Santander UK is a large retail and commercial bank based in the UK and a wholly owned subsidiary of the major global bank Banco Santander.
“Four years ago, we realized that while data was a big asset for the organization, we were not leveraging it in the best way,” said Antonio Alvarez, director of Data Innovation at Santander UK.
Like many financial institutions, Santander UK had a large number of legacy data warehouses spread across its many business units. Each business unit—from risk to finance to capital markets—had built their own data warehouse using different sources with inconsistent data and different ways of calculating the same metrics leading to different results. As a result, the company couldn’t get the comprehensive customer insights it needed and business staff often worked with “multiple versions of the truth.”
As Santander UK began its evolution towards a data-driven company and to better understand its customers, it needed to modernize its data platform.
“We wanted to create a new operating model around data to transform the business and create new value for our customers,” said Alvarez.
Santander UK worked with Cloudera to implement a single data platform that could support all its workloads, including self-service analytics, operational analytics, and data science. Processing 10 million transactions daily (30,000 transactions per second at peak times) and bringing together nearly two petabytes (PB) of data, the platform provides unprecedented customer insight and business value across the organization:
New customer insight analytics, drawing from more than 40 million customer records, streaming transaction data, and 10 years of historical data, have led to greater personalization and relevancy, and improved customer experiences. Now the bank can engage with customers at the right moment—whether it’s identifying in real time if someone needs help filling out a loan application or understanding when customers may be going through a difficult life situation and proactively offering new loan payment options to help ease the stress.
Nearly 15 million active customers get data from the architecture through branches, telephone banking and mobile services, such as Santander Wallet--a mobile banking application, helping simplify and streamline interactions.
The company’s financial crime investigative unit can better detect terrorism financing, human trafficking and money laundering thanks to new machine learning models that analyze streams of customer transactions and interactions against decades-old data.
Data scientists and analysts can draw from a nexus of transaction, interaction, payment and application data to identify new business opportunities and increase the pace of innovation.
Business staff can conduct self-service analytics across vastly larger data sets to uncover ways to better manage risk and improve operational efficiency. Approximately 5,000 queries are performed daily—a two times increase in just nine months—and this number continues to grow.
“We started with an innovation mindset to explore new opportunities and create new services for customers,” said Alvarez. “Cloudera provides all the capabilities that we need in a single platform, with a single consistent data context through SDX; this is critical for the success of our data initiative. The more we use the Cloudera platform, the more value we create.”
“We envision a world where all our products and customer interactions are powered by machine learning and artificial intelligence to provide better experiences to customers,” said Alvarez.
To achieve this goal, Santander UK needed to put in place capabilities so its data scientists and data engineers could experiment and deploy new models faster. “Cloudera Data Science Workbench is a great tool for both data scientists and data engineers to create value quickly and then move into production faster. Our platform is growing quickly with new use cases and Cloudera Data Science Workbench is a foundational tool that can help us pursue new opportunities at scale.”
Currently, Santander UK brings in data to its modern data platform from a wide range of legacy data warehouses. However, ultimately, Alvarez expects to simplify the infrastructure and create a centralized streaming data warehouse, decommissioning costly legacy data warehouses that are no longer needed. “In the long term, we want to consolidate our data warehouses inside Cloudera to create a streaming data warehouse where real-time information is available in that central platform,” said Alvarez.
Already, the bank has realized impressive benefits spanning new revenues, cost savings, and risk reductions including:
Creating analytics for corporate customers with near-real-time shopping behavior and helping identify 7,000 new corporate customer prospects
Reducing capital expenditures by US$3.2 million and decreasing operating expenses by US$650,000
Enabling marketing to realize US$2.4 million in annual savings on cashback on commercial transactions.
Protecting 3.7 million customers from financial crime impacts through 95 new proactive control alerts
Improving risk and capital calculations to reduce the amount of money it must set aside as part of risk mandates. For example in one instance, the risk team was able to release US$5.2 million that it had withheld for non-performing credit card loans by identifying healthy accounts miscategorized as high risk.
And with Cloudera’s support, he expects greater opportunities ahead. “It can be difficult for banks to stay on the forefront of analytics and machine learning,” said Alvarez. “Cloudera provides us with a long-term view of the industry and value-add services to help us identify new opportunities that aren’t on our radar yet.”