Hello everyone and thank you for joining me today. It's truly an honor to be here at conference 42 ml Ops 2025. Let's start this. Start with my introduction. This is NA ti and I'm currently working at State Sheet Financial Corporation and I'm bring with me over 19 years of experience in FinTech technologies, specializing in digital transformation, system modernization and regulatory compliance. So in this session, I'm excited to share, insert some, how we can architect responsible AI at scale, particularly in a highly regulated world of financial services. So financial services is an industry where innovation must always be balanced with trust and compliance. So the big question is how we bring the cutting edge AI into this space. Without compromising on responsible responsibility. So for our next 15, 20 minutes, we will unpack that together. So let's begin with some context. The AI revolution in financial services. We are in the middle of AI revolution in finance, but by 2029, the global. AI in FinTech revenue is projected to exceed $61 billion. That tells us AI is no longer an experiment. It's becoming the backbone of how financial institutions operate. So nowadays, the banks are investing heavily in machine learning pipelines, explainable models and complaints, first workflows. But here is the reality. As air adoption accelerates, regulators are watching more closely than ever before. So the challenge is not just building high performing models, it's building models that are trustworthy, explainable, and auditable. And this is where MLO Mops becomes absolutely essential. So let us jump into the agenda. For today's stock, and here is how I structure today's stock. We will start with the fundamentals of ML ops in FinTech and the unique challenges the industry faces. Then I will walk through some high impact AI applications that are transforming finance today. From there. We'll explore how financial institutions are architecting complaint ML platforms. And finally, I'll share best practices that leading organizations are adapting. By the end of this talk, you will see why ML Ops is not optional in financial services anymore, but it's the backbone of scaling AI responsibility. So ML ops as the backbone. So when people hear the term ML ops, they often think that basically think about pipelines and our automation. But in finance it's much bigger than that. So automation ensures that every model is reproducible with a clear audit trial. Monitoring. Monitoring keeps an eye on models in real time, detecting drift or anomalies before any cause. Damage. Governance adds, checks and balances, ensuring approvals, documentation, and complaints are part of the process. And explainability. This is critical in finance. You can't deny someone a mortgage. Flag a fraud case without being able to explain why. So without transparency, you risk both the trust and regulatory ities. So ML Ops is not just a technical discipline, it's the A, it's a very framework that makes responsible AI possible in financial services. So let us discuss the challenges in financial services. By using ai. Of course, none of these comes easy. On the technical side, we face real time processing demands. Fraud detection systems, for example, can't be delayed even by a second. Integration with the legacy systems make this even harder, and all of this happens while we are handling some of the most sensitive personal and financial data imaginable. So on the regulatory side, the bar is even higher. So studies shows that nearly 60% of models still exhibit by us regulatory requires complete audit trials for every decision, and they expect rigorous validation before models go live for global institutions. Complaint is not just one f. It's many from GDPR in Europe to CCPA in California, plus some sector specific banking laws. So building in building AI in finance is building an aircraft engine. You need precision, safety and accountability at every step. So let us look at the high impact applications despite these challenges. Is already driving major impact. Fraud detection is one of the best examples. Modern systems can analyze thousands of attributes per transaction in milliseconds flagging suspicious activity instantly. So if you take MasterCard's example, it uses AI to monitor billions of transactions daily and reducing false positives while meeting global compliance requirements. That's a great example of responsible AI at scale. Risk modeling and credit scoring are also being reshaped. JP Morgan Ha. JP Morgan Chase has used advanced ML to strengthen credit scoring models and embed explainability so that decisions are transparent. This helps them extend credit more fairly while staying regulatory ready. Compliance is another area where AI VA shines. So HSBC, for example, has tested NLP powered systems to pass regulatory filings, improving accuracy and reducing manual compliance workloads. So that's a huge efficiency gain in a high stake areas. And finally, customer intelligence. Productive AI can help financial institutions offer proactive guidance, even identifying elderly signals of financial distress before they escalate. Each of these examples show the same pattern. Success is not just about model, it's about the ML lops framework that supports the model. So next we'll discuss architecting for scale. So how do we make as scalable and sustainable in finance? The