Books

Concepts

A book is a container for one deal one or more documents that get parsed together. Books have a parse_status that progresses through: Documents can be bank statements, credit reports, tax forms, or loan applications. The book API returns different output shapes depending on which document types were parsed.

Submit a book

POST /submit-book

Multipart form upload. Pathway creates the book, uploads the files to S3, and starts parsing in the background. Returns a book_id immediately.

files

file[]

required

One or more PDF files. Pathway auto-classifies each file as bank statement, credit report, tax form, or loan application.

book_name

string

required

Name for the book.

description

string

Optional description.

webhook_url

string

If provided, Pathway will POST to this URL when parsing completes. If omitted, poll GET /books/:id.

import requests

API_BASE = "https://api.lendpathway.com/api"
TOKEN = "pat_your_token_here"
headers = {"Authorization": f"Bearer {TOKEN}"}

with open("statement.pdf", "rb") as f:
    r = requests.post(
        f"{API_BASE}/submit-book",
        headers=headers,
        files={"files": ("statement.pdf", f, "application/pdf")},
        data={
            "book_name": "Q3 Statements",
            "webhook_url": "https://your-server.com/webhook"
        }
    )

print(r.json())
# {"book_id": "...", "status": "processing", "message": "Processing started"}

import requests, time

API_BASE = "https://api.lendpathway.com/api"
TOKEN = "pat_your_token_here"
headers = {"Authorization": f"Bearer {TOKEN}"}

with open("statement.pdf", "rb") as f:
    r = requests.post(
        f"{API_BASE}/submit-book",
        headers=headers,
        files={"files": ("statement.pdf", f, "application/pdf")},
        data={"book_name": "Q3 Statements"}
    )

book_id = r.json()["book_id"]

while True:
    r = requests.get(f"{API_BASE}/books/{book_id}", headers=headers)
    status = r.json()["parse_status"]
    if status in ["completed", "failed", "cancelled"]:
        break
    time.sleep(5)

Submit response

{
  "book_id": "99cc93e6-1f3f-42b1-9fe4-ba5a95be9c78",
  "status": "processing",
  "message": "Processing started"
}

Webhook payload (POST to your webhook_url when done)

{
  "book_id": "99cc93e6-1f3f-42b1-9fe4-ba5a95be9c78",
  "status": "completed",
  "book_url": "https://app.lendpathway.com/books/99cc93e6-1f3f-42b1-9fe4-ba5a95be9c78"
}

Get a book

GET /books/:id

Returns the Book object parse status, name, timestamps, and book_meta.

r = requests.get(f"{API_BASE}/books/{book_id}", headers=headers)
book = r.json()
print(book["parse_status"])           # "completed"
print(book["parse_status_message"])   # "Processed 3 bank_statements"

Book fields

Field	Type	Description
`id`	string	UUID
`parse_status`	string	`new`, `processing`, `completed`, `failed`, `cancelled`
`parse_status_message`	string	Human-readable status message, e.g. “Processed 4 bank_statements”
`name`	string	Book name
`description`	string
`book_tag`	string	Optional label/tag for the book
`is_starred`	bool
`created_at`	string	ISO timestamp
`last_parsed_at`	string	ISO timestamp of last parse
`salesforce_opportunity_id`	string	Set if the book was imported from Salesforce
`book_meta`	object	Raw parser output (see below)

book_meta keys

Key	Present when	Description
`parser_v2_mca_result`	Bank statements parsed	Raw `HolyMCAResult` full account ledgers, transactions, positions, business info
`parser_v2_credit_report`	Credit report parsed	`CRMeta` bureaus, accounts, FICO scores
`parser_v2_tax_forms`	Tax forms parsed	Raw tax extraction result
`parser_v2_loan_application`	Loan application parsed	Extracted loan app fields
`excluded_document_ids`	Always	List of document UUIDs excluded from analytics
`excluded_account_ids`	Always	List of account IDs excluded from analytics
`excluded_position_ids`	Always	List of position IDs excluded from revenue
`revenue_exclusion_tags`	Always	List of tags excluded from true revenue computation

For computed analytics, use /analytics and /tax-analytics rather than reading raw book_meta.

