Artificial Intelligence for Finance Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Finance Directors, Financial Controllers and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 7 Nov 2026 Course code SD-FM-098

Before you join: Participants should understand core finance processes such as budgeting, forecasting, accounts payable, reconciliations, financial reporting or financial controls.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Finance teams face increasing pressure to shorten reporting cycles, improve forecast accuracy, detect irregular transactions earlier and provide management with timely analysis. Artificial intelligence can support these aims, but finance professionals must distinguish suitable use cases from unsuitable ones, understand data and model limitations, and retain control over material judgements. Poorly governed AI can introduce data exposure, unsupported conclusions, bias, weak audit evidence and unreliable financial outputs.

This course equips finance and accounting professionals to assess, apply and govern AI in core finance processes. Participants examine AI use cases for forecasting, cash management, accounts payable, reconciliations, financial close, budgeting, profitability analysis, fraud screening and management reporting. They learn how machine learning, generative AI, optical character recognition and anomaly detection work; how to prepare financial data; how to test outputs; and how to establish human review, documentation and monitoring controls.

Instructor-led sessions combine short technical briefings with finance cases, group analysis, simulations and structured application planning. Cases reflect realistic finance decisions such as reducing invoice exceptions, improving a 13-week cash forecast, reviewing journal entries and drafting management commentary from controlled sources. Participants leave with a finance AI use-case assessment, a model governance checklist, a controlled prompt template and an implementation roadmap for a selected finance process.

The course suits finance professionals who need to make informed decisions about AI initiatives without becoming data scientists or software developers. It is particularly relevant for finance leaders, controllers, planning teams, internal audit professionals and transformation staff responsible for improving finance performance while protecting financial integrity.

What you will be able to do

  • Assess finance AI use cases using value, feasibility and control criteria
  • Prepare financial data for forecasting, classification and anomaly detection models
  • Apply structured prompting and source-checking methods to generative AI outputs
  • Build a 13-week cash forecast using time-series and driver-based techniques
  • Evaluate AI-supported controls for invoices, journal entries and reconciliations
  • Test model performance using error rates, bias measures and business materiality
  • Design AI governance controls using NIST AI RMF and ISO/IEC 42001 concepts
  • Produce an implementation roadmap for a controlled finance AI pilot

Benefits of attending

For you

  • Assess vendor and internal AI proposals with finance-specific evaluation criteria
  • Produce more reliable forecasts using error measures, scenarios and documented overrides
  • Use generative AI for finance tasks without accepting unsupported outputs
  • Identify control gaps in AI-supported accounting and reporting workflows
  • Lead a practical AI pilot conversation with technology, risk and audit teams

For your organisation

  • Prioritise AI investments with clear value, feasibility and risk criteria
  • Reduce manual effort in invoice review, reconciliations and reporting preparation
  • Improve forecast discipline through measurable accuracy and bias monitoring
  • Retain accountability and audit evidence for AI-supported financial decisions
  • Establish consistent governance for finance AI pilots and production models

Course modules

10 modules over 5 days

Participants can distinguish finance AI applications and prioritise use cases against value, feasibility and control exposure.

  • AI, machine learning, generative AI and automation distinguished by finance use
  • Supervised, unsupervised and generative models matched to accounting problem types
  • Data, model, process and decision risks mapped before implementation
  • Human-in-the-loop review points assigned for material financial judgements
  • Probability, confidence scores and error rates interpreted for finance decisions
  • Finance AI use-case inventory ranked by value, feasibility and control exposure
  • Public, internal and confidential data classified before model access

Participants can assess whether finance data is fit for an AI-supported process and specify required controls.

  • Chart of accounts, subledgers and operational data mapped to target outcomes
  • Data quality dimensions assessed: completeness, accuracy, timeliness, consistency and validity
  • Duplicate suppliers, missing fields and outliers identified using profiling rules
  • ETL pipeline stages documented from source extraction through controlled outputs
  • Master-data governance assigns owners, definitions, change controls and retention periods
  • Training, validation and test datasets separated to prevent misleading performance results

Participants can use structured prompts and review methods for controlled generative AI finance tasks.

  • Structured prompting specifies role, source data, constraints and output format
  • Few-shot examples improve classification consistency for recurring finance narratives
  • Retrieval-augmented generation grounded in approved policies and accounting guidance
  • Hallucinated citations, calculations and policy claims detected through source checking
  • Prompt templates protect confidential data through minimisation and redaction rules
  • Management commentary drafts reconciled to approved figures and evidence

Participants can select and evaluate AI-supported methods for cash flow and financial forecasting.

  • Time-series forecasting combines historical patterns, drivers and finance judgement
  • 13-week rolling cash forecast built from receipts, payroll and supplier data
  • ARIMA, exponential smoothing and regression compared using forecast error metrics
  • Mean absolute percentage error interpreted alongside bias and business materiality
  • Scenario assumptions modelled for volume, price, collection and payment timing
  • Forecast overrides documented with rationale, approver and subsequent variance review

Participants can design AI-supported review processes for invoice exceptions and transaction irregularities.

  • Invoice data extracted with OCR and validated against purchase orders
  • Three-way matching exceptions prioritised using value, risk and ageing criteria
  • Anomaly detection flags duplicate payments, split invoices and unusual vendors
  • Benford's Law used cautiously for screening irregular transaction populations
  • False positives and false negatives evaluated against investigation capacity
  • Segregation-of-duties controls retained when automating payment exception workflows

Participants can identify controlled AI applications for close activities, reconciliations and financial reporting.

