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.
Up your liquidity risk management game
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:
- Support liquidity monitoring and forecasting in real time. Incorporate data from novel sources — social media feeds, alternative credit histories (utility and rental payments), geo-spatial systems, and IoT streams — into liquidity risk models. For example, an institution that has significant liquidity risk exposure could monitor customer sentiment via social media and financial news and events combined with liquidity indicators such as deposit inflows and outflows, loan repayments, and transaction volumes. Thus identifying trends that may impact liquidity and take preemptive action to manage their position.
- Apply emerging technology to intraday liquidity management. Look for ways to integrate predictive analytics and ML into liquidity risk management — for example, by monitoring intraday liquidity, optimizing the timing of payments, reducing payment delays and/or dependence on intraday credit.
- Enhance counterparty risk assessment. Use predictive analytics and ML to formalize key intraday liquidity metrics and monitor liquidity positions in real time. Design forecasting models that more accurately predict intraday cash flows and liquidity needs. Deliver real-time analytic dashboards, suitable for different stakeholders, that integrate data from payment systems, nostro accounts, internal transactions, and other sources.
- Transform stress testing
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:
- Enable transparent access to financial data. It all starts with implementing a modern data architecture, which affords a comprehensive view of data across all core processes and systems — from loan portfolios and investment portfolios, to trading positions, customer profiles, and financial market data. It also makes it easier to manage, integrate, analyze, and govern data, increasing efficiency, improving risk management, and simplifying compliance.
- Use ML to more realistically model and simulate stress scenarios. Create predictive and ML models to simulate known credit, market, and liquidity risks in different kinds of stress scenarios, embedding them into existing risk-management processes. Design automation to manage and govern this lifecycle — automating data input, model execution, and monitoring — and configure alerts that trigger whenever risk levels change or exceed predefined thresholds.
Streamline KYC and AML, too
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:
- Improved customer risk profiling. Aggregate data from internal and external sources — including transaction histories, credit reports, sanctions lists, reputation-screening reports, and social media feeds. Apply predictive-analytic and ML techniques to this data to create more accurate profiles and proactively identify high-risk customers.
- Automated KYC and AML compliance. Modernize KYC and AML by optimizing existing automation, reducing manual touchpoints and increasing efficiency. Look to automate workflows that perform routine checks, such as screening against lists of sanctioned individuals, or Politically Exposed Persons (PEPs), to streamline operations..
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.