Finance

AI & Machine Learning COURSE FOR Finance Professionals

AI Course Outline

Artificial Intelligence and Machine Learning has revolutionized the world of work and no profession has been spared .Either you learn to leverage AI to do work or it will replace you. The Accounting, Finance and Auditing professions are no exception. This AI and machine learning course is offered to equip finance professionals to be up to date with the ever evolving digital landscape and improve their productivity . The course focuses on skills needed by Finance professionals like data wrangling, financial modeling, forecasting, anomaly/fraud detection, audit automation, NLP for contracts/taxes, visualization and governance.

Course summary

Title: AI & Machine Learning for Accounting Professionals
Target audience: Accountants, Auditors, Financial Analysts, Controllers, Tax professionals
Prerequisites: Accounting knowledge, Excel competency; basic statistics and familiarity with spreadsheets. No prior programming required for beginner track; programming introduced progressively.
Outcomes: Prepare financial datasets, build predictive and anomaly detection models, automate repetitive accounting tasks, create dashboards, apply NLP to contracts/invoices, understand model governance and auditability.

4 Week Intensive Bootcamp

Learning objectives: To understand data science lifecycle, types of data used in accounting, data ethics and privacy, data cleaning, transformation, and summarize financial data; producing meaningful KPIs.

Course Module

1. Orientation, tools, ethics, and data governance for finance
2. Advanced Excel & introduction to Python for accountants (pandas, Jupiter)
3. Databases & SQL for accounting (transactions, ledgers, joins)
4. Data cleaning, reconciliation, and feature engineering for financial data
5. Exploratory data analysis and finance KPIs (ratio analysis, trend detection)
6. Probability & inferential statistics for audit and sampling
7. Time-series analysis & forecasting for budgeting and cashflow modeling
8. Supervised learning: regression & classification for credit rating client churn
9. Unsupervised learning: anomaly detection for fraud/error and irregularities
10. NLP & document intelligence: contracts, invoices, SOWs, tax forms
11. Automation & RPA integration; APIs and workflow automation
12. Dashboard, visualization, reporting, model explainability, compliance, and capstone submission

Assessments and deliverables
1. Weekly labs (hands-on notebooks, SQL queries).
2. Projects: (1) Forecast/budget model; (2) Anomaly detection for expense claims.
3.Capstone project (team or individual): end-to-end pipeline (data ingestion, cleaning, model, dashboard, governance doc).
4.Final demo and written audit trail documenting model logic and controls.

Optional practical projects / case studies
1. Fraud/anomaly detection: supplier invoice duplicates, fake vendors, expense claim anomalies.
2. Forecasting & budgeting: rolling forecasts, scenario analysis, cashflow prediction.
3. Revenue recognition automation: classify transactions against revenue rules.
4. Audit sampling & error estimation: stratified sampling tools and sample-size calculators.
5. Invoice processing pipeline: OCR -> NLP extraction -> GL coding -> exceptions dashboard.
6. Tax compliance automation: extract taxable items and map to tax codes.
7. KPI dashboards: executive and audit dashboards with drilldowns and alerts.

Tools & technologies
1. Programming: Python (pandas, scikit-learn, statsmodels, Prophet, PyTorch/TensorFlow optional), R (tidyverse).
2. Databases: SQL (Postgres, MySQL), basic NoSQL exposure.
3. BI: Power BI, Tableau, Looker.
4. NLP/OCR: spaCy, Hugging Face transformers, Tesseract, AWS Textract (or Azure Form Recognizer).
5. MLOps/Deployment: Docker, basic cloud (AWS/GCP/Azure), MLflow (optional).
6. Automation: UiPath/Automation Anywhere (RPA) or Python scripts.
7. Excel & VBA for legacy/quick tasks.

Instructional design recommendations
1. Mix lectures with hands-on labs: 60% practical.
2. Use real or realistic accounting datasets (anonymized GL, AP/AR, payroll).
3. Emphasize explainability, audit trails, documentation — critical for auditors and regulators.
4. Include guest lectures from audit/regulatory practitioners.
5.Provide templates for model risk documentation, data lineage, and controls.

Evaluation & certification
– Assessments: quizzes, lab notebooks, projects, peer reviews.
– Passing criteria: assessments + capstone; optional proctored final.
– Issue digital badge/certificate; recommend adding a capstone portfolio to LinkedIn.

Reading & resources
1. Python for Data Analysis — Wes McKinney (pandas)
2. Practical Statistics for Data Scientists — Peter Bruce et al.
3. Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow — Aurélien Géron
4. Papers/guidance: IFRS/IAS guidance on accounting automation, AICPA tech insights
5. Online: Coursera/edX specializations for machine learning and NLP (as supplements)

Implementation tips for accounting organizations
1. Start with low-risk automation: data extraction, reconciliation, reporting.
2. Ensure data stewardship and access controls before modeling.
3. Implement model-versioning and logging for auditability.
4. Keep humans in the loop: triage exceptions, validate models periodically.