At Zafin, our mission is to assist banks modernize their core infrastructure to ship distinctive, personalised experiences to their clients. To find out buyer relationship tiers and supply personalised rewards, we wanted to course of vital knowledge volumes—100 million accounts with over 10 billion balances. Historically, we might have applied this tiering utility utilizing an open-source framework, Java Spring Boot, with a devoted PostgreSQL server. Nonetheless, constructing and scaling this resolution with a conventional tech stack proved inefficient and unsustainable at scale.
Conventional knowledge infrastructure doesn’t scale
Like many organizations in monetary providers, we initially relied on devoted knowledge warehousing options to energy our analytics. Whereas useful, this strategy got here with vital challenges: excessive compute prices, upkeep overhead, and efficiency bottlenecks.
With a single-tenant structure, each buyer deployment requires devoted infrastructure. Since some clients use analytics sporadically whereas others depend on it actively, this setup was costly and inefficient. Devoted infrastructure demanded fixed fine-tuning and incurred fastened prices, no matter utilization—a big effort for our engineering groups. Scaling to accommodate large knowledge volumes throughout tens of millions of accounts, balances, and transactions usually stretched our conventional programs to their limits.
Complicated computations, akin to processing billions of information for buyer segmentation, tiering, and analyzing relationship patterns, ran as batch jobs that have been gradual to finish. These bottlenecks delayed time-to-insight, and our conventional strategy couldn’t sustain with the size and pace banks required.
To seamlessly present superior analytics to our clients, we wanted an economical, scalable platform able to dealing with large knowledge volumes, delivering excessive efficiency, and conserving prices below management.
Migrating to Databricks to speed up knowledge intelligence
We ran a proof of idea (POC) to match our earlier Java Spring Boot/PostgreSQL setup with the Databricks Knowledge Intelligence Platform. Right here’s how the POC empowered us to enhance our knowledge infrastructure.
Databricks delivered as much as 10x sooner computation speeds in comparison with our earlier resolution. For instance, duties that beforehand took 4,000 seconds on our earlier system at the moment are accomplished in simply 300 seconds. With elastic scalability, we are able to course of billions of transactions and balances throughout tens of millions of accounts with out compromising efficiency.
Migrating analytics workflows lowered time-to-market for brand spanking new capabilities by 30–40% whereas requiring fewer engineering sources. In comparison with our conventional setup, Databricks helped us speed up the supply timeline of our tiering utility by 30%. With much less effort spent on infrastructure administration, our engineering staff can deal with constructing modern options for our clients.
Databricks’ Serverless capabilities have been a sport changer, permitting us to separate compute from storage to make sure we solely paid for the compute we wanted. This strategy eradicated the associated fee burden of idle infrastructure. Cloud prices for analytics dropped by 50–70%, relying on utilization profiles. The flexibility to auto-scale based mostly on knowledge hundreds meant that each high-traffic and sporadic-use clients may depend on a seamless, responsive expertise with out guide tuning.
The outcomes have been clear: Databricks provided a transformative resolution that delivered superior efficiency, scalability, and cost-efficiency—all whereas assembly the stringent governance necessities of extremely regulated monetary establishments.
Delivering worth to our clients
Databricks’ unified atmosphere permits our groups to deal with knowledge ingestion, transformation, and analytics in a single place—bettering productiveness and collaboration. From knowledge ingestion to advanced SQL-based transformations and embedded analytics, the whole lot runs seamlessly on Databricks the place even non-technical customers can derive significant insights. When talking with Terry Hickey, Zafin’s Chief Income Officer, he expressed, “With the modernization of our knowledge infrastructure, we are able to now assist banks effortlessly uncover actionable insights to drive development and deepen buyer relationships.”
With Databricks because the spine of our trendy knowledge stack, we’ve been in a position to innovate sooner and ship higher worth to our clients, together with:
- Personalization: Utilizing Databricks SQL and Notebooks, we constructed a data-intensive tiering utility that evaluates buyer balances, product holdings, and transaction patterns to find out relationship tiers (e.g., Gold, Silver, Platinum). This enables banks to ship personalised rewards and advantages to clients whereas nudging clients towards the following tier with focused engagement, rising retention and loyalty.
- Enterprise Intelligence (BI): Our embedded analytics resolution, Zafin Analytics, helps line-of-business bankers achieve actionable insights into product efficiency and supply effectiveness. With Databricks’ built-in atmosphere, we offer out-of-the-box analytics to reply vital questions like: How is a checking account performing available in the market? What’s the adoption price of a promotional supply? How do buyer transaction patterns differ throughout tiers?
- Value-Environment friendly Transformations: With Databricks Autoloader, we seamlessly ingest billions of information from numerous file sorts (Avro, CSV) saved in Azure Blob Storage. This, mixed with Serverless scaling, ensures that we are able to course of knowledge effectively, with out over-provisioning or incurring pointless prices.
- Guaranteeing Knowledge Governance and Compliance: Unity Catalog allows sturdy knowledge governance, permitting us to isolate single-tenant buyer environments and keep compliance with monetary providers rules.
The impression for our banking clients is profound. The identical tiering utility that beforehand took a big financial institution two years to develop can now be deployed by their clients in simply months. This can be a vital time-to-market benefit that drives sooner innovation and monetization.
“It’s thrilling to see how Zafin is leveraging the Databricks platform to ship knowledge and AI-driven improvements that empower banks worldwide. By modernizing their infrastructure, Zafin is enabling monetary establishments to unlock new alternatives for personalization, buyer engagement, and growth-faster and extra effectively than ever earlier than.”
— Junta Nakai, Vice President – World Head of Monetary Providers, Cybersecurity and Public Sector GTM | Databricks
Wanting forward: Tapping into real-time improvements
Along with enabling sooner, cheaper knowledge processing, the Databricks Platform has positioned us for future innovation. We’re exploring alternatives in AI and GenAI, akin to real-time tiering insights, profitability modeling, and real-time provides with Databricks’ Delta Stay Tables and streaming capabilities. To additional streamline our analytics capabilities, we additionally plan to allow Lakehouse Federation for seamless integration throughout knowledge sources.
As we proceed to scale and innovate, we’re excited to unlock much more highly effective capabilities for our clients—reworking knowledge into actionable insights that drive the way forward for banking.
About Zafin
Based in 2002, Zafin is a world supplier of SaaS options for core banking modernization and transformation. Our award-winning platform allows banks to innovate their enterprise fashions whereas modernizing their know-how, making certain transparency and equity for banks and their clients. By enhancing operational effectivity, boosting income, and bettering buyer experiences, Zafin empowers monetary establishments to realize their strategic targets.
Zafin is headquartered in Vancouver, Canada, with a presence spanning places of work and clients worldwide, together with main banks like ING, CIBC, HSBC, Wells Fargo, Navy Federal Credit score Union, PNC, and ANZ.
Be taught extra
Executives and Enterprise Leaders can achieve insights from friends on the Monetary Providers Discussion board–Shifting to Monetary Intelligence, in New York Metropolis on Mar 20, 2025. Register immediately.
For technical professionals, go to the Knowledge Intelligence for Monetary Providers webpage for extra use circumstances and sources.
