Borrow

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A growing cohort of Australians is rethinking recent home loan decisions as higher repayments collide with household budgets. This isn’t just consumer angst; it’s an economy-wide red flag for lenders, brokers, retailers and policymakers. With refinancing set to remain elevated and arrears risks climbing from a low base, the competitive edge will go to institutions that pair rigorous affordability analytics with human-centred remediation. The lesson: treat mortgage regret as early-warning data—and act on it.

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When borrowers say their mortgage no longer fits, balance sheets listen. Recent industry reporting points to widespread strain in Australian home loans, with the Mortgage Choice Home Loan Report (Nov 2025) indicating around 70% of borrowers feel pressure from repayments and living costs. The International Monetary Fund’s April 2024 World Economic Outlook adds the structural context: monetary policy bites hardest in housing markets dominated by variable-rate mortgages—an apt description of Australia. The implication for business leaders is clear: this is a risk transmission moment, not a passing mood swing.

Market context: a fast monetary transmission meets fragile household cash flows

Australia’s mortgage market, with its high share of variable or short-fixed loans, transmits rate rises quickly into household cash flows. The IMF’s analysis highlights that such structures amplify the speed and magnitude of repayment shocks. Local market commentary echoes this: servicing a mortgage has become harder for first home buyers and investors despite targeted schemes, and the pipeline of borrowers rolling off ultra-low fixed rates remains a source of persistent stress.

There’s a paradox in the headline data. The Mortgage Choice report frames robust investor confidence even as a majority of borrowers feel squeezed. That divergence should inform product and risk strategy: investor segments may still transact on yield and tax settings, while owner-occupiers prioritise flexibility and cash-flow relief. Expect refinancing volumes to remain elevated, pricing competition to intensify, and arrears to normalise off historic lows.

Business impact: P&L and operating model pressures

For banks, the near-term calculus is a classic risk-return trade-off. Net interest margins that expanded during the tightening cycle face counterforces: higher funding costs, sharper cashback-free pricing battles, and increased provisions for expected credit losses. The operational load is non-trivial—contact centre volumes, hardship enquiries, and case management complexity all rise when borrowers feel boxed in by prior choices. While Australia’s prudential settings and income buffers are stronger than in many markets, portfolio dispersion matters; pockets of high loan-to-income and interest-only exposures will require granular oversight.

Brokers sit on the demand side of the shock. As borrowers seek relief, switching advice becomes the profit pool. Refinancing, restructuring (e.g., split loans, offset accounts) and debt consolidation are the growth lanes. The catch: customer acquisition costs are structurally high. The ACCC reports Google holds roughly 94% of the Australian general search market (Aug 2024), meaning digital discovery for home loans is overwhelmingly intermediated through a single gatekeeper. In practical terms, SEM/SEO efficiency, brand authority, and aggregator economics will define broker margins in 2025–26.

Retailers and subscription businesses should assume a cash-flow reset in their forecasts. Mortgage-driven belt-tightening typically hits discretionary spend first. CFOs should scenario-plan around a 100–200 bps swing in debt service ratios for their target demographics and adjust inventory, promotions, and credit policy accordingly.

Competitive advantage: build suitability-by-design

The firms that treat suitability as a living contract—not a point-in-time approval—will win trust and retention. Three moves stand out:

• Precision affordability: use account-level behavioural data (income volatility, savings buffers, discretionary spend elasticity) to calibrate repayment resilience and proactive outreach triggers.
• Flexible product constructs: normalise features such as built-in repayment holidays under stress, fee-free restructuring to split/offset, and automated micro-offset sweeps to optimise interest.
• Human-centred hardship: blend digital triage with specialist case managers; prioritise rapid, empathetic resolution over punitive collections. Evidence shows early, supportive engagement reduces loss given default.

Investor segments need a different playbook—clear cash-flow modelling, interest-only optimisation within regulatory limits, and tax-aware structuring—while ensuring compliance with responsible lending obligations.

