Specialised Track · Banking and Insurance

Put AI to work in banking and insurance.

A four-day, hands-on programme for banking, insurance and technology professionals. Credit, financial crime, compliance, claims and customer engagement with applied AI and agentic automation. Every delegate leaves with a prompt library, a working agent built on a process they own and a costed implementation plan for their institution.

4 days 16 modules 60%+ hands-on Two lab tracks STOA certified
Daniel Sto
Why this programme exists
"Alert triage, KYC extraction, claims intake. Each is an agent waiting to be built. The credit decision stays with a human."
Daniel Sto · Director of Faculty, STOA Institute
Duration
4 days
Format
Live online or on site
Next open date
Private cohorts
Fee
On request
per delegate
For teams
Private cohort from 8 delegates
Schedule a TNA call
Programme overview

Built around credit, financial crime, compliance and claims.

This four-day programme is built for banking, insurance and technology professionals who have to turn artificial intelligence into working practice inside a regulated institution. It sets out what the current generation of AI systems does well, where each type of system belongs across credit, financial crime, operations, customer engagement and underwriting, and what changes when a system is given a goal rather than a question. Business and technology participants work the same material from their own side of the house, so the conversation that normally happens across two departments happens in one room.

Day one establishes the foundations: how large language models work, where they fail, prompt engineering with the C.L.E.A.R. framework, and a sweep of generative AI use cases across every function of a bank and an insurer. Day two goes into the core processes where AI already carries weight, covering credit scoring, underwriting and risk assessment, fraud and financial crime, AML and sanctions compliance, and closing on the decision frameworks that separate processes which should stay rule-based from genuine candidates for AI. Day three covers agentic AI and enterprise architecture: participants build a working agent on a banking or insurance process, then weigh cloud, hybrid and on-premise deployment against data residency obligations and the security controls an AI application needs. Day four is governance and implementation, covering use case design from concept to costed pilot, model risk management, the governance and regulatory picture, and the capstone action plan.

Participants leave with a prompt library for their own work, a working agent built on a process they own, a governance starting point they can take to their own risk and compliance functions, and a costed AI implementation plan for their institution and role. The programme closes on where agentic AI is heading: context engineering, agentic retrieval, the Model Context Protocol and agent-initiated payments.

Who should attend

This programme is designed for business, operations, technology, data and leadership professionals across banking, insurance and financial technology:

  • Credit, Lending and Underwriting Managers and Analysts
  • Risk, Model Risk and Treasury Professionals
  • Compliance, AML and Financial Crime Officers
  • Internal Audit, Shariah Audit and Shariah Compliance Officers
  • Operations, Trade Finance and Back Office Managers
  • Customer Experience, Contact Centre and Collections Managers
  • Insurance and Takaful Underwriting and Claims Professionals
  • Product Managers and Digital Channel Owners
  • Business Analysts and Financial Analysts
  • Investment Analysts and Portfolio Managers
  • Branch and Regional Managers
  • Chief Information Officers, Chief Technology Officers and Chief Digital Officers
  • Heads of IT, Enterprise Architects and Solution Architects
  • Data and Analytics Managers, Data Scientists and Data Engineers
  • Application Development and Systems Integration Managers
  • Information Security and Cyber Risk Officers
  • Technology Risk, IT Governance and Vendor Management Officers
  • Banking and Insurance Executives, General Managers and Senior Managers
  • Heads of Digital Transformation, Innovation and Strategy

Industries: retail, commercial, corporate, investment and Islamic banking; insurance, takaful and reinsurance; asset management; digital banks and financial technology; payment providers, switches and processors; central banks, regulators and development finance institutions; credit unions, cooperatives and microfinance institutions.

Learning outcomes

By the end of this programme, participants will be able to:

  • Distinguish AI hype from practical AI applications in banking and insurance operations
  • Explain how large language models work and where they fail
  • Apply the C.L.E.A.R. framework and K-shot prompting to banking and insurance tasks
  • Evaluate AI use cases across credit, fraud, compliance, claims, operations and customer engagement
  • Distinguish generative AI from agentic AI and their respective roles
  • Build a working agent on a banking or insurance process
  • Assess cloud, hybrid and on-premise deployment options against data residency obligations
  • Identify AI security risks and the controls that contain them
  • Apply model risk management and governance controls to AI systems
  • Map their institution's regulatory obligations onto an AI control framework
  • Take an AI use case from concept to a costed pilot plan
  • Build an AI implementation plan for their institution and role
Programme outline

Sixteen modules. Four days. One working agent.

