AI-Driven Financial Planning, Forecasting, and Automation

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

12 hours 22 minutes
Sessions

On-Demand
Subtitles

English

Found in

Part of

Financial Analyst: AI, Excel, and Power BI Skills

Overview

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Create multi-year financial forecasts, stress-test business plans, and automate analysis using AI-driven workflows. In this course, you’ll learn how modern financial analysts combine budgeting, predictive modeling, and automation to improve decision-making. You’ll apply zero-based budgeting to control costs and analyze budget-to-actual variances to identify root causes. Then, you’ll build integrated financial projections and stress-test them against adverse scenarios. You’ll explore supervised learning techniques to forecast key business metrics and uncover value drivers. Finally, you’ll evaluate AI models for credit-risk classification and design automated pipelines that update forecasts using structured financial data. What makes this course unique is its focus on AI in finance. You won’t just build static spreadsheets—you’ll design scalable, automated workflows that reflect how finance teams operate today. The course concludes with a portfolio-ready project where you prepare a 12-month financial forecast and scenario analysis brief for leadership review.

Syllabus

Budgeting: Analyze & Control Costs: Building a Zero-Based Departmental Budget

You will apply zero-based budgeting principles to build a departmental budget from the ground up. You’ll structure and code expenses clearly, allocate costs accurately, and design a template that supports disciplined cost management and transparency.

Budgeting: Analyze & Control Costs: Analyzing Budget-to-Actual Variances

You will analyze budget-to-actual variances to determine their root causes and assess their business impact. You’ll interpret deviations, identify operational drivers, and prepare clear explanations that support corrective action and decision-making.

Project and Stress-Test Financial Plans: Building a Multi-Year Financial Projection

You will create a multi-year P&L projection by integrating top-down market assumptions with bottom-up sales and cost plans. You’ll model revenue and expense drivers across multiple years and ensure assumptions are logically connected.

Project and Stress-Test Financial Plans: Stress-Testing the Financial Plan

You will evaluate the resilience of a financial plan by stress-testing it against adverse scenarios. You’ll run downside cases, assess margin pressure, and propose adjustments that preserve financial stability.

Analyze Financial Data: Reconciliation Fast: Analyze Margin Drivers Using ERP Data

You will analyze transaction-level data to isolate key drivers of revenue, cost, and margin. You’ll build pivot-based reports, interpret performance patterns, and identify the operational factors shaping financial outcomes.

Analyze Financial Data: Reconciliation Fast: Reconcile Data Across Systems

You will evaluate data completeness and reconcile discrepancies between source systems such as ERP, General Ledger, and data warehouse platforms. You’ll document findings and ensure financial accuracy across reporting environments.

Forecast Business Metrics: Uncover Value Drivers: Forecasting with Supervised Learning

You will apply supervised-learning algorithms to forecast key business metrics using structured datasets. You’ll build and tune predictive models and evaluate forecast accuracy using appropriate performance metrics.

Forecast Business Metrics: Uncover Value Drivers:Uncovering Value Drivers with Explainable AI

You will analyze feature importance using explainable AI techniques such as SHAP and feature importance scores. You’ll interpret model outputs to identify the variables that most strongly influence business performance

Automate Financial Analysis with AI Pipelines: Evaluate AI Models for Credit-Risk Classification

You will evaluate competing AI models for credit-risk classification using financial datasets. You’ll compare model performance using metrics such as F1 score and AUROC to determine which approach best supports risk assessment.

Automate Financial Analysis with AI Pipelines: Create Automated Pipelines for Earnings Forecasts

You will create an automated pipeline that retrieves financial data from SEC filings, retrains models, and updates earnings forecasts. You’ll design a workflow that keeps financial insights accurate, efficient, and continuously updated.

Project: 12-Month Financial Forecast and Scenario Analysis Brief

In this project, you will build a 12-month financial forecast using structured revenue and cost assumptions. You will develop base, optimistic, and downside scenarios to evaluate profitability under different business conditions. You will perform variance analysis by comparing forecasted results to recent performance and assess financial sensitivity to key value drivers. Finally, you will prepare a professional recommendation brief summarizing risk exposure and strategic actions. This project simulates a real FP&A assignment and demonstrates your ability to translate financial assumptions into structured analysis and executive-ready insights.