Context: A mature market meets a platform challenger

Australia’s mortgage distribution is broker‑led and getting more so. Industry data indicates brokers accounted for a record 77.6% of new residential lending in the June 2025 quarter, underscoring the channel’s centrality to customer acquisition and lender throughput. Against that backdrop, a fresh aggregator entrant—Viking—arrives after more than 15 years without a new player of scale. The timing matters: cost pressures on brokers, rising compliance demands, and lenders’ desire for cleaner files have pushed the battleground from headcount and commission splits to software‑driven productivity and conversion.

Accendo Financial’s Trent Carter has framed the stakes succinctly: the real contest is about differentiation and the switching risk brokers face when contemplating a move. In other words, only clear, bankable value will shift behaviour in a market that prizes stability.

Decision: Where new aggregators must differentiate

A practical way to assess the entrant’s strategy is to combine Porter’s Five Forces with platform flywheel logic:

• Economics: Competitive commission structures and transparent fees are necessary but not sufficient. The decisive edge lies in reducing brokers’ cost‑to‑serve (time per file, rework, resubmissions) and improving lender approval rates.
• Technology: An integrated stack—CRM, document capture, bank‑statement and income parsing, digital ID/KYC, and straight‑through lodgement—backed by AI assistance for file preparation and policy matching.
• Compliance & governance: Compliance‑by‑design that automatically records best‑interest duty evidence, product comparisons, and advice rationale. Australia’s AI Ethics Principles (2019) and the Australian Government’s 2024 interim response on AI guardrails set expectations for explainability, fairness and accountability that must be embedded in product design.
• Ecosystem & data: Lender integrations, accreditation orchestration, and consented data sharing. The flywheel is simple: more brokers drive more data; more data sharpens policy‑matching and quality checks; better quality accelerates approvals; faster approvals attract more lenders and brokers.
• Bargaining dynamics: Lenders want distribution but punish poor quality with credit policy friction and lower conversion. Brokers, meanwhile, hold substantial power; switching is rare unless value is unequivocal. High platform concentration elsewhere—e.g., the ACCC noted Google’s ~94% share in Australian general search in 2024—illustrates how entrenched digital intermediaries become without compelling differentiation.

Implementation: Building the aggregator operating system

Winning here requires an operating system for brokers, not just a portal. Key capabilities:

• Data plumbing: API‑first connections to lender lodgement systems; automated document ingestion; bank‑statement, income and expense categorisation; and consent management that complies with privacy obligations.
• AI that’s enterprise‑grade: Policy‑matching copilots, file completeness scoring, and pre‑submission quality checks. Australia’s public sector examples—such as the ATO’s emphasis on governance for general‑purpose AI—highlight the need for model validation, risk controls, bias testing and robust audit trails.
• Compliance‑by‑design: Evidence capture for best‑interest duty, product comparison artefacts, and immutable logs for regulatory reviews. This should be surfaced as guided workflows, not after‑the‑fact paperwork.
• Observability: Event telemetry across the broker workflow (time on task, error rates, resubmission causes) to power continuous improvement and lender SLA analytics.
• Security posture: Least‑privilege access, customer data segregation, anomaly detection, and breach‑readiness. Align with Australia’s AI Ethics Principles on transparency and accountability, with user‑facing explanations for any AI‑generated recommendations.

Australia’s AI ecosystem research has repeatedly flagged a commercialisation gap. That’s an opening: the entrant that productises AI into measurable broker outcomes—measured in minutes saved and approvals won—can convert curiosity into market share.

Results: The economics that justify switching

Brokers rarely move unless the numbers make sense. An illustrative model shows the hurdle:

• Time savings: If baseline file prep and lodgement consume ~6 hours per application, AI‑assisted document parsing, policy checks and statement analysis that trim 20–25% (to ~4.5–4.8 hours) free capacity for one additional file every 4–5 files processed.
• Conversion uplift: Moving a broker’s lodgement‑to‑approval rate from 65% to 72% (via completeness scoring and policy alignment) yields roughly 0.4 more settlements per 6 applications.
• Payback logic: Combined, time saved plus conversion gains can add ~1 extra settlement every 6–8 weeks for an average broker. If switching friction temporarily reduces throughput for a month or two, breakeven typically requires sustained 10–15% productivity uplift and 5–7 percentage‑point conversion gains. Anything less is a hard sell.

Lenders benefit too: higher‑quality submissions compress credit cycle times. Even modest SLA improvements reduce cost‑to‑acquire and increase net promoter outcomes for borrowers—valuable in a competitive, rate‑sensitive market.

Market context and competitive dynamics

Incumbent aggregators hold scale, brand and deep lender relationships. Experience from concentrated digital markets suggests that without step‑change capability, new platforms struggle to prise users from incumbents. But lenders have reasons to back credible entrants: diversification, competitive tension on aggregator economics, and—most importantly—submission quality. If a new platform consistently delivers cleaner files, lenders will preferentially accredit and may offer faster turnaround for those channels.

With brokers writing most loans, even single‑digit percentage shifts in aggregator share can reprice the competitive map. As Carter noted, the switching hurdle is real; the remedy is empirical proof, not promises.

Future outlook: Rates, consolidation and AI commercialisation

Macro conditions matter. Major bank forecasts pushed expectations for cash‑rate cuts into 2026, keeping refinance activity lively while purchase volumes remain rate‑sensitive. In such conditions, brokers prioritise speed to yes and certainty for clients; aggregators that compress time‑to‑approval will win mindshare. Given Australia’s documented AI commercialisation gap, the window is open for platforms that turn responsible AI into operational leverage—expect M&A as incumbents buy capability if they cannot build at pace.

Lessons and playbook: What leaders should do next

For new entrants: Anchor the proposition in quantifiable broker outcomes: publish time‑on‑task and conversion benchmarks by lender, audited by a third party. Offer migration toolkits (data transfer, accreditation orchestration) to cut switching pain to weeks, not months. Embed AI governance that meets Australian public‑sector‑grade standards from day one.

For incumbents: Treat this as a software race. Invest in consented data infrastructure, pre‑submission quality engines, and explainable AI. Bundle economic incentives with demonstrable productivity tools, not just commission tweaks.

For lenders: Tier SLAs by submission quality, not just volume. Share structured policy feedback loops with aggregators to train their quality engines; the payoff is fewer touches and faster, safer credit decisions.

The bottom line: in Australia’s broker‑first market, the winners won’t simply be the biggest. They’ll be the platforms that turn data, AI and compliance‑by‑design into broker productivity and lender conversion gains—strong enough to overcome the gravitational pull of the status quo.