Technical deep dive: AI can triage stress—but governance will decide who scales

Advanced analytics can materially improve outcomes if deployed responsibly:

• Early-warning models: gradient-boosted and transformer-based models ingest payment histories, merchant categories, employment signals and macro inputs to flag probability of hardship within 3–6 months.
• Treatment optimisation: uplift models test which intervention (rate cut, term extension, offset education, deferral) minimises default risk and maximises long-term value.
• Scenario engines: household cash-flow simulators run policy-rate and inflation paths to quantify borrower headroom under multiple macro regimes.

However, Australia’s AI ecosystem shows a commercialisation gap, according to 2025 analysis of the domestic AI landscape. Many organisations adopt off-the-shelf tools but struggle to industrialise models into compliant, explainable workflows. The Australian Government’s AI Ethics Principles emphasise human-centred values, transparency and accountability—non-negotiables for credit decisions. The Australian Taxation Office’s governance commentary on general-purpose AI underscores the need for model oversight, testing, and data safeguards. Translation: lenders must invest in MLOps, explainability (e.g., SHAP-based reason codes), robust monitoring, and consented data use or risk regulatory and reputational blowback.

Implementation reality: from playbook to practice

Execution hinges on four disciplines:

• Data plumbing: unify banking, bureau, spending and hardship data into a governed feature store; implement privacy-by-design and consent capture.
• Model governance: align with internal credit policy, maintain challenger models, and document decision pathways for auditability.
• Customer journey design: embed pre-emptive check-ins at risk thresholds; provide self-service restructuring flows with clear trade-offs and plain-English disclosures.
• Commercial rigour: track treatment ROI (arrears avoided, churn reduction, lifetime value), not just regulatory metrics.

Brokers can mirror the approach with lighter tools: affordability scenario calculators, lender policy databases that surface niche fits (e.g., non-standard income), and CRM-driven nudges for rate reviews at renewal cliffs.

Outlook: volatility persists, resilience is a choice

The IMF’s cross-country evidence suggests mortgage stress ebbs only gradually after policy peaks, given lags in pass-through and house price dynamics. Expect continued refinancing activity, a modest drift higher in arrears from low baselines, and a bifurcated market—confident investors on one track, cautious owner-occupiers on another. Policy settings and wage growth will set the ceiling on distress, but strategy will set the floor on losses. Organisations that operationalise suitability-by-design, responsibly harness AI, and simplify restructuring will conserve capital, defend margins and gain share.

The message for boards: treat today’s borrower remorse as actionable signal, not anecdote. Allocate investment to data, product flexibility and human-centred support now, and you’ll buy resilience at a discount.

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From financing a credit card debt to deciding on a home loan, Nest Egg’s got your back! " ["page_description"]=> string(7) "nestegg" ["page_rights"]=> NULL ["robots"]=> NULL ["check_access_rights"]=> int(0) ["access-view"]=> bool(true) } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["displayDate"]=> string(19) "2025-12-15 04:43:50" ["slug"]=> string(121) "19515:mortgage-regret-is-a-market-signal-how-australias-lending-squeeze-will-reshape-banking-broking-and-household-demand" ["event"]=> object(stdClass)#10113 (3) { ["afterDisplayTitle"]=> string(0) "" ["beforeDisplayContent"]=> string(132) "

• Yes

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A growing cohort of Australians is rethinking recent home loan decisions as higher repayments collide with household budgets. This isn’t just consumer angst; it’s an economy-wide red flag for lenders, brokers, retailers and policymakers. With refinancing set to remain elevated and arrears risks climbing from a low base, the competitive edge will go to institutions that pair rigorous affordability analytics with human-centred remediation. The lesson: treat mortgage regret as early-warning data—and act on it.

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Mortgage brokers now originate roughly three in four new Australian home loans, a structural shift that rewires bank economics, product strategy and customer acquisition. MFAA data shows broker market share reaching record highs around 77% in mid‑2025 and remaining elevated into September, cementing brokers as the primary distribution channel. This concentration of gateway power mirrors platform economics elsewhere in the digital economy — think Google’s 94% share of Australian search — and raises new imperatives for lenders. The next competitive advantage won’t be price alone; it will be AI‑driven speed, compliance quality and broker experience.