  • Journal entries classified for preparer review using explainable prediction features
  • Account reconciliations matched through rules, tolerances and exception queues
  • Close-task status predicted from dependencies, workload and historical completion data
  • Variance analysis separates price, volume, mix and timing drivers
  • Management reports drafted from governed data with mandatory human review
  • IFRS and GAAP disclosure support checked against authoritative source materials

Participants can apply AI-supported analysis to planning models, commercial decisions and profitability reviews.

  • Driver-based budgets connect operational volumes, rates and resource consumption
  • Machine-learning demand forecasts feed revenue and capacity planning models
  • Customer profitability combines activity-based costing, service costs and margin
  • Price optimisation recommendations tested against commercial constraints and approvals
  • Working-capital drivers modelled through receivables, inventory and payables cycles
  • Monte Carlo simulation quantifies ranges around budget assumptions and outcomes

Participants can establish finance AI governance requirements, model documentation and monitoring controls.

  • NIST AI RMF functions applied to finance model governance
  • ISO/IEC 42001 management-system requirements translated into finance control activities
  • Model inventory records purpose, owner, data, limitations and approval status
  • Bias testing examines disparate outcomes in credit, hiring and supplier decisions
  • Explainability evidence links material outputs to inputs, rules and assumptions
  • Model monitoring thresholds trigger retraining, suspension or escalation decisions
  • Audit trails retain prompts, versions, approvals and user actions

Participants can plan, govern and assess a finance AI pilot from business case through acceptance.

  • Use cases scored with a value-risk-feasibility prioritisation matrix
  • Business case quantifies cycle-time, error-rate, cash and control impacts
  • Build, buy and configure options assessed against integration requirements
  • Pilot scope defines baseline measures, acceptance criteria and exit conditions
  • RACI matrix assigns finance, technology, risk and audit responsibilities
  • Change plan addresses role redesign, training, communications and adoption metrics

Participants can prepare a controlled action plan for a priority AI opportunity in their finance function.

  • Finance AI opportunity mapped from pain point to controlled workflow
  • Current-state process documented using SIPOC boundaries, handoffs and control points
  • Target-state design specifies data inputs, model outputs and human decisions
  • Key risk indicators selected for data drift, accuracy and exceptions
  • Implementation roadmap sequences pilot, validation, deployment and post-launch monitoring
  • Personal application plan identifies sponsor, first use case and milestones

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Finance AI use-case prioritisation matrix
  2. Financial data readiness assessment checklist
  3. Controlled finance prompt template
  4. 13-week cash forecast model specification
  5. AI model governance checklist
  6. Finance AI pilot implementation roadmap

Who should attend

  • Finance Directors — need informed oversight of AI investments and finance controls
  • Financial Controllers — need controlled automation for close, reporting and reconciliations
  • FP&A Managers — need improved forecasting, budgeting and management insight
  • Chief Accountants — need reliable AI use within accounting policies and reporting
  • Internal Audit Managers — need evidence-based assessment of AI risks and controls
  • Finance Transformation Managers — need practical methods for selecting and piloting AI use cases

Skills and tools covered

Finance AI assessmentFinancial data preparationCash flow forecastingPrompt engineeringModel risk governanceAI implementation planning

Tools and standards

CRISP-DM, NIST AI RMF, ISO/IEC 42001, COSO Internal Control—Integrated Framework

How the course runs

Training methodology

The course uses short instructor briefings to explain methods, followed by finance-focused case work, group exercises, simulations and application planning. Participants examine realistic data and process scenarios involving cash forecasting, invoice exceptions, close activities and management reporting. Facilitated discussions compare alternative control designs, model outputs and implementation choices. Throughout the programme, participants apply structured templates to a finance process relevant to their role, building evidence for a practical post-course action plan.

Requirements and prerequisites

Participants should understand core finance processes such as budgeting, forecasting, accounts payable, reconciliations, financial reporting or financial controls. Experience using spreadsheets and interpreting financial data is expected. Familiarity with basic accounting terminology, management reporting and internal control responsibilities will help participants apply the material quickly. Prior exposure to AI, machine learning, programming, statistics or data science is not required. This course does not teach software development, model coding or advanced quantitative forecasting methods.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

No previous AI, data science or programming experience is required. Participants should understand core finance processes and be comfortable interpreting financial information.

Participants should bring a laptop if attending in person, plus an example of a finance process they want to improve. No proprietary data is needed, and participants should not bring confidential records for use in class.

The course is designed for finance managers, controllers, FP&A professionals, accountants, internal auditors and finance transformation staff. It is also suitable for technology and risk professionals working closely with finance teams.

This course focuses on finance processes, accounting controls, financial data and governance decisions. It does not teach programming or advanced model development; it teaches participants how to assess, apply and control AI in finance work.

Participants can use the templates to assess a priority AI opportunity, define data and control requirements, and prepare a pilot roadmap. The course materials support discussions with finance leadership, technology teams, risk functions and vendors.

The five days provide approximately 32.5 contact hours, delivered from 08:30 to 16:30 each day. Time is divided across ten modules covering AI foundations, finance applications, data, controls, governance and implementation planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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