Insights from Banking Leaders at Databricks Data + AI Summit

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McKinsey‘s research indicates that banks that quickly adopt digital innovations see up to a 15% increase in revenue and a 20% reduction in costs. Despite this, 65% of banking executives believe outdated data management systems significantly hinder their operational efficiency. Leaders in banks and credit unions are now prioritizing data modernization to enhance operational efficiency and customer satisfaction.

How can your institution use improve efficiency and customer relationships? We share highlights from a leader panel at the Data Modernization for Banking and Credit Union Growth event that Zennify hosted at the Databricks Data + AI Summit earlier this month. The goal? Showcase leading financial institutions that are driving data unification and AI-readiness within their organization.

Below, we share highlights from our conversation with Barb MacLean, SVP of Technology Operations and Implementation at Coastal Community Bank and Gabe Arce, VP of Salesforce & Omnichannel Centers of Excellence at Axos Bank.

Embracing Data as a Strategic Asset

Barb MacLean shared valuable insights into her bank’s journey to data modernization. Under her leadership, the bank undertook an aggressive timeline to overhaul their data systems, achieving what typically would have taken years in just nine months.

Barb detailed the transition from traditional batch processing to real-time data integration. This strategic shift is vital for financial institutions that require immediate access to accurate data for making informed decisions. Real-time data processing not only accelerates operational workflows but also improves the responsiveness of customer service, creating a more dynamic banking experience. This feat was not just about speed but strategic foresight and execution.

Turn data into a critical asset for decision-making and operational efficiency.

Barb MacLean, Coastal Community Bank

Implementing Change Effectively

  1. Start with Leadership Commitment: Successful data initiatives require strong leadership. The executive buy-in at Coastal Community Bank was crucial, reflecting a shared understanding of data’s central role in future-proofing the business.
  2. Choose the Right Partners and Tools: Coastal Community Bank’s switch to Databricks from traditional data solutions was a game-changer, enabling them to handle real-time and batch data effectively. This strategic tool choice was pivotal in addressing their unique challenges, particularly in integrating disparate data systems from various partners.
  3. Leverage Data to Drive Business Decisions: The ability to quickly integrate and analyze data has allowed Coastal to make informed decisions swiftly, a competitive edge that is vital in the fast-paced financial sector.
  4. Focus on Scalability and Flexibility: As customer markets grow and regulatory demands increase, having a scalable and flexible data architecture ensures that banks can adapt quickly without compromising performance.
  5. Cultural Shift Towards Data-Driven Operations: Moving from a traditional IT support role to a strategic, data-centric approach requires a cultural shift within the organization. Training and empowering teams to think data-first is crucial.

Barb’s narrative underscored the importance of not just having a technological shift but also fostering an organizational culture that embraces change. The transition from legacy systems to modern data platforms like Databricks involved clear communication, setting realistic expectations, and gradual integration into daily operations, ensuring that the team not only adopts new technologies but also thrives using them.

The Role of AI in Data Modernization

Gabe Arce, VP of Salesforce & Omnichannel COEs at Axos Bank, emphasized the role of artificial intelligence (AI) in enhancing data capabilities.

His insights align with industry trends that suggest AI is set to transform banking significantly, enhancing productivity by up to 30% and potentially increasing revenue by 6%​ (Accenture | Let there be change)​.

  1. Enhancing Customer Interactions with AI: Gabe discussed the potential of AI in transforming customer service, where AI can automate routine interactions, providing faster and more accurate responses to customer inquiries.
  2. Data Sharing and Integration: The importance of seamless data integration was a key theme. Gabe sees firsthand the benefit that seamless data sharing between systems is having in materializing the benefit of Generative AI efforts to enhance the operational agility of Banks.
  3. Preparatory Steps for AI Implementation: The journey to effective AI usage begins with understanding the available technologies and their potential integration into existing systems. Gabe’s proactive approach in evaluating and planning AI applications sets a foundational blueprint for others in the industry.

Cultivating Partnerships for Enhanced Integration

Gabe also emphasized the value of strategic partnerships in adopting and integrating new technologies effectively.

Collaborative relationships with leading technology providers can enable financial institutions to rapidly augment their capabilities through both declarative and programmatic solutions to best serve any number of strategic needs.

These partnerships are crucial for ensuring that data flows seamlessly across platforms, enhancing overall system functionality and efficiency, and enabling Generative AI strategies.

Zennify and Databricks: Your Guides to Data Modernization

Gartner predicts that by 2025, more than 75% of financial institutions will have incorporated AI and machine learning into their operations to further enhance data analytics and customer interactions.

At Zennify, we understand that upgrading your data systems is a complex but critical endeavor. Together with Databricks, we provide the expertise and tools necessary to streamline this transition. Our approach ensures that financial leaders can leverage their data to its fullest potential, enhancing decision-making processes and operational efficiency.

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