Redefining Data Management for Leading Financial Institution: 360-Degree Customer View, 40% Improved Risk Detection
Summary:
A leading international bank struggled with cost-effective data processing and analysis due to the lack of a centralized repository. This impeded advanced analytics and accurate reporting. They sought a comprehensive solution to centralize data, enable advanced analytics, and enhance data management efficiency.
Challenges:
Absence of Centralized Data Location:
The lack of a centralized repository for enterprise data posed significant challenges.
Cost-Effective Data Processing:
The daily volume of approximately 10TB and a total database size of 400TB needed to be ingested and processed for reporting and analytics, making cost-effective data processing difficult.
Unified Platform for Advanced Analytics:
The absence of a central foundational platform hindered the ability to conduct fit-for-purpose advanced and exploratory analytics on large and diverse datasets.
Objectives:
- Primary Goal- Centralize data to enable efficient, cost-effective processing and analysis
- Secondary Goal- Create a unified platform for advanced analytics and accurate reporting
Suggested Solution:
Relanto delivered a forward-looking solution by crafting a custom data model specifically designed to meet the bank’s evolving needs. Central to this solution was the development of an Enterprise Data Lake (EDL), a comprehensive repository that integrates various data sources into a unified framework. This EDL was constructed in adherence to strategic enterprise data lake standards, ensuring it met stringent governance and operational criteria, including aspects of maintainability and auditing.
To enhance the effectiveness and efficiency of the EDL, Relanto implemented advanced automated frameworks. These frameworks included table creators, installers, and workflow automation tools, which streamlined the data management processes and ensured the solution's robustness and scalability. By automating these components, Relanto not only optimized data integration and processing but also facilitated easier maintenance and future expansions, aligning with the bank’s long-term strategic goals.
Outcome:
- 360 View of Customer: Integrated data from various touchpoints to provide a unified perspective on customer interactions and preferences, enhancing decision-making and customer service by consolidating all relevant information into a single profile.
- Personalized and Predictive Product Offering: Utilized data analytics to tailor product recommendations and offers to individual customer needs and behaviors. Predictive modeling anticipated customer preferences, driving targeted marketing and increased engagement.
- Fraud and Risk Management: Implemented advanced analytics and machine learning to detect and mitigate fraudulent activities and assess risk factors. This proactive approach enhanced security and minimized potential financial losses.
- Risk Data Aggregation Server: Served as the foundational infrastructure for Anti-Money Laundering (AML) solutions by consolidating risk-related data into a central server. This aggregation supported comprehensive analysis and effective detection of suspicious activities.
- Streamlining and Optimizing Data and Workflows: Improved efficiency by automating and refining data processing and workflow management. Streamlined operations reduced manual errors, enhanced productivity, and ensured timely and accurate data handling.
