Hello everyone and thank you for joining me today. My name is Heh Bja and I'm a software engineering leader creating enterprise software solution from the last 15 years. Currently my work is focused on HCM and FinTech space, creating solution that provide small and the medium business workers, which are SMBs the backbone for US economy with the tools they need to focus on their jobs without. Taking the burden for the financial stress. The topic for our session is how to architect Scalable platform for financial wellness for employees through the HCM and the FinTech integration. This is just not a technical problem to solve or a challenge. This is a growing financial stress into the ecosystem industry. For employees and they're directly impacting their productivity engagement and also impacting employer for the employer retention. Traditionally, HCM system often operates in the isolation from financial wellness tools. What are we going to explore today? How to bridge that gap? Build a unique opportunity to reimagine how workforce data and the financial insight can work together. To create a meaningful employee experience. So let's spend some time, maybe 15, 20 minutes with deep dive into the business use cases for this integration, the technical architecture required to build our scalable and the secure platform, the core principles, and some of the business use cases. Let's start with the business use case, the impact for the financial asset. The impact for the financial stresses are very much well documented. Employees who are experiences financial difficulties demonstrate decreased focus, increase healthcare utilization, and the higher turnover rates. The core issue of the problem is the missed opportunities. Here, most enterprise architecture treat workforce management and the financial wellness as a completely separate domain. They work in isolation, meaning the missed critical chances to provide intervention and support when needed the most to the workers on the technical front. All modernized enterprise must navigate complex requirement, maintain strict privacy standards, and ensure a seamless user experience across a variety of different and often cross-functional use cases. To build our effective platform, we need a strong technical foundation. There are few core pillars for the architecture that we have to follow. Whenever actually we are thinking about to moving any data, we need a sophisticated data orchestration layer, which just goes beyond the traditional ETL processes. A key part of this is privacy design as we deal with the employee data, which is more sensitive. We want to make sure that all our platform are deal with the privacy and security in the mind from the day one central of this is the event driven communication pattern. All the modernized platform need real data for the workforce event, like when employee salary changes new benefit enrollments. They move, that means their effective dating are changing their performance reviews, which can trigger the relevant financial wellness intervention or an opportunity. Our communication layer must handle both synchronous and the asynchronous pattern effectively, and we can achieve this using even streaming platform like Kafka, which is widely used across the industries as the backbone for our hybrid communication model. When we architect the system, we can leverage a number of proven design pattern to address specific challenges. These are not unique. This has been used in the multiple industries, so we can learn from there. Number one is architecture, which allow us. That how are core business logic to remain independent of the integrations providing the clear interfaces for the external system for integration. This is vital for a system that connect with the various different providers. Second is the API Gateway pattern, which provides a single entry point for all of our integration offering unified authentication, how to throttle the request rail. And monitor and tracking. The lastly is circuit brick or circuit breaker pattern, which is crucial for stability. It's prevent cascading failures and ensure graceful degradation of functionality. When a connected system experiences any issue. In case of any failure, we can look into more advanced topic like data mesh architecture for organization with complex data needs. Privacy and the security consolidation, as I said in the past, these are the must consolidation we should take Whenever we're talking about the HCM and the financial system. Whenever we're dealing with the employee financial data, privacy and security are paramount. We should adopt. In fact, we must adopt a zero trust architecture, authenticating authorization, each and every request. We should know regardless of. What is the source? Who is trying to accept the data? This provides the gran control, which is essential for the financial data. We should also implement multi-layer encryption. We should encrypt data at all stages and rest. While in transit, even while in use through the advanced encryption techniques token piece authentication, which also allows to support custom roles find grade and access control, and with token scope to the specific data types. And finally, we must practice data minimization. We should actively minimize data exchange to only what is absolutely necessary for the specific business functions. Let's talk about what AI. Can play a role here. AI can make those systems more proactive. It can transform our reactive financial wellness program into our proactive intervention systems. By analyzing the pattern, by looking into the workforce data and the financial behavior, AI model can definitely identify employees who are at risk for financial stress before it become a crisis for them and impact their home. Capabilities. We can also do this without centralized sensitive data by using federating learning approaches. AI can also leverage NLP techniques to analyze communication pattern for stress indicator and a behavioral analytics to extent beyond direct financial data to include metrics like productivity sick leave. These are, these are the scoring and the metrics we should. Aligned to each and every worker in their workforce. The goal is to use realtime scoring system to enable the immediate response to identified risk factor. The feedback loop is very much critical here. In fact, that's that's a key for the ai. We learn from our system and continuously refine the accuracy and effectiveness for our AI models over time. Another one of the important applications for HCM FinTech integration is. Personalized nudging. We can leverage the combination of workforce context and the financial data to deliver precise, timely, and the very relevant information. For example, if I imply situation changes, take example, they can promote, then they need, immediate changes