Analytics

Analytics are computed from the current Book every time you request them. The response reflects transaction tags, excluded documents and accounts, excluded positions, organization revenue rules, business-day settings, and screening policy.

Bank analytics

GET /books/{book_id}/analytics

Call this after the Book reaches parse_status: "completed" and contains bank statement or Plaid data.

import requests

API_BASE = "https://api.lendpathway.com/api"
TOKEN = "pat_your_token_here"
BOOK_ID = "99cc93e6-1f3f-42b1-9fe4-ba5a95be9c78"

response = requests.get(
    f"{API_BASE}/books/{BOOK_ID}/analytics",
    headers={"Authorization": f"Bearer {TOKEN}"},
    timeout=60,
)
response.raise_for_status()

analytics = response.json()
print("True revenue:", analytics["true_revenue"])
print("Average daily balance:", analytics["average_daily_balance"])
print("Debt positions:", len(analytics["positions"]))

const API_BASE = "https://api.lendpathway.com/api";
const TOKEN = "pat_your_token_here";
const BOOK_ID = "99cc93e6-1f3f-42b1-9fe4-ba5a95be9c78";

const response = await fetch(`${API_BASE}/books/${BOOK_ID}/analytics`, {
  headers: { Authorization: `Bearer ${TOKEN}` },
});

if (!response.ok) {
  throw new Error(`${response.status}: ${await response.text()}`);
}

const analytics = await response.json();
console.log({
  trueRevenue: analytics.true_revenue,
  averageDailyBalance: analytics.average_daily_balance,
  positions: analytics.positions.length,
});

curl --fail-with-body \
  "https://api.lendpathway.com/api/books/99cc93e6-1f3f-42b1-9fe4-ba5a95be9c78/analytics" \
  -H "Authorization: Bearer pat_your_token_here"

How to read the response

The response has several useful levels:

Book totals such as true_revenue, average_daily_balance, total_loan_payments, and debt_to_income_ratio
statements, organized by statement period with an account breakdown inside each period
merged_accounts, containing the transaction history enriched with cleaned tags and position information
positions, containing detected debt relationships and their payment schedules
screening_metrics and screening_result, containing the fact sheet and current organization policy decision

All monetary values are dollar amounts. Ratios such as debt_to_income_ratio and holdback_pct are percentage values, so 12.5 means 12.5%.

Response models

These models mirror the current public response. Default values are included because older Books can have sparse data.

BookAnalytics

from typing import Any

from pydantic import BaseModel, Field


class BookAnalytics(BaseModel):
    statements: list[StatementAnalytics]
    merged_accounts: dict[str, MergedAccount] | None = None
    loan_summary: list[LoanSummary] = Field(default_factory=list)
    positions: list[DebtPosition] = Field(default_factory=list)

    total_deposits: float
    total_withdrawals: float
    total_loan_disbursements: float = 0.0
    total_loan_payments: float = 0.0

    opening_balance: float = 0.0
    closing_balance: float = 0.0
    peak_balance: float = 0.0
    lowest_balance: float = 0.0
    largest_deposit: float = 0.0
    avg_deposit: float = 0.0
    largest_withdrawal: float = 0.0
    avg_withdrawal: float = 0.0
    num_deposits: int = 0
    num_withdrawals: int = 0
    avg_transaction: float = 0.0
    total_days: int = 0
    average_daily_balance: float | None = None
    days_negative_balance: int = 0
    days_under_threshold: int = 0
    low_balance_threshold: float | None = None

    true_revenue: float = 0.0
    num_true_revenue_transactions: int = 0

    nsf_total: float = 0.0
    num_nsf: int = 0
    overdraft_total: float = 0.0
    num_overdraft: int = 0
    owner_transaction_total: float = 0.0
    num_owner_transaction: int = 0
    internal_transfer_total: float = 0.0
    num_internal_transfer: int = 0
    bank_fee_total: float = 0.0
    num_bank_fee: int = 0
    payment_processor_total: float = 0.0
    num_payment_processor: int = 0
    stop_payment_total: float = 0.0
    num_stop_payment: int = 0
    reversal_total: float = 0.0
    num_reversal: int = 0