A progressive structure from AI foundations and prompt engineering, through the core banking and insurance processes, to agentic AI, enterprise architecture, governance and a costed implementation plan.

Day 1: Foundations of AI in Banking and Insurance

01
Day 1 · Module

The AI Landscape in Banking and Insurance

  • What AI is and what it is not in financial services
  • History of AI and the scaling laws
  • The system families (classical machine learning, generative AI, reasoning models, agentic AI)
  • Rule-based automation, robotic process automation and AI
  • How leading banks and insurers are deploying AI
  • Digital banks, takaful operators and the competitive picture
  • Common misconceptions and realistic expectations for AI adoption
Hands-on exercise AI readiness self-assessment against a structured maturity framework
02
Day 1 · Module

Inside a Large Language Model

  • Tokens, embeddings, context windows and inference
  • Pre-training, fine-tuning and retrieval
  • Reasoning models and when they earn their cost
  • Hallucination, non-determinism and verification of output
  • Frontier, open-weight and small language models
  • Model selection criteria (capability, cost, latency, data control)
  • Multilingual and multi-script customer channels
Live demonstration the same banking task run across models and settings
03
Day 1 · Module

Prompt Engineering for Banking and Insurance Work

  • Prompt engineering fundamentals: the C.L.E.A.R. framework
  • K-shot prompting (zero-shot, one-shot, few-shot, multi-shot)
  • Chain-of-thought and structured output techniques
  • Role, context and constraint design
  • Tool call and no tool call
  • Prompt libraries and reusable skills for banking and insurance teams
  • Customer data, PII and confidentiality in prompts
Hands-on lab prompt engineering on participants' own banking and insurance tasks
04
Day 1 · Module

Generative AI Use Cases Across the Bank and the Insurer

  • Retail and branch banking
  • Credit, lending and loan origination
  • Operations and back office (trade finance documents, remittance repair, reconciliation, KYC extraction)
  • Compliance, risk and internal audit
  • Treasury, markets and investment research
  • Customer service, complaints and collections
  • Insurance and takaful (underwriting support, claims triage, first notice of loss, policy documents)
  • Internal functions (human resources, finance, procurement, IT service desk)
  • When generative AI is appropriate and when it introduces risk
Gamified task scoring AI opportunities across the functions represented in the room

Day 2: AI Across the Core Banking and Insurance Processes

05
Day 2 · Module

AI-Driven Credit Scoring, Underwriting and Risk Assessment

  • Traditional scorecards and AI-driven credit models
  • Alternative and behavioural data
  • Key variables in predictive risk assessment
  • Underwriting and pricing models in insurance and takaful
  • Model outputs, decision thresholds and cut-offs
  • Explainability and reason codes (SHAP, LIME, adverse action reasons)
  • Bias, fairness and proxy discrimination
  • Regulatory expectations for automated credit and underwriting decisions
  • Expected credit loss modelling and model governance
Case study analysis an AI-based credit scoring implementation, its business impact and its regulatory implications
06
Day 2 · Module

Fraud Detection and Financial Crime

  • Rule-based and AI-driven anomaly detection
  • Real-time transaction monitoring on instant payment rails
  • False positives, alert triage and analyst productivity
  • Authorised push payment scams and mule account detection
  • Claims fraud and organised claims networks
  • Behavioural biometrics and device intelligence
  • Deepfake, voice-clone and synthetic identity fraud in onboarding and contact centres
  • Graph analytics for network detection
Live demonstration a synthetic identity and deepfake onboarding attempt, and the controls that catch it
07
Day 2 · Module

AI in AML, Sanctions and Regulatory Compliance

  • Transaction monitoring model tuning and thresholds
  • Sanctions and politically exposed person screening
  • Name matching across languages and scripts
  • Perpetual KYC and customer risk rating
  • Suspicious transaction reporting and narrative drafting
  • Regulatory reporting and obligation mapping
  • Horizon scanning and policy change management
  • Model validation and audit evidence for AI in compliance
Hands-on lab drafting and source-grounding a compliance narrative, then verifying it
08
Day 2 · Module

