Objectives

Upon completion of this course, participants will be able to:

• Understand the data analytics lifecycle and its application in accounting and finance.
• Acquire, clean, and prepare financial data from disparate sources for analysis.
• Apply key descriptive and diagnostic analytical techniques to understand past performance.
• Utilize data visualization best practices to communicate financial insights effectively.
• Apply basic predictive modeling techniques (e.g., regression) for forecasting and risk assessment.
• Identify and use analytical techniques for continuous auditing and fraud detection.
• Select and apply appropriate analytical tools (e.g., Python/R basics, specialized software) for specific tasks.
• Develop and implement financial Key Performance Indicators (KPIs) driven by data analysis.
• Use analytics to improve efficiency in core financial processes (e.g., Accounts Payable, General Ledger).
• Communicate the business value of data-driven insights to non-technical stakeholders.

Target Audience

• Financial Analysts and Senior Accountants
• Internal and External Auditors
• Financial Planning & Analysis (FP&A) Professionals
• Financial Controllers and Risk Managers
• Business Intelligence (BI) Specialists in Finance
• Data Analysts looking to specialize in finance/accounting
• CFOs and Finance Leaders guiding digital transformation

• Hands-on workshops using a common analytics tool (e.g., Excel, specialized software) for data cleaning and manipulation
• Case studies applying analytical techniques to audit samples and fraud scenarios
• Group exercises developing and presenting a financial dashboard based on raw data
• Individual assignments on performing descriptive statistics and simple regression analysis
• Discussions on defining key financial analytical use cases (e.g., optimizing working capital)
• Practical lab on using Benford's Law to check for transactional anomalies

Unit 1: The Analytics Mindset and Data Foundation

• Evolution of the finance function: from scorekeeper to strategic advisor
• The four types of analytics: Descriptive, Diagnostic, Predictive, Prescriptive
• Frameworks for applying analytics across GL, AP, AR, and Payroll
• Understanding the data analytics project lifecycle
• Ethics, privacy (e.g., GDPR), and security considerations in financial data
• The concept of data quality and its impact on analytical results

• Connecting to common financial data sources (ERP, Data Warehouse, CRM)
• Techniques for data extraction and integration from disparate systems
• Data cleansing and transformation: handling missing values, outliers, and inconsistencies
• Data profiling and validation for quality assurance
• The importance of relational database principles and normalization
• Introduction to basic data manipulation commands (e.g., SQL/Python Pandas concepts)

Unit 2: Descriptive and Diagnostic Analytics

• Summary statistics: mean, median, standard deviation, and quartiles
• Identifying and interpreting trends, seasonality, and cyclical patterns
• Using correlation and scatter plots to understand variable relationships
• Grouping and aggregation techniques for performance segmentation
• Distribution analysis (histograms) to understand underlying data patterns
• Applying Pareto analysis (80/20 rule) to sales, costs, or exceptions

• Performing variance analysis and drill-down techniques
• Hypothesis testing: framing and testing financial questions with data
• Techniques for identifying anomalies and outliers in transactions
• Creating custom metrics and ratios for deep performance insights
• Using segmentation analysis to understand cost or revenue drivers
• Developing a repeatable workflow for root cause investigation

Unit 3: Predictive Analytics and Risk Modeling

• Simple Linear Regression for forecasting financial metrics (e.g., revenue)
• Time Series Analysis and moving averages for short-term prediction
• Understanding the concepts of model training, testing, and validation
• Evaluating model performance and measuring forecasting accuracy (e.g., MAPE)
• Limitations and common pitfalls of predictive financial models
• Introduction to more advanced techniques (e.g., machine learning concepts)

• Using Benford's Law analysis for fraud detection in transaction data
• Developing Continuous Auditing (CA) and Continuous Monitoring (CM) routines
• Applying analytics to assess risk in Accounts Payable and expense reports
• Techniques for predicting credit risk or default rates in Accounts Receivable
• Analyzing journal entries for unusual patterns or control overrides
• Using clustering to segment customers or suppliers for risk targeting

Unit 4: Visualization, Reporting, and Implementation

• Best practices for communicating analytical results through dashboards
• Selecting the appropriate visualization for the financial metric (e.g., waterfall charts)
• Using visualization to highlight exceptions, trends, and future predictions
• Principles of dashboard design for different user roles (e.g., CFO vs. Analyst)
• Utilizing BI tools (e.g., Tableau, Power BI) and their connection to financial data
• Creating interactive and drillable reports for root cause discovery

• The technology landscape for finance analytics (tools, infrastructure, skills)
• Building a finance analytics team and fostering an analytical culture
• Developing an analytics strategy and prioritizing use cases (e.g., high ROI)
• Communicating analytical results effectively to drive business action
• Case studies on successful finance and accounting analytics adoption
• Overcoming organizational resistance to data-driven decision-making