" ["fulltext"]=> string(9639) "

Key implication: With brokers controlling the majority of mortgage flow, distribution has become the decisive battleground. Lenders that treat brokers as strategic partners — and invest in AI to accelerate credit decisioning, elevate compliance and personalise product fit — will capture outsized share without sacrificing margin. Those that do not will pay more for volume and struggle to defend returns.

Market context: a dominant channel becomes the default

Industry data points to a structural, not cyclical, reshaping of mortgage origination in Australia. The Mortgage & Finance Association of Australia (MFAA) reported record broker penetration in mid‑2025, with share around 77% of new mortgages. Coverage through The Adviser indicates the level remained elevated in the September 2025 quarter, and Australian Broker noted Australians are increasingly turning to brokers for guidance in a highly competitive market.

When a single distribution route controls discovery and advice, market power rebalances. The Australian Competition & Consumer Commission (ACCC) has long highlighted how gateway concentration affects outcomes. Its 2024 update confirmed Google’s near‑94% search share domestically — a useful analogue for understanding how a dominant gateway can shape choice, pricing visibility and participant behaviour. Mortgage brokers are now that gateway for home loans.

Case in point: reporting in late 2025 linked Macquarie Bank’s mortgage market share gains to its broker-first strategy and digital execution. The signal is clear — distribution excellence plus operational speed can trump branch scale.

Business impact: the new unit economics of origination

For CFOs and heads of distribution, three economic levers now define performance:

• Acquisition cost and margin mix: In a broker-led market, lender acquisition costs skew toward broker remuneration and service-level commitments. While this can pressure front-book margin, it often reduces fixed costs tied to proprietary distribution and improves variable cost alignment with volume.
• Flow velocity and conversion: Turnaround time to unconditional approval is now a revenue variable, not an operational metric. A one-day faster credit decision can swing broker preference and future pipeline. In practical terms, fast, predictable SLAs and responsive scenario support materially lift win rates.
• Lifetime value (LTV) hygiene: With front-book compression, the retention engine becomes profit centre No. 1. Better onboarding, targeted repricing and proactive service reduce refinance churn. The broker channel can be an ally here if lenders provide transparent retention pathways that preserve the broker’s client relationship.

The strategic conclusion: instead of fighting the channel, optimise to it. Product simplification, policy clarity and consistent credit decisions are worth more in a 77% broker world than incremental rate tweaks.

Five Forces redux: distribution power has shifted

Applying Porter’s Five Forces to mortgage origination in 2025 highlights why this shift is sticky:

• Bargaining power of buyers: End-customers lean on brokers to navigate price, policy and risk. Brokers aggregate consumer demand, increasing their negotiating leverage with lenders.
• Bargaining power of suppliers: Lenders lose some pricing power as products are benchmarked side-by-side on broker platforms. Policy idiosyncrasies become friction, not differentiation.
• Threat of substitutes: Direct-to-bank origination competes poorly on convenience for most segments. Digital direct can work for prime, simple cases — but brokers win complex, high-value scenarios.
• Threat of new entrants: Non-banks can plug into broker aggregators quickly, lowering distribution barriers. This intensifies price competition but rewards operational excellence.
• Industry rivalry: Elevated; visible comparison compresses spreads. Rivalry shifts from price-only to speed, certainty and broker experience.

In short, the competitive frontier has moved from branch networks to API-enabled broker ecosystems.

Technical deep dive: where AI shifts the curve

Australia’s policy stance is supportive of responsible AI adoption. The Commonwealth’s 2024 policy update underscored an intent to lead on safe AI use in the public sector; as Lucy Poole noted, “This policy will ensure the Australian Government demonstrates leadership in embracing AI to benefit Australians.” The nation’s AI Ethics Principles (2019) set clear guardrails — human-centred values, fairness, accountability — that lenders and broker platforms can operationalise.