    num_loan_disbursements: int = 0
    num_loan_payments: int = 0
    debt_to_income_ratio: float | None = None
    num_mca_positions: int = 0
    num_active_mca_positions: int = 0
    num_factoring_positions: int = 0
    num_reversal_credits: int = 0
    num_missed_payments: int = 0
    num_modified_payments: int = 0

    total_mca_daily_remit: float = 0.0
    total_mca_monthly_remit: float = 0.0
    total_mca_paid_net: float = 0.0
    total_mca_holdback_pct: float | None = None

    reconciliation_results: list[ReconciliationResult] = Field(default_factory=list)
    revenue_exclusion_tags: list[str] = Field(default_factory=list)
    excluded_position_ids: list[str] = Field(default_factory=list)
    excluded_document_ids: list[str] = Field(default_factory=list)
    average_statement_metrics: AccountStatementMetrics | None = None
    counterparty_clusters: list[CounterpartyCluster] = Field(default_factory=list)

    deposits_by_weekday: dict[str, float] = Field(default_factory=dict)
    withdrawals_by_weekday: dict[str, float] = Field(default_factory=dict)
    deposit_count_by_weekday: dict[str, int] = Field(default_factory=dict)
    withdrawal_count_by_weekday: dict[str, int] = Field(default_factory=dict)
    bank_holidays: list[dict[str, str]] = Field(default_factory=list)

    most_recent_transaction_date: str | None = None
    most_recent_statement_end_date: str | None = None
    most_recent_mca_disbursement_date: str | None = None
    most_recent_mca_payment_date: str | None = None

    screening_metrics: ScreeningMetrics | None = None
    screening_result: ScreeningResult | None = None

Statements and account metrics

from typing import Literal

from pydantic import BaseModel, Field


class AccountStatementMetrics(BaseModel):
    account_id: int
    account_name: str
    account_number: str
    document_id: str | None = None
    starting_balance: float
    ending_balance: float
    num_deposits: int
    total_deposits: float
    num_withdrawals: int
    total_withdrawals: float

    is_reconciled: bool
    reconciliation_skipped: bool = False
    discrepancy: float | None = None
    reconciliation_message: str | None = None
    computed_ending_balance: float | None = None
    expected_ending_balance: float | None = None

    loan_disbursements: float = 0.0
    loan_payments: float = 0.0
    average_daily_balance: float | None = None
    days_negative_balance: int = 0
    days_under_threshold: int = 0
    days_in_period: int = 0
    true_revenue: float = 0.0
    num_true_revenue_transactions: int = 0

    nsf_total: float = 0.0
    num_nsf: int = 0
    overdraft_total: float = 0.0
    num_overdraft: int = 0
    owner_transaction_total: float = 0.0
    num_owner_transaction: int = 0
    internal_transfer_total: float = 0.0
    num_internal_transfer: int = 0
    bank_fee_total: float = 0.0
    num_bank_fee: int = 0
    payment_processor_total: float = 0.0
    num_payment_processor: int = 0
    stop_payment_total: float = 0.0
    num_stop_payment: int = 0
    reversal_total: float = 0.0
    num_reversal: int = 0
    debt_to_income_ratio: float | None = None


class StatementAnalytics(BaseModel):
    document_id: str
    document_ids: list[str] = Field(default_factory=list)
    document_name: str
    document_type: str | None = None
    statement_start_date: str
    statement_end_date: str
    statement_period: str
    accounts: list[AccountStatementMetrics]


class LoanSummary(BaseModel):
    loan_type: str
    total_disbursements: float
    total_payments: float
    disbursement_count: int
    payment_count: int
    is_excluded: bool = False


class CounterpartyCluster(BaseModel):
    cluster_id: str
    counterparty: str
    direction: Literal["credit", "debit"]
    total: float
    count: int
    transaction_ids: list[int]


class ReconciliationResult(BaseModel):
    month: str
    account_name: str
    reconciled: bool
    reconciliation_skipped: bool = False
    discrepancy: float
    attempts_made: int
    reason: str | None = None

Each statement normally contains individual account rows plus an account_id: 0 row named COMBINED. The combined row represents the period-wide cash line across included accounts.

