Forensic Cash-Flow Analysis
Budget: $250 – $750 AUD
I have 1,100 PDF pages of bank statements covering five accounts held by my ex-wife from November 2017 to today. I need a forensic review that traces every inflow and outflow, flags any misuse of funds, and clearly separates the deposits I personally made from everything else.
Your main objective is to deliver a concise summary report that spotlights:
• large withdrawals
• unusual deposits
• frequent transfers, especially movements between the five accounts that disguise cash on hand
Along the way, highlight any spending patterns that contradict an asserted financial-hardship position.
I will supply the PDFs in date order. You can extract the data however you like—Excel, SQL, Python, Power BI, or specialist forensic tools—as long as the final report is easy for a lawyer to navigate, cites page references to the original statements, and includes a clear narrative of findings plus a transaction log of all items you flag.
Please outline the approach you would take to convert the PDFs, reconcile cross-account transactions, and ensure accuracy over the six-year span.
1. Transfers between accounts
• Identify and record all transfers between:
• ANZ Access ↔ Trust Accounts (Grace & Ava)
• ANZ Access ↔ Credit Card
2. Credit Card Analysis
• Record all cash advances.
• Reconcile all deposits/payments into the credit card (from any account).
3. Cash Movements
• Tally all ATM withdrawals from the ANZ Access account.
• Tally all cash deposits into the ANZ Access account.
4. Third-Party Deposits
• Identify and list all deposits into ANZ Access account from:
• “Ol’ Mate Motor Co”
• “Graham Hampson”
• These show my contributions.
5. Expenditure Analysis (ANZ Access Account)
• Create two lists:
a) By payee/vendor highest to lowest spend
b) By category/type (e.g. groceries, utilities, retail, travel, cash withdrawals).
6. Deposits by Payee (ANZ Access)
• List all deposits into the ANZ Access account, grouped by payee/source.
7. Combined Daily History
• Merge all 4 accounts into a single daily timeline (chronological order).
• This allows identification of patterns (e.g. cash deposits → transfer → withdrawal).
Your main objective is to deliver a concise summary report that spotlights:
• large withdrawals
• unusual deposits
• frequent transfers, especially movements between the five accounts that disguise cash on hand
Along the way, highlight any spending patterns that contradict an asserted financial-hardship position.
I will supply the PDFs in date order. You can extract the data however you like—Excel, SQL, Python, Power BI, or specialist forensic tools—as long as the final report is easy for a lawyer to navigate, cites page references to the original statements, and includes a clear narrative of findings plus a transaction log of all items you flag.
Please outline the approach you would take to convert the PDFs, reconcile cross-account transactions, and ensure accuracy over the six-year span.
1. Transfers between accounts
• Identify and record all transfers between:
• ANZ Access ↔ Trust Accounts (Grace & Ava)
• ANZ Access ↔ Credit Card
2. Credit Card Analysis
• Record all cash advances.
• Reconcile all deposits/payments into the credit card (from any account).
3. Cash Movements
• Tally all ATM withdrawals from the ANZ Access account.
• Tally all cash deposits into the ANZ Access account.
4. Third-Party Deposits
• Identify and list all deposits into ANZ Access account from:
• “Ol’ Mate Motor Co”
• “Graham Hampson”
• These show my contributions.
5. Expenditure Analysis (ANZ Access Account)
• Create two lists:
a) By payee/vendor highest to lowest spend
b) By category/type (e.g. groceries, utilities, retail, travel, cash withdrawals).
6. Deposits by Payee (ANZ Access)
• List all deposits into the ANZ Access account, grouped by payee/source.
7. Combined Daily History
• Merge all 4 accounts into a single daily timeline (chronological order).
• This allows identification of patterns (e.g. cash deposits → transfer → withdrawal).
Related categories:
Python
Excel
SQL
Financial Analysis
Data Extraction
Data Analysis
Power BI
Forensic Consulting
Data Management