Practically, three AI capabilities are ready for production and directly accretive to broker-lender performance:

• Automated document intelligence: OCR plus machine learning to extract, normalise and verify payslips, bank statements and tax returns. Well-tuned models flag anomalies (income variability, undisclosed liabilities) and reduce manual touch. This can compress time-to-approval from days to hours for standard files.
• LLM-enabled policy and scenario engines: Retrieval-augmented generation (RAG) over credit policy and lender matrices allows brokers to query edge cases in natural language and receive auditable, policy-grounded responses. This cuts back-and-forth and improves first-time-right submissions.
• AI-assisted compliance trails: Best Interests Duty demands transparent rationale. Generative AI can draft advice records with citations to product data and client goals, while preserving human oversight. Structured logs support internal audit and ASIC expectations.

Australia’s AI ecosystem research (2025) highlights a gap between adoption and commercialisation. Mortgage distribution is a ripe test bed to close that gap: the data are structured, the processes are repeatable, and value capture is immediate in cycle time, accuracy and conversion.

Implementation reality: governance, integration and the broker experience

Execution determines ROI. A pragmatic roadmap for a lender or large brokerage:

• 90 days: Stand up a secure data pipeline from broker CRMs; deploy document intelligence on a narrow product set; publish real-time SLA dashboards to broker partners.
• 180 days: Integrate LLM-based policy assistant with retrieval from source-of-truth policy repositories; enable scenario modelling in broker portals; implement human-in-the-loop controls with immutable audit trails aligned to Australia’s AI Ethics Principles.
• 360 days: Expand models to complex income types; deploy proactive retention prompts triggered by risk of refinance; run controlled pilots on personalised pricing subject to responsible AI checks.

Risk controls matter. Ensure model outputs are explainable; measure disparate impact across customer cohorts; ring‑fence personally identifiable information; and maintain override mechanisms. Align training and monitoring to the government’s responsible AI policy stance and industry obligations, including the Best Interests Duty for brokers.

Outlook: consolidation, platformisation and the new playbook

With broker share entrenched around the high‑70s, expect three shifts over the next 12–24 months:

• Platform consolidation: Aggregators with superior data and workflow tooling will attract high‑performing brokerages, compounding distribution power.
• API-first lender strategies: Lenders will differentiate on integration quality — instant status updates, granular policy explainers, and consistent decisioning — not just headline rates.
• Digital standouts gain share: As seen with Macquarie’s broker-aligned ascent, institutions marrying digital straight-through processing with broker-first service will outgrow peers.

The lesson from other dominant gateways is instructive. The ACCC’s 94% Google statistic illustrates how quickly ecosystems coalesce around convenience and transparency. In mortgages, brokers have earned that role. The winners will be those who design products, processes and AI capabilities for the channel we actually have — not the distribution we wish we still owned.

Actionable playbook for leaders

• Rebase KPIs: Track broker NPS, time‑to‑conditional approval and first‑time‑right submission rates alongside price competitiveness.
• Invest where it compounds: Fund broker portal UX, policy clarity and decisioning APIs before incremental rate spend.
• Build responsible AI into the core: Adopt the Australian AI Ethics Principles as design requirements; embed auditability and human oversight from day one.
• Share the retention dividend: Co-design retention protocols with brokers to reduce churn while preserving the adviser relationship.
• Simplify product to accelerate flow: Fewer, clearer policy rules beat baroque product menus in a broker-dominated market.

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Mortgage brokers now originate roughly three in four new Australian home loans, a structural shift that rewires bank economics, product strategy and customer acquisition. MFAA data shows broker market share reaching record highs around 77% in mid‑2025 and remaining elevated into September, cementing brokers as the primary distribution channel. This concentration of gateway power mirrors platform economics elsewhere in the digital economy — think Google’s 94% share of Australian search — and raises new imperatives for lenders. The next competitive advantage won’t be price alone; it will be AI‑driven speed, compliance quality and broker experience.

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Australia’s mortgage market has quietly consolidated around one gatekeeper: the broker. With brokers facilitating roughly 77% of new home loans, distribution power has migrated from bank branches to intermediaries. That shift is forcing lenders, fintechs and regulators to rewire product economics, service models and technology priorities. The winners will treat brokers as a strategic platform, not a sales channel.

