The use of Artificial Intelligence (AI) and Machine Learning (ML) techniques within banks is rising, especially for risk management purposes. The question arises whether the commonly used three lines of defence model is still fit for purpose given these new techniques, or if changes to the model are necessary. If AI and ML models are developed with involvement of second line functions, or for pure risk management purposes, independent oversight should be performed by a separate function. Other prerequisites to apply AI and ML in a controlled way are sound governance, a risk framework, an oversight function and policies and processes surrounding the use of AI and ML.

Following the financial crisis, quantitative liquidity risk regulation was introduced by means of the Liquidity Coverage Ratio (LCR). This literature study aims to investigate whether the introduction of the LCR leads to better liquidity risk management in banks. It elaborates on the drivers and definition of liquidity risk as well as the history, benefits and goals of this regulation. It also delves into the exact composition of the ratio and the assumptions used. The impact on bank lending as well as banks' business model and risk management is addressed, as well as the interaction with monetary policy operations and capital regulation. This paper then describes the operational differences that were observed after the implementation, and behavioral aspects. We also address the Net stable Funding Ratio (NSFR) and the discussion on interaction between the two indicators and possible redundancy. We have found that the introduction of the LCR leads to better management of liquidity risk for most financial institutions, but more harmonious implementation throughout the sector could reduce liquidity risk even further.