How Data Analytics Helps Financial Advisors Serve Clients

Data-driven mortgage recommendations use verified information rather than figures recalled from memory. Useful inputs include transaction data shared with consent through the Consumer Data Right, verified credit and identity records, property valuation models, and clear risk measures such as debt-to-income ratios and serviceability buffers. (Source)

The guardrails are equally important. On May 28, 2026, APRA confirmed that the mortgage serviceability buffer would remain 3 percentage points above the loan rate. From December 10, 2026, new transparency requirements will apply to privacy policies when automated systems make decisions that significantly affect people. Better analytics must therefore be paired with clear records and human oversight.

What “data-driven” means in Mortgages

Three data layers do most of the work. The first is consented bank transaction data, which can show income patterns, recurring commitments, and liabilities a client may overlook. Frollo for Brokers, for example, gives brokers access to verified bank data through the Consumer Data Right, reducing manual work during the fact-find and assessment.

The second layer covers verified identity and credit information.

The third covers property data, including automated valuation models. Cotality provides real-time residential property estimates for Australian property data users.

These inputs can improve suitability checks and speed, but they don’t create certainty. An automated valuation model provides an estimate and confidence range, not a credit decision or a lender-approved security value. Use it to screen equity and loan-to-value scenarios, then confirm the result through the lender’s required valuation process.

Designing for regulatory guardrails

APRA settings shape how lenders assess borrowing capacity. Alongside the 3 percentage point serviceability buffer confirmed on May 28, 2026, each authorised deposit-taking institution may write up to 20 per cent of its new mortgage lending at a debt-to-income ratio of six or more from February 2026. The limit applies separately to owner-occupier and investor portfolios. It is a portfolio limit rather than an automatic ban on an individual loan, so a high-DTI application may be workable at one lender but not another.

ASIC’s best interests duty governs how mortgage brokers justify recommendations. The transparency requirements starting December 10, 2026 apply when automated decisions significantly affect a person’s rights or interests. Good file management should record which rules or model outputs shaped a shortlist, retain consent receipts with the relevant data pull, and explain which alternatives were considered. If a scoring tool ranks products, describe the criteria in language the client can understand.

Building a practical analytics toolkit

Start with cash flow. Categorise recurring income and essential outgoings from consented data, then compare them with the client’s declared liabilities. Source data reduces rekeying and can reveal inconsistencies earlier. Where possible, use Consumer Data Right channels instead of screen scraping or shared account credentials.

Add property analytics. A valuation range, comparable sales, and rental yield estimates provide a reasonable starting point for equity discussions. Then consider debt-to-income risk. RBA analysis found that about one-fifth of housing investors had debts greater than six times their income in 2021. Because that figure is several years old, use it as background rather than as a description of current borrowers. This supports a clearer view of data-driven lending risk when comparing loan scenarios.

Finally, connect the tools to the workflow. NextGen’s consolidated Open Banking view creates a single financial picture of applicants after consent, which can help with joint applications. Adoption remains uneven. A June 2025 NextGen industry survey found that many brokers still relied on older bank-statement processes or were unaware that Open Banking features were available. Current usage may differ, but the finding highlights the need for staff training and clear procedures.

Where the market is moving

Data coverage is widening. On July 13, 2026, Consumer Data Right product-data obligations began for non-bank lenders, with consumer data sharing scheduled to start on November 9, 2026. Broader coverage should support better comparisons, but advisers still need to verify marketing claims.

Inovayt’s Melbourne page, for example, lists access to more than 40 lenders and notes that some approvals can occur in as little as four hours. Such figures describe possible outcomes, not guarantees or proof of whole-of-market coverage. Apply the same standard when reviewing any firm’s claims.

Human expertise remains central 

Models identify patterns, while people handle exceptions, fairness checks, and disputes about data-derived conclusions. Assign a named reviewer when automation materially influences a shortlist, and give clients a clear way to question the result.

People seeking local expertise alongside Open Banking data and valuation models can compare appropriately licensed professionals, including financial advisors in Melbourne​ and specialist mortgage brokers who handle complex borrower profiles in Victoria.

Mini playbooks for data-driven mortgage decisions

• First-home buyer. Use consented data to verify regular saving patterns rather than relying on a single balance snapshot. Stress-test repayments at the product rate plus 3 percentage points. Record why the recommended product suits the client beyond its headline rate, including any automated screening used.
• Refinancer. Review transaction data for essential outgoings and outdated direct debits. Run an initial property estimate to check equity, then test the position against debt-to-income guardrails before shortlisting lenders. Record the comparison method, not only the outcome.
• Self-employed borrower. Combine bank-statement analysis with accountant-prepared records. Note where seasonal revenue, retained profits, or mixed personal and business accounts may distort automated categories. If you override a model’s output, explain why.

A sensible implementation sequence

Data and governance work best together. A practical sequence is to introduce Open Banking data first, property analytics second, and explainable models third. Whether the work is managed internally or through a firm such as Inovayt, the same discipline applies: record the inputs, explain the reasoning, retain evidence of consent, and document the options that were set aside.