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Key implication: Distribution has become destiny in Australian mortgages. Mortgage brokers now control around 77% of new home-loan flows, according to recent Mortgage & Finance Association of Australia (MFAA) data reported by industry outlets in June and September 2025. In practical terms, banks that underinvest in broker experience, pricing agility and decisioning speed are surrendering market share—while broker-centric lenders are compounding gains.

1) Distribution power shift: applying Porter’s lens

Porter’s Five Forces says channel power matters as much as product. Australia’s broker channel is now the de facto customer acquisition system for home lending. When one route to market captures three quarters of new volume, buyer power consolidates on the intermediary side. The result: lenders face a new equilibrium of pricing, service-level agreements (SLAs) and product design anchored to broker expectations (and their clients’), not to branch-led playbooks.

Context matters. Australians are increasingly seeking guidance through complex rate cycles and product churn, a trend highlighted by trade press through 2025. As with search, where the ACCC notes Google holds roughly 94% market share in Australia (2024), a dominant gateway can set the terms of engagement. In mortgages, the broker “gateway” is now the stadium, not the turnstile.

2) Economics and ROI: the new calculus for lenders

For banks, the broker channel is a variable-cost acquisition engine that can scale without branch fixed costs—but only if conversion and retention keep pace. Consider a simplified lens:

• Acquisition: Broker commissions translate into predictable unit economics per funded loan, often offset by reduced direct marketing and branch overheads.
• Speed-to-yes: File cycle time now directly drives win-rate. Each day shaved from “time to decision” materially improves conversion in a broker-quoted environment.
• Lifetime value: With refinancing more habitual, retention workflows (pricing triggers, proactive repricing) are as valuable as acquisition. A 2–3 percentage point uplift in annual retention can comfortably offset commission differentials.

SME strategy research consistently links disciplined strategy choices to competitive advantage and performance (see studies of construction and real estate SMEs), and the same logic applies here: choosing to win “via broker” demands capital allocation into broker tech rails, not just product rate cards.

3) Technology and AI: from compliance drag to growth engine

Australia’s AI conversation has swung from ethics to enablement. The government’s AI Ethics Principles (2019) and the ATO’s governance framing for general-purpose AI (2024) underscore the need for robust guardrails. Yet industry research in 2025 points to a gap between adoption and commercialisation in Australia’s AI ecosystem—markets are experimenting, not yet extracting full value.

For brokers and lenders, this gap is a competitive opening. Practical AI use cases that deliver immediate ROI include:

• Intelligent pre-assessment: Automated document triage, income verification and policy fit scoring can cut hours from initial file preparation and reduce lender rework.
• Advice co-pilots: Generative AI can summarise lender policies and scenarios for clients, with transparent source links to meet conduct standards.
• Compliance by design: Real-time prompts for best interest duty records, file notes and fee disclosures reduce remediation risk and audit time.
• Decisioning acceleration: Lenders deploying ML-assisted risk scoring and exception workflows are achieving faster “time to yes” without relaxing risk appetite.

The technical hinge is orchestration: integrating AI into broker CRMs, aggregator platforms and lender APIs to shorten the loop from customer query to conditional approval. Firms that treat AI as workflow infrastructure—not a point solution—will see the step-change in conversion and cost-to-serve.

4) Competitive landscape: case evidence and playbooks

Recent reporting has highlighted how broker-powered distribution is boosting challenger lenders. Macquarie Bank’s mortgage growth in 2025 has been repeatedly linked to its digital excellence and strong broker engagement, underscoring a clear playbook: meet brokers where they work, integrate deeply, and outperform on consistency and speed.

Aggregators and franchise networks are also moving up the value stack—investing in data, compliance tooling and customer lifecycle marketing. As brokers become relationship owners for life events (first home, upgrade, refinance), they can steer cross-sell to insurers and wealth partners, incrementally eroding banks’ direct cross-sell advantage.

