Natural language processing in finance is redefining how institutions analyze text data, assess risk, and extract insights from markets. Quantum computing, which allows machines to explore many possibilities in parallel so certain tasks can run dramatically faster than on today’s computers, will not instantly transform finance — but that day is coming, and practitioners should plan for it, according to the author of this chapter of AI in Asset Management: Tools, Applications, and Frontiers.
The author argues that quantum computing will not remake finance overnight, but firms can gain near-term value from hybrid quantum–classical methods for hard optimization and simulation while preparing for quantum-safe security. In summary, the authors suggest that practitioners experiment pragmatically now (portfolio optimization, Monte Carlo, targeted machine learning) and begin their shift to post-quantum cryptography.
Firms that begin testing mixed quantum-and-classical methods will grab early wins (faster optimization and simulations) and reduce cyber risk. Reliable, large-scale quantum computers are still far off, so near-term benefits will come from practical, small-scale quantum techniques and a careful shift to new, post-quantum encryption.
This chapter shows what the move to quantum means in practice and refreshes machine learning (ML) basics — supervised, unsupervised, and neural nets — behind credit scoring, fraud detection, market/risk analytics, and portfolio construction. It spotlights the workhorses: k-Nearest Neighbor (kNN) for credit and fraud calls via nearest-neighbor similarity; k-means to flag anomalies and surface anti-money-laundering (AML) patterns; and principal component analysis (PCA) to compress correlated factors for cleaner risk and smarter allocation.