Parse Bank and Brokerage Statements in Seconds

Bank reconciliation is accounting's most predictable recurring task. Every month, for every bank account, someone downloads or receives a statement, opens the accounting system, and matches each bank transaction against the corresponding GL entry. Matches are cleared. Discrepancies — outstanding checks, deposits in transit, bank fees not yet recorded, errors — are investigated and resolved. The process is essential for financial accuracy and fraud detection, but the matching step is mechanical work that does not require professional judgment. EezyAutomation automates the extraction half of reconciliation. The parser reads bank statements from any institution — major banks, regional banks, credit unions, online banks — and extracts every transaction with its date, description, amount, running balance, and reference number. The output is a structured transaction feed that maps directly to your reconciliation workflow in EezyBooks, QuickBooks, Xero, or any system that supports bank feed imports. The extraction engine handles the quirks that make bank statement parsing harder than it appears. Some banks combine multiple transactions into a single line ('3 deposits totaling $4,521.00'). Some truncate descriptions. Some split transactions across page breaks. Some include memo fields that wrap to the next line and look like a separate transaction. The fuzzy-logic parser uses amount validation, date sequencing, and running balance verification to correctly parse these edge cases. For organizations reconciling 10+ accounts monthly, the time savings compound. A single bank account with 100 transactions takes 30-45 minutes to reconcile manually when the matching data must be visually compared between the statement and the GL. When both sides are structured data, matching becomes an automated comparison that completes in seconds, with only the exceptions requiring human attention. The running balance verification built into the parser also provides an integrity check. If the parser's extracted transactions do not sum to the statement's ending balance, it flags the statement for review before the data enters your accounting system. This catches parsing errors before they become reconciliation discrepancies, ensuring that the structured output you receive is reliable from the start.

Brokerage statements are the most data-dense documents in financial services. A single monthly statement from a well-diversified portfolio might contain 50 positions, 30 transactions, 15 dividend payments, 8 interest accruals, realized gain/loss summaries, unrealized gain/loss calculations, margin balances, and fee schedules — all presented in the custodian's proprietary format. EezyAutomation's brokerage statement parser handles this complexity by extracting data at multiple levels. At the summary level, the parser captures beginning and ending portfolio values, net deposits and withdrawals, income received, and realized gains. At the position level, it extracts each holding with security description, CUSIP or ticker, quantity, market value, cost basis, and unrealized gain/loss. At the transaction level, it captures buys, sells, dividends, interest, distributions, and transfers with dates, amounts, and settlement information. Cost basis extraction deserves special attention because it is the data most likely to be needed for tax reporting and the hardest to extract accurately. Brokerage statements report cost basis in various ways: per lot, average cost, FIFO, specific identification. The parser identifies the cost basis method used by the custodian and extracts both the total cost basis and any lot-level detail provided. For tax-loss harvesting strategies, this per-lot visibility is essential for identifying positions with embedded losses. The parsed output supports multiple downstream uses simultaneously. Wealth managers use it for performance reporting and client portfolio reviews. Accountants use it for investment account reconciliation and tax preparation. Compliance officers use it for personal trading surveillance and restricted list monitoring. Fund administrators use it for NAV calculation inputs. Each use case requires the same underlying data extracted from the same statements — parsing once and serving multiple consumers eliminates the redundant extraction that occurs when each team reads the same statements independently. For custodians and asset classes where electronic data feeds are available (e.g., Schwab, Fidelity, Pershing), electronic feeds are always preferable to statement parsing. EezyAutomation fills the gap for custodians without feeds, alternative investment statements (hedge funds, private equity, real estate), and historical statements where electronic data is unavailable.

Family offices and independent wealth advisors face a problem that grows with every new client relationship and every new account: aggregation. A single family might have checking accounts at three banks, brokerage accounts at two custodians, retirement accounts at another custodian, a private equity fund account, a real estate investment account, and an alternative investment portfolio. Getting a unified financial picture requires extracting data from 8-12 statements per month — per family. Traditional aggregation tools like Plaid and Yodlee solve this for accounts that support API-based data access. But many accounts do not: private bank accounts, alternative investment funds, offshore accounts, insurance policies with cash value, and any institution that has not built an API connector. For these accounts, the only data source is the periodic statement — and extracting data from statements has historically meant manual entry into an aggregation spreadsheet. EezyAutomation closes the aggregation gap by parsing statements from institutions that electronic feeds do not cover. Upload the private bank statement, the hedge fund quarterly report, the real estate partnership capital account statement, and any other document-based account report. The parser extracts positions, balances, income, and transactions into the same structured format that electronic feeds provide. The unified output from EezyAutomation and electronic feeds flows into EezyFinance for consolidation. Families and advisors see a complete asset allocation across all accounts, all custodians, and all asset classes in a single view. Performance measurement spans the full portfolio, not just the electronically connected portion. Net worth calculations include every asset, not just the ones that Plaid can see. For family offices managing multi-generational wealth across dozens of entities and accounts, this comprehensive aggregation transforms the quarterly reporting process. Instead of spending the first two weeks of each quarter extracting data from statements, the office parses all statements in a batch, reviews the exceptions, and spends their time on analysis — asset allocation drift, liquidity planning, tax-lot optimization, estate planning scenarios — the advisory work that families actually pay for. At $2 per statement, parsing 15 statements per family per month costs $30. The alternative — an analyst spending 30 minutes per statement — costs $225 at $30/hour. The math is clear, and it scales linearly with the number of families and accounts under management.