Bank statement analytics

GET /books/:id/analytics

Returns BookAnalytics computed metrics across all bank statements in the book. Applies your org’s exclusion config (excluded documents, excluded accounts, excluded positions, revenue exclusion tags) before computing. This is the primary endpoint for underwriting data.

r = requests.get(f"{API_BASE}/books/{book_id}/analytics", headers=headers)
a = r.json()

Top-level fields

Field	Type	Description
`total_deposits`	float	Sum of all credits across all accounts and months
`total_withdrawals`	float	Sum of all debits
`true_revenue`	float	Credits excluding loans, transfers, NSF, owner draws, and other non-revenue tags
`average_daily_balance`	float	Weighted average daily balance across the period
`days_negative_balance`	int	Total days with a negative ending balance
`debt_to_income_ratio`	float	`(total_loan_payments / true_revenue) × 100`
`opening_balance`	float	Balance at the start of the earliest statement
`closing_balance`	float	Balance at the end of the most recent statement
`num_deposits`	int	Count of deposit transactions
`num_withdrawals`	int	Count of withdrawal transactions
`total_loan_disbursements`	float	Total money in tagged as loan disbursements
`total_loan_payments`	float	Total money out tagged as loan payments
`num_mca_positions`	int	Number of detected MCA positions

statements[] per-document breakdown All documents appear in this array, including excluded ones. Excluded documents will still show their statement_period and document_name, but their metrics are zeroed. Filter by excluded_document_ids from book_meta if you want to exclude them. Each statement has document_id, document_name, statement_start_date, statement_end_date, statement_period, and an accounts[] array. Each account has:

Field	Type	Description
`account_id`	int	0 = combined aggregate for this statement; 1+ = individual accounts
`account_name`	string	Account name from the bank
`starting_balance`	float
`ending_balance`	float
`total_deposits`	float
`total_withdrawals`	float
`loan_disbursements`	float
`loan_payments`	float
`average_daily_balance`	float
`days_negative_balance`	int
`true_revenue`	float
`debt_to_income_ratio`	float
`is_reconciled`	bool	Whether the parser’s transaction sum matches the bank-reported ending balance
`discrepancy`	float	Difference between computed and bank-reported ending balance

loan_summary[] loan type rollup

Field	Type	Description
`loan_type`	string	One of the loan tag values
`total_disbursements`	float
`total_payments`	float
`disbursement_count`	int
`payment_count`	int
`is_excluded`	bool	True if this loan type is excluded from revenue computation

average_statement_metrics average row across all statements An AccountStatementMetrics object representing the per-statement average. Same fields as the per-account table above. Used for the Average row in CSV exports. positions[] detected debt positions Each position is a group of transactions the parser identified as belonging to the same loan or MCA.

Field	Type	Description
`position_id`	string
`name`	string	Display name (e.g. “ONDECK CAPITAL”)
`loan_type`	string	`merchant_cash_advance`, `bank_loan`, `factoring`, `credit`, `lease`, etc.
`total_disbursements`	float	Money received from this lender
`total_payments`	float	Money paid to this lender
`disbursement_count`	int
`payment_count`	int
`first_disbursement_date`	string	YYYY-MM-DD
`last_payment_date`	string	YYYY-MM-DD
`avg_payment`	float	Average payment size
`is_active`	bool	Still making payments at end of statement period
`is_excluded`	bool	True if excluded from revenue calculation
`returns_count`	int	Reversal-tagged credits within this position
`returns_total`	float
`funder_title`	string	Matched funder name from your org’s funder directory
`funder_link`	string	Funder website
`funder_contact`	string
`funder_email`	string
`payment_schedules`	array	Detected payment schedule objects each has `frequency` (`daily`, `weekly`, `monthly`, `irregular`), `payment_dates[]`, and `next_expected_date`

merged_accounts transaction-level data Keyed by account number. Each account has a transactions[] array. Each transaction:

Field	Type	Description
`transaction_id`	int
`transaction_date`	string	YYYY-MM-DD
`description`	string	Raw description from the bank
`amount`	float	Always positive
`transaction_type`	string	`credit` or `debit`
`ledger_balance`	float	Running balance after this transaction
`tag`	string[]	Classification tags see tags reference below
`position`	object	If this transaction belongs to a debt position, contains `position_id`, `position_name`, `loan_type`, `funder_title`

Transaction tags Tags classify what a transaction represents. Revenue computation excludes all tags in revenue_exclusion_tags.

Tag	Meaning
`true_revenue`	Counts toward revenue (synthetic tag added post-classification)
`merchant_cash_advance`	MCA disbursement or payment
`bank_loan`	Bank loan
`factoring`	Factoring advance or payback
`credit`	Credit card payment
`lease`	Equipment/vehicle lease
`internal_transfer`	Transfer between accounts owned by the same entity
`owner_transaction`	Owner draw or owner contribution
`nsf_overdraft`	NSF fee or overdraft fee
`bank_fee`	General bank fee
`payment_processor`	Payment processor deposit (Stripe, Square, etc.)
`reversal`	Transaction reversal
`untagged`	Could not be classified

Tag-based aggregate fields on BookAnalytics:

Field	Description
`nsf_total`	Total NSF fee amount
`num_nsf`	Count
`overdraft_total`	Total overdraft fee amount
`num_overdraft`	Count
`owner_transaction_total`	Total owner draw/contribution amount
`num_owner_transaction`	Count
`internal_transfer_total`	Total internal transfer amount
`num_internal_transfer`	Count
`bank_fee_total`	Total bank fee amount
`num_bank_fee`	Count
`payment_processor_total`	Total payment processor credits
`num_payment_processor`	Count
`reversal_total`	Total reversal amount
`num_reversal`	Count
`stop_payment_total`	Total stop payments
`num_stop_payment`	Count

The nsf_overdraft tag is a single tag applied to transactions. The aggregate splits into nsf_total and overdraft_total at the metrics level.