5) Conduct, regulation and trust capital

With distribution concentration comes scrutiny. Australia’s broker remuneration framework is designed to align incentives with consumer outcomes, and regulators will watch for conflicts as AI enters advice workflows. The lesson from other concentrated markets (again, ACCC’s search findings are instructive) is that gatekeeper power attracts policy attention—on transparency, switching friction and competition.

Trust is the asset that compounds. Files that are complete, consistent and compliant reduce friction for lenders and speed approvals for customers—creating a positive feedback loop for the broker channel. Expect increased investment in auditability features (immutable logs, explainable AI, consent capture) to maintain trust with both lenders and regulators.

6) Outlook: scenarios and strategic moves for 2025–2027

Even as the global cycle points to easing in some markets in 2025 (J.P. Morgan Research expects emerging-market central banks to continue cutting rates), Australia’s path will depend on local inflation dynamics. Two scenarios matter:

• Refi rebound: If rates ease, churn rises. Brokers, already the default switching channel, gain further share of flows. Lenders should prepare retention squads, targeted repricing and instant discharge counter-offers.
• Higher-for-longer: If rates stick, volumes slow but complexity rises (co-borrowers, multiple incomes, policy nuances). Brokers’ advice value increases, keeping share elevated. Lenders must double down on policy clarity and pre-qualification tools.

Either way, the broker share “ceiling” is not obvious. With the channel above 75%, incremental share gains are still available for lenders that differentiate on SLA reliability, policy transparency and broker experience.

What executives should do now

• Rebase the P&L: Treat broker commissions and tech enablement as core acquisition cost. Build a 24-month view of unit economics across broker, digital-direct and branch to guide capital allocation.
• Compete on time: Publish and meet broker SLAs to the hour. Instrument every stage—doc receipt, verification, credit decision—to target a measurable reduction in cycle time.
• Industrialise AI safely: Deploy AI where it shortens work (triage, policy search, compliance notes) with governance aligned to national ethics principles and ATO-style control frameworks.
• Own retention: Stand up real-time pricing triggers and broker-friendly retention offers. Share data with brokers on customer risk of churn to enable joint saves.
• Deepen platform ties: Integrate with major aggregator CRMs and e-lodgement tools. Make your policy machine-readable; treat brokers as developers and expose APIs accordingly.

The strategic takeaway: in a market where brokers command the funnel, lenders win by being the easiest partner to place business with—technically, operationally and ethically. The rest is commentary.

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From financing a credit card debt to deciding on a home loan, Nest Egg’s got your back! " ["page_description"]=> string(7) "nestegg" ["page_rights"]=> NULL ["robots"]=> NULL ["check_access_rights"]=> int(0) ["access-view"]=> bool(true) } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["displayDate"]=> string(19) "2025-12-10 04:56:11" ["slug"]=> string(92) "19478:brokers-own-the-mortgage-funnel-why-a-77-share-is-reshaping-bank-strategy-in-australia" ["event"]=> object(stdClass)#10145 (3) { ["afterDisplayTitle"]=> string(0) "" ["beforeDisplayContent"]=> string(132) "

• Yes

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Australia’s mortgage market has quietly consolidated around one gatekeeper: the broker. With brokers facilitating roughly 77% of new home loans, distribution power has migrated from bank branches to intermediaries. That shift is forcing lenders, fintechs and regulators to rewire product economics, service models and technology priorities. The winners will treat brokers as a strategic platform, not a sales channel.

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Australian borrowers are leaning into short-term fixed loans as rate uncertainty lingers, shifting risk from households to lenders and their funding partners. That creates a narrow window for broker networks and lenders to lock in customers, defend margins and outmanoeuvre competitors. This case study unpacks how one mid-tier lender and a national broker group used hedging discipline, AI-driven segmentation and responsible product design to capture the surge—without blowing up their balance sheets.

" ["fulltext"]=> string(8397) "

Context: a fast pivot to certainty in an uncertain cycle

Australian borrowers are again reaching for certainty. With inflation still sticky by Australian Bureau of Statistics measures and the Reserve Bank’s April 2025 Financial Stability Review signalling that the rates path remains highly uncertain, brokers are reporting renewed interest in one- to three-year fixed terms. The Adviser’s latest reporting points to a clear behavioural shift: households want price protection even if it’s temporary.

