A case study for unlocking the potential of deep learning in asset-liability-management

T Krabichler, J Teichmann - Frontiers in Artificial Intelligence, 2023 - frontiersin.org
The extensive application of deep learning in the field of quantitative risk management is still
a relatively recent phenomenon. This article presents the key notions of Deep Asset-Liability …

Adoption of artificial intelligence and machine learning in banking systems: a qualitative survey of board of directors

A Eskandarany - Frontiers in Artificial Intelligence, 2024 - frontiersin.org
The aim of the paper is twofold. First to examine the role of the board of directors in
facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore …

[HTML][HTML] Application of deep reinforcement learning in asset liability management

TA Wekwete, R Kufakunesu, G van Zyl - Intelligent Systems with …, 2023 - Elsevier
Abstract Asset Liability Management (ALM) is an essential risk management technique in
Quantitative Finance and Actuarial Science. It aims to maximise a risk-taker's ability to fulfil …

Evaluating the Impact of Liquidity and Debt Management Strategies on Business Growth and Stability

B Ranha - Journal Development Manecos, 2023 - scieclouds.com
This paper explores how firms use assets and liabilities management strategies to enhance
business prospects for growth as well as stability. As this was a quantitative study, data was …

[PDF][PDF] Intelligent Systems with Applications

TA Wekwete, R Kufakunesu, G van Zyl - researchgate.net
Asset Liability Management (ALM) is an essential risk management technique in
Quantitative Finance and Actuarial Science. It aims to maximise a risk-taker's ability to fulfil …