Transactional data can be very messy. The information recorded from each transaction across a person’s financial life can come from many different banks and merchants, and the resulting data lacks structure. This makes it hard for financial institutions and their customers to use it to make financial decisions.
At Bud Financial Ltd (‘Bud’), we use machine learning (ML) technology to make sense of financial data so that financial companies can instead focus on building better services for their customers.
Bud began life in 2015 as an education platform to help people manage their money and improve their financial wellbeing. Now, we operate as a business to business firm. Global financial companies use our APIs to combine transactional data from any source so they can harness the power of Bud’s data intelligence to open up more financial opportunities for their customers. Throughout this journey, we’ve always relied on Google Cloud to develop and scale our technology.
Pushing the boundaries of financial technologies, with security in mind
As a fast-moving business that works with global banks, we need a cutting-edge platform that scales and helps us to maintain a high degree of trust from our clients.
Google Cloud makes it easy for us to demonstrate that we’re meeting industry compliance and security requirements with self-serve, painless reports such as SOC 2 and ISO 27001. The Operations Suite (formerly Stackdriver) gives us visibility into all of our data assets, making tracking and management easier. We always know what data is where, who has access to what, what data is interacting with other elements of the platform, and how. This traceability is key.
Google Cloud has an excellent reputation in the financial sector, which has made our conversations with clients easier from the get-go.
Using ML to open up lending opportunities for underserved communities
Personalization, made possible with ML, has flourished in other industries. But in financial services, it can be difficult to innovate at speed while taking all the necessary precautions for this to be done in a privacy-preserving, and compliant way that benefits end-users. Nonetheless, many pressing challenges are pushing organizations to seek answers.
The ongoing cost of living crisis is motivating financial services companies to identify and support customers who might be experiencing financial difficulties. Our clients want to engage with their customers in relevant, timely ways. They use Bud’s data intelligence to give their customers personalized insights into their spending and influence positive behavior to build financial resilience and achieve their goals.
Bud’s platform is also being used to improve lending processes. Current lending services may not meet the needs of every customer. If you’re someone who has moved countries, never had a credit card, or doesn’t have a perfect credit score, it can be difficult to get access to loans. This isn’t always fair because your history is not necessarily reflective of your current ability to pay back a loan. Bud wants to change this by using transactional data to provide companies with a better understanding of someone’s real-time financial situation and affordability. That way, without increasing their risk, these companies can open up their services to more people who need them. It’s not just underserved customers who are seeing this benefit. The simple affordability checks and credit risk insights we provide mean that every customer can get faster access to the products that are right for them.
Banks and financial institutions can use this data to improve their decisions around areas like credit affordability and application processes. At the same time, Bud helps these companies to deliver that context to their customers, alerting them to ways that they can improve their financial decisions. But all this relies on transactional data used in real time, covering millions of customers, which creates huge and highly-variable volumes of data.
Bud uses DataStax Astra DB on Google Cloud to handle this data volume and run this critical service for banking customers. With DataStax Astra DB on Google Cloud, Bud developers can take full advantage of different data models and APIs to accelerate new product and service launches. This frees up our developers to focus on banking data services rather than operational database tasks. Moreover, Bud only pays for the time and compute resources it uses.
Leveraging a scalable, flexible platform to process billions of transactions each month
Just as financial services companies need to be flexible and adapt to change, we’re always using new technologies at Bud to figure out how we can solve evolving challenges in this space. Google Cloud provides us this flexibility to experiment. For example, although we use Cloud SQL, as we’ve grown as a business we’ve also experimented with no-relational databases. Because it’s so flexible, Google Cloud enables us to be future-proof as an organization. We follow multicloud principles, but in practice we use Google Cloud as our solid foundation.
With Google Kubernetes Engine, we deploy and scale everything on our platform, not least because it gives us the flexibility to build and test in different kinds of environments. Our recent launch in the U.S. took only eight weeks. It was made easy by Google Kubernetes Engine, which enables us to spin up U.S. environments and scale them up to accommodate the different needs of local clients. Scaling the Bud platform has been very straightforward with Google Cloud. We now serve millions of our enterprise clients’ customers, enriching billions of transactions each month on our platform.
Meanwhile in the backend, we’ve got a team responsible for ensuring that our platform is reliable and scalable, providing a world-class developer experience so clients can integrate our APIs seamlessly. These two things are directly impacted by our platform provider. Having self-managed services such as Google Kubernetes Engine and CloudSQL means we can continue to focus on our core services instead of managing our 30 clusters and instances.
Simplifying transactional data to help more people make sense of their finances
Over the years, we increased our use of BigQuery for logging events in our business intelligence. It is easy to integrate it with tools such as Looker, which enables all teams across the company to better visualize how our product is performing and understand all of our key business metrics.
We concluded 2022 with an exciting launch on Google Cloud Marketplace in the U.K. and the U.S. As one of the first U.K. fintech on Google Cloud Marketplace, our focus for 2023 is to scale our solution to even more financial businesses and make it easier than ever for them to get started. Many companies still aren’t getting the full value of their transactional data, so we’re expecting this launch to open more distribution channels.