In today’s landscape of AI-driven innovation, financial institutions are increasingly looking to generative AI as a transformative technology. However, the success of such initiatives hinges on the foundational management of data.
Hossein Zahed, Senior Director of Data Science and Machine Learning at Capital One, tells Emerj Managing Editor Matthew DeMello on the ‘AI in Business’ podcast that effective AI deployment begins with solving business problems at scale. “Understanding the problem first is key,” Zahed states, noting that an AI initiative without precise alignment to customer needs risks creating downstream inefficiencies.
He insists that data management serves as the bedrock for AI functionality, and in industries like financial services, the stakes are particularly high. Effective data governance ensures not only that AI systems have access to high-quality inputs but also that these systems can adapt to emerging customer demands.
Robust data governance frameworks are critical for scaling AI capabilities across organizational silos, allowing for consistent and accurate outcomes. According to the Open Data Institute, current data governance frameworks often fall short in covering all phases of AI, from design through to deployment, leading to high-performing models in tests that may fail in real-world applications.
For enterprises transitioning from legacy systems, adopting cloud-based solutions and centralized data repositories represents a critical first step toward this goal. However, a Deloitte survey found that only 19% of organizations have a strong data governance structure currently in place, highlighting the significant challenge of modernizing outdated infrastructures to support the complex data needs of generative AI.
A significant challenge for financial enterprises lies in modernizing their legacy systems. Many organizations still rely on outdated infrastructures that cannot support the complex data needs of generative AI.
Zahed’s conversation with Emerj highlights Capital One’s unique position in hybrid strategy in this area, having transitioned fully to the cloud to enhance data accessibility and scalability. This transition enables data scientists and engineers to focus on solving customer problems rather than battling fragmented or inaccessible datasets.
This article examines three critical strategies for AI adoption in financial services, as outlined by Hossein Zahed during his podcast appearance:
This article examines three critical strategies for AI adoption in financial services Zahed explains at length during his podcast appearance:
- Leveraging Generative AI (GenAI) for enhanced customer interactions: GenAI drives personalized customer interactions by analyzing data touchpoints and providing actionable insights, while backend improvements ensure seamless integrations.
- Scaling AI solutions through hybrid strategy: Scalable cloud systems and cross-functional collaboration are essential for supporting the growing complexity of AI-driven initiatives.
- Embedding ethical considerations in AI deployment: Establishing robust ethical frameworks that include proactive bias detection, regulatory compliance, and transparency mechanisms from the earliest stages of AI implementation mitigates risks and fosters long-term customer trust.
Listen to the full episode below:
Guest: Hossein Zahed, Senior Director of Data Science and Machine Learning at Capital One
Expertise: Generative AI, data governance, cloud infrastructure, AI-driven customer experience
Brief Recognition: Hossein Zahed, Senior Director at Capital One, has spearheaded AI-driven innovations, including modernizing legacy systems and enhancing data governance. With extensive experience in financial technology and cloud infrastructure, he is a recognized leader in aligning generative AI with customer-centric strategies.
Leveraging Generative AI for Enhanced Customer Interactions
GenAI’s ability to transform customer interactions has been a key focus for enterprise leaders. From personalizing financial products to predicting customer needs, AI-driven insights allow companies to create seamless and engaging experiences.
Zahed points out that while customer-facing applications like chatbots and virtual assistants garner much attention, the real value lies behind the scenes. AI-powered systems that enhance data architecture and processing capabilities enable these visible applications to perform efficiently and effectively. However, many organizations overlook the importance of backend investments, which are often the foundation of customer satisfaction. A study published in MDPI reveals that companies leveraging AI for backend operations achieve significantly higher customer satisfaction rates by addressing the critical disconnect between user-facing tools and the unseen technological infrastructure that drives them.
These backend enhancements often include advanced sentiment analysis, real-time fraud detection algorithms, and predictive analytics that inform proactive customer service strategies. Such innovations not only provide a more secure and personalized experience for end-users but also ensure that visible customer-facing tools perform optimally. The critical insight here is that the success of customer satisfaction initiatives often hinges on the unseen technological investments that enable these seamless experiences.
Zahed emphasizes the importance of understanding customer needs at a granular level. For instance, analyzing data on when and how customers engage with financial services provides actionable insights to optimize touchpoints and tailor offerings. By addressing the often-overlooked backend capabilities, organizations can better align their technology investments with customer expectations, fostering deeper engagement and loyalty.
AI systems capable of processing this data can recommend actionable strategies to enhance user experiences, such as offering real-time support during high-traffic periods or tailoring product suggestions based on historical behavior.
