Transforming Banking With Data: Insights From Jamie Clisham

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TL;DR:

• Community banks must prioritize business-led, IT-enabled data initiatives to maximize impact and maintain compliance in a regulated space.
• Starting small with core data and scaling systematically prevents analysis paralysis and accelerates decision-making.
• Partnerships with tested third-party vendors help smaller banks achieve enterprise-level capabilities efficiently.
• AI and machine learning in fraud and risk management enable rapid, scalable decision-making, enhancing security while maintaining customer experience.
• Data governance and explainability are crucial for adopting AI in regulated industries, ensuring compliance, transparency, and auditability.

Before we dive into the key takeaways from this episode, be sure to catch the full episode here:

Meet Jamie - VP of Data and Analytics at Machias Savings Bank

Jamie Clisham, VP of Data and Analytics at Machias Savings Bank, leads data-driven transformations at a mid-sized community bank with 15 locations across Maine.

With a background in engineering and years of experience in sales, operations, and revenue management, Jamie developed a passion for using data to drive profitability and improve business outcomes.

Since joining Machias Savings Bank, Jamie has spearheaded the development of a modern analytics function, leveraging tools like Snowflake and Tableau while collaborating with third-party partners to scale efficiently.

An advocate for balancing innovation with regulatory compliance, Jamie ensures her team delivers secure, actionable insights.

Jamie’s philosophy centers on building resilient teams and fostering partnerships that solve problems and drive efficiency, ensuring banks of all sizes can thrive in today’s data-driven landscape.

The Challenges of Starting Small: Building an Analytics Foundation

Building a robust analytics foundation is no small feat, especially for community banks with limited resources.

For Machias Savings Bank, the journey began by identifying critical data sources and prioritizing the organization of core information.

Jamie emphasized the importance of starting with manageable, bite-sized initiatives. Attempting to ingest all available data at once can result in delays, inefficiencies, and frustration.

Instead, her team focused on consolidating data from essential systems like the core banking platform, ensuring compliance and data quality.

The iterative approach allowed for early wins, enabling the team to demonstrate value quickly and gain buy-in for larger projects.

This strategy highlights that even small-scale initiatives, when well-planned, can lay the groundwork for significant, scalable data-driven advancements.

“The worst thing you can do is not get started at all. You don’t have to be on the bleeding edge.” — Ankur Patel

Balancing Business-Led and IT-Enabled Strategies

Success in banking analytics requires a delicate balance between business leadership and IT enablement.

Jamie believes that while IT provides the critical infrastructure—the “pipes”—business teams must lead the charge to ensure analytics initiatives align with strategic goals.

At Machias Savings Bank, the analytics team adopted a collaborative approach, where IT enabled the technical foundation, but business teams defined priorities and ensured that outcomes addressed operational needs.

This model prevents disconnects between technology capabilities and business requirements. It also fosters a sense of shared ownership, where teams work together to ensure that analytics efforts drive tangible business results.

Ultimately, this balanced approach empowers organizations to achieve scalability and innovation while maintaining focus on delivering meaningful value to stakeholders.

The Role of Third-Party Vendors in Scaling Community Banks

For community banks like Machias Savings Bank, partnerships with third-party vendors are vital to achieving enterprise-level capabilities.

Smaller institutions often lack the internal resources to manage complex data engineering or advanced analytics tasks.

Jamie’s team strategically outsourced data engineering and architecture to a trusted vendor, freeing internal resources to focus on business-driven insights.

Selecting the right partner, however, requires careful vetting. Clisham’s framework prioritized vendors that were proven, reliable, and capable of aligning with the bank’s specific use cases.

References and real-world case studies played a crucial role in the decision-making process.

By leveraging external expertise, community banks can accelerate innovation and scale their data capabilities while maintaining a cost-effective, resource-efficient model—a necessity in today’s competitive banking environment.

Fraud and Risk Management: AI’s Tried-and-True Use Case in Banking

Fraud and risk management remain the cornerstone use cases for AI in banking.

Jamie highlighted how AI and machine learning have long been leveraged to combat fraud and assess credit risk, enabling faster, more accurate decisions.

By analyzing vast datasets, these technologies identify patterns and anomalies that would be impossible for human analysts to detect in real time.

This capability is particularly crucial in fraud detection, where rapid responses can mitigate significant financial losses. Similarly, AI-powered credit risk models go beyond traditional metrics like credit scores, incorporating diverse variables to predict long-term customer behavior more accurately.

These applications demonstrate the practical, transformative power of AI in banking, showcasing its ability to enhance both security and decision-making efficiency.

“Fraud and risk require rapid decisions; machine learning models make them scalable and efficient.” — Ankur Patel

Generative AI vs. Traditional Models: A Balanced Approach

While generative AI garners significant attention, traditional machine learning models remain indispensable in banking analytics.

Jamie’s approach to balancing these technologies underscores the importance of aligning tools with specific business needs.

Traditional models excel in areas requiring high levels of regulatory compliance, such as fraud detection and credit risk assessment, due to their transparency and proven track record.

Conversely, generative AI’s potential lies in areas like customer experience and workflow optimization. However, its adoption requires careful consideration of security and data privacy concerns.

“AI helps us deliver better customer experiences while maintaining security—it’s where the magic happens.” — Jamie Clisham

Clisham advocates for a “crawl, walk, run” approach: leveraging tried-and-true methods while gradually incorporating newer, more experimental technologies.

This strategy ensures that innovation doesn’t come at the expense of trust, compliance, or operational reliability.

The Importance of Data Governance and Transparency in Regulated Spaces

Data governance and transparency are non-negotiable in regulated industries like banking.

Jamie emphasized the dual role of data governance: ensuring data quality and maintaining regulatory compliance. Her team’s approach involved engaging operational teams to identify pain points in data entry processes and implementing rules engines to clean and monitor data continuously.

Governance extends beyond preparation; it includes post-production oversight.

For instance, Clisham highlighted the importance of tracking AI-driven decisions to create an audit trail. This level of transparency not only satisfies regulatory requirements but also builds trust internally and externally.

By embedding governance into every stage of data and AI initiatives, banks can maintain the rigor and accountability needed to operate in a highly scrutinized environment.

Looking Ahead: AI’s Role in Enhancing Decision-Making and Efficiency

AI’s potential to transform banking lies in its ability to enhance decision-making and drive efficiency.

Jamie envisions AI enabling smaller banks to compete with larger institutions by automating routine tasks and freeing employees to focus on high-value activities. She sees tools like machine learning models and AI agents improving scalability, delivering actionable insights, and streamlining workflows.

In the face of industry challenges like margin compression, these advancements are essential for growth.

Clisham’s team’s focus on iterative improvement and responsible AI adoption ensures that the technology not only meets current needs but also evolves alongside business objectives.

As AI becomes more integrated into daily operations, it promises to redefine the banking landscape by enhancing both operational efficiency and customer satisfaction.

Want to learn more about how AI transforms banking? Check out this episode about enterprise solutions on a credit union scale with Jonathon Allen & Joseph Pellissery.