How does expense categorisation compare to HEM benchmarks?

HEM provides statistical benchmarks based on household surveys. CDR expense analysis shows actual spending. Lenders typically use the higher of actual expenses or HEM for serviceability — having both allows brokers to present the most accurate picture.

Can the API detect BNPL commitments from transaction data?

Yes, the API identifies BNPL payments from providers like Afterpay, Zip, Humm, and others by recognising their transaction patterns and merchant identifiers. These are flagged as recurring commitments.

How far back should transaction data go for expense analysis?

We recommend 3-6 months as a minimum. This captures regular billing cycles, quarterly payments, and enough data to calculate reliable monthly averages. Longer periods improve accuracy for seasonal expenses.

What happens when borrower spending varies significantly month to month?

The API calculates monthly averages and flags months with unusual spikes. It also identifies seasonal patterns (e.g. higher utility costs in winter) so lenders can assess normalised expense levels.

Does this satisfy responsible lending expense verification requirements?

CDR-sourced expense data provides evidence of genuine inquiry into borrower expenses, which is a core requirement of responsible lending under the National Consumer Credit Protection Act. It goes well beyond self-declaration.

Banking API — Mortgage Brokers

Comprehensive Expense Categorisation for Mortgage Serviceability

Replace HEM benchmarks with real expense data from CDR bank transactions. Fiskil categorises borrower spending into discretionary and non-discretionary expenses, identifies recurring commitments, and produces lender-ready serviceability evidence.

Lender serviceability assessments increasingly demand actual expense evidence rather than Household Expenditure Measure (HEM) benchmarks alone. APRA and ASIC have made clear that responsible lending requires genuine inquiry into borrower expenses. Fiskil analyses CDR transaction data to categorise spending, detect recurring commitments, and distinguish discretionary from non-discretionary expenditure — giving mortgage brokers the evidence lenders need.

The Challenge

Relying on HEM benchmarks or borrower-declared expenses leads to inaccurate serviceability assessments, application rework, and regulatory risk.

The Solution

Fiskil analyses real transaction data to produce a structured breakdown of borrower expenses that satisfies lender serviceability requirements and responsible lending obligations.

Capabilities

How Fiskil Helps

Discretionary vs Non-Discretionary Classification

Every transaction is classified as essential (housing, utilities, groceries, transport, insurance) or discretionary (dining, entertainment, travel, retail), aligned with lender assessment categories.

Recurring Commitment Detection

Automatically identify loan repayments, BNPL instalments, rent, child support, subscriptions, and other fixed commitments from transaction patterns.

HEM Comparison Analysis

Compare actual expenses against HEM benchmarks for the borrower's household composition, highlighting where real spending exceeds or falls below benchmarks.

Spending Trend Analysis

Track expense trends over 3-12 months to identify whether spending is stable, increasing, or contains seasonal spikes that affect serviceability.

Implementation

How It Works

Obtain CDR Consent

Borrower authorises access to transaction accounts covering day-to-day spending, credit cards, and any accounts used for regular expenses.

Retrieve and Categorise Transactions

Fiskil retrieves 3-6 months of transactions and automatically categorises each into expense types aligned with lender assessment frameworks.

Generate Expense Summary

The API produces a structured expense report showing monthly averages by category, recurring commitments, and HEM comparison data.

Include in Serviceability Package

Submit the expense analysis alongside income verification as part of a complete serviceability evidence package for the lender.

In Practice

How Teams Use This

Franchise Broker Group

A national franchise broker group adopted CDR expense analysis to replace manual bank statement reviews across 400 broker offices.

Lender expense queries dropped by 54%, and the average application rework cycle reduced from 3.2 rounds to 1.1 rounds per submission.

First Home Buyer Specialist

A broker specialising in first home buyers used expense analysis to identify hidden BNPL commitments and subscriptions that borrowers forgot to declare.

Undisclosed commitment detection prevented 23% of applications from proceeding at amounts the borrower could not sustainably service.

Refinance and Debt Consolidation Broker

A refinance specialist used detailed expense categorisation to demonstrate that borrowers' actual living costs were lower than HEM benchmarks, unlocking higher borrowing capacity.

For 41% of refinance applications, verified expenses below HEM increased borrowing capacity by an average of $38,000.

Technical Details

What You Integrate With

API endpoints

GET /v1/accounts/{accountId}/transactions
POST /v1/expenses/categorise
GET /v1/expenses/summary
GET /v1/commitments/recurring

Data returned

Categorised expenses

Recurring commitments

Discretionary spending totals

HEM comparison metrics

Monthly expense averages

Authorisation

OAuth 2.0 / CDR consent

Real-time data access.

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FAQs

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