answer lies in that picture. From an infrastructure point of view, secure and isolated training environments are crucial. Hybrid cloud and on-premise setups allows flexibility while respecting data residency requirements. Containerization provides security and portability. And redundancy across regions. Ensure systems don't fail when they are most needed. And operationally institutions are building human in the loop of workflows for I risk cases, running rigorous testing before deploying models and planning disaster recovery with fallback models and automating compliance reporting. This combination of robust infrastructure and discipline operation is. What enables financial institution to innovate quickly while remaining compliant and resilient? And let's talk about legal guard rails, but even the best architecture is not enough without ethics, isn't it? So we know the, we know that models can be highly accurate and still unfair. Leading algorithms, for example, but lending algorithms, in fact. I been slow shown to unintentionally penalize 10 groups. This is why institutions are embedding ethical guardrails into entire AI life cycle. It starts with ethical data practices that make sure data sets are representative. It continues with the responsible modeling where fairness constraints are applied. Deployment is transparent with documentation of how decisions are made, and monitoring is not just for accuracy, it's also for fairness. So if you look at the results that speaks for themselves, some institutions have reduced bias by more than 70, 70% without sacrificing the performance after implementing this model. So that's a real win for both business and the society. Now let us look at the data governance. For compliant ai. Data governance is the foundation of compliant ai. Think of data lineage like a supply chain. You need to know exactly where each dataset originated, how it was transformed, and how it flows into the model. Without this visibility, regulators won't sign off. So privacy is equally essential. Auto automated tools can detect personally identifiable information, anonymize it, and apply advanced productions like differential privacy. Data quality must be validated continuously because bad data equals bad outcomes, and access management ensures that only the right people touch sensitive data with every action logged far from being a bottleneck. Good data governance actually accelerates innovation because it's builds trust with regulators, executives and customers alike. So the validation and monitoring bring rigor to the entire ML life cycle before deployment models, under undergoes test testing, statistical checks, and compliance reviews. The real test starts after deployment when live data begins flowing. And here the continuous monitoring is also a key. It detects drift anomalies or degradation. Automated response can retrain models, trigger alerts, or switch to fallback models before problem escalates. Complaints documentation. Once a heavy manual process is increasingly automated, that means regulators can get full transparency in minutes instead of weeks. So one major retail bank saw model related incidents draw by 64% after implementing continuous validation. That's the power of discipline. ML ops. Now let us look at the best practices. So what does success look like? Successful institutions design modular platforms with the clear separation of data model and serving layers. So they use infrastructure as code to make environments reproducible. They maintain model, so every version is trackable. And when it comes to implementation, they start with low risk use cases like compliance automation. Before actually moving to the sensitive areas like lending, they develop standardized reference architectures, create center of excellence for governance, and involve compliance teams from day one. This is exactly how institutions like MasterCard, JP Morgan, and HCPC have been able to innovative responsibly while satisfying regulators. So here the last is very clear. Building compliance in from the start is far easier than bolt bolting it later. So now the key takeaways from this session. So we will wrap up here with some key points to remember. First, ML ops is non-negotiable for financial services. Without this, AI simply is not scalable or safe. Second one. Ethical AI requires continuous vance bias detection. Fairness testing and explainability must leave throughout the life cycle. And the third one, innovation and compliance can coist. With the right platforms, financial institutions can move fast and still remain audit ready. And finally, success requires collaboration across roles, which includes. Data scientists, engineers, compliance officers, and business stakeholders must all work together. So responsible AI is not just technical goal, it's an organizational commitment. So that brings us to closing this talk. And thank you very much for spending your time with me today. I hope this session has shown you why ML Ops is the backbone of responsible AI in financial services. And how institutions can invo innovate while staying compliant and trustworthy. If you would like to continue the conversation or share your own challenges, please feel free to connect with me, and I would like to thank everyone once again, and I wish you success in building AI that's not only powerful, but also responsible.