Transactions

from typing import Literal

from pydantic import BaseModel, Field


class TransactionPosition(BaseModel):
    position_id: str
    position_name: str
    loan_type: str
    funder_title: str | None = None


class EnrichedTransaction(BaseModel):
    transaction_id: int
    document_id: str | None = None
    transaction_date: str
    description: str
    amount: float
    transaction_type: Literal["credit", "debit"]
    ledger_balance: float | None = None
    tag: list[str] = Field(default_factory=list)
    position: TransactionPosition | None = None


class MergedAccount(BaseModel):
    account_number: str
    account_name: str | None = None
    transactions: list[EnrichedTransaction] = Field(default_factory=list)

merged_accounts is keyed by the account ID serialized as a string. Transaction tags have already been cleaned for credit/debit direction. Qualifying credits also receive the synthetic true_revenue tag, and transactions without another tag receive untagged.

Debt positions and payment schedules

from typing import Literal

from pydantic import BaseModel, Field

Frequency = Literal["daily", "weekly", "monthly", "irregular"]
ScheduleState = Literal["active", "closed"]
EpisodeState = Literal["pending", "active", "closed"]
EpisodeRole = Literal["initial", "renewal", "stack", "orphan"]
PositionStatus = Literal["just_funded", "active", "closed"]


class Disbursement(BaseModel):
    disbursement_id: str
    transaction_ids: list[int] = Field(default_factory=list)
    date: str
    amount: float
    is_merged: bool = False


class ReversalCredit(BaseModel):
    transaction_id: int
    date: str
    amount: float
    description: str = ""
    attributed_schedule_id: str | None = None


class Miss(BaseModel):
    evidence: Literal["reversal", "gap"]
    date: str | None = None
    date_window: tuple[str, str] | None = None
    expected_amount: float
    count_estimate: int = 1
    reversal_txn_id: int | None = None
    severity: Literal["single", "multi", "long"] = "single"
    schedule_id: str | None = None


class Modification(BaseModel):
    date: str
    before_amount: float
    after_amount: float
    delta_pct: float
    type: Literal["cross_stream", "within_stream"]
    schedule_id: str


class PaymentSchedule(BaseModel):
    schedule_id: str
    transaction_ids: list[int] = Field(default_factory=list)
    avg_amount: float = 0.0
    frequency: Frequency = "irregular"
    pull_day: str | None = None
    remit_daily: float | None = None
    amount_variance: float = 0.0
    is_holdback_style: bool = False
    first_payment: str = ""
    last_payment: str = ""
    payment_count: int = 0
    total_paid: float = 0.0
    total_paid_net: float = 0.0
    term_est_days: int | None = None
    state: ScheduleState = "active"
    misses: list[Miss] = Field(default_factory=list)
    modifications: list[Modification] = Field(default_factory=list)


class Episode(BaseModel):
    episode_id: str
    advance: Disbursement | None = None
    schedules: list[PaymentSchedule] = Field(default_factory=list)
    role: EpisodeRole = "initial"
    state: EpisodeState = "pending"
    first_payment: str | None = None
    last_payment: str | None = None


class PositionTransaction(BaseModel):
    transaction_id: str
    document_id: str | None = None
    date: str
    description: str
    amount: float
    type: str


class DebtPosition(BaseModel):
    position_id: str
    name: str
    loan_type: str
    is_excluded: bool = False

    funder_uuid: str | None = None
    funder_title: str | None = None
    funder_link: str | None = None
    funder_contact: str | None = None
    funder_email: str | None = None
    transactions: list[PositionTransaction] = Field(default_factory=list)

    total_disbursements: float = 0.0
    total_payments: float = 0.0
    transaction_count: int = 0
    disbursement_count: int = 0
    payment_count: int = 0
    avg_disbursement: float = 0.0
    avg_payment: float = 0.0
    first_disbursement_date: str | None = None
    last_disbursement_date: str | None = None
    last_payment_date: str | None = None
    returns_count: int = 0
    returns_total: float = 0.0

    episodes: list[Episode] = Field(default_factory=list)
    reversal_credits: list[ReversalCredit] = Field(default_factory=list)
    status: PositionStatus = "closed"
    daily_remit_burden: float = 0.0
    monthly_remit_burden: float = 0.0
    holdback_pct: float | None = None
    n_active_schedules: int = 0
    n_active_episodes: int = 0
    est_total_payback: float | None = None
    total_paid_net: float = 0.0
    progress_pct: float | None = None
    has_renewal: bool = False
    has_stack: bool = False
    potential_missed_payments: list[Miss] = Field(default_factory=list)
    potential_modified_payments: list[Modification] = Field(default_factory=list)