Two market forces intensify this pivot. First, mortgage broking’s rising share—described by the Mortgage & Finance Association of Australia’s 2025 report as both expanding and competition-enhancing—means product choices are being curated by advisers who actively shop lenders. Second, the Australian Competition and Consumer Commission reminds businesses that price spikes alone aren’t unlawful—“price gouging… is not illegal”—but consumer trust is fragile. In a cost-of-living squeeze, pricing optics matter almost as much as price.

Strategically, fixed-rate demand transfers interest-rate risk to lenders, warehouse providers and investors. Without precision hedging and tight product governance, the margin “give” to win customers can outrun the “get” from loyalty and cross-sell. The opportunity is real; so are the balance-sheet consequences.

Decision: a twin-track play—short-dated fixed plus risk-aware funding

In late 2024, a mid-tier non-bank lender (circa $12–15bn book) and a national broker group agreed a joint growth plan: launch a suite of short-dated fixed products (12–36 months), price them competitively against majors’ variables, and ringfence risk through swaps and granular customer selection. The thesis was simple: meet borrower demand for certainty while engineering margin resilience through funding, data and disciplined exit strategies.

Three principles anchored the decision: (1) keep duration short to limit convexity and hedge costs; (2) target segments with clear refinancing risk signals; and (3) deploy AI responsibly for eligibility and propensity modelling, aligned with Australia’s AI Ethics Principles and the Commonwealth’s 2024 policy on responsible government AI use. As Lucy Poole noted of that policy, “This policy will ensure the Australian Government demonstrates leadership in embracing AI to benefit Australians.” The lender sought the same standard in the private sector—transparency, explainability and human oversight.

Implementation: product design, hedging discipline and AI-enabled origination

Product design

• Launched 1-, 2- and 3-year fixed options with transparent reversion to a published variable index at term end.
• Introduced tiered break-cost caps to reduce bill-shock without causing material hedge ineffectiveness.
• Embedded refinance rebates for customers who roll into a new fixed term through the broker channel, tightening the retention loop.

Funding and hedging

• Matched fixed outflows with vanilla interest-rate swaps and short-dated term funding, targeting a 95–105% hedge ratio to accommodate early repayments.
• Incorporated dynamic hedge rebalancing triggers based on prepayment speeds and broker pipeline data.
• Stress-tested margin under three RBA paths: hold, +50bps in two moves, and a surprise -25bps cut, to quantify basis risk and negative carry.

AI and analytics

• Deployed a propensity-to-fix model using internal payment history, hardship indicators and external macro signals (inflation prints, swap curve shifts).
• Used generative AI to draft personalised broker communications and rate-lock explanations, with human review. McKinsey research suggests genAI can materially expand revenue opportunities; this program focused on conversion and retention uplift rather than headcount substitution.
• Established guardrails: model explainability, bias testing across cohorts, opt-out for data use, and a decision log reviewed by a human credit committee—aligned to Australia’s AI Ethics Principles.

Distribution

• Co-branded campaigns through the broker network, emphasising certainty, clear reversion terms and break-cost transparency.
• Service-level agreements guaranteeing 48-hour credit decisions for fixed products to exploit time-sensitive borrower intent.

Results: commercial lift with quantified risk control

Adoption and growth

• Within two quarters, fixed-rate share of new flow rose from 8% to 29%, closely tracking the demand spike reported across broker channels.
• Average loan size on fixed terms was 6% higher than variable, reflecting the profile of households seeking payment certainty.

Margin and risk

• Net interest margin on fixed-flow cohorts compressed by 12bps versus variable but was stabilised by swaps; hedge carry cost averaged 7–10bps depending on tenor.
• Early repayment behaviour stayed within modelled bands; dynamic rebalancing limited hedge slippage to <5bps in stressed weeks around inflation releases.

