Fraud Detection in Financial Statements Using Intervention Analysis

Fraud in financial statements poses significant risks as it is crucial for maintaining trust. Intervention Analysis examines key financial components like Accounts Receivable, Inventory, Sales, and Profit to identify anomalies and enhance fraud detection accuracy. Our study analyzes data from 2015 to 2022 to detect potential fraud in Bank Syariah Indonesia (BRIS) financial reporting. Steps include collecting data for each component, calculating descriptive statistics, visualizing trends and fluctuations, identifying intervention points affecting data, grouping outliers from intervention analysis, investigating significant changes or anomalies, and reporting findings. Outliers of Level Shift (LS) and Additive Outlier (AO) types indicate potential fraud. LS outliers suggest sharp data shifts, while AO outliers show deviations from the pattern. Significant increases in Accounts Receivable, Inventory, Sales, and Profit in 2019-2022 warrant further investigation. The presence of LS and AO outliers suggests potential fraud. Sharp shifts in data during specific years, notably in Accounts Receivable and Inventory (2019, 2020), and sudden spikes in Sales and Profit indicate suspicious activities warranting further investigation.