to their retirement contribution or some other financial decision they have to take care of. Or if something happened in their family or there's a time for enrolled for the new benefit or a new deadline, which could change their personalized decisions. So these kind of situations we should handle with the right kind of nudges. We should also look into the behavioral economics also, and also for the effective delivery, we should use multi-channel approach. It can be SMS, it can be the mobile apps. It can be through the notification also, so we can nudges worker with the right information at the right time for all the workflows. Moving beyond traditional batch processing and even, even architecture enable immediate response to the employee life event as financial stress indicator that we spoke about in the last site. The key component of these architecture includes event sourcing pattern that captured the complete history of an employee financial wellness journey. Stream processing for real time analysis of the workforce even as they occur. And carefully design event schema that support the evolution over the time. By building a system with massive durability, we ensure critical events are never lost. This approach allows for the powerful cross system correlation connecting workforce, even with the financial behavior pattern to provide real time analysis. Let's look into the microservice design. The microservice design is the perfect for this kind of platform because it's allowed them to evolve rapidly while maintaining the system reliability and scalability. This is a particularly valuable when we are integrating with. Diverse and complex set of and the financial service provider. We designed our service boundaries to align with the business capabilities rather than the technical consideration. We have to find a balance between performance and the reliability in our interservice communication model. Our API design should abstract implementation details while providing flexibility for different integration scenarios. For data consistency, we should consider eventual consistency pattern for appropriate data types and for our surface discovery to enable dynamic scaling and the deployment pattern that will handle the variable demands. Our reality for our many enterprises that the core system are legacy. We hardly change the system and upgrade the system sometime as, as pricey, sometime as need more, more investment or they're, they, they're hard to upgrade. This system often lacks the modern API capability. They are still using the old data format and very limited real time integration option. So what are the solution approaches? We can modernize our ETL with the change data capture techniques and build data transformation layer to map between legacy format and the modern API and use adapter pattern. To isolate the legacy integration logic. We must also accommodate batch processing windows with catching strategies, time to live and build a comprehensive error handling and the retrial logic. Let's touch base a little bit of compliance and regulatory architecture, consolidation. Also, compliance is not a side project. It's it required. It's a core architectural consolidation, and we should adopt it from the day one, like security. We need a robust data governance framework, and a consent management is critical because we are talking about the employee person leader. We should. Take their consent to use their data for the different purposes. We need clear data retention policies. Also that balance regulatory requirement with operational needs using automated management of prevent compliance gap and also audit trails must that must be in detail, inimitable to support regulatory examiner and the legal discovery processes. We should build these capabilities into our platform from start to reduce the compliance burden and improve accuracy. Whenever we serving the large employee population, performance optimization is a constant challenge. Our architecture decision must anticipate peak uses. When is the time where we have like the high load, maybe the year end, maybe the quarter trend, or maybe whenever there payroll runs to talk about the peak while maintaining the responsive performance during the normal operations? Our scalability strategies include. Multi-tiered caching architecture optimized for different users patterns, database charting strategies that account for the diverse access patterns, using CDNs for financial execution materials and the load balancing that considers the stateful nature of financial wellness interactions. We must also ensure our performance monitoring extent beyond simple infrastructure metrics to include business relevant measurements. Let's look at a few examples of this pattern in action. Let's talk about a healthcare organization, a healthcare organization that started small by connecting emergency saving program to their payroll system. This focus approach, established core integration pattern and demonstrated value before they expanded to the more complex feature resulting in significant improvement in the employee financial resilience, a technology company. Leverage a microservice architecture to create personalized financial education program that adopted. To the employee career trajectories, their event driven approach enable real time responses to promotions and the equity investing events, which improved retirement saving participation, a manufacturing organization that adopted adapted pattern to integrate modern financial wellness capabilities without disturbing their established HR processes. This shows how architecture pattern can enable innovation even without within a constrained technical. Environment and a financial services company face unique compliance challenges. They use sophisticated consent management and the data governance pattern to provide valuable employee benefit while maintaining the strict regulatory compliance. I. As we look into the future, we'll see separate ER emerging technologies that continue to shape this space, including the advanced AI capabilities beyond simple pattern equalization, blockchain for trust, transparency in the quantum resistant encryption. We also anticipate greater API standardization across the HM and the FinTech industries. To build truly sustainable platform, we need to follow some key success principles. Start with the career use case and the success metrics designed for privacy and security from the beginning. Leverage event driven pattern for responsiveness and maintain marginalized architecture that can support evolution. Ultimately, this integration is more than just a technical project. It isn't app opport to reimagine how technology supports human potential. Does take us to the end. Thank you so much. I appreciate you all taking some time to listen. Thank you so much.