In many ways, community banks are the backbone of the US financial services system. Typically focusing on smaller, local markets and operating from just a few locations, around 95% of banks in the United States fall into the “community bank” category.
It is their relatively small size (less than $10 billion in assets), traditional product offerings, and geographic reach that creates a point of difference. Community banks are pillars of a local community, playing an outsized role in lending to local businesses, families, and homeowners. Indeed, many community banks were the first to step up with local support during COVID-19 before their national competitors.
However, as banking becomes increasingly digital, community banks face new competitive threats. Typically, community banks lack the IT resources of major national or regional operators, often relying on legacy technology. Many are unable to access the benefits of cutting-edge analytics and data mining. Worryingly, they are often underserved or overlooked by global fintech operators.
The fact that community banks operate on a smaller scale to national rivals should not undermine the importance of data. If anything, data becomes more important. Greater insight into real-time data enables community banks to be more local and more personal.
Neocova, a fintech start-up based in Austin, Texas, aims to address this glaring gap. It wants to help community banks leverage data to drive improved outcomes for themselves and their customers.
“We saw a huge opportunity to modernize data and the accessibility of data for community banks,” says Matt Beecher, CEO at Neocova. “Our focus has been to build a modern data and analytics platform uniquely structured for those banks whose customers could be small to medium-size businesses, established companies, or ordinary people.”
Many community banks have, what Matt Almeida, Vice President of Engineering at Neocova, describes as “messy data,” the result of randomly adding tables to existing normalized databases.
“What works for one bank in extracting a piece of data may not work for another because it can be a totally different structural framework,” he says. “It’s the problem we aim to solve.”
To reimagine data possibilities in the community banking sector, Neocova needed a solution that would allow it to ingest data from multiple sources in multiple structures. It then needed to build a front-end environment to provide banks with access to the insights buried within.
As they searched for a solution, Neocova was already familiar with MongoDB, as many on the team had used Atlas in previous roles. Almeida says MongoDB Atlas, a developer data platform, offered a set of clear answers to the requirements Neocova was looking for:
“We needed a flexible, distributed, document-store database with good community support. The decision to use MongoDB Atlas was one of the earliest pieces of infrastructure we agreed upon. It was highly recommended by our cloud architects, it provided exceptional levels of security and data isolation, and displayed ease of use when plugged into other cloud resources. “
MongoDB Compass then provided a perfect complement to Atlas, Almeida adds: “Atlas is in a class of its own in terms of stability, performance, maintainability, and its tool and feature set. Compass is a phenomenal GUI for when we need to do something more complicated with any dataset. Maintaining data isolation between customers is incredibly simple to manage. Switching between different customer clusters is like second nature with the native UX.”
Matt Almeida, Vice President of Engineering, Neocova
Additionally, Neocova is exploring Atlas Charts to help their customers better visualize their own powerful data. “We want to embed this in a per customer instance strategy,” shares Almeida. “The MongoDB team has been kind enough to meet with us, go over the product roadmap and see how we can work together to take this product to the next level.”
MongoDB Atlas now enables Neocova to take a community bank’s data, transform it into a single unified language, store it, and create an application layer. This application layer can then be plugged into both the bank’s own and best-in-class third-party analytics tools.
Almeida adds: “MongoDB has been the easiest piece of our infrastructure to tame. We built the resources into Hashicorp Terraform modules early on, and we haven’t had any struggles since.”
Neocova had a very specific set of objectives, and MongoDB ensured they were met. Data is secure, resource maintenance and configuration are simple, and the start-up can now build a series of flexible data models. These models can then be stored in a distributed environment and can scale as Neocova grows and evolves.
More importantly, the solution delivers results for customers of Neocova. “We’re dramatically shortening the line between data and revenue by augmenting existing legacy technology systems and data that banks already have in place,” says Neocova CEO, Matt Beecher. “This approach allows for the delivery of actionable results quickly without the heavy lift of replacing existing legacy tools.”
Neocova’s solution allows customers to harness their data to make meaningful improvements.
Beecher sees two key areas where clients are reaping benefits. Firstly, improved operational efficiency means banks can now access critical insights without having to commit vital resources to time-consuming data-modeling processes. Secondly, the speed of analysis, using ML and AI, creates actionable insights that allow banks to make informed, timely decisions. For personal banking, this could include analyzing an account holder’s transactions, identifying their mortgage payments and provider, and seeing if there is an opportunity for the bank to offer a better deal. Similar possibilities also apply to commercial customers, where the potential business benefits are even greater.
Beecher cites the example of one commercial bank that estimated it was missing out on around $1 million a year in revenue from lost merchant fee opportunities. Neocova’s analysis, however, was something of a shock. “We ran through the analytics and the figure wasn’t $1 million, it was $5 million in revenue transacting outside the bank,” he recalls. “It was something the bank could offer at a much cheaper rate, but just didn’t know about it.”
For Beecher, these results are just the beginning. “There are so many real-life examples of being able to harness data and translate it into something truly actionable for the banks,” he concludes. “And we're not talking about basis point differences: these are massive improvements for a bank. It's so powerful.”
Matt Beecher, CEO at Neocova