Bank statement detailed view

GET /books/:id/statements

Account-centric view of the same parsed data. Structured as a list of accounts, each with their full statement history. Useful when you need transaction-level data, daily balance maps, or per-lender activity broken down by month. Two key differences from /analytics:

Organized by account first, then statements under each account (vs /analytics which groups by document first)
Does not apply org-level exclusions — all documents and accounts are included
Includes daily_balances (a full {"YYYY-MM-DD": balance} map for every day in the period) and funders[] (per-lender activity within that statement)

r = requests.get(f"{API_BASE}/books/{book_id}/statements", headers=headers)
accounts = r.json()

for account in accounts:
    print(f"\n{account['account_name']} ({account['bank_name']})")
    print(f"  {account['business_name']}")
    for stmt in account["statements"]:
        print(f"  {stmt['statement_start_date']} to {stmt['statement_end_date']}")
        print(f"    Credits: ${stmt['sum_credits']:,.2f}  Debits: ${stmt['sum_debits']:,.2f}")
        print(f"    Revenue: ${stmt['revenue_credits']:,.2f}  DTI: {stmt.get('debt_to_income_ratio', 'N/A')}%")
        print(f"    Transactions: {len(stmt['transactions'])}")
        if stmt["funders"]:
            for f in stmt["funders"]:
                print(f"    Funder: {f['name']} ({f['loan_type']}) "
                      f"in=${f['total_disbursements']:,.2f} out=${f['total_payments']:,.2f} "
                      f"freq={f.get('payment_frequency', 'N/A')}")

SimpleAccount fields

Field	Type	Description
`account_id`	int
`account_name`	string
`account_number`	string
`bank_name`	string
`routing_number`	string
`account_type`	string	e.g. `checking`, `savings`
`business_name`	string	Business name extracted from the statement
`business_address`	object	`street_address`, `city`, `state_province`, `postal_code`, `country`
`statements[]`	array	Per-statement data for this account, sorted by date ascending

SimpleStatement fields

Field	Type	Description
`document_id`	string
`document_name`	string
`statement_start_date`	string	YYYY-MM-DD
`statement_end_date`	string	YYYY-MM-DD
`starting_balance`	float
`ending_balance`	float
`min_balance`	float	Lowest daily balance in the period
`max_balance`	float	Highest daily balance in the period
`sum_credits`	float	Total deposits
`sum_debits`	float	Total withdrawals
`net_deposits`	float	`sum_credits - sum_debits`
`num_deposits`	int
`num_withdrawals`	int
`average_daily_balance`	float
`days_negative_balance`	int
`days_in_period`	int
`revenue_credits`	float	True revenue for this account in this statement
`loan_disbursements`	float	Total loan disbursements received
`total_mca_disbursements`	float	MCA-specific disbursements only
`loan_payments`	float	Total loan payments made
`debt_to_income_ratio`	float
`transactions[]`	array	All transactions for this account in this statement period. Each has `transaction_id`, `transaction_date`, `description`, `amount`, `transaction_type`, `ledger_balance`, `tag[]`
`daily_balances`	object	`{"YYYY-MM-DD": balance}` for every calendar day in the period
`funders[]`	array	Per-lender activity in this statement period (see below)

SimpleFunder fields (inside funders[]) Each entry represents one detected loan or MCA position and its activity within this specific statement month.

Field	Type	Description
`position_id`	string
`name`	string
`loan_type`	string
`funder_title`	string	Matched funder from your org’s funder directory
`funder_link`	string
`funder_contact`	string
`funder_email`	string
`transaction_count`	int	Total transactions (disbursements + payments) in this month
`total_disbursements`	float	Money received from this lender in this month
`total_payments`	float	Money paid to this lender in this month
`funded_date`	string	Date of first disbursement in this month
`first_payment_date`	string
`last_payment_date`	string
`payment_count`	int	Number of payment transactions
`avg_payment_amount`	float	Average payment size
`payment_frequency`	string	`daily`, `weekly`, `monthly`, `irregular`. Null if fewer than 3 payments in the period

Credit report data

GET /books/:id

Credit report data lives in book_meta from GET /books/:id. Access it at book["book_meta"]["parser_v2_credit_report"].

