The recent failures of regional banks in the US, such as Silicon Valley Bank (SVB), Silvergate, Signature, and First Republic, were caused by multiple factors. To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure.
Technology alone would not have prevented the banking crisis, but the fact remains that financial institutions still aren’t leveraging technology as creatively, intelligently, and cost-effectively as they should be. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices. Implementing a modern data architecture makes it possible for financial institutions to break down legacy data silos, simplifying data management, governance, and integration — and driving down costs.
Historically, technological limitations made it difficult for financial institutions to accurately forecast and manage liquidity risk. Thanks to the growth and maturity of machine intelligence, institutions can potentially analyze massive volumes of data at scale, using artificial intelligence (AI) to automatically identify problems, as well as apply pre-defined remediations in real time.
However, because most institutions lack a modern data architecture, they struggle to manage, integrate and analyze financial data at pace. By addressing this lack, they can responsibly and cost-effectively apply machine learning (ML) and AI to processes like liquidity risk management and stress-testing, transforming their ability to manage risk of any kind.
Financial institutions can use ML and AI to:
The recent regional bank collapses also highlighted the crucial role stress-testing plays in modeling economic conditions. Institutions can use ML and AI to transform stress testing — improving accuracy and efficiency, identifying weaknesses, and enabling improvements that traditional methods miss.
Use cases include:
While Know Your Customer (KYC) and Anti-Money-Laundering (AML) processes didn’t play a role in the recent collapses, institutions can also leverage the combination of a modern, open data architecture, advanced analytics, and machine automation to transform KYC and AML .
Possible applications include:
Financial institutions need a flexible data architecture for managing, governing, and integrating data at scale across the on-premises and cloud environments. This architecture should provide a secure foundation for leveraging ML and AI to manage risk, particularly liquidity risk and stress-testing.
Cloudera Data Platform (CDP) facilitates a transparent view of data across on-premises and cloud data sources, while its built-in metadata management, data quality-monitoring, and data lineage-tracking capabilities simplify data management, governance, and integration. CDP also enables data and platform architects, data stewards, and other experts to manage and control data from a single location.
A scalable platform like CDP provides the foundation for streamlining risk management, maximizing resilience, driving down costs, and gaining decisive advantages over competitors.Learn more about managing risk with Cloudera.
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