An episode represents one observed advance and its payment streams. renewal and stack describe how a later advance relates to earlier active schedules. orphan means payments are visible but the original advance predates the statement window.

Screening

from typing import Any, Literal

from pydantic import BaseModel, Field


class ScreeningMetrics(BaseModel):
    avg_daily_balance: float | None = None
    negative_days: float | None = None
    days_under_threshold: float | None = None
    true_revenue: float | None = None
    avg_monthly_revenue: float | None = None
    avg_monthly_deposits: float | None = None
    total_loan_payments: float | None = None
    debt_to_income_ratio: float | None = None
    closing_balance: float | None = None
    opening_balance: float | None = None
    total_days: float | None = None
    lowest_balance: float | None = None
    num_deposits: float | None = None
    num_withdrawals: float | None = None
    num_true_revenue_transactions: float | None = None
    num_mca_positions: float | None = None
    time_in_business_in_days: float | None = None
    account_holder_ownership_percentage: float | None = None
    requested_loan_amount: float | None = None
    most_recent_month_revenue: float | None = None
    most_recent_month_negative_days: float | None = None
    most_recent_month_avg_daily_balance: float | None = None
    most_recent_month_deposits: float | None = None
    most_recent_month_loan_payments: float | None = None
    num_nsf: float | None = None
    num_overdraft: float | None = None
    num_active_mca_positions: float | None = None
    num_factoring_positions: float | None = None
    days_since_last_transaction: float | None = None
    days_since_last_statement_end: float | None = None
    days_since_last_mca_disbursement: float | None = None
    days_since_last_mca_payment: float | None = None
    total_deposits: float | None = None
    total_withdrawals: float | None = None
    avg_monthly_withdrawals: float | None = None
    avg_monthly_deposit_count: float | None = None
    avg_monthly_negative_days: float | None = None
    avg_monthly_loan_payments: float | None = None
    nsf_total: float | None = None
    overdraft_total: float | None = None
    num_reversal_credits: float | None = None
    num_missed_payments: float | None = None
    num_modified_payments: float | None = None
    business_name: str | None = None
    state_code: str | None = None
    industry: str | None = None


class ResolvedScreeningRule(BaseModel):
    target_type: Literal["state", "industry", "all"]
    target_value: str
    equation: dict[str, Any] | None = None
    deny_all: bool = False
    lendsaas_decline_reason_ids: list[str] | None = None
    result: Literal["PASS", "REJECT"]
    reason: str
    rule_as_variables: str
    rule_after_substituting: str


class ScreeningResult(BaseModel):
    result: Literal["PASS", "REJECT"]
    num_passed: int
    num_total: int
    resolved_rules: list[ResolvedScreeningRule] = Field(default_factory=list)

screening_result can be absent when screening is disabled or when a fact sheet cannot be built. A missing metric causes the individual rule that needs it to pass.

Account-first statement view

GET /books/{book_id}/statements

This view groups data by account and then by month. It includes daily balance maps and monthly lender activity. It does not apply organization-level document, account, or revenue exclusions.

from typing import Any

from pydantic import BaseModel, Field


class SimpleFunder(BaseModel):
    position_id: str
    name: str
    loan_type: str
    funder_uuid: str | None = None
    funder_title: str | None = None
    funder_link: str | None = None
    funder_contact: str | None = None
    funder_email: str | None = None
    transaction_count: int
    total_disbursements: float
    total_payments: float
    funded_date: str | None = None
    first_payment_date: str | None = None
    last_payment_date: str | None = None
    payment_count: int = 0
    avg_payment_amount: float | None = None
    payment_frequency: str | None = None


