THE SCALE OF modern banking is truly staggering. For example, Wells Fargo—one of the biggest banks in the world—has roughly $1.9 trillion in assets, almost 250,000 employees worldwide, and about 70 million customers in the United States. This means the company sees customers at thousands of branches and ATMs, while also managing 35 million daily web sign-ins and serving 27 million mobile users.
All of which means that a huge number of people depend on Wells Fargo for secure, reliable transactions. Customers also increasingly expect that all of their online and app interactions are equally personalized and easy—regardless of whether they’re shopping or banking.
Customer happiness by itself is hard enough to achieve in any industry, but banks also face a number of other, unique challenges—from strict regulatory requirements, to macroeconomic shocks, to competing with other financial organizations and tech companies for talent. In addition, banks are carefully adopting new technologies, but change often takes time. For example, while many financial institutions now have some data in the cloud, financial industry migration to the cloud remains in its early stages, and many banks have adopted a multi-cloud or hybrid (i.e., a mixture of data stored on the cloud and on-premises) setup.
“There may be regulatory requirements around data sources and they need to be maintained for transaction integrity, but they still need to be flexible enough to build the kind of experience that matches up with the current expectation of the customers,” noted Swarup Pogalur, SVP, CTO of Digital Engineering at Wells Fargo. “We needed to discover how you consistently achieve that within our technology stack.”
Solving Complex Issues with MongoDB
To update the company’s customer authentication interface—and to improve its scalability, synchronization capabilities, and security—Wells Fargo tapped its longtime collaborator MongoDB.
Specifically, Pogalur and his team of engineers relied on MongoDB’s expertise in large scale application modernization efforts to power Wells Fargo’s legacy authentication platform modernization initiative, which sought to achieve a number of goals, including:
- A highly resilient database that would support a phased migration
- Accelerated product release cycles, and the ability to introduce feature updates without downtime
- Reverse sync capabilities, and a solution that would allow Wells Fargo’s modernized and legacy systems to coexist and support phased traffic migration
Achieving these goals would both boost Wells Fargo’s customer authentication platform with respect to regulatory and security standards, while also delighting customers with its performance.
To achieve its modernization goals, Wells Fargo and MongoDB co-created a blueprint and data migration approach with MongoDB as the developer data platform, which allowed Pogalur and the Wells Fargo development team to build high-performance, highly resilient patterns within the Wells Fargo ecosystem. This modernization “playbook” helped change the way Wells Fargo could offer authentication services to online customers—starting with how they log in.
Before the “playbook” was introduced, Wells Fargo’s Secure Identity Management Services (SIMS) authentication platform posed challenges for the company’s tech stack, in terms of its agility and ability to scale to increasing demand for the services.
As a set of services that all require a secure login, SIMS-related updates and feature enhancements often required making bulky changes that required significant lead time for updates and market rollouts, as well as delayed reaction times to customer feedback. Wells Fargo needed a solution that could unify service enhancements without impacting resiliency of the services, and they found that in a new deployment architecture with MongoDB .
For the SIMS Project, the team built upon their “playbook” patterns with new stacks created in MongoDB, modernizing its applications with technologies like Apache Kafka and Apache Flink, streaming platforms that easily connect to MongoDB. This new architecture stack coexisted alongside the legacy platform stack used during migration, which allowed Wells Fargo to run both systems in parallel so the team could confirm that the new stack worked and scaled as designed.
“One of the capabilities that we required during this modernization process was reverse sync,” Pogalur said. “Meaning, ‘How do we make sure that in this process of coexistence what gets written into one database also gets replicated into the legacy database?’” Reverse sync capabilities allowed Wells Fargo to switch over to the new functionality completely, without any customer service impact or data integrity loss.
Now the Wells Fargo stack—powered by MongoDB—manages 70 million transactions per day, 35 terabytes of data, 20 separate microservices, and 2.5 billion MongoDB documents.
“MongoDB was built by developers, for developers, to empower teams to build better and faster in a more organic way, on a foundation built for the most demanding security, durability, reliability, availability, and performance at scale requirements while solving their data challenges,” said Andrew Davidson, Senior Vice President of Product Management at MongoDB. “Our fully managed platform boasts a range of powerful features—from multi-cloud capabilities, to support for integrated search and vector search-native tooling—and takes care of time-consuming tasks so teams can focus on innovating with technology like generative AI.”
Scaling for the Future
The successful modernization project with MongoDB has enabled Pogalur and team to continue to innovate and prototype more quickly, while also focusing on industry-expanding technologies, specifically, generative AI (gen AI).
Pogalur and his team are now thinking about options for using gen AI, from how Wells Fargo’s developers can use gen AI, to how gen AI might impact the company’s virtual assistant, Fargo, which allows customers to send money, search transactions, and receive spending summaries.
While large language models create several innovative opportunities Pogalur’s team is also interested in leveraging small language models (SLM)—smaller neural networks, trained on smaller and domain specific datasets, that are ideal for edge or offline uses—to provide great customer experiences.
“If we think about the edge, we are looking at generative AI models that work very well on the device,” he said. “It's interesting because we can use a small language model that understands the domain and context very well—and allows us to build context aware, personalized experiences for our customers .”
A SLM-powered app or assistant would enhance the user experience by providing richer contextual data to enable deeper personalization. Something that might take six to seven clicks to complete could be completed simply by telling the virtual assistant, “I want to send $50 tomorrow morning from my checking account.” The model’s contextual understanding would translate into an actionable insight behind the scenes that can be executed in an intuitive and easy to use interaction, Pogular said.“That's where the evolution will happen over time,” he added.
Overall, Wells Fargo’s work with MongoDB has not only helped them modernize their current apps, it’s also given them the space to focus on next-generation innovations and user experiences
“In terms of the developer experience, [MongoDB] provided more flexibility,” said Pogalur. “In the traditional way of doing application development using relational databases, there's a dependency that you generally have with a database architect, database management team, database design team. With our playbook, there's a lot more flexibility for the developers to drive those decisions while ensuring conformance with enterprise data management standards and frees up database experts to focus on scaling the database infrastructure – it’s a win-win.”
To learn how MongoDB helps financial organizations modernize legacy infrastructure, protect customer information, and build better customer experiences, visit MongoDB for Financial Services.
