Open Banking for Lending: How Lenders Use Banking APIs for Faster Decisions

Lending decisions depend on understanding a borrower's financial position — their income, expenses, existing debts, and spending patterns. Traditionally, this meant collecting payslips, requesting bank statements, and manually reviewing documents. Open banking APIs replace this with real-time, verified access to the same data directly from the borrower's bank.

For lenders, this means faster decisions, lower operational costs, more accurate risk assessment, and a better applicant experience. Here's how it works in practice.

The Traditional Lending Process and Its Problems

A typical loan application process without open banking looks like this:

1. Applicant fills out an application form with self-reported income and expenses
2. Lender requests supporting documents — payslips, bank statements, tax returns
3. Applicant uploads or emails documents (often taking days)
4. Lender manually reviews documents, cross-referencing against the application
5. Credit check is run
6. Decision is made

This process is slow (days to weeks), expensive (manual review costs), and unreliable (self-reported data can be inaccurate or fraudulent, PDF bank statements can be forged).

How Open Banking Changes Lending

With open banking, the process becomes:

1. Applicant fills out an application
2. Applicant connects their bank account(s) through a secure consent flow
3. Lender receives verified transaction data, account details, and balances via API
4. Automated analysis extracts income, expenses, and risk indicators
5. Credit check is run
6. Decision is made — often in minutes

Steps 2–4 happen in real time. The data comes directly from the bank, so it can't be forged or selectively edited. The result is faster time-to-decision, lower cost per application, and more accurate underwriting.

Key Use Cases for Lenders

Income Verification

The most immediate use case. Instead of asking for payslips, pull transaction data and identify regular income deposits. Open banking income verification can detect:

• Employment income — regular salary or wage deposits, identified by amount, frequency, and payer description
• Self-employment income — variable business revenue deposits, with patterns analysed over 3–12 months
• Government income — Centrelink payments, pensions, and other government transfers
• Rental income — regular rental payments received
• Multiple income sources — gig economy workers, part-time roles, side businesses

Automated income verification is faster and more accurate than payslip review. It also captures income sources that payslips miss — like rental income or secondary employment.

Expense Analysis and Affordability

Responsible lending requires understanding a borrower's expenses, not just their income. Transaction data reveals:

• Fixed commitments — rent or mortgage payments, loan repayments, insurance premiums
• Recurring expenses — utilities, subscriptions, childcare, school fees
• Discretionary spending — dining out, entertainment, travel
• Existing debt obligations — credit card repayments, BNPL payments, personal loan repayments

By categorising transactions and separating essential from discretionary spending, lenders can model affordability with far greater precision than the Household Expenditure Measure (HEM) benchmarks that many lenders still rely on.

Bank Statement Analysis

Beyond income and expenses, transaction data provides signals about financial behaviour:

• Account balance trends — is the applicant's balance generally stable, growing, or declining?
• Overdraft usage — how often does the applicant go into overdraft, and by how much?
• Dishonoured payments — failed direct debits or returned payments indicate financial stress
• Gambling transactions — identified through merchant categorisation, a key risk indicator
• Payday lending — use of short-term high-cost lenders suggests financial difficulty
• Savings behaviour — regular transfers to savings accounts indicate financial discipline

These behavioural signals complement credit scores and provide a more nuanced picture of credit risk.

Identity and Account Verification

As part of the loan origination process, open banking can verify that the applicant is who they claim to be and that the bank account provided is theirs. The bank-verified account holder name is matched against the applicant's identity documents, reducing identity fraud in lending.

Impact on Lending Metrics

Lenders who adopt open banking typically see improvements across several key metrics:

Metric	Without Open Banking	With Open Banking
Time to decision	Days to weeks	Minutes to hours
Document collection cost	Manual, per-application cost	Automated via API
Application drop-off	High (document upload friction)	Lower (single consent flow)
Income verification accuracy	Based on self-reported + payslips	Bank-verified, multi-source
Fraud detection	Limited to credit checks	Bank data cross-referencing
Default rates	Benchmark	Improved with better affordability data

Regulatory Context

In Australia, responsible lending obligations under the National Consumer Credit Protection Act require lenders to make reasonable inquiries about a borrower's financial situation. Open banking data is increasingly recognised as a reliable source for these inquiries — it provides verified income and expense data directly from the bank, which is harder to dispute than self-reported information.

The planned expansion of CDR to non-bank lenders from mid-2026 means that BNPL and other non-bank lending data is expected to become available through open banking in phases — product data from July 2026, with consumer data following from late 2026 through 2027. This will fill a significant gap, enabling lenders to eventually see a borrower's BNPL obligations alongside their bank account activity.

In New Zealand, open banking under the Customer and Product Data Act enables similar data access patterns for lenders. Globally, open banking for lending is one of the most established use cases, with adoption accelerating across the UK, EU, and other regulated markets.

Integration Approaches

Lenders typically integrate open banking data at one of two points in their workflow:

Pre-Qualification

Use open banking data early in the application funnel — before a full credit check — to pre-qualify applicants. This reduces the number of applications that proceed to full assessment only to be declined, saving cost and improving applicant experience.

Full Assessment

Integrate open banking data into the full underwriting workflow, replacing or supplementing manual bank statement review. This is the most common integration point and delivers the largest efficiency gains.

Build vs. Buy

Lenders can build direct integrations with bank APIs (requiring CDR accreditation in Australia, or similar accreditation in other markets) or use a managed open banking platform that handles connectivity, accreditation, consent management, and data enrichment.

Building directly gives maximum control but requires significant investment in infrastructure, security, and ongoing compliance. A managed platform reduces time-to-market and operational complexity.

Fiskil provides open banking infrastructure for lenders — income verification, expense analysis, account verification, and transaction data through a single API. Learn how we work with lenders.