Data Engineering for Banks [Part 8]: Top Success and Failure Stories

Raiffeisen Bank International (RBI), headquartered in Austria, implemented a real-time data architecture across 12 countries, enhancing its marketing performance by 60%.

By transitioning from manual monthly reporting to automated, near real-time insights, RBI improved its multi-channel marketing strategies, including social media, TV, YouTube, and web platforms.

This transformation was achieved within eight months and ensured GDPR compliance across all regions.

Implemented a high-speed, Big Data platform using Apache Spark over their Data Lake to compute anti-fraud predictors.

It fully automates the processing of 1.5 billion transactions daily, which enables scalable, customizable fraud detection unmatched by previous relational database systems.

Deployed a Databricks-based analytics platform on Microsoft Azure with MLflow, Unity Catalog, a feature store, and a model factory.

They achieved a dramatic reduction in model development and deployment from weeks to hours, which enabled near-real-time personalization and predictive analytics, all under strict ISO 27001 governance.

Although more of a proprietary internal system than a public case study, Goldman’s creation of SecDB, a distributed ledger-like global data store with object retrieval and replication, underpinned its risk calculations.

Paired with the “Slang” scripting language, it delivered a crucial edge in risk management, especially during the 2008 financial crisis.

Inspired by Goldman’s SecDB, Bank of America integrated its systems post-Merrill Lynch acquisition and launched Quartz, a successor risk platform combining advanced data architecture and analytics, which elevated its risk-handling capabilities across global markets.

TSB Bank’s attempt to migrate its IT systems from Lloyds Banking Group to a new platform, Proteo4UK, led to a major crisis.

The migration was poorly executed, resulting in widespread outages and errors, such as customers seeing others’ transactions.

The crisis highlighted systemic IT and management flaws, including unrealistic deadlines, a lack of proper communication, and the addition of new functionality during the ongoing migration process.

In 2019, Capital One experienced a data breach affecting over 100 million customers. The breach was due to a misconfigured firewall in a cloud server, which allowed an external attacker to access sensitive data.

This incident underscored the importance of robust cloud security measures and the need for continuous monitoring and auditing of cloud configurations.

Between 2023 and 2025, Barclays experienced 33 IT service outages, which highlights vulnerabilities in their complex and legacy IT systems.

Despite significant investments in modern technologies and cybersecurity, the bank faced ongoing challenges such as frequent service disruptions.

These incidents emphasized the risks posed by legacy technology and the need for modernization to ensure service continuity.

JPMorgan Chase acquired the fintech startup Frank for $175 million, believing it had four million users.

However, it was later discovered that Frank had only 300,000 users, and the user data had been fabricated.

This case highlighted the importance of due diligence and data verification in acquisitions, especially when dealing with startups and new technologies.

HSBC and Santander each experienced 32 IT service outages between 2023 and 2025, indicating systemic issues in their IT infrastructures.

These failures were attributed to a combination of technical debt, data silos, and challenges associated with rapid digital transformation.

The incidents underscored the need for comprehensive risk management and modernization strategies in banking IT systems.