r = requests.get(f"{API_BASE}/books/{book_id}", headers=headers)
book = r.json()
cr = book["book_meta"]["parser_v2_credit_report"]  # CRMeta shape

CRMeta shape (inside book_meta.parser_v2_credit_report):

{
  "primary_entity": {
    "full_name": "John Smith",
    "date_of_birth": "1985-03-14",
    "address": [
      {
        "address_line_1": "123 Main St",
        "city": "New York",
        "state": "NY",
        "zip_code": "10001",
        "is_primary": true
      }
    ]
  },
  "credit_report_body": [
    {
      "credit_bureau": { "name": "experian", "display_name": "Experian" },
      "fico_score": { "score": 720, "date_of_score": "2025-06-01" },
      "underwritten_accounts": [
        {
          "account_name": "CHASE SAPPHIRE",
          "account_number": "xxxx1234",
          "is_open": true,
          "account_type_normalized": "revolving",
          "account_status_normalized": "current",
          "collateral_type": null,
          "recent_balance": 2400.00,
          "credit_limit": 15000.00,
          "monthly_payment": 45.00,
          "date_opened": "2018-04-01"
        }
      ]
    }
  ]
}

Account status values: current, delinquent, charged_off, collection, bankruptcy, foreclosure, repossession, settled, closed, unknown Account type values: revolving, installment, mortgage, charge, other Collateral type values: unsecured, real_estate, vehicle, cash_deposit, education

Tax form analytics

GET /books/:id/tax-analytics

Returns TaxCashFlowAnalysis Fannie Mae 1084-style qualifying income computation from parsed 1040, Schedule C, 1065, and 1120S forms.

r = requests.get(f"{API_BASE}/books/{book_id}/tax-analytics", headers=headers)
tax = r.json()

for year in tax["years"]:
    print(f"{year['tax_year']}: ${year['total_qualifying_income']:,.2f}")

TaxCashFlowAnalysis fields

Field	Type	Description
`years[]`	array	One entry per tax year
`most_recent_year`	int
`avg_qualifying_income`	float	Average qualifying income across all years

TaxYearAnalysis per year

Field	Type	Description
`tax_year`	int
`total_qualifying_income`	float	Sum of all income sources for this year
`total_wages`	float	W-2 wages
`total_schedule_c`	float	Self-employment income
`total_partnership`	float	Partnership K-1 income
`total_s_corp`	float	S-corp K-1 income
`total_depreciation_addback`	float	Depreciation added back to income
`reported_agi`	float	AGI from line 11 of 1040
`wage_sources[]`	array	Per W-2 income source detail
`schedule_c_sources[]`	array	Per Schedule C income source detail
`partnership_sources[]`	array	Per K-1 (partnership) income source
`s_corp_sources[]`	array	Per K-1 (S-corp) income source
`warnings[]`	array	Reconciliation warnings (missing K-1, missing return, income mismatch)

Export as CSV

GET /books/:id/csv-export

Downloads the bank statement UW table as a CSV. Applies org-level exclusions (same as /analytics).

table_format

string

default:"month_as_row"

month_as_row one row per statement period per account (default).
month_as_col metrics as rows, months as columns (pivoted). Uses the combined account (account_id=0) per statement.

# Default
curl "https://api.lendpathway.com/api/books/{book_id}/csv-export" \
  -H "Authorization: Bearer pat_xxx" \
  -o uw.csv

# Pivoted
curl "https://api.lendpathway.com/api/books/{book_id}/csv-export?table_format=month_as_col" \
  -H "Authorization: Bearer pat_xxx" \
  -o uw_pivoted.csv

Columns: Period, Document, Account, Starting Balance, Deposits, Deposit Count, Withdrawals, Withdrawal Count, Ending Balance, Loan In, Loan Out, Avg Daily Balance, Days Negative, True Revenue, DTI % Includes NET and Average summary rows.

Export as Excel

GET /books/:id/spreadsheet-export?sheet_type=mca

Returns an .xlsx file using your org’s configured spreadsheet template.

sheet_type

string

required

mca bank statements. vlad credit reports. tax tax forms.

curl "https://api.lendpathway.com/api/books/{book_id}/spreadsheet-export?sheet_type=mca" \
  -H "Authorization: Bearer pat_xxx" \
  -o statements.xlsx

Overview

Endpoints

Cookbook

Concepts

Submit a book

Get a book

Bank statement analytics

Bank statement detailed view

Credit report data

Tax form analytics

Export as CSV

Export as Excel

​Concepts

​Submit a book

​Get a book

​Bank statement analytics

​Bank statement detailed view

​Credit report data

​Tax form analytics

​Export as CSV

​Export as Excel

Concepts

Submit a book

Get a book

Bank statement analytics

Bank statement detailed view

Credit report data

Tax form analytics

Export as CSV

Export as Excel