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Data Analytics

Fintech startup, Branch makes data analytics easy with BigQuery

#finserv

Editor’s note: Here we take a look at how Branch, a fintech startup, built their data platform with BigQuery and other Google Cloud solutions that democratized data for their analysts and scientists.  

As a startup in the fintech sector, Branch helps redefine the future of work by building innovative, simple-to-use tech solutions. We’re an employer payments platform, helping businesses provide faster pay and fee-free digital banking to their employees. As head of the Behavioral and Data Science team, I was tapped last year to build out Branch’s team and data platform. I brought my enthusiasm for Google Cloud and its easy-to-use solutions to the first day on the job. 

We chose Google Cloud for ease-of-use, data & savings 

I had worked with Google Cloud previously, and one of the primary mandates from our CTO was “Google Cloud-first,” with the larger goal of simplifying unnecessary complexity in the system architecture and controlling the costs associated with being on multiple cloud platforms. 

From the start, Google Cloud’s suite of solutions supported my vision of how to design a data team. There’s no one-size-fits-all approach. It starts with asking questions: what does Branch need? Which stage are we at? Will we be distributed or centralized? But above all, what parameters in the product will need to be optimized with analytics and data science approaches? With team design, product parameterization is critical. With a product-driven company, the data science team can be most effective by tuning a product’s parameters—for example, a recommendation engine for an ecommerce site is driven by algorithms and underlying models that are updating parameters. “Show X to this type of person but Y to this type of person,” X and Y are the parameters optimized by modeling behavioral patterns.  Data scientists behind the scenes can run models as to how that engine should work, and determine which changes are needed.  

By focusing on tuning parameters, the team is designed around determining and optimizing an objective function. That of course relies heavily on the data behind it. How do we label the outcome variable? Is a whole labeling service required? Is it clean data with a pipeline that won’t require a lot of engineering work? What data augmentation will be needed? 

With that data science team design envisioned, I started by focusing on user behavior—deciding how to monitor and track it, how to partner with the product team to ensure it’s in line with the product objectives, then spinning up A/B testing and monitoring. On the optimization side, transaction monitoring is critical in fintech. We need to look for low-probability events and abnormal patterns in the data, and then take action, either reaching out to the user as quickly as possible to inform them, or stopping the transaction directly. In the design phase, we need to determine if these actions need to be done in real-time or after the fact. Is it useful to the user to have that information in real time? For example, if we are working to encourage engagement, and we miss an event or an interaction, it’s not the end of the world. It’s different with a fraud monitoring system, for which you’ve got to be much more strict about real-time notifications.

Our data infrastructure

There are many use cases at Branch for data cloud technologies from Google Cloud. One is with “basic” data work. It’s been incredibly easy to use BigQuery, Google’s serverless data warehouse, which is where we’ve replicated all of our SQL databases, and Cloud Scheduler, the fully managed enterprise-grade cron job scheduler. These two tools, working together, make it easy to organize data pipelining. And because of their deep integration, they play well with other Google Cloud solutions like Cloud Composer and Dataform, as well as with services, like Airflow, from other providers. Especially for us as a startup, the whole Google Cloud suite of products accelerates the process of getting established and up and running, so we can perform the “bread-and-butter” work of data science. 

We also use BigQuery as a holder of heavier stats, and we train our models there, weekly, monthly, nightly, depending on how much data we collect. Then we leverage the messaging and ingestion tool Pub/Sub and its event systems to get the response in real time. We evaluate the output for that model in a Dataproc cluster or Dataform, and run all of that in Python notebooks, which can call out to BigQuery to train a model, or get evaluated and pass the event system through.

Full integration of data solutions

At the next level, you need to push data out to your internal teams. We are growing and evolving, so I looked for ways to save on costs during this transition. We do a heavy amount of work in Google Sheets because it integrates well with other Google services, getting data and visuals out to the people who need them; enabling them to access raw data and refresh as needed. 

Google Groups also makes it easy to restrict access to data tables, which is a vital concern in the fintech space. The infrastructure management and integration of Google Groups make it super useful. If an employee departs the organization, we can easily delete or control their level of access. We can add new employees to a group that has a certain level of rights, or read and write access to the underlying databases. As we grow with Google Cloud, I also envision being able to track the user levels, including who’s running which SQLs and who’s straining the database and raising our costs. 

A streamlined data science team saves costs

I’d estimate that Google Cloud’s solutions have saved us the equivalent of one full-time engineer we’d otherwise need to hire to link the various tools together, making sure that they are functional and adding more monitoring. Because of the fully managed features of many of Google Cloud’s products, that work is done for us, and we can focus on expanding our customer products. We’re now 100% Google Cloud for all production systems, having consolidated from IBM, AWS, and other cloud point solutions. 

For example, Branch is now expanding financial wellness offerings for our customers to encourage better financial behavior through transaction monitoring, forecasting their spend and deposits, and notifying them of risks or anomalies. With those products and others, we’ll be using and benefiting from the speed, scalability, and ease of use of Google Cloud solutions, where they always keep data—and data teams—top of mind. 

Learn more about Branch. Curious about other use cases for BigQuery? Read how retailers can use BigQuery ML to create demand forecasting models.