- Our client, the bank required a big data architecture to leverage analytics use cases for CRM and Financial data that would allow it to meet its vision for digital transformation to fuel innovation.
- The company confronted several difficulties stemming from an obsolete data infrastructure, which hindered its efforts to capitalize on data as a valuable resource.
- They required fully automated and integrated data pipelines to support the end-to-end business processes of account creation, funding, and internal financial reporting.
- They needed a single source of truth to power these applications.
About the Bank
The client is a pioneer in building the next generation of cross-border embedded finance. It stands as an illustrious digital banking institution, committed to delivering impeccably smooth and avant-garde financial experiences to its esteemed clientele. With a global presence, this bank bestows upon its customers the privilege of multi-currency accounts and an array of international financial services. This eminent establishment endeavours to provide a platform of utmost convenience, enabling the effortless management of finances, seamless cross-border fund transfers, expedient payment facilitation, and comprehensive access to an array of online banking amenities.
In the realm of data, the bank undertakes the noble responsibility of stewarding and safeguarding highly sensitive customer information, encompassing personal particulars, intricate financial transactions, and intricate account specifics. Pledged to unwavering data security and privacy, the bank adheres steadfastly to stringent protocols, ensuring the utmost confidentiality and incorruptibility of its esteemed patrons’ invaluable data. In tandem with our unwavering support, the bank fortifies its fortress of data protection through the implementation of resolute measures, ensuring the impregnability of customer information, whilst dutifully complying with the discerning requisites of prevailing data protection regulations.
Built a unified data platform at the back end of the account creation and financial insights applications to automate the flow of data throughout the organization and processes with numerous automated data pipelines. This enabled the complete elimination of siloed data sets and fragmentation and duplication of assets which was previously hindering their ability to meet their innovation transformation goals.
- Future state design which laid out a modern data architecture, GDPR readiness, and data governance model.
- Data flow automation using spark-powered automated data pipelines on Azure Synapse and ADF.
- Data analytics environment setup on Microsoft Azure
- Data governance, BI reports, and Implementation of data-driven financial forecasts and insights.
- Strong middleware design to act as an intermediary between data architecture and business applications.
- Fully automated account creation and funding process, along with end-to-end integration and automation of financial insights reporting process.
- Integration with Microsoft Purview for strong data governance and management capability
- Integration with Log Analytics for proactive metadata management, auditing and cataloguing of data.
Agile Approach to Data Lake Development
Our agile approach to data-lake development helped the bank launch its analytics programs quickly and establish a data-friendly culture for the long term.
Stage 1: Landing zone for raw data
Scalable, low-cost “pure capture” environment
The data lake serves as a thin data-management layer within the company’s technology stack that allows raw data to be stored indefinitely before being prepared for use in computing environments.
Stage 2- Machine learning environment
Data Lake is actively used for Use Cases
Data scientists have easy, rapid access to data—and can focus more on running experiments with data and analysing data, rather than focusing solely on data collection and acquisition.
Stage 3- Offload for data silos
Data Lake is integrated with existing enterprise data sources.
Taking advantage of the low storage costs associated with a data lake, clients can house “cold” (dormant, or inactive) data. This data can be used to generate insights without pushing or exceeding storage limitations.
Stage 4- Data operations
A data lake is a core part of data infrastructure.
Much of the information that flows through the company is going through the data lake.
Full advantage of the distributed nature of data-lake technology as well as its ability to handle computing-intensive tasks, such as advanced analytics or to deploy machine-learning programs.
Joining the Next Generation of Data-Driven Banking
Embracing big data is no longer an option but a necessity for banks to thrive in the digital era. By harnessing the power of data-driven insights, banks can unlock new opportunities, mitigate risks, deliver personalized experiences, improve operational efficiency, and combat fraud effectively. The statistics underscore the immense potential and tangible benefits that await banks on their big data journey. The time for banks to leverage big data is now, as those who fail to embrace this transformative technology risk being left behind in an increasingly data-driven world.