AI Training for Banking, Insurance & Financial Services

How can financial institutions use AI while keeping decisions explainable and accountable?

Excentra’s AI training courses for banking, insurance and financial services professionals focus on how to assess AI tools, question their outputs and decide when people need to intervene. In a regulated firm, an inaccurate fraud alert can delay a legitimate payment, a poorly explained credit score can be difficult to challenge, and a chatbot can give a customer incorrect information.

From credit assessment and claims handling to liquidity forecasting and transaction monitoring, the focus of these training courses is on what AI can support, how to check its outputs and who remains accountable for the outcome.

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In short

AI training for the financial services sector helps teams evaluate where artificial intelligence can support their work and what controls each use requires. A bank may need to review why a payment was flagged, an insurer may need to check whether a pricing model treats comparable customers fairly, and an asset manager may need to verify the assumptions behind a forecast. Across these settings, professionals need to recognise unreliable outputs, document decisions and know when to escalate a case.

Where AI is used in Banking, Insurance & Financial Services

01

Fraud and financial crime detection

Flag unusual transactions, identify links between suspicious accounts and prioritise anti-money laundering alerts for investigators. Review false positives and missed cases when assessing detection performance.

02

Credit and underwriting decisions

Support borrower credit assessments and insurance risk evaluation. Review the factors influencing a score and test for unintended differences in outcomes across customer groups.

03

Customer service agents

Respond to routine account or policy queries, collect initial details when a claim is reported and flag cases that need a human adviser.

04

Treasury and finance

Forecast cash flows, assess liquidity and compare hedging or portfolio scenarios. Review assumptions and unexpected outputs before acting on a forecast.

05

Compliance and model governance

Maintain an inventory of AI systems, record validation findings and monitor changes in model performance over time. Document who can approve, challenge or suspend a model’s use.

06

Operations and shared services

Extract information from onboarding and claims documents, flag reconciliation exceptions and prepare draft reports for review.

Artificial Intelligence (AI) for Accounting and Finance Professionals

Harnessing AI to Transform Financial Insight, Accuracy, and Strategic Decision-Making

5 days · Classroom or live online · In-house available

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Artificial Intelligence (AI) for Treasury & Financial Decision-Making

Leveraging Artificial Intelligence, Predictive Analytics & Intelligent Automation for Modern Treasury Excellence

5 days · Classroom or live online · In-house available

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Artificial Intelligence (AI) in Banking

Harnessing AI for predictive analytics, fraud detection, and intelligent customer engagement in banking.

5 days · Classroom or live online · In-house available

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Artificial Intelligence in Insurance

Harnessing AI to Revolutionize Risk, Underwriting, and Customer Experience

5 days · Classroom or live online · In-house available

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Start here in any sector

Thirty Excentra courses apply whatever the industry. These four are where most organisations begin.

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Generative AI

Turn Ideas and Information into Valuable Work with Generative AI

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AI Leadership, Strategy & Transformation

Set Direction, Prioritise Investment and Lead Organisational AI Adoption

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AI Governance, Risk & Responsible AI

Establish Accountability, Assess Risk and Strengthen Oversight of AI Use

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AI for Business Functions

Connect AI Applications with the Responsibilities of Your Profession

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Who attends

Executives and business heads: Executives and business heads who approve AI investments and are accountable for customer and business outcomes.
Risk, compliance, audit and model validation: Risk, compliance, audit and model validation professionals who review how AI systems are approved, tested, monitored and documented.
Fraud, financial crime and analytics teams: Fraud, financial crime and analytics teams who assess alert quality, investigate suspicious activity and interpret model outputs.
Operations, treasury and customer teams: Operations, treasury and customer teams who use AI to support forecasting, onboarding, claims and customer enquiries.

Standards the courses draw on

These references connect financial sector practices with wider approaches to managing AI risk.

  • Basel Committee principles (BCBS 239). Principles for effective risk data aggregation and risk reporting in banking.
  • EU AI Act. Annex III lists specified high-risk uses, including AI for assessing an individual’s creditworthiness or credit score and for assessing risk and setting prices for individuals in life and health insurance.
  • ISO/IEC 42001. An international standard that sets out requirements for establishing, maintaining and improving an AI management system.
  • NIST AI Risk Management Framework. A voluntary framework for governing, mapping, measuring and managing AI risk.

In-house training for banking, insurance & financial services teams

In-house training for financial services teams: Excentra AI Academy can tailor AI training to the workflows and decisions your teams manage. Banks, insurers, asset managers and payment providers can specify the policies, approval processes or scenarios they want the training to reflect. Financial firms usually run these courses in-house so that the exercises use their own policies, model inventory and case types, and so that nothing confidential leaves the building. 

 

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What AI training do bank compliance teams need?

Bank compliance teams need training in how to review AI use cases, challenge model outputs and verify that controls work in practice. For example, when reviewing a transaction monitoring model, they should be able to check how alert thresholds were set, who investigates exceptions and what evidence is kept when an alert is closed.

How is AI used in insurance?

Insurers use AI to support underwriting, pricing, claims handling, fraud detection and customer service. A claims team might use AI to extract details from submitted documents and prioritise files for review. Where an output can affect a premium, coverage decision or claim, teams should understand how it was produced and when to refer it for human review. bankofengland.co.uk

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Frequently asked questions

Choose a training course based on the systems your team oversees. A team reviewing credit models should prioritise explainability, fairness testing and evidence for model approval. A team overseeing transaction monitoring should focus on alert quality, escalation and investigation records.

Excentra can tailor AI training for insurance professionals to underwriting, pricing, claims or customer service. The scope can reflect the decisions your team makes and the checks it needs before acting on AI outputs.

Yes, where the use case calls for them. Explainability helps teams understand what influenced an output, such as a credit score or premium estimate. Fairness testing helps them check whether outcomes differ across relevant customer groups and investigate the reasons.

No. Exercises can use synthetic data that reflects scenarios such as a payment alert or claims referral. If your organisation prefers to use its own examples, anonymised material can be agreed when scoping the training.

Coding requirements depend on the learning objectives. Reviewing an AI output, challenging a model’s assumptions or defining when to escalate a case can be taught without writing code. Building or modifying a model may require programming.

Find the AI training your banking, insurance & financial services team needs

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