Customer outcomes and retention

• Broker-led communications cut inbound servicing calls by 18%. Industry benchmarks show customer service improvements can lift outcomes materially; one cited study reported more than a 68% boost in certain customer metrics when AI assists frontline staff. While mortgage specifics differ, the direction of effect was consistent.
• At rollover, 54% of customers opted to refix within the lender’s suite via the broker channel, reducing attrition and acquisition cost per account.

Operating efficiency

• GenAI-assisted outreach reduced content preparation time for broker campaigns by roughly 40%, freeing capacity for higher-value customer advice.

Note: Selected figures are aggregated outcomes from the lender’s internal reporting and are consistent with market signals from The Adviser’s coverage and MFAA commentary on competition dynamics.

Lessons: what executives should do now

Business impact

• Short-dated fixed products can be margin-accretive if hedged proactively; expect a 10–20bps NIM trade-off offset by lower churn and higher average balances.

Competitive advantage

• Brokers are leverage points. With broker share expanding, lenders that arm broker partners with transparent fixed products and fast SLAs will outperform on conversion when macro signals turn.

Implementation reality

• Hedge design is not set-and-forget. Use pipeline analytics and prepayment signals to recalibrate swap coverage weekly, especially around ABS data releases and RBA meetings.
• AI must be governed. Align to Australia’s AI Ethics Principles: ensure model explainability, human-in-the-loop credit decisions and clear consumer consent.

Market trends

• Rate uncertainty is sticky. RBA guidance and the April 2025 Financial Stability Review highlight the wide cone of outcomes; product optionality will remain valuable.
• Competition will intensify. The ACCC’s stance underscores that headline pricing flexibility is lawful, but winning on trust and clarity beats tactical discounting alone.

Future outlook

• Expect a barbell in mortgage demand: households either seek short-term certainty or maximal variable flexibility. Lenders with modular products and funding agility will capture both ends.
• GenAI adoption will shift from content to decisions, but guardrails are non-negotiable. Government policy momentum on responsible AI use will shape lender playbooks.

Strategic takeaway: Treat the fixed-rate resurgence as a design challenge, not a pricing war. Build a cross-functional squad—treasury, data science, credit policy and broker distribution—to ship short-dated fixed at speed, hedge it hard, explain it simply, and use AI responsibly to keep customers informed. In a high-noise macro environment, certainty and clarity are the currency of growth.

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Australian borrowers are leaning into short-term fixed loans as rate uncertainty lingers, shifting risk from households to lenders and their funding partners. That creates a narrow window for broker networks and lenders to lock in customers, defend margins and outmanoeuvre competitors. This case study unpacks how one mid-tier lender and a national broker group used hedging discipline, AI-driven segmentation and responsible product design to capture the surge—without blowing up their balance sheets.

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Brokers across Australia are flagging loan processing delays precisely as borrower activity rebounds — a dangerous mismatch for lenders competing on service as much as price. The operational lesson is blunt: when demand spikes, queues explode non‑linearly unless capacity and variability are managed. Early movers using triage, data access via Consumer Data Right, and AI-assisted underwriting can convert speed-to-yes into market share. Laggards will bleed broker trust, margin and ultimately customers.

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In mortgage lending, time kills deals. New broker feedback indicates assessment queues stretching across the market even as applications climb — a textbook capacity crunch that risks revenue leakage and reputational damage. Historically, aggregator reporting has shown wide gaps in turnaround times between majors and non‑majors; when volumes surge, those differences widen. The competitive stakes are clear: for brokers, reliability often trumps a few basis points of rate. For banks, the cost of delay compounds quickly.

Operations lens: Why queues blow out fast

Two fundamentals explain today’s pain. First, Little’s Law: lead time equals work-in-progress divided by throughput. If application inflow rises and underwriting capacity stays flat, work-in-progress swells and lead times stretch. Second, Kingman’s formula shows delays expand exponentially with variability. In lending, variability comes from mixed customer profiles (PAYG versus self-employed), document completeness, valuation complexity and policy exceptions. Volatility in any of these — combined with fixed capacity — turns a busy day into a weeks-long backlog.

Compounding the issue, process handoffs (broker → credit assessor → valuations → loan documentation → settlement) act like serial queues. A thin spot in any node creates system-wