AI in Finance Summit Fall 2026

Attendees, exhibitors & solutions

Who this event is for

Industry trends

Key trends shaping this market and its wider industry.

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A range of accommodation options near the venue and across key areas.

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Frequently asked

Glossary

1

LLM (large language model)

A large language model trained on text data to generate or analyse language; at AI in Finance Summit New York the practical questions are accuracy, data leakage, auditability and cost of inference in regulated workflows.

2

Model risk management (MRM)

The bank-style discipline of inventorying, validating, monitoring and documenting models; for AI systems it extends to prompts, feature pipelines, third-party models and ongoing performance drift.

3

SR 11-7

US Federal Reserve supervisory guidance on model risk management, widely used by banks as a reference for model validation, governance and documentation expectations, including for AI and machine-learning models.

4

Model validation

Independent review of a model's conceptual soundness, data quality, performance and limitations; AI finance buyers typically ask vendors for validation evidence and reproducible benchmarks.

5

Explainability (XAI)

Methods that help users understand why a model produced an output; in finance this supports adverse-action explanations, fair-lending reviews, model risk committees and supervisory conversations.

6

Alternative data

Non-traditional datasets such as transaction, satellite, web or sensor data used to generate investment or credit signals; buyers at the summit assess coverage, lineage, consent and backtest robustness.

7

Backtesting

Testing a strategy or model on historical data to estimate how it would have performed; in AI finance it must account for look-ahead bias, data snooping and regime change.

8

RegTech

Technology that helps financial firms meet regulatory obligations, such as surveillance, reporting, AML and compliance monitoring; AI RegTech vendors at the event focus on alert triage and workflow automation.

9

AML (anti-money laundering)

Controls and monitoring designed to detect money laundering; machine learning is used for transaction monitoring and alert scoring, but explainability and false-positive rates remain key buying criteria.

10

KYC (know your customer)

Identity verification and customer due-diligence processes required before and during a financial relationship; AI tools are applied to document checks, screening and ongoing monitoring.

11

Synthetic data

Artificially generated data designed to preserve statistical patterns for testing or training; finance teams use it to overcome data scarcity and privacy constraints, subject to validation against real-world behaviour.

12

RAG (retrieval-augmented generation)

A pattern where a language model retrieves relevant documents before answering; financial-services deployments use it for research and policy Q&A with access controls and citation requirements.

13

Agentic workflow

An AI system that plans and executes multi-step tasks with limited human intervention; in finance the debate at the summit centres on approval boundaries, audit logs and containment of unintended actions.

14

Data lineage

A record of where data came from and how it was transformed; it is a prerequisite for model reproducibility, regulatory reporting and third-party AI risk reviews.

15

Model drift

Degradation in model performance as market conditions or data distributions change; monitoring for drift is a core part of post-deployment governance for AI in finance.