Oracle Financial Services

Banks are modernizing finance to enhance efficiency and agility. While updating legacy systems is challenging and costly, can intelligent automation—combining RPA, AI, and generative AI—help banks scale and drive greater value in their transformation journey? During our Finance Transformation session at #OCW this week, Yogi Singh from Oracle hosted an engaging conversation with executives Morgan Klein-MacNeil from TD, Andy Coffey from M&T Bank and Vishnu Mangal from Scotiabank, where they shared their insightful transformation journeys. One mentioned that they focused on consolidating data and profitability insights, and their transformation included a major shift to a single general ledger system, emphasizing change management and consistent data practices. While another is on a SaaS transformation journey, as they are working to improve efficiency by consolidating data from four ledgers. The goal is to go live with profitability-focused applications by 2025, using a lift-and-shift approach. One said they have began their journey in 2010, focusing on standardizing global operations. They learned that building a solid foundation before layering new capabilities was essential to success. When it comes to discuss change management challenges, all speakers advocated for tackling transformation one use case at a time and stressed the importance of integrating core systems, like retail and capital markets, to drive impact. They also shared some best practices for data consolidation: - Focus on specific use cases rather than trying to overhaul everything at once. - Establish a single source of data and ensure integration with core systems to identify key financial impacts like cash flow and taxes. - Build data foundations first, then layer advanced capabilities like profitability analysis. They discussed their Gen AI and automation use cases and lessons learned: - Using Gen AI to support CFO office with insights, analytics, and reporting, driving product innovation and profitability. - Highlighting the need to separate AI from RPA, noting that many end users mistakenly interchange these technologies. - Emphasizing the need for permanent, rule-based solutions in automation to avoid short-term fixes and ensure lasting business impact. Banks are prioritizing building robust data foundations, leveraging Gen AI to enhance financial insights, and streamlining legacy systems to improve overall efficiency. As automation and AI continue to evolve, the focus remains on strategic application, effective change management, and continuous learning to drive sustainable finance transformation. #FinanceTransformation #Banking #ai #genai #IntelligentAutomation #financemodernization #roboticprocessautomation #RPA Sabrina Scott Jason Wynne Mark Atherton Gina D'Onofrio Brian Tom

The way financial services firms manage profitability is evolving rapidly, fueled by AI and data-driven insights that enhance decision-making and growth potential. By moving 1/3 of the zero profitable customers up one segment could generate an increased client profit contribution of $75m per year! During this learning session On the Revenue Growth Path: How Al Is Reshaping FS Institutions’ Profitability at #OCW this week, Jason Wynne from Oracle shared four opportunities for AI in maximizing profitability: 1. Clustering and Classification: Use labeled data to train models and discover new customer segments. 2. Anomaly Detection: Identify seasonal patterns and detect irregularities. 3. Simulation and Forecasting: Employ traditional models and synthetic databases for market predictions. 4. Generative AI Agents: Experiment with Gen AI to simulate realistic market conditions. Then our prestigious customer speakers Mark Downey from Resolution Life and Jyotsna S. from Voya Financial shared their best practices on leveraging AI to increase revenue potentials. Here are the highlights: - Understanding your target audience and making impactful decisions is crucial. But don’t forget the intangible elements like customer experience, which also drive profitability. - AI helps analyze profitability and customer lifetime value (CLV). Data quality, ownership, and a collaborative culture are essential for maximizing these insights. Consistent data curation and engaging external factors can help retain customers. - Breaking down data silos and communicating data's cross-functional value is critical. - AI applications in customer segmentation, risk management, and proactive engagement are key to adapting to economic changes. - Engage with leadership to drive AI adoption and explore innovative use cases. - Establish data ownership and ensure data consistency for clean, actionable insights. Increasing profitability in financial services isn’t just about leveraging technology—it’s about fostering a collaborative culture first that supports AI experimentation and integration to make smarter, data-driven decisions. The future is bright for those ready to embrace AI’s potentials. #riskandfinance #profitablity #ai #financialservices #banking #CLV #customerexperience #revenuegrowth Sabrina Scott Mark Atherton Yogi Singh

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#OCW day 1 - SaaS native cloud core banking 🚀During the session “Revolutionizing Industry Cloud with SaaS Native Cloud Banking”, Steve Smith from Accenture and Vikram Gupta and Aubrey Hawes from Oracle discussed how SaaS-native cloud core banking is transforming the industry from the following perspectives: challenges and landscape, key design elements, business strategies, AI, and security & compliance. * The rise of highly scalable, cloud-native solutions is the trend. Banks need to understand the differences between true SaaS applications vs. PaaS applications and adapt rather than remain static. Also, it is important for cost management in core transformations, particularly for regional and community banks. Consumers expect better products and force banks to increase speed to market. * When it comes to design, our approach is to provide unlimited scalability with API-first approach. We offer high performance & maximum resilience with robust disaster recovery. Our solution meets stringent US regulatory standards. * Building vertical apps that drive true cloud transformation. Also, differentiating between ‘me too’ solutions and unique, impactful offerings. We also ensure real-time, scalable banking experiences with data grids enhancing transaction performance. * Banks are still muddling through the AI wave but they are looking into uses cases that can improve customer support, empower associates and enhance decision-making processes. Also, as a bank, have the ability to solve customer’s real problem at the moment is crucial! Let’s transform your banking experience with cloud-native core banking solutions that are built for the future! #coremodernization #SaaSBanking #CloudCoreBanking #saas #cloud #banking #oraclecloudcorebanking

#OCW day 1 - AI Thrilled to have witnessed an engaging session on Gen AI for banking, expertly moderated by Sanjay Mathew! The conversation among USAA, Accenture and Oracle dived into best practices and where and how to get started to seamlessly ingest AI into business processes. Key Takeaways for me are: 1. AI Transformation Journey: It is all about turning AI concepts into practical applications. Panelists emphasized the importance of validating AI use cases, especially in financial and accounting areas, to remove manual work and deliver value to the end user. 2. Insights on AI adoption: Tony Cyriac from Accenture highlighted the shift from last year’s focus on efficiency to this year’s emphasis on driving revenue and customer engagement through AI. He stressed the importance of building organizational muscle in legal, compliance, and HR to learn and grow together. Also Tony highlighted AI’s role in automation and augmentation, with the potential to significantly transform client experiences. He encouraged financial institutions to embed AI into their core processes and shift from isolated use cases to more comprehensive, value-driven strategies. All panelists suggested starting small to improve efficiency in banking, such as configuring process flows for loan origination. With AI, processes like trade finance can be accelerated, customized, and scaled with specific bank logic and rules. 3. Navigating the Complexity of AI: Common challenges include data governance, compliance, and addressing AI biases. The panelists underscored the need for a clean, trustworthy data foundation and transparent AI models that can withstand legal scrutiny. Across the industry, there’s a consensus on the importance of validating AI use cases rigorously. As banks navigate new regulations and use AI to predict impacts and streamline workflows, the focus remains on ensuring data accuracy and transparency. The financial services sector is at the forefront of AI transformation, but success hinges on data readiness, clear value propositions, and an ongoing commitment to responsible AI deployment. It’s clear that Gen AI is not just a trend—it’s the future of banking. Excited to see how these innovations continue to evolve and transform the industry! Kudos all the thought leaders who are pioneering this journey! #banking #genai #ai Aubrey Hawes Robin Smith Conor Colleary Sam Olson David Schinckel Steve Smith Tushar Chitra Vikram Gupta

#OCW day 1 What an incredible keynote session by our executive Sonny Singh! Sonny highlighted how Oracle’s comprehensive banking suite, built on a microservices architecture with SaaS ground-up is revolutionizing the industry. Then, Sonny brought our SVP of product, Vikram Gupta to discuss embedded AI. Vikram showcased an innovative use case where generative AI streamlines collections call compliance checks—truly exciting advancements! We also heard from Sovan Shatpathy, CTO at Navy Federal Credit Union about their inspiring cloud modernization journey, driven by a commitment to serve their members. Sovan outlined three key drivers: 1. Banking is now a technology and data business: Modernization is essential. 2. Member-centric approach: Members expect their financial institution to support their financial well-being. 3. Future-proofing: Ensuring the institution is ready for what’s next. With $180 billion assets, Navy Federal’s full-scale modernization focuses on these strategic steps: 1. Modern, deconstructed, and slim cores 2. Data-driven, API-led, and cloud-first approach 3. Simplifying and scaling operations 4. Ubiquitous security measures 5. Agile product delivery Their transformation starts at the core—accounts—then expands to products, channels, engagement, and data, all with the member at the center. Lastly, Sovan encouraged other financial institutions to adopt an API-first approach, embrace the cloud, and begin their modernization journey sooner rather than later to stay ahead of the curve. Exciting times ahead for the industry! #banking #corebanking #cloud #genai #ai #saas #modernization Aubrey Hawes Robin Smith Conor Colleary Sanjay Mathew Sam Olson Tushar Chitra

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