Context: Inflation’s signal, competition’s squeeze

The trimmed mean – the Reserve Bank of Australia’s preferred gauge because it filters out volatile items – has printed at 3.3%, keeping underlying inflation above the RBA’s 2–3% target band. That rekindles the risk of a policy move, a view echoed by two of the big four banks recently flagging the prospect of near-term hikes. One major bank went further, indicating that tighter conditions would likely reduce the number of active competitors in home lending – a reminder that funding costs, capital constraints and serviceability standards typically cull weaker players first.

For brokers, this macro cue arrives at a sensitive operating point: acquisition costs remain elevated, refinancing appetite is choppy, and lenders are repricing frequently. The downstream impact is immediate. On a standard 30-year principal-and-interest mortgage, a 25-basis-point rise lifts monthly repayments by roughly $80 per $500,000 of balance (around $160 per $1 million). Back-of-the-envelope, maximum borrowing capacity falls by about 2–3% for the same income and expense profile when rates lift by 25 bps, tightening the pipeline for first-home buyers and stretching debt-to-income ratios for investors.

Market dynamics amplify the challenge. With Google retaining approximately 94% share of general search in Australia, digital lead generation is effectively a one-platform game, limiting brokers’ ability to sidestep rising cost-per-click when refinance traffic spikes. In short: higher rates threaten both the demand side (eligibility and appetite) and the supply side (marketing costs and lender turnarounds).

Decision: A three-track play for resilience and edge

Our focal organisation is a composite of mid-tier Australian brokerages (20–200 brokers) that, following the inflation print, made a coordinated decision on three fronts:

• Protect the book by prioritising at-risk customers likely to fall into payment stress if rates rise another 25–50 bps, and by accelerating repricing and hardship pathways.
• Sharpen acquisition economics to counter higher cost-per-lead in a Google-dominated market, leaning into first-party data and partner ecosystems to diversify away from pure paid search.
• Industrialise serviceability triage using lightweight AI assistance – aligned with Australia’s AI Ethics Principles and public-sector governance approaches – to speed compliant fact-finding, document checks and scenario modelling without replacing licensed judgement.

The goal was not prediction, but readiness: reset operating tempo to weekly rate scenarios, translate them into borrower-level actions, and convert cost pressure into a retention advantage while competitors hesitated.

Implementation: From macro signal to borrower-level action

The network stood up a cross-functional rate-response cell for 12 weeks, with the following technical and operational steps:

• Segment and score: Using CRM and lender policy rules, the team scored every customer by exposure to a 25–50 bp rise (loan-to-value, debt-to-income, fixed-to-variable rollover proximity, and expense buffers). The model was simple, auditable and deterministic to satisfy compliance review.
• Serviceability scenarios: For each segment, advisers generated side-by-side repayment schedules and borrowing-capacity deltas under +25 and +50 bps. A $750,000 balance example helped normalise expectations: +25 bps implies around +$120/month, +50 bps about +$240/month.
• Proactive repricing and product switches: Advisers queued repricing requests with current lenders for eligible borrowers and presented alternatives only where the net benefit exceeded switch costs (valuation, discharge, application fees) under the scenario horizon.
• AI-enabled workflow assist: Simple, retrieval-based tools summarised policy documents, pre-populated fact-finds from structured data, and generated first-draft client communications with plain-English explanations of rate scenarios. The AI was bounded: no autonomous recommendations; every output required broker validation, consistent with the Australian Government’s AI consultation emphasis on human oversight.
• Lead diversification: Marketing shifted budget from pure search to co-marketing with accountants and buyers’ agents, and nurtured own-audience channels (email, SMS) to blunt cost-per-click inflation in a market where one platform dominates discovery.
• Compliance-by-design: Call scripts captured explicit consent, affordability checks referenced lender policy excerpts, and all AI prompts/outputs were archived for audit, echoing governance practices seen in public entities such as the ATO’s approach to AI oversight.

Results: Measured outcomes and quantifiable impacts

While the policy outcome remained uncertain, the brokerage network tracked leading indicators and customer-level finances to validate the approach:

• Borrower impact maths: For a typical $500,000 loan on a 30-year term, the monthly repayment increased from roughly $3,000 at 6.00% to about $3,080 at 6.25% (+$80). For $1,000,000, the rise was about +$160/month. These figures framed hardship risk thresholds and guided repricing urgency.
• Eligibility compression: Based on repayment maths, advisers communicated a capacity delta of approximately 2–3% reduction in maximum borrowing capacity per 25 bps increase (income and expenses held constant), helping clients right-size expectations and timelines.
• Operational throughput: AI-assisted document summarisation and templated scenario emails reduced preparation time per customer review cycle, allowing advisers to contact materially more customers within the 12-week window. The uplift in throughput was driven by automation of low-risk admin, not credit advice, maintaining compliance with human-in-the-loop controls.
• Acquisition cost discipline: By shifting a portion of spend from paid search – where Google’s ~94% market share leaves limited bargaining power – to partner referrals and first-party channels, the network reduced exposure to sudden spikes in click prices commonly seen when refinance interest surges alongside rate headlines.

Taken together, these moves did not bet on the rate decision; they priced the risk, defended the existing book, and repositioned acquisition towards channels the firm could control.

Lessons: Strategy under uncertainty, with numbers and guardrails

• Translate macro to micro fast: A trimmed-mean print at 3.3% is a headline. Converting it to +$80 per $500,000 and a 2–3% capacity squeeze per 25 bps makes it actionable for clients and advisers.
• Retention is the cheapest hedge: With a major bank signalling that tighter conditions can reduce active competitors, brokers that reach at-risk clients first can defend share while others retrench.
• Control your demand side: In a one-platform search market, diversify lead sources. Partnerships and owned channels hedge cost-per-click volatility when rate news floods the funnel.
• Use AI where it’s strong, govern where it must be: Retrieval and summarisation lift speed; licensed humans still decide credit suitability. Anchor tools to Australia’s AI Ethics Principles and emerging governance practices for trust and auditability.
• Scenario cadence matters: Weekly playbooks (repricing queues, hardship triage, policy updates) beat monthly reviews in a fast-moving rate environment, particularly when two of the big four are guiding to hikes.

Future outlook: Tightening bias, thinner margins, clearer advantages

If inflation proves sticky and the RBA tightens again, lenders will juggle funding costs and risk weights, and brokers will face stricter serviceability across segments. Early adopters of the playbook above gain a durable edge: lower effective acquisition costs, higher client retention, and faster, compliant response cycles. If the Board ultimately holds, none of the preparation is wasted; repricing wins, cleaner data, and stronger referral pipes all accrete to medium-term economics. Either way, the market narrative is the same: uncertainty rewards operational discipline.