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 future liabilities. ALM is especially critical
in environments of elevated interest rate changes, as has been experienced globally between 2021 and 2023.
Traditional ALM implementation is still heavily dependent on the judgement of professionals such as Quants,
Actuaries or Investment Managers. This over-reliance on human input critically limits ALM performance due
to restricted automation, human irrationality and restricted scope for multi-objective optimisation. This paper
addressed these limitations by applying Deep Reinforcement Learning (DRL), which optimises through trial, and
error and continuous feedback from the environment. We defined the Reinforcement Learning (RL) components
for the ALM application: the RL decision-making Agent, Environment, Actions, States and Reward Functions.
The results demonstrated that DRL ALM can achieve duration-matching outcomes within 1% of the theoretical
ALM at a 95% confidence level. Furthermore, compared to a benchmark weekly rebalancing traditional ALM
regime, DRL ALM achieved superior outcomes of net portfolios which are, on average, 3 times less sensitive
to interest rate changes. DRL also allows for increased automation, flexibility, and multi-objective optimisation
in ALM, reducing the negative impact of human limitations and improving risk management outcomes. The
findings and principles presented in this study apply to various institutional risk-takers, including insurers, banks,
pension funds, and asset managers. Overall, DRL ALM provides a promising Artificial Intelligence (AI) avenue
for improving risk management outcomes compared to the traditional approaches.
