Hello everyone. Welcome to the session on cloud native hyper automation that's transforming compliance and risk management in financial services. I'm Ma Swami. I work as an architect at Microsoft Corporation. And I have worked with financial services industries. For several years now. In this presentation, we will explore how hyper automation powered by AI and Kubernetes is fundamentally transforming compliance and risk intelligence across financial systems. The financial services industry faces mounting pressure to process exponentially growing transaction volumes while maintaining rigorous compliance standards and. Mitigating the emerging risks in real time. Traditional approaches to compliance and risk management are no longer sufficient. We'll examine how cloud native architecture enables scalable, resilient, and fully auditable workflows. We'll look at certain use cases like know your customer, KYC, verification, fraud detection, and some sophisticated credit risk modeling. All of these use cases, as we will talk further through the session, are supported by machine learning models that can be containerized, and we will talk about how GI tops and certain methodologies can help with these use cases. Throughout this presentation, I'll share key architecture, design principles, implementation strategies, real world lessons. From having worked on building adaptive regulation of your systems that evolve seamlessly with rapidly changing financial landscape. Alright, let's dig in. So the hyper motivation refers the strategic convergence of artificial intelligence, machine learning, robotic process automation, and cloud native technologies to transform. Manual error prone enterprise operations into these intelligent self optimizing systems. The transformation enables financial institutions to process thousands of compliance checks simultaneously. It can also adapt to changing regulatory requirements and. Be able to generate the re required reports in a matter of couple of hours instead of months. It can also help in maintaining consistent policy enforcement across global operations. The core technologies that power hyper automation are machine learning. Which has these deep learning models for pattern recognition, anomaly detection, predictive analytics, deployed as containerized services. Now, these models continuously learn from transactional data, identifying subtle patterns and credit risk indicators that traditional rule-based systems cannot detect natural language processing. Provides these large natural language processing supported by these large language models. Help with say document analysis, regulatory, text interpretation, and automated report. Generation NLP systems extract critical information from unstructured regulatory documents and generate compliant reports in multiple languages. RPA, which has these intelligent bots handle routine compliance tasks, do data validation and regulatory submissions. These RPA systems work 24 by seven, processing thousands of transactions with perfect consistency while lots of freeing up human analyst for complex decision making. Computer vision on the other hand, helps with document verification, biometric authentication, and visual fraud detection capabilities. Kubernetes serves as this foundational orchestration layer for all of these technologies to come together and support hyper. Automation Kubernetes is able to do that as it is able to consistently scale and provide secure systems for every component from machine learning and inference engines to workflow automation bots. We'll talk about the architecture details a little more as we discuss the use cases. It's modern. Financial institutions leverage Kubernetes to orchestrate complex compliance workflows, which are distributed across these microservice architectures, the containerized approach fundamentally transforms how financial systems operate, enabling elastic scaling during peak regulatory reporting periods. Seamless deployment and versioning of machine learning models and fault tolerant processing of hypersensitive financial data that is geographically distributed. A critical Kubernetes features enable. Truly adaptive systems. This horizontal pod autoscaling dynamically adjusts the resources based on the transactional value. The service mesh technology provides zero trust security between microservices and ops workflows to. That allows teams to deploy policy changes across global infrastructure in minutes rather than weeks. But these capabilities all allow financial institutions to build systems that respond dynamically to changes in the regulatory requirements or market conditions while maintaining unwavering compliance and operational resilience. This architecture fund provides the foundation for truly cloud native financial operations where infrastructure adapts automatically to business needs while maintaining rigorous security and compliance standards. Now let's look at a couple of use cases. The first use case that I've got here is how KYC can be transformed with multimodal authentication. The multimodal biometric verification combines facial recognition, document authentication, and behavioral analysis with containerized microservices. Computer vision algorithms powered by computer vision or open CV and tensor flow. Validate identity documents while machine learning models assign, assess the risk profiles in real time. This approach uses biometric APIs like say Azure face API for precise identity matching, coupled with behavioral analytic models that detect anomalies in user interaction patterns. Each verification T runs as independent containerized microservice on Kubernetes, enabling truly parallel processing of. Identity verification steps. So when a user uploads the identity document and a selfie, for example, for this picture, Kubernetes orchestrates simultaneous document verification or validation, and the facial matching with the behavioral scrolling, delivering a comprehensive. Confidence core for risk assessment in seconds rather than hours. That dramatically reduces the customer onboarding friction while exceeding regulatory compliance standards. The other use case that we have is streaming analytics for fraud detection. Now, real time fraud detection requires processing millions of transactions per second while identifying subtle patterns that indicate fraudulent activity. Streaming analytics, leverages even driven. Can, it's even driven architecture and Kubernetes to create a resilient, scalable fraud prevention system. This architecture can process transactions with latency measured in milliseconds, enabling institutions to block fraudulent transactions before they complete, while minimizing false positives that frustrate legitimate customers. So what I've represented on screen is just four different steps and where that ingestion is being done by this event driven architecture which are listening to the signals and Kubernetes is leveraged for the containerized implementations. The models are helping detect the patterns and alerts are the ones that help. Financial systems, financial services organizations detect and manage the frauds. Now, AI driven customer service is another scenario, which you must all be very familiar with. Or at least anybody who has tried contacting customer service in the recent past. So any powered customer service agents deployed across Kubernetes clusters can provide consistent compliance responses while dynamically adapting to the changing requirements. These agents leverage natural language understanding to resolve complex financial queries without human intervention, maintaining conversation context across multiple interactions. As an example, these can be built on Azure OpenAI services and deployed through Azure Kubernetes service. These virtual agents understand customer intent. They can access account information securely and provide personalized financial guidance. All this can be done while maintaining strict compliance with privacy and regulatory standards in the financial services