Artificial intelligence is reshaping financial services from the inside out — credit underwriting, fraud detection, wealth management, and customer service are all being rebuilt around machine learning models trained on alternative data. Coverage in this section tracks the models being deployed at scale, the vendors winning major bank contracts, and the operational risks accompanying autonomous decisioning.
Synthetic data promises a way around AI's privacy problem in finance. How banks and fintechs use algorithmically generated data to train models without exposing real customers.
Robo-advisers were just the start. Machine learning is now embedded in portfolio construction, tax optimisation, risk profiling, and research analysis. What it cannot do, though, is as instructive as what it can.
A primary-source control benchmark for rogue-trading surveillance: what the Kerviel and Adoboli cases reveal, where analytics helps, and which decisions must remain human.
A function-by-function map of where AI is actually deployed across banking and finance in 2026, and why the durable value sits in the back office, not the demo-stage chatbot.
A practical evidence framework for faster credit decisions, alternative data, adverse-action explanations and model risk.
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