In this paper, we present Deep-MacroFin, a comprehensive framework designed to solve partial differential equations, with a particular focus on models in continuous time economics. This framework leverages deep learning methodologies, including conventional Multi-Layer Perceptrons and the newly developed Kolmogorov-Arnold Networks. It is optimized using economic information encapsulated by Hamilton-Jacobi-Bellman equations and coupled algebraic equations. The application of neural networks holds the promise of accurately resolving high-dimensional problems with fewer computational demands and limitations compared to standard numerical methods. This versatile framework can be readily adapted for elementary differential equations, and systems of differential equations, even in cases where the solutions may exhibit discontinuities. Importantly, it offers a more straightforward and user-friendly implementation than existing libraries.

AI and data science will probably bring about the most significant changes to the financial services industry that the industry has witnessed in its short history. Yet, today, many financial institutions are still struggling to find their way to start the AI journey1. We propose a concept called T-shaped teams (See Exhibit 1) as the solution for financial institutions to overcome the hurdles in AI adoption. The idea is simple: Technology, more specifically AI and data science, is such a distinct discipline from investments that it takes an additional function, which we refer to as the innovation function, to join them and form the cohesive AI-age investment team. Globally, the financial services industry is eagerly looking to transform from Stage 0 in AI adoption to Stage 1 (See Exhibit 2).