The banking sector is undergoing radical transformation. Changing business models, disruptive technologies, fintechs, and compliance pressures challenge incumbent firms. To this full-service bank, the new data platform enables data-driven outcomes as steppingstone of a new ecosystem with robust compliance and structured/unstructured data access.
For one of Cognizant’s clients, a Nordic-Baltic banking group, every day brings a multitude of customer interactions, channels, and data. Structured and unstructured data – and thus hard to harvest and turn into insights.
Utilizing data is crucial to enable more proactive sales and service offerings in any business. In the banking industry, with its disrupted competitive landscape, it’s even more important. As the bank company realized it needed do drive more value from its existing data assets by combining, analyzing and using traditional and new types of data, it turned to a number of vendors. The mission was to build a new digital data foundation, where a data lake was central. This is central in becoming a digital, data-driven bank that amplifies offerings and provides meaningful customer experiences, while also improving profitability, risk management, and predictions.
A New Data Engine
Why was Cognizant chosen as a partner? The vast skill set and the capability to handle the whole value chains – from strategy, digital use cases, implementation and maintenance to support – determined. In just six weeks, Cognizant’s team designed a state-of-the-art data lake with GDPR compliance and won the project.
A scalable, future-proof data lake – as a part of the bank’s central data operational capability – is a prerequisite for capturing diverse types of data, encouraging experimentation with data, generating comprehensive insights, and integrating data with decision-making. This, in turn, makes it possible to get to know the customers, and to create engaging digital channels that drives profitability and sustainability.
Enterprise-Wide Data Access and Analytics
By combining existing data sources with a data lake, supported by information management, Cognizant now enables access to a broad range of extended analytical capabilities across the bank. The data lake is also a cost-effective way to store and manage large volumes of data from internal and external data sources to enable AI, to provide enterprise-wide access to data to several strategic business initiatives across the bank, and to provide the ability to ingest and process near real time events.
So far, the bank company experiences several new capabilities and advantages. Among other things, the bank has a better understanding of customer’s purchasing behavior and can tap into their transactional data to provide relevant and timely “next best action”. The bank can also make usage of Swish more engaging to end-customers and predict the risk of customers that will terminate their mortgage agreement within the next two months.
Cognizant met the designated goals of delivering different use cases by connecting the Cognizant data lake business analyst with relevant stakeholders, systematically extract and analyze the current and future KPIs and requirements, and map big data capabilities to the identified data source, use cases and business needs. A number of strategic and tactical data outcomes are delivered, such as the following:
• Anti-money laundering & Risk classifications
• Cash flow prediction & Financial behaviour clustering
• Mortgage churn model execution on data lake
• The next best actions for customer relationship management
Moving forward, the bank plans to automate artificial intelligence outcomes in a direct data pipeline with the data lake and execute more valuable anti-money laundry and bank’s sustainability use cases.
Process automation is vital to banking’s future; 90% of the 302 industry leaders in our study appear convinced that process automation is either important or critical to their business. Yet many are struggling to move beyond early proofs of concept. Why is that?