Decision Frameworks and Use Case Mapping

  • Deterministic and probabilistic processes
  • Mapping a process to the right AI approach (rules, classical machine learning, generative AI, agents)
  • Value, feasibility and risk scoring
  • Build, buy or configure
  • Total cost of ownership and cost per decision
  • Risk and uncertainty management in AI implementations
  • Candidates and non-candidates for automation
Pair and small group work mapping participants' own processes to determine which are candidates for AI and which should remain rule-based

Day 3: Agentic AI and Enterprise Architecture

09
Day 3 · Module

Introduction to Agentic AI

  • What is an AI agent?
  • Agents, assistants and workflow automation
  • The agent loop (goal, observe, reason, act, evaluate, repeat)
  • Agent types (simple reflex, model-based, goal-based, utility-based, learning)
  • Tools, function calling and memory
  • Single-agent and multi-agent systems, orchestration and hand-offs
  • Levels of autonomy and human-in-the-loop
  • Agentic AI capabilities and current limitations in financial services
Live demonstration an agent completing a multi-step banking task end to end
10
Day 3 · Module

Agentic AI Use Cases in Banking and Insurance

  • Compliance and financial crime agents
  • Credit file preparation and loan origination agents
  • Trade finance and document checking agents
  • Customer service and complaint handling agents
  • Portfolio and treasury monitoring agents
  • Claims handling agents in insurance and takaful
  • Back office reconciliation agents
  • The human-in-the-loop question: when to let agents act and when to require approval
  • Agent-initiated payments and agentic commerce
Hands-on lab building a first working agent on a banking or insurance process, on a business track or a technical track
11
Day 3 · Module

Enterprise AI Architecture and Deployment

  • The three deployment models (cloud, hybrid, on-premise)
  • Data sovereignty and residency requirements
  • In-country cloud regions and options for regulated workloads
  • Local and private inference (Ollama, LM Studio, llama.cpp, vLLM)
  • Model gateways, routing and multi-model strategies
  • Retrieval architecture (vector databases, chunking, embeddings, hybrid search)
  • Integration patterns (core banking systems, policy administration systems, APIs, data warehouses, ISO 20022 message flows)
  • Building versus buying AI, vendor evaluation and total cost of ownership
  • What IT teams need from business teams, and what business teams need from IT
Live demonstration a private AI deployment running on synthetic customer data with no external calls
12
Day 3 · Module

Security, Data Protection and Running AI in Production

  • The AI attack surface (prompt injection, data exfiltration, insecure output handling, supply chain)
  • The OWASP Top 10 for large language model applications
  • PII redaction, tokenisation and data minimisation
  • Access control, tenancy and audit logging
  • Secrets, keys and third-party model terms
  • Observability for AI systems (traces, metrics, latency, cost per run)
  • The Agent Health Check (groundedness, source citation, escalation and abstention behaviour, prompt-injection resistance, cost and latency)
  • Evaluation sets, regression testing and model change control
  • AI in the software lifecycle (coding assistants, test generation, legacy system documentation)
Hands-on lab probing an AI application for prompt injection and configuring guardrails

Day 4: Governance, Regulation and Implementation

13
Day 4 · Module

Building AI Use Cases from Concept to Deployment

  • A practical framework from concept to deployment
  • Problem definition and success criteria
  • Data readiness, quality and access
  • Pilot scope, design and acceptance criteria
  • Validating, costing and pitching an AI initiative
  • The business case and benefit measurement
  • From pilot to production, ownership and the support model
Hands-on exercise the use case builder, designing an AI use case for the participant's own institution
14
Day 4 · Module

