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AI for FP&A Automation & Modeling

IBM via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

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By the end of this course, you will write structured AI prompts that clean and standardize messy financial data, use AI to draft and extend model logic, apply AI to anomaly detection and reconciliation, and build the governance habits — guardrails, output logging, and audit evidence — that make AI-assisted finance defensible. This is where AI stops being a buzzword and becomes practical leverage. You will reclaim the hours lost to manual data prep and model-building, while keeping human judgment and control firmly in place — the balance finance leaders, auditors, and regulators now expect. What sets this course apart is that governance is built in from the start, not bolted on at the end. Under one clear principle — AI surfaces, the analyst owns — you learn to tier AI uses by risk, verify every output, and document how AI was used. The skills are taught tool-agnostically and demonstrated on AI tools, so they transfer as the tools evolve. No coding background required.

Syllabus

Data Prep Automation

Manual data cleanup quietly consumes much of every finance team's time. This module teaches financial planning and analysis (FP&A) analysts how to automate data preparation with generative AI while keeping human judgment and governance firmly in control. Learners identify which data-cleaning tasks are worth automating, write structured AI prompts that standardize messy, multi-source financial data, and design validation checks that confirm cleaned outputs are accurate, complete, and audit-ready. The module covers data quality, data profiling, error and anomaly detection, account-code mapping, reconciliation, duplicate removal, and exception handling, and it shows how rules-based automation and AI-assisted cleaning work together in a single workflow. It is built for analysts, accountants, and finance professionals who want practical, no-code AI skills for faster, more reliable financial reporting, data analysis, and month-end close.

AI-Assisted Model Building

Use generative AI to build and extend financial models faster, without giving up accuracy or control. This module shows finance professionals how to write effective AI prompts that generate spreadsheet formulas and model structures, how to translate AI suggestions into clean, auditable models with a clear input-calculation-output architecture, and how to review AI-generated logic for errors, hard-coded values, and bias before it reaches a live model. Topics include AI-assisted financial modeling, prompt engineering for finance, driver-based modeling, spreadsheet model design and structure, formula auditing, model validation, error and bias detection, and responsible AI use with human oversight. Designed for FP&A analysts, financial analysts, and accounting and finance teams adopting AI across budgeting, forecasting, and planning workflows, it builds practical, governance-ready model-building skills.

Model Validation with AI

Learn to validate AI-assisted financial models so their numbers can be trusted and defended. This module on model validation, anomaly detection, and financial reconciliation shows FP&A analysts and finance teams how to catch errors before they reach decisions. Explore how AI flags unusual transactions and balances, how to reconcile model outputs against control totals and the general ledger, and how to design review trails and approval workflows that keep AI-assisted forecasting, budgeting, and reporting auditable. Build practical skills in exception management, financial controls, risk-based escalation, and AI governance for finance — no coding required. Ideal for financial planning and analysis, accounting, internal audit, and corporate finance professionals who want to adopt AI responsibly and produce decision-ready numbers.

Responsible AI Practices

Use generative AI across FP&A, budgeting, and financial reporting with confidence — and keep every output defensible. This module teaches responsible AI practices for finance: how to recognize where AI fails, including hallucination, model drift, and bias, and how to tell low-risk uses from high-risk ones. You'll learn to tier AI use cases by risk, design prompt and output guardrails, log AI interactions for audit, and build approval workflows with defined roles, thresholds, and escalation. Topics include AI governance, AI risk management, model risk, AI auditability, data lineage, prompt guardrails, output logging, audit evidence, and human-in-the-loop oversight. Designed for FP&A analysts, financial analysts, accountants, controllers, and finance, risk, and compliance teams adopting AI in budgeting, forecasting, and reporting, it builds practical, audit-ready governance skills.