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 would have liked to course of important information volumes—100 million accounts with over 10 billion balances. Historically, we might have carried out this tiering utility utilizing an open-source framework, Java Spring Boot, with a devoted PostgreSQL server. Nevertheless, constructing and scaling this resolution with a standard tech stack proved inefficient and unsustainable at scale.
Conventional information infrastructure doesn’t scale
Like many organizations in monetary companies, we initially relied on devoted information warehousing options to energy our analytics. Whereas practical, this method got here with important 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 mounted prices, no matter utilization—a major effort for our engineering groups. Scaling to accommodate huge information volumes throughout thousands and thousands of accounts, balances, and transactions usually stretched our conventional methods to their limits.
Complicated computations, comparable 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 method couldn’t sustain with the dimensions and pace banks required.
To seamlessly present superior analytics to our clients, we would have liked an economical, scalable platform able to dealing with huge information volumes, delivering excessive efficiency, and retaining prices underneath management.
Migrating to Databricks to speed up information 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 information infrastructure.
Databricks delivered as much as 10x quicker computation speeds in comparison with our earlier resolution. For instance, duties that beforehand took 4,000 seconds on our earlier system are actually accomplished in simply 300 seconds. With elastic scalability, we will course of billions of transactions and balances throughout thousands and thousands of accounts with out compromising efficiency.
Migrating analytics workflows diminished time-to-market for brand 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 crew can give attention to constructing revolutionary options for our clients.
Databricks’ Serverless capabilities have been a recreation changer, permitting us to separate compute from storage to make sure we solely paid for the compute we would have liked. This method eradicated the fee burden of idle infrastructure. Cloud prices for analytics dropped by 50–70%, relying on utilization profiles. The power to auto-scale based mostly on information hundreds meant that each high-traffic and sporadic-use clients may depend on a seamless, responsive expertise with out handbook tuning.
The outcomes have been clear: Databricks supplied 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 setting permits our groups to deal with information ingestion, transformation, and analytics in a single place—enhancing productiveness and collaboration. From information ingestion to complicated 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 information infrastructure, we will now assist banks effortlessly uncover actionable insights to drive development and deepen buyer relationships.”
With Databricks because the spine of our trendy information stack, we’ve been in a position to innovate quicker and ship larger 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 permits 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 provide effectiveness. With Databricks’ built-in setting, we offer out-of-the-box analytics to reply essential questions like: How is a checking account performing available in the market? What’s the adoption charge of a promotional provide? How do buyer transaction patterns fluctuate throughout tiers?
- Value-Environment friendly Transformations: With Databricks Autoloader, we seamlessly ingest billions of information from varied file varieties (Avro, CSV) saved in Azure Blob Storage. This, mixed with Serverless scaling, ensures that we will course of information effectively, with out over-provisioning or incurring pointless prices.
- Making certain Knowledge Governance and Compliance: Unity Catalog allows sturdy information governance, permitting us to isolate single-tenant buyer environments and preserve compliance with monetary companies rules.
The affect 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. It is a important time-to-market benefit that drives quicker innovation and monetization.
“It’s thrilling to see how Zafin is leveraging the Databricks platform to ship information 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 – International Head of Monetary Providers, Cybersecurity and Public Sector GTM | Databricks
Wanting forward: Tapping into real-time improvements
Along with enabling quicker, cheaper information processing, the Databricks Platform has positioned us for future innovation. We’re exploring alternatives in AI and GenAI, comparable to real-time tiering insights, profitability modeling, and real-time affords with Databricks’ Delta Reside Tables and streaming capabilities. To additional streamline our analytics capabilities, we additionally plan to allow Lakehouse Federation for seamless integration throughout information sources.
As we proceed to scale and innovate, we’re excited to unlock much more highly effective capabilities for our clients—reworking information 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, guaranteeing transparency and equity for banks and their clients. By enhancing operational effectivity, boosting income, and enhancing buyer experiences, Zafin empowers monetary establishments to realize their strategic targets.
Zafin is headquartered in Vancouver, Canada, with a presence spanning workplaces and clients worldwide, together with main banks like ING, CIBC, HSBC, Wells Fargo, Navy Federal Credit score Union, PNC, and ANZ.
Study extra
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For technical professionals, go to the Knowledge Intelligence for Monetary Providers webpage for extra use instances and sources.
