Natural language processing in finance is redefining the way in which institutions analyze text data, assess risk, and derive insights from markets. Quantum computing, which allows machines to explore many possibilities in parallel in order that certain tasks will be performed significantly faster than today’s computers, is not going to transform the world of finance immediately – but that day is coming, and practitioners should plan for it, in line with the writer of this chapter of .
The writer argues that quantum computing is not going to transform finance overnight, but within the short term, corporations can profit from hybrid quantum classical methods for hard optimization and simulation while preparing for quantum security. In summary, the authors suggest that practitioners now experiment pragmatically (portfolio optimization, Monte Carlo, targeted machine learning) and start transitioning to post-quantum cryptography.
Companies that start testing mixed quantum and classical methods will see early wins (faster optimization and simulations) and reduce cyber risk. Reliable large-scale quantum computing continues to be a great distance off, so near-term advantages will come from practical small-scale quantum techniques and a cautious transition to recent post-quantum encryption.
This chapter shows what the shift to quantum technology means in practice and refreshes the basics of machine learning (ML) – supervised, unsupervised and neural networks – behind credit scoring, fraud detection, market/risk evaluation and portfolio construction. It highlights the workhorses: -Nearest Neighbor (kNN) for credit and fraud calls via nearest neighbor similarity; – technique of flagging anomalies and detecting anti-money laundering (AML) patterns; and Principal Component Analysis (PCA) to compress correlated aspects for cleaner risk and smarter allocation.