industry. Alright, next one that we have here is QAPs driven compliance automation, large language model based compliance pipelines. Deployed through GitHub's workflows ensure consistent, auditable regulatory processes across all of the environments. Essentially, infrastructure as a code principle can extend beyond the traditional DevOps to compliance rules themselves, enabling version controlled regulatory logic, and fully automated policy updates. Today most financial and regulatory enterprises struggle with fragmented manual compliance processes that creates significant operational land operational risk. And these compliance rules are often managed in spreadsheets, word documents, and siloed systems. Making it nearly impossible to ensure version control, consistency, or even traceability across streams and geographies. So in the traditional systems. When regulatory frameworks change, updates to policies or workflows are applied manually by different teams leading to implementation deals that are measured in typically months to years. Human errors in policy interpretation and dangerous gaps in audit coverage are not uncommon. These. There's also extremely limited visibility for the compliance teams that they're unable to easily verify whether the applied systems are fully aligned and the reg with the latest regulatory standards. Now, that's where GitHubs can help. It can. Draft dramatically reduce the operational risk because everything is traceable and auditable With compliance updates deployed in hours instead of weeks, it can completely eliminate the drift between environments and automated validation that all of the systems comply with the. Current regulations, it simplifies certification of the systems within the financial services industry. Credit risk sorry about the typo. It's not revolution, but evolution mission learning algorithms. Analyze vast data sets include including comprehensive transaction history, behavioral spending patterns credit bureau data and external risk factors to generate sophisticated, multi-dimensional credit scores that far exceed the predictive power of traditional FIO based models. Containerized model serving architecture enables sophisticated AB testing of risk models in production environments that allows continuous improvement of credit positioning, accuracy. Models are deployed. Models can be deployed as independent microservices, enabling real time scoring of loan applications while maintaining strict regulatory compliance and extensibility requirements. Advanced techniques like gradient boosting natural neural networks and ensemble methods identify complex linear relationships in credit data. They can improve default default predict prediction accuracy. Now one thing to bear in mind when thinking about credit risk evaluation using these technologies is the potential bias in these models it's a significant challenge that. Models may unintentionally favor or disadvantage a loan applicant based on the factors that are correlated with protected characteristics such as gender, race, geography, socioeconomic status. For example, this often occurs when the training data reflects historical lending patterns that embody the past discrimination rather than objective financial behavior or creditworthiness. This comprehensive approach of detecting a bias, having explainable ai, knowing which models are used and being cognizant of the models that are being selected, and continuous monitoring of what these models are resulting or showing the results will help in reducing the bias. Reducing the bias is essentially important to ensure fairness while also ensuring that you are able to assess the risk accurately. It is also important to be able to explain how a risk is determined to be compliant with regulations and ensure transparency and accountability. Looking at some of the implementation strategy and best practices. Firstly I would suggest looking at high impact use cases, these high impact use cases, for example, say KYC verification, which is which is very much required in financial services industry, but it's also one of the critical processes that impact. The customer of the financial services industry in addition to the operations teams internally. So essentially, these high impact use cases will help showcase a return on investment that will that'll help the firm adopt these, latest technologies that can help them become one of these frontier firms. The second best practice is to firstly build the cloud native foundation that is required to support these forward looking technologies. Kubernetes infrastructure, for example with proper security controls and monitoring and alerting can can help. We saw all of these core technologies that power hyper automation. The third one is to make sure these changes are gradual and not happening all at once. Using cannery deployments, which is essentially some, a type of blue-green deployment to reduce their risk involved in, in rolling out these technologies or also consider using AB testing framework to validate what these machine learning models are how these machine learning models are performing in production and how you could reduce risk if required. The last one I have on screen is to ensure that there is regulatory alignment. This is most important, as you all know, within financial services industry. All of the systems that are used. It is highly recommended to maintain comprehensive audit trails and ensure that there is explainable AI capability and automated compliance validation built directly into the architecture. So these considerations should be taken right at the beginning of your implementations, right when you're designing the system, not as an afterthought. And successfully implementing hyper automation requires these strategic phased approaches that balances innovation and operational stability alongside the regulatory compliance. Continuing with some of these best practices, technical risk mitigation is, can be achieved using multi-region deployment for your business continuity and disaster recovery capabilities. And monitor your models and retrain the pipelines to maintain model accuracy, ensure that are circuit breakers built in to have fail safe mechanisms for the ML or machine learning service failures. We've talked about reducing bias, and that's a very important factor. Compliance considerations, explainable ai, privacy, preserving machine learning, immutable audit logs that cannot be altered essentially, and data quality monitoring. These are some of the factors that we do wanna make sure are taken into consideration, followed through the implementation to ensure excellence. They essentially create technically robust and compliant system when you're evolving in ensure the sustainability of these hyper automation initiatives. Wrapping up with the future of hyper automated finance. Finance hyper automation represents a fundamental paradigm shift from reactive manual compliance processes to proactive, intelligent risk systems By, and you achieve this like we've talked about by strategically combining the AI capabilities with cloud native architecture patterns. Financials institutions can build truly adaptive systems that evolve seamlessly with regulatory changes while maintaining exceptional operational efficiency and customer experience. The convergence of machine learning, containerization and automation technologies creates unprecedented opportunities for financial innovation organizations that. Successfully implement these comprehensive strategies will gain substantial competitive advantages through dramatically reduced operational costs, significantly improved customer experiences in enhanced regulatory compliance posture, and the ability to respond to market changes with speed and accuracy. And to wrap it up. I would like to say that the future belongs to institutions that embrace transformation. Building systems that are not just automated, but are truly intelligent and adaptive. Thank you for your time. I appreciate you listening to this session.