Good morning and good afternoon everyone. I'm truly excited to be here with you today as part of Con F 42 conference. My name is Rag and I bring over a decade of a specialized experience in data warehousing, business intelligence, and advanced analytics. Primarily focused on the banking and financial services sectors. Currently working at one of the leading financial organizations in the industry. I lead AI driven analytics initiatives that directly support making. Regulatory complaints and operational performance enhancement. My team works at the intersection of the data science and business strategy, translating complex information into actionable insights. Today we are going to explore how advanced AI techniques, particularly privacy aware approaches like federated learning and differential privacy are fundamentally reshaping the predictive analytics landscape in finance. This transformation isn't just about implementing smarter algorithms or faster computing. It's about building systems we can genuinely trust with our most sensitive information. Moving on to the slide number two, AI and ml transforming financial Predictive analytics. Covering smarter decisions in risk, fraud, and client strategy. What used to take weeks of manual analysis can now happen in real time, creating opportunities for more responsive customer experiences. As models, advance data privacy and complaints have become non-negotiable in finance, trust currency and regulations like GDPR and DPDP are changing how we work with the data. Customer expectations are also evolving. People want personalization, but not at the expense of privacy. Today's roadmap focuses on responsible AI that delivers better predictions alongside governance, transparency, and business value. This approach represents a new co advantage in financial services, the ability to innovate without compromising trust. Moving on to the slide number three. Analytics in finance has evolved from basic reporting to predictive and prescriptive models. Now we are entering the era of responsible AI building systems that privacy respects meet co complex demands and deliver powerful insights. Our models can't be black back boxes anymore. Regulations like GDPR and India DPDP Act require us to be intentional about data usage. Creating both challenges and opportunities for financial institutions. Companies that adapt quickly gain advantages in both customer trust and operational efficiency. This evolution isn't just about technology, right? It's about fundamentally rethinking how we derive value from data while respecting boundaries. The techniques we will discuss represent practical solutions to this challenge. The next slide here talks about this is where the things get truly exciting from both technical and business perspective. Federated learning represents an advanced form of machine learning, which fundamentally changes how we approach model training, right? Federated learning lets us train models across multiple data sources without data living it's original location. The model travels to the data, not vice versa. This means sensitive constrain customer information stays secure in its original environment. When we add differential privacy, we can protect individual identities even in aggregator results. This provides mathematical guarantees about privacy preservation, not just procedural safeguards. Secure multi-party computation allows different departments or institutions to collaborate without revealing underlying data. This isn't just good data science, it's responsible engineering for regulated environments. For example, different banks could build collaborative fraud detection systems without sharing their actual customer transactions Together, these techniques form a powerful toolkit that addresses both technical and compliance requirements. Next slide. In banking, these techniques enable better credit risk modeling across global branches and more effective fraud detection. While mentioning compliance ability to learn from diverse data sources without centralizing them, creates models that are both more accurate and more compliant. We have seen fraud detection accuracy improve by double digits, using federated approaches that learn patterns across multiple different payment channels. While staying within co complex boundaries, these improvements directly impact the bottom line through reduced losses, excuse me, for a ML and transactional monitoring, excuse me. These approaches reduce false positives without compromise, compromising security or customer privacy. I'm so sorry. This means. Legitimate transactions plot for review, meaning flag for review, improving both operational efficiency and customer experience without sacrificing regulatory complaints. The key insight here is that privacy, preservation and moral performance are a necessary trade-offs. They can be complimentary with the right approach. Excuse me. Wealth management involves deeply personal. Financial data that reveals not just assets, but life goals and tolerances. Using federated learning, we can train models across client segments without centralizing sensitive information. This enables personalized portfolio recommendations while maintaining strict data separation. The insights flow to advisors and clients, but sensitive details stay protected. For example, market segment trends can inform personalized advice without exposing individual client portfolios. These applications aren't theoretical. They're being implemented today by forward thinking institutions that allow wealth managers to provide more. Consistent advice across different advisors. Were preserving the human relationship that makes wealth management valuable. The result is enhanced service delivery that maintains a confidentiality. Clients expect on regulations. Demand the