AI Governance, Risk and Regulation in Financial Services

  • AI governance models (accountability frameworks, AI inventories and registers, oversight committees, three lines of defence)
  • Model risk management applied to AI systems (Federal Reserve SR 11-7, PRA SS1/23)
  • Bias, fairness and practical mitigation strategies
  • Explainability and customer disclosure duties
  • Data protection, confidentiality and cross-border transfer
  • Third-party and vendor risk for AI services
  • International standards and frameworks (ISO/IEC 42001, NIST AI Risk Management Framework, the EU AI Act)
  • Shariah governance considerations for AI in Islamic banking and takaful (AAOIFI, IFSB)
  • Mapping local regulatory expectations onto an AI control framework
Controlled group discussion the governance guardrails each institution needs in place before scaling AI
15
Day 4 · Module

Emerging Methods

  • Context engineering
  • Loop engineering and graph engineering
  • Agentic retrieval-augmented generation
  • The Model Context Protocol (MCP) and tool ecosystems
  • Agent payment protocols and agentic commerce
  • Computer use and browser agents
  • On-device and edge inference in the branch network
Live demonstration an agent reaching banking systems through the Model Context Protocol
16
Day 4 · Module

Organisational Readiness and Capstone

  • Building an AI-literate institution
  • Change management, upskilling and role redesign
  • Operating model options (centre of excellence, federated, embedded)
  • Measuring adoption and value
  • Communicating AI use to customers, boards and regulators
  • The AI implementation roadmap
  • The ninety-day plan (next steps, priority use case, timeline, owners)
Personal action planning each participant presents a ninety-day AI action plan for their role and institution, with peer feedback and instructor guidance
Day 4 · Course closing

Course closing

  • Course debrief and key takeaways
  • Final Q&A
  • Official closing ceremony and award of certificates
  • Group photo
Our training methodology

Three frameworks behind every programme.

Whatever programme you choose, it follows three proven instructional frameworks, from design to delivery to evaluation.

Delivery

TEA™ Delivery Framework

  • T Theory, core concepts & frameworks
  • E Examples, live demos & case studies
  • A Application, hands-on exercises & practice

Our signature approach structures every module into three phases for maximum retention and practical application.

Design

ADDIE Instructional Design

  • A Analyse, assess learner needs
  • D Design, structure the learning path
  • D Develop, build materials & activities
  • I Implement, deliver the programme
  • E Evaluate, measure outcomes & refine

The industry-standard model for course design and learning alignment.

Evaluation

Kirkpatrick Model

  • L1 Reaction, participant satisfaction
  • L2 Learning, knowledge & skills gained
  • L3 Behaviour, on-the-job application
  • L4 Results, business impact & ROI

The globally recognised four-level framework for measuring training effectiveness, from initial reaction to long-lasting post-training effects.

Upcoming sessions

Choose your date.

Private cohorts for banks, insurers and financial technology firms, live online or on site. Schedule a TNA call and receive a custom proposal within 48 hours.

Date
Format
Duration
Price
Private cohorts run on request.
Live virtual or On-site
1/2 to 5 days
Custom
What every participant gets

You keep everything.

No vendor lock-in, no paywalled materials. Every delegate leaves with the working files, the templates, and the post-programme support to actually use them.

Personal prompt library

8 – 12 tested prompts built during the session on your actual work scenarios.

Templates & dashboards

Dashboards, workflow templates, and evaluation frameworks ready to use.

Learning materials

Full soft copy of all session materials, handouts, and reference guides.

STOA certificate

Certificate of Completion plus a guided pathway to the Google AI certificate.

30-day support

Post-programme Q&A availability and implementation guidance.

Lifetime updates

Access to updated prompt libraries and resources as AI tools evolve.

Delivery options

Choose how your team learns.

In-Person

Instructor-led on-site

Delivered at your offices or venue. Maximum engagement for teams of 8 – 25 participants. All exercises use your team's actual work scenarios.

Live Online

Virtual instructor-led

Real-time delivery via Zoom, Meet, or Teams. Interactive breakout rooms, same content depth as in-person. Ideal for distributed teams.

Hybrid

Blended approach

Combine in-person and virtual sessions. Flexible scheduling across time zones. Pre-work materials and follow-up resources included.

Training catalogue

104 courses in nine families, overview level, methodology up front.

Download the catalogue
Ready to build

Bring your business and technology teams into one room with AI.

Book a private programme for your bank, insurer or financial technology firm. Custom proposal within 48 hours. Delivery live online or on site.