Additionally, prioritizing backend improvements ensures that AI systems can seamlessly integrate with existing workflows. By enhancing internal operations, companies not only improve their responsiveness to customer needs but also position themselves to adapt more quickly to market changes. These efforts collectively drive a superior end-user experience while reinforcing customer trust and loyalty.
Zahed points to one example of driving back office efficiencies into higher quality customer experiences can be found in the use of AI for predictive analytics. By anticipating customer needs based on behavior and preferences, financial institutions can proactively offer solutions, such as customized loan options or personalized investment advice.
These predictive capabilities not only foster deeper customer engagement but also generate higher revenue by aligning products with individual requirements.
Scaling AI Solutions Through Strategic Investment in Infrastructure
Effective AI implementation depends not just on data quality but also on the scalability of the underlying infrastructure. Many enterprises grapple with the decision to modernize their tech stacks, weighing the benefits of cloud adoption and advanced storage solutions against the costs of transition.
Zahed emphasizes that starting with scalable foundations is critical for long-term success. “Trying to pivot later on is much harder,” he advises, pointing to the advantages of building adaptable systems from the outset.
For companies aiming to future-proof their operations, investing in scalable infrastructure such as object-based storage and advanced cloud systems is non-negotiable.
These technologies enable organizations to handle the growing volume and complexity of data required for AI solutions. According to a study published in ESP Journals, hybrid strategies that balance cloud-based systems with object-based storage have emerged as critical solutions to these challenges. Enterprises adopting such scalable technologies report significant improvements in both operational efficiency and customer retention.
Zahed also underscores the value of cross-functional collaboration in scaling AI initiatives. Teams across data science, engineering, and business strategy must align their efforts to ensure that AI solutions are not only technically viable but also aligned with organizational goals. Alignment between the two is particularly crucial in industries like financial services, where customer trust and regulatory compliance are paramount.
Moreover, companies that invest in robust infrastructure gain a competitive edge by reducing time-to-market for new AI-driven solutions. Rapid deployment capabilities allow businesses to capitalize on emerging opportunities while maintaining the flexibility to scale operations as needed. He notes that a faster time-to-market approach minimizes the risks associated with infrastructure bottlenecks, enabling sustained innovation and growth.
Furthermore, scalable AI solutions offer enhanced resilience during periods of uncertainty. For instance, during the COVID-19 pandemic, organizations with robust cloud-based infrastructures were better equipped to adapt to remote operations and shifting market demands. By investing in such technologies, businesses not only prepare for current challenges but also ensure readiness for future disruptions.
Embedding Ethical Considerations in AI Deployment
As enterprises expand their AI capabilities, ethical considerations must remain at the forefront. Ensuring data privacy, preventing algorithmic bias, and maintaining transparency is critical for building trust with customers and regulators alike.
Zahed highlights the importance of establishing frameworks to evaluate the risks associated with emerging technologies. “Thinking about risks early on can save a lot of trouble down the line,” he advises.
He elaborates, “Governance frameworks must address not just the immediate technical risks but also long-term ethical considerations, such as algorithmic fairness and data privacy. This means designing systems that can adapt as regulations evolve, ensuring compliance without sacrificing innovation.”
This detailed approach underscores the necessity of proactive governance as a foundational element in successful AI deployment.
Incorporating ethical frameworks into AI strategies not only mitigates risks but also enhances brand reputation. According to AIMultiple Research, companies perceived as ethical innovators enjoy higher customer loyalty and greater market share. The trend highlights the dual benefit of ethical AI: it safeguards stakeholders while driving business success.
By embedding ethical considerations into their AI deployment strategies, Zahed notes organizations can ensure compliance with regulations while maintaining customer trust — the balance between innovation and responsibility positions companies as leaders in the increasingly competitive landscape of AI-driven industries. Ethical practices serve as a foundation for sustainable growth, enabling enterprises to navigate complex challenges while fostering long-term success.
Zahed emphasizes the critical role of regular audits in maintaining compliance with ethical standards.
“Auditing AI systems isn’t just a checkbox for compliance—it’s an ongoing process of refinement. Regular audits help us uncover hidden biases and vulnerabilities before they become systemic problems. For instance, we’ve used audits to identify discrepancies in credit approval algorithms, ensuring fairer outcomes for customers.”
– Hossein Zahed, Senior Director of Data Science and Machine Learning at Capital One
This process not only builds trust with regulators but also strengthens the overall data governance framework, enabling organizations to address potential risks before they escalate. Proactively engaging with stakeholders, including regulators and customers, in these audit frameworks further demonstrates a commitment to ethical AI practices.
