RESOLUTION BAZAAR

{

"agentName": "Financial Statement Analyzer Agent (FSAA)",
"agentDescription": "Automates the detailed review and analysis of corporate debtors' financial statements (Balance Sheet, Income Statement, Cash Flow Statement, and Notes) to detect anomalies, inconsistencies, and red flags potentially indicative of misstatement, window dressing, or fraudulent activities. Provides forensic auditors with targeted reports highlighting areas requiring deeper investigation.",
"version": "1.0",
"status": "Conceptual",
"goals": [
  "Automate the ingestion and parsing of financial statement data from various formats.",
  "Perform integrity checks on financial statements (e.g., balance sheet equation, inter-statement consistency).",
  "Conduct comprehensive ratio analysis, focusing on ratios sensitive to financial manipulation (e.g., profitability, leverage, asset quality, working capital ratios).",
  "Execute trend analysis (horizontal analysis) over multiple periods to identify unusual fluctuations or growth patterns.",
  "Perform structural analysis (vertical analysis) to spot abnormal compositions within statements.",
  "Apply statistical techniques (e.g., Benford's Law) to identify potentially manipulated figures.",
  "Detect anomalies and outliers in key financial metrics compared to historical performance or industry benchmarks.",
  "Generate detailed reports flagging specific discrepancies, anomalies, and red flags with supporting calculations and visualizations.",
  "Reduce manual effort in routine FS review, allowing auditors to focus on complex issues."
],
"keyCapabilities": [
  "Financial Statement Parsing: Extracts data from PDFs, spreadsheets (Excel), XBRL, or scanned documents (using OCR).",
  "Data Normalization: Standardizes account names and structures across different periods or formats.",
  "Statement Integrity Validation: Automatically checks if assets equal liabilities plus equity, and verifies linkages between statements (e.g., net income to retained earnings and cash flow).",
  "Ratio Calculation Engine: Computes a wide range of financial ratios, including those used in fraud scoring models (e.g., components of Beneish M-Score like DSRI, GMI, AQI, SGI, DEPI, SGAI, LVGI, TATA).",
  "Trend Analysis: Calculates period-over-period percentage and absolute changes for all line items and ratios.",
  "Common-Size Analysis (Vertical Analysis): Expresses statement items as percentages of a base figure (e.g., total assets or total revenue).",
  "Benford's Law Analysis: Tests the distribution of first digits in numerical data against the expected logarithmic distribution.",
  "Anomaly Detection Models: Utilizes statistical methods (e.g., Z-score, Interquartile Range) or ML models to identify significant deviations from norms.",
  "Red Flag Rule Engine: Applies predefined rules based on known accounting fraud schemes (e.g., revenue recognized too early, unusual inventory growth, excessive capitalization of expenses).",
  "Reporting and Visualization: Creates clear reports summarizing findings, highlighting flagged items, and presenting trends/anomalies graphically."
],
"targetUsers": [
  "Forensic Auditors",
  "Insolvency Professionals / Resolution Professionals / Liquidators",
  "External Auditors (for fraud risk assessment)",
  "Internal Audit Departments"
],
"inputDataRequirements": [
  "Corporate Debtor's Financial Statements (Balance Sheet, Income Statement, Cash Flow Statement) for multiple consecutive periods.",
  "Notes to Accounts (crucial for understanding accounting policies and disclosures).",
  "Trial Balance / Detailed Ledgers (for drill-down on flagged items - secondary input).",
  "Preferably in structured format (Excel, CSV, XBRL) or high-quality PDF.",
  "Industry Benchmark Data (Optional, for comparative analysis).",
  "Previous Audit Reports (Optional, for context)."
],
"outputFormats": [
  "Financial Statement Anomaly Report (PDF, HTML) - detailing findings, flagged items, reasons for flagging, supporting calculations.",
  "Ratio Analysis Summary and Trend Report (XLSX, PDF).",
  "Benford's Law Test Results.",
  "List of Identified Red Flags (Categorized by type/severity).",
  "Data Visualizations (Charts for trends, ratios, anomalies).",
  "Structured data export (JSON, CSV) of calculated ratios and flagged items."
],
"potentialBenefits": [
  "Accelerates the financial statement review process significantly.",
  "Enhances the detection of potential financial irregularities and fraud indicators.",
  "Provides a systematic and consistent approach to FS analysis.",
  "Uncovers subtle anomalies that might be missed during manual reviews.",
  "Helps auditors prioritize areas for substantive testing and deeper investigation.",
  "Provides objective, quantitative support for forensic findings."
],
"requiredTools": [
  {
    "toolCategory": "Data Acquisition & Parsing",
    "tools": [
      "File Parsers (Libraries for reading PDF - PyPDF2, PDFMiner; Excel - Pandas, openpyxl; XBRL - Arelle, python-xbrl)",
      "Optical Character Recognition (OCR) Engines (Tesseract, AWS Textract, Google Vision AI - if dealing with scanned documents)"
    ]
  },
  {
    "toolCategory": "Data Processing & Analysis",
    "tools": [
      "Data Manipulation Libraries (Pandas - essential, NumPy)",
      "Statistical Libraries (SciPy - for statistical tests including Benford's Law analysis, StatsModels)",
      "Financial Calculation Libraries (NumPy Financial, or custom implementations)"
    ]
  },
  {
    "toolCategory": "Machine Learning & Rule Engines",
    "tools": [
      "ML Frameworks (Scikit-learn - for anomaly detection algorithms like Isolation Forest, Local Outlier Factor)",
      "Rule Engine Software/Libraries (Optional, or custom logic in Python/Java)"
    ]
  },
  {
    "toolCategory": "Data Storage",
    "tools": [
      "Relational Databases (e.g., PostgreSQL, SQLite - for storing parsed statements, calculated ratios, results)"
    ]
  },
  {
    "toolCategory": "Reporting & Visualization",
    "tools": [
      "Data Visualization Libraries (Matplotlib, Seaborn, Plotly)",
      "Reporting Libraries (ReportLab, FPDF, WeasyPrint)",
      "Spreadsheet Writing Libraries (openpyxl, XlsxWriter)"
    ]
  }
]

}