Enterprise analytical platform for smarter banking decisions
Leading financial institution builds a unified analytics foundation to power strategic, data-driven management
Challenge
As the country’s leading state-owned financial institution, BROU needed to break down data silos spread across multiple systems.
Inconsistent definitions, limited traceability, and fragmented reporting made it difficult to standardize KPIs and confidently support commercial, risk, and executive decisions. The organization required an enterprise-wide analytics environment capable of formalizing data governance while enabling descriptive, predictive, and prescriptive insights across the bank.
Solution
An end-to-end analytics platform was implemented to centralize governed, high-quality data, integrating multiple data sources into a unified environment.
The solution includes predictive models for loans and credit cards, a commercial portal featuring four management frameworks, and a smart segmentation component for campaign tracking, along with SRCM and management KPIs aligned with strategic business goals.
Additionally, self-service analytics capabilities were introduced to accelerate access to information and democratize data usage across the organization.
10
Integrated data sources
3700
Reports and dashboards available
1200
Active users across 20+ business areas
Benefits
The value behind the solution
Enterprise-grade Data Governance
A formal governance model institutionalized common definitions, quality controls, and accountability, creating a single, trusted source of truth across the bank.
Analytics embedded into core banking decisions
Descriptive, predictive, and prescriptive models now directly support lending, credit card strategies, and commercial performance management.
Data-driven commercial execution
The commercial portal enables structured segmentation, campaign monitoring, and KPI tracking, strengthening alignment between strategy and execution.
Our methodology
The initiative followed a structured, collaborative approach focused on building a strong and sustainable analytics foundation. More than ten data sources were progressively integrated into a unified model, supported by over 30 data models and a formal governance framework that defined 450+ business terms and 380+ data quality rules.
Over 120 business stakeholders actively contributed to defining and validating key information, ensuring alignment with the bank’s operational needs. In parallel, more than 500 employees were trained to promote self-service analytics, drive cross-functional adoption, and establish consistent performance indicators across commercial, risk, and management areas.
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