  In this paper, we develop a deep neural network approach to solve a lifetime
expected mortality-weighted utility-based model for optimal consumption in the
decumulation phase of a defined contribution pension system. We formulate this
problem as a multi-period finite-horizon stochastic control problem and train a
deep neural network policy representing consumption decisions. The optimal
consumption policy is determined by personal information about the retiree such
as age, wealth, risk aversion and bequest motive, as well as a series of
economic and financial variables including inflation rates and asset returns
jointly simulated from a proposed seven-factor economic scenario generator
calibrated from market data. We use the Australian pension system as an
example, with consideration of the government-funded means-tested Age Pension
and other practical aspects such as fund management fees. The key findings from
our numerical tests are as follows. First, our deep neural network optimal
consumption policy, which adapts to changes in market conditions, outperforms
deterministic drawdown rules proposed in the literature. Moreover, the
out-of-sample outperformance ratios increase as the number of training
iterations increases, eventually reaching outperformance on all testing
scenarios after less than 10 minutes of training. Second, a sensitivity
analysis is performed to reveal how risk aversion and bequest motives change
the consumption over a retiree's lifetime under this utility framework. Third,
we provide the optimal consumption rate with different starting wealth
balances. We observe that optimal consumption rates are not proportional to
initial wealth due to the Age Pension payment. Forth, with the same initial
wealth balance and utility parameter settings, the optimal consumption level is
different between males and females due to gender differences in mortality.