next slide here. Insurance deals with sensitive, structured and unstructured data, spanning health information, property details, and behavior patterns, predictive pricing and fraud detection benefit from ai, but only when privacy is preserved. Our privacy preserving models have reduced false claim approvals and optimum optimized. Premiums while aligning with regulatory guidelines like solvency. Two, this dual benefit business improvement and complaints make these approaches particularly valuable. These approaches don't replace traditional models. They enhance them by incorporating diverse data signals without increasing complex risk per stance, we can now assess risk across placeholders segments without exposing individual ries, enabling more precise pricing with appropriate confidentiality. Insurance represents an excellent test case for responsible AI because the business fundamentally depends on data analysis, yet operates under strict privacy constraints. Next slide. Deployment presents significant hurdles, inconsistent data quality, organizational silos, legacy systems as strict complex requirements. Each organization will face its own unique combination of these challenges. Addressing these challenges require combinations. Between you, to say like combination and coordination between data scientists. Compliance officers, IT teams, and business stakeholders. Without this cross-functional collaboration, even technically, sound solutions may fail to deliver a value. Successful implementations begin with clear governance frameworks before any code is written, establishing proper guardrails for innovation. This framework should define roles, responsibilities, and escalation path prevent questions arise. The organization seeing the most success are those. That approach, responsible AI as a business transformation initiative, not just a technical project. Next slide here. Before we talk about a specific AI toolkit available today, I want to briefly reference a research paper, which I co-authored with my colleagues, which was recently accepted for publication by IEE. In this work, we developed an AI driven scheduling framework that uses long short-term memory networks to workload prediction and isolation forest algorithms. For anomaly detection in cloud ETL environments, this is particularly relevant for a big data processing in banking and financial systems where efficiency directly impacts both costs and a customer experience. We extensively tested this framework across major cloud platforms like AWS Azure and Google Cloud using real workload traces and current pricing data. The results were very compelling, meaning we achieved up to 40% reduction in cloud infrastructure cost with significantly improved responsiveness and. Intelligent auto-scaling behavior that adapted to changing conditions. This research matters to our discussion today because it forms a foundation for scalable privacy aware data processing pipelines. In fact, our next research phase is actively exploring how the same system can be extended using a federated learning approaches to support compliance heavy financial workloads. So we are not just discussing theoretical concepts here. We are building a proven technology that already working in real enterprise environments and evolving toward even stronger privacy protections and operational efficiency. Next slide. Number 10. The ecosystem around responsible AI is maturing rapidly. Enterprise Ready Tools Now make privacy aware AI deployment feasible in production environments, not just research labs, right? Measure cloud providers support federated learning capabilities paid with confidential computing environments for comprehensive data protection. These manage services reduce the implementation burden while maintaining strong security guarantees. Open source differential privacy libraries from Google IBM and Open Mind gives teams flexibility in implementing privacy protections. These tools have. Active communications and regular updates make them wearable options for protection use. What used to be r and d concepts are now deployment already. Techniques with enterprise support and growing communities of practice, this maturity means financial institutions can implement these approaches with confidence in their sustainability. Next slide. You're wondering where to begin. Start small, but think strategically. Pilot a federated use case where privacy matters. Fraud detection is an excellent candidate given its sensitivity and high business impact. Build a cross-functional team spanning analytics, legal and IT capabilities. This diverse key of prospective helps. Navigate technical, regulatory, and operational complexities from the beginning. Define success metrics upfront, not just model accuracy, but regulatory alignment, risk reduction, and business value. These broader metrics help demonstrate the full impact of responsible AI approaches culture, establishing a center of excellence that can document learnings and establish best practices. Remember, it's about building a momentum responsibly, not achieving perfection immediately. Each successful implementation creates institutional knowledge that makes the next one easier. Next slide here to wrap this conversation up. The future of AI in finance is exciting because it'll demand trust and transparency like never before. Explainability, secure collaborations, real time competence checks. These are not nice to haves anymore. They're becoming requirements. And my last slide for today, if you take one thing away today from this topic, it's this. Privacy is just a roadblock to ai. It's a foundation for its success in financial services. Thank you so much for your attention. Feel free to contact me over LinkedIn and thanks again for giving me this opportunity, conf 42. Thank you. Bye now.