class SimpleStatement(BaseModel):
    document_id: str
    document_name: str
    statement_start_date: str
    statement_end_date: str
    starting_balance: float
    ending_balance: float
    min_balance: float = 0.0
    max_balance: float = 0.0
    sum_credits: float
    sum_debits: float
    net_deposits: float
    num_deposits: int
    num_withdrawals: int
    average_daily_balance: float | None = None
    days_negative_balance: int = 0
    days_in_period: int = 0
    revenue_credits: float = 0.0
    loan_disbursements: float = 0.0
    total_mca_disbursements: float = 0.0
    loan_payments: float = 0.0
    debt_to_income_ratio: float | None = None
    transactions: list[dict[str, Any]]
    daily_balances: dict[str, float] = Field(default_factory=dict)
    funders: list[SimpleFunder] = Field(default_factory=list)


class SimpleAccount(BaseModel):
    account_id: int
    account_name: str
    account_number: str
    bank_name: str
    routing_number: str | None = None
    account_type: str
    business_name: str
    business_address: dict[str, Any] | None = None
    statements: list[SimpleStatement]

Tax cash-flow analysis

GET /books/{book_id}/tax-analytics

Returns qualifying-income analysis derived from the tax forms stored on the Book.

from typing import Any, Literal

from pydantic import BaseModel, Field


class TaxEntity(BaseModel):
    name: str
    tin: str | None = None
    address: str | None = None


class IncomeSource(BaseModel):
    source_type: Literal["wages", "schedule_c", "partnership", "s_corp"]
    source_name: str
    entity: TaxEntity | None = None
    ownership_pct: float | None = None
    ordinary_income: float = 0
    rental_income: float = 0
    guaranteed_payments: float = 0
    depreciation_addback: float = 0
    other_income: float | None = None
    other_deductions: float | None = None
    subtotal: float = 0
    has_k1: bool = False
    has_return: bool = False
    k1_return_match: bool | None = None


class ReconciliationWarning(BaseModel):
    entity_name: str
    warning_type: Literal[
        "missing_k1",
        "missing_return",
        "income_mismatch",
        "missing_from_schedule_e",
    ]
    message: str


class ParsedFormField(BaseModel):
    key: str
    label: str
    value: Any
    format: Literal["currency", "percent", "text", "count"]


class ParsedFormCard(BaseModel):
    title: str
    entity_name: str | None = None
    entity_tin: str | None = None
    badge: str | None = None
    fields: list[ParsedFormField] = Field(default_factory=list)


class TaxYearAnalysis(BaseModel):
    tax_year: int
    borrower: TaxEntity | None = None
    wage_sources: list[IncomeSource] = Field(default_factory=list)
    schedule_c_sources: list[IncomeSource] = Field(default_factory=list)
    partnership_sources: list[IncomeSource] = Field(default_factory=list)
    s_corp_sources: list[IncomeSource] = Field(default_factory=list)
    schedule_e_entity_count: int = 0
    k1_count: int = 0
    warnings: list[ReconciliationWarning] = Field(default_factory=list)
    parsed_forms: list[ParsedFormCard] = Field(default_factory=list)
    total_wages: float = 0
    total_schedule_c: float = 0
    total_partnership: float = 0
    total_s_corp: float = 0
    total_depreciation_addback: float = 0
    total_qualifying_income: float = 0
    reported_agi: float | None = None


class TaxCashFlowAnalysis(BaseModel):
    years: list[TaxYearAnalysis] = Field(default_factory=list)
    most_recent_year: int | None = None
    avg_qualifying_income: float | None = None

Response conditions

Status	Meaning
`400`	The Book has not completed parsing or does not contain the required data
`401`	Missing or invalid PAT
`403`	The token cannot access this Book
`404`	The Book does not exist in the token’s organization

For practical selection and aggregation patterns, continue with Working with analytics data.

Overview

Endpoints

Cookbook

Bank analytics

How to read the response

Response models

Account-first statement view

Tax cash-flow analysis

Response conditions

​Bank analytics

​How to read the response

​Response models

​Account-first statement view

​Tax cash-flow analysis

​Response conditions

Bank analytics

How to read the response

Response models

Account-first statement view

Tax cash-flow analysis

Response conditions