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Applied Sciences OPEN ACCESS

AI-Powered Monitoring Systems for Liquidity Risk: Predicting Cash Flows, Liquidity Coverage Ratio, and Liquidity Shortfall with Deep Learning and Real-Time Analytics

Shaurya Shounik
MS in Finance, Brandeis University Independent Researcher, USA
tajas 2024
VOL. 6 / NO. 06 JUNE
FULL TEXT PP. 56-65
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VOLUME 6
ISSUE 06
YEAR 2024
PAGES 56-65
RESEARCH OVERVIEW

Abstract

As the COVID-19 pandemic and the financial crisis of 2008 have shown, liquidity risk is still an important factor in maintaining financial stability. Although buffers were enhanced by the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR), day-to-day monitoring is still dependent on scenario analysis, which relies on historical assumptions, and stress testing. This research has gathered information from universities, government organizations, and companies to find out whether AI may help close that gap. Across studies, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models generally outperform statistical baselines such as ARIMA and GARCH, cutting forecast errors by roughly 20–40% and improving predictions of cash flows, LCR components, and liquidity shortfalls. Technically, continuous, low-latency monitoring is possible with event-driven data stacks like Apache Kafka with Flink, but there have been few practical installations for liquidity risk. With the use of AI-enabled monitoring, which seems to be more accurate and responsive, institutions are moving toward a unified, real-time view of financing risk. This development enables banks to efficiently manage risk and enhance systemic regulatory monitoring. Still, regulator-auditable compliance procedures, data-quality controls, model-risk governance, and flexibility will likely be necessary for adoption.

RESEARCH TOPICS

Keywords

Liquidity risk Liquidity Coverage Ratio (LCR) Real-time analytics Risk management Artificial intelligence in finance Deep learning Financial Stability Liquidity shortfall

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