Video
Alisa Rusanoff discusses the growing complexity of financial modeling in today’s volatile global environment. With her expertise in trade finance, embedded finance, and commodity trading, she emphasizes the importance of layering multiple factors into models—especially macroeconomic, geopolitical, and supply chain variables. The presence of multicollinearity, where variables are highly interrelated, makes it vital to distinguish correlation from causation.
Rusanoff advocates a top-down approach: starting with macro-level threats such as sanctions, wars, or trade restrictions, and then tracing their ripple effects on supply chains, company revenues, and consumer behavior. Scenario analysis is key, with models built to assess outcomes under best-case, worst-case, and "Black Swan" scenarios. The goal is to understand elasticity of demand and how pricing, cost of goods, and vendor relationships shift under different pressures.
AI plays a pivotal role in managing this complexity. Rosenoff and her team use multi-agent AI systems to simulate scenarios in real time, helping to catch emerging patterns faster than human analysts could. Yet she likens AI to a junior analyst—promising, but still learning. She urges cautious implementation, starting with simple tasks before trusting AI with more strategic decisions.
She closes by underscoring the interconnectedness of global markets. Even seemingly distant events—like unrest in one region—can cascade into energy markets, inflation, and shifts in consumer demand worldwide. No business is insulated, and the scale and timing of these effects must be part of every institution’s strategic analysis. The conference itself offered further insights into AI model risk, ethics, and the power of collaboration in such an evolving field.
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