An artificial intelligence keynote for banking and financial professionals: retail banking, investment banking, wealth management. Romain Rissoan illuminates the highest-value use cases — scoring, anti-fraud, compliance, the augmented adviser — and the risk-management considerations that come with them.
Banking creates enormous value with artificial intelligence, yet operates under regulatory constraints that are among the most demanding in any industry. Credit decisions, fraud prevention, AML/CFT compliance, customer relationships, operational automation: AI is now present across the entire banking value chain. Yet a model that cannot explain itself will not survive internal audit or regulatory scrutiny — and in banking, trust, once lost, is very hard to recover.
This AI keynote for banking and finance addresses all players in the sector: retail banks, investment and corporate banks, wealth and asset managers. Romain Rissoan, who speaks at leading financial institutions (references include Rothschild, CIC…), offers a grounded reading of high-value use cases and their safeguards, free from both technological hype and unwarranted doom. Every case is anchored in the reality of a real institution, with realistic expected gains and the conditions required for success.
The talk is designed for executive committees as much as for risk, compliance, IT and business teams. The challenge is not only to understand what AI makes possible, but to know how to prioritise and arbitrate: which use cases to deploy first, at what level of risk, and under what governance model. The keynote provides that decision-making framework by systematically linking business value to risk control and regulatory compliance.
Battle-tested use cases, from credit to customer relationship.
AI improves the reliability of lending decisions by drawing on a broader range of data than traditional models, while remaining explainable and compliant with transparency requirements. Properly governed, it sharpens decision quality, shortens response times and enables better service for profiles that were previously difficult to assess — without ever turning credit into a black box.
By analysing millions of transactions on the fly, AI identifies fraud patterns that static rules miss and continuously adapts to new fraudster techniques. It enables faster blocking of fraudulent transactions while reducing false positives — unjustified alerts that degrade the customer experience and saturate surveillance teams.
In the fight against money laundering and terrorist financing, AI improves transaction monitoring, prioritises genuinely relevant alerts and drastically reduces the false positives that tie up entire teams. It also helps document and audit-trail decisions — essential when facing the regulator — and sustains compliance at the transaction volumes a bank actually handles.
An AI co-pilot prepares client meetings, summarises a customer's situation, suggests responses and instantly retrieves the relevant product or regulatory information. The adviser gains in responsiveness and relevance, spends less time searching and updating records, and more time on personalised guidance — which remains the heart of the banking relationship.
Generative AI processes high volumes of documents and back-office tasks at scale: document reading, checks, reconciliations, reporting, standardised responses. Processing times fall, error rates drop and teams are freed from repetitive work so they can focus on complex cases and quality control.
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