where fraud is intercepted before it even reaches its customer, where AI systems don't just reach, react to the threats, but predicts and prevent them with unparalleled precision. This is The feature where we are building advanced analytics. The next wave of AI will harness the quantum computing and deep learning to develop hyper intelligent models with unmatched predictive accuracy. This means fraud will be spotted not just in milliseconds, but even anticipated before it occurs based on dynamic patterns across millions of transactions. Real time risk assessment. AI driven risk, risk engines will evaluate threats in real time, enabling proactive decisions within milliseconds. Reactive security will transform into proactive defense systems, reducing vulnerabilities and losses to near zero levels. Personalized security measures. AI will craft in individual security protocols tailored to each Customer's unique behavioral patterns and with their transactional history. These dynamic measures will enhance the trust and significantly reduce the false positive. Collaborative fraud prevention. AI platforms will enable the secure and decentralized network for institutions to share their threat intelligence. This collective effort will create a powerful and adaptive defense ecosystem that benefits the entire financial sector. For an instance, a new type of fraud detection in one institution is immediately shared across the network, preventing its spread. Conclusion. Let's bring everything together. AI powered fraud detection is not just a technique. It's a transformation, a way of doing things. To build trust, protect assets, and stay ahead of ever evolving threats. With all this approach, there is a 97 percent accuracy rate in the real time threat detection, 63 percent reduction in the false positive, and saving time and resources, and 73 percent faster investigation, enabling quicker resolutions. With AI Financial institutions can move from reactive to proactive, from static to dynamic, and from traditional to transformative. The result, a world where fraud is no longer a growing threat, but a manageable challenge. Thank you all for your attention. I hope you are excited about the possibilities of AI powered fraud detection as I am. Thank you. Thank you everyone.