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[[File:Deep backward stochastic differential equation method.png|thumb|upright=
{{Artificial intelligence|Approaches}}
{{differential equations}}
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But as financial problems become more complex, traditional numerical methods for BSDEs (such as the [[Monte Carlo method]], [[finite difference method]], etc.) have shown limitations such as high computational complexity and the curse of dimensionality<ref name="Han2018">{{cite journal | last1=Han | first1=J. | last2=Jentzen | first2=A. | last3=E | first3=W. | title=Solving high-dimensional partial differential equations using deep learning | journal=Proceedings of the National Academy of Sciences | volume=115 | issue=34 | pages=8505-8510 | year=2018 }}</ref>.
#In high-dimensional scenarios, the Monte Carlo method requires numerous simulation paths to ensure accuracy, resulting in lengthy computation times. In particular, for nonlinear BSDEs, the convergence rate is slow, making it challenging to handle complex financial derivative pricing problems<ref name="puc">{{cite web | title = Real Options with Monte Carlo Simulation | url = http://www.puc-rio.br/marco.ind/monte-carlo.html | access-date = 2010-09-24 | archive-url = https://web.archive.org/web/20100318060412/http://www.puc-rio.br/marco.ind/monte-carlo.html | archive-date = 2010-03-18 | url-status = dead }}</ref><ref>{{cite web | title = Monte Carlo Simulation | url = http://www.palisade.com/risk/monte_carlo_simulation.asp | publisher = Palisade Corporation | year = 2010 | access-date = 2010-09-24 }}</ref>. [[File:Pi monte carlo all.gif|thumb|upright=1.
#The finite difference method, on the other hand, experiences exponential growth in the number of computation grids with increasing dimensions, leading to significant computational and storage demands. This method is generally suitable for simple boundary conditions and low-dimensional BSDEs, but it is less effective in complex situations<ref name="GrossmannRoos2007">{{cite book|author1=Christian Grossmann|author2=Hans-G. Roos| author3=Martin Stynes|title=Numerical Treatment of Partial Differential Equations| url=https://archive.org/details/numericaltreatme00gros_820|url-access=limited| year=2007| publisher=Springer Science & Business Media| isbn=978-3-540-71584-9|page=[https://archive.org/details/numericaltreatme00gros_820/page/n34 23]}}</ref>.
===Deep BSDE method===
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