Sallie Mae Saves $1.2M a Year While Helping Students Soar

When you’re helping millions of students navigate one of life’s biggest investments — college — every decision counts. That’s why Sallie Mae, a pioneer in student lending for over 50 years, has evolved far beyond loans. Today, the institution is a trusted partner in the full college journey, offering a variety of tools and resources to help students find the right school, search for scholarships, secure gap funding and step confidently into life after graduation. Serving millions of customers and managing billions in private student loans, Sallie Mae makes higher education more accessible and less stressful, with a reputation for innovation in the financial services space.

With data at its core, Sallie Mae needed a modern solution to unify insights across its loan services and educational tools. Sallie Mae turned to the Snowflake AI Data Cloud to deliver smarter, faster and more personalized experiences, whether it’s matching a student to the right college or making nuanced loan decisions in real time. “We use Snowflake across both our lending and college planning services, all in service of our mission: to help students before, during and after college. That means guiding them to the right school, helping them find scholarships, supporting them through funding, and empowering their next step — whatever their future holds,” says Mark Perrault, Senior Director, Enterprise Data Services at Sallie Mae.

Before migrating to Snowflake in 2018, Sallie Mae relied on an on-premises solution that couldn’t keep up with its growing data needs. Severe storage limitations forced regular decisions about what data to purge. As business demands grew, inflexible infrastructure created significant constraints. Siloed data and operational inefficiencies made it especially difficult to test new models or run multiple variations of analyses. Tools like SAS ran slowly and incurred high costs, limiting the company’s ability to iterate quickly or experiment with different scenarios.

“The lack of scalability and flexibility made it hard to innovate and respond to new opportunities,” Perrault says. Building long-term relationships with students and borrowers has always been a core part of Sallie Mae’s mission — but without scalable infrastructure, maintaining engagement across the entire loan lifecycle was a challenge. “We weren’t able to proactively engage with customers,” Perrault says. “With Snowflake, we stay connected throughout their academic journey without missing a beat.”

This transformation expanded Sallie Mae’s data capabilities across multiple business units, improved customer engagement and streamlined analytical workflows — all without requiring constant infrastructure upgrades. “Since switching, we’ve kept our warehouse spend flat, while our data volume has grown from 100TB to over 600TB,” Perrault says. Teams now have the flexibility to test multiple models in a single day, run complex analyses in real-time and maintain continuous engagement with customers.

When the federal government introduced the current expected credit loss (CECL) standard, Sallie Mae faced a new requirement: to project loan repayment risk monthly instead of annually. Built around static projections, its legacy system couldn’t respond fast enough. Each loan recalculation took 12–16 hours, and the infrastructure couldn’t support multiple executions, making economic scenario testing impossible. 

Sallie Mae’s diverse set of loan products, each with distinct repayment behaviors, made recalculations resource-intensive and prone to errors. Scaling infrastructure just to support the massive, single-threaded monthly run created bottlenecks and drove up costs. Auditing or verifying past calculations was manual and cumbersome, requiring users to retrace steps through inconsistent logs and recreated settings.

To unlock scalability and flexibility, Sallie Mae rebuilt the CECL calculation engine in Snowflake. “We finished our first product run of CECL on Snowflake in two hours,” Perrault says. “Now we can run multiple scenarios,  evaluate economic impact scenario weights and give our finance team more flexibility.” The platform scales on demand, reducing costs and enabling finance teams to test options in near real time.

Critically, Snowflake’s architecture gave Sallie Mae something the old system never could: effortless transparency. Each CECL model run stores its configurations and results automatically. There’s no need for manual logging and tracking, no risk of using the inappropriate model versions and no difficulty verifying or auditing historical numbers. To recreate a scenario or audit a result, users simply reference saved settings and rerun the process exactly as it was.

The move also eliminated the dependency on third-party tools for data extraction and processing, which had added hours to every cycle. The team focused their efforts on bringing the work to the data, not moving the data to the work. Now, everything runs natively in Snowflake, streamlining operations and cutting processing time by over 80%.

As Sallie Mae’s infrastructure matured, marketing and customer engagement saw major gains. Before Snowflake, campaign workflows depended on manual file transfers across systems, creating delays, errors and data mismatches. Even simple actions, like sending a welcome email after a loan application, could take up to 48 hours to execute.

Now, Snowflake serves as the single source of truth for analytics and campaign operations. The marketing team works directly from live production data, ensuring alignment between insights and actions. Partners access the same datasets through Snowflake Secure Data Sharing for faster segmentation, lead generation and campaign execution without duplication or lag.

Tasks that once took days now take minutes. Customer data integration (CDI) processes that previously ran for two days now complete in a few hours, allowing near real-time messaging. If someone abandons a loan application, a follow-up email goes out within 15 minutes — boosting re-engagement and conversions.

Beyond transactional messaging, Sallie Mae personalizes the customer journey by linking touch points across scholarships, college planning and loan tools. “Snowflake allows us to seamlessly collaborate with marketing partners and get the right message to the right person faster,” Perrault says.

Snowflake also boosted resiliency. Previously manual backups and recovery are now automated with daily cross-region replication and growing failover capabilities. Its extensibility allows for the easy integration of external data, like FEMA updates for disaster response or Department of Education data for college search tools.

Sallie Mae maps each involved party’s journey across four key lifecycle stages: member, consumer, customer and alumni. Progression through these stages reflects a growing understanding of each individual’s needs and circumstances. At the heart of this strategy is a customer identity system, fully built and managed in Snowflake. Sallie Mae handles classification, householding and lifecycle tracking in one unified environment, enabling more relevant and timely support at every step.

Using Snowflake Secure Data Sharing, Sallie Mae securely shares identity data with marketing and servicing partners. Each interaction — whether a marketing offer, service alert or repayment message — draws from the most current information available.

The same infrastructure powers marketing and lead generation. Instead of shuttling files between systems, vendors access live data directly through Snowflake, which streamlines campaign execution and removes delays between insight and action. This supports rapid segmentation, targeted outreach and more precise messaging across all customer touchpoints.

As borrowers progress through their loan lifecycle, data continues to shape the experience. Snowflake helps Sallie Mae analyze repayment behavior, segment customers by risk and engagement and adjust communication strategies to support financial success. Whether offering repayment guidance or tailoring outreach, Sallie Mae grounds every decision in real-time analytics flowing through Snowflake.

Today, Snowflake handles more than 4 million queries each month. Seventy percent of this volume comes from systems running operational processes, while analysts and business teams generate the rest — experimenting, iterating and building new solutions. When an ad hoc table or process gains traction, teams promote it to a fully operational workflow that scales organically into critical business functions.

Snowflake’s flexibility has fostered greater cross-department collaboration, empowering employees to turn insights into scalable processes. Successes like the CECL overhaul have sparked broader innovation. Teams are reimagining years-old workflows by using Snowflake features like Streamlit to modernize operations. Emerging use cases — from real-time customer engagement to FEMA-driven disaster response — show how Snowflake has become not just a tool, but a key enabler of business agility.

With a modern data foundation powered by Snowflake, Sallie Mae is better equipped than ever to support students at every step — from their first campus visit to building financial futures of their own.