Identify the key characteristics of fraud, including intentional deception and deliberate misrepresentation, to prevent losses and assess indicators like concealment and unfair gain.

Explore a seven-factor framework to assess an organization's fraud management maturity across levels, covering internal controls, hotlines, training, resources, audits, data metrics, and predictive prevention.

Explore how fraud manifests across industries and processes, highlighting financial statement fraud, misstatement of assets and liabilities, delayed revenue recognition, and intellectual property fraud.

Explore fraud across processes and sectors, from fake invoices and ghost customers to lapping, fictitious sales, duplicate payments, and counterfeiting, with red flags and prevention strategies.

Learn to detect fraud before it happens using Excel-based outlier analysis and Benford's law, explore artificial intelligence methods for spotting patterns, visuals, and unusual behaviors.

Apply fundamental rules for fraud detection across processes, including duplicates, rounded amounts, weekends, holidays or after working hours activity, threshold analysis, and aging analysis, with user-id tracking in Excel.

Explore Benford's law, the first-digit distribution guiding invoice numbers, purchase orders, and other transactions. See how deviations signal potential non-compliant transactions and uncover fraud patterns, as shown in Enron investigations.

Learn how standard deviation complements the mean to reveal variation and potential fraud indicators. Use histograms and cross-location or time comparisons to flag non-compliant transactions and guide deeper investigations.

Perform aging analysis in excel to categorize invoices by days past due, compute not-due amounts, and allocate pending totals 1–30, 31–60, 61–90, and over 90 days using if and ifs.

Apply the Pareto principle to fraud analytics by creating a Pareto chart in Excel, highlighting the vital few customers, vendors, or claims that drive 80% of impact.

Apply Benford's Law in Excel to compare the first-digit distribution of invoice numbers against the Benford distribution, using formulas, pivot tables, and charts to flag potential anomalies for deeper investigation.

Learn to create a box and whisker plot in Excel to identify and label outliers in invoice amounts, then investigate those data points as a fraud detection starting point.

Apply Benford's law to the first two digits using log(1+1/d) and multiply by 100 to get percentage. Excel reveals deviations, e.g., 39 numbers show 1.09% expected vs 12.2% observed.

Develop skill in fraud risk analytics by identifying root causes with the five way analysis, assign ultimate responsibility, and act at the root-cause level to prevent fraud.

Test fraud hypotheses with historical data and sampling to detect patterns using Excel and AI tools. Ensure random, representative samples with a minimum of 30 observations for reliable inferences.

Explore how to test hypotheses with correlation in Excel, using scatter plots and trend lines to measure how transaction amount influences time to detect fraud, with r squared indicating strength.

Apply chi-square tests to assess whether fraud varies with customer tenure, using pivot tables to build a contingency table, compute observed and expected frequencies, and interpret the p-value against 0.05.

Explore fraud detection using ai and autoML tools like Power BI, enabling proactive and predictive insights without coding, while integrating traditional rule-based, threshold-based, and aggregate score-based methods.

Explore how AI, from weak AI to artificial general and artificial super intelligence, uses pattern recognition, machine learning, and deep learning to power analytics in Industry 4.0 with IoT.

Measure your data daily to drive improvement and reveal how dependent and independent variables shape predictions. Explore regression, classification, and supervised versus unsupervised learning in fraud risk analytics.

Explore how supervised learning uses defined characteristics to classify objects and how unsupervised learning groups data and identifies outliers useful for fraud detection; distinguish regression from classification.

Explore linear and multiple regression for fraud prediction, using transaction amount to predict time to detect fraud, with r-squared and mean absolute error as accuracy metrics.

Explore logistic regression as a classification algorithm for fraud, including training-test splits, optimization, and evaluating with accuracy, precision, recall, F1, probability between 0 and 1, and a confusion matrix.

Apply unsupervised learning with k-means clustering to identify data clusters and outliers, using z scores and a threshold to flag anomalies for fraud risk analytics.

Explain the reasons behind anomalies in an unsupervised fraud detection model using Shap, identify driving factors among 32 variables, and build preventive controls for motor insurance.

Explore time based anomalies by removing seasonal and trend components with Prophet to detect residual anomalies, forecast using historical data, and visualize future and actual anomalies in Colab.

Explore image anomaly detection using isolation forest on HSV color histograms to distinguish normal from anomalous images, train on normal data, save and test models, and visualize results.

Explore fraud detection with AutoML, enabling no-code development by dragging and dropping data, and Power BI's AutoML capabilities.

Identify anomalies in a dataset with Power BI desktop’s anomaly detection and a date-based run chart. Import Excel data, adjust sensitivity, and interpret anomaly points to support fraud prevention.

Apply AutoML to build a classification model for fraud detection in motor claims using Power BI service, training and evaluating with confusion matrix, precision, recall, and AUC.

Explore regression analytics for fraud detection using simple and multiple linear regression, the regression line, and R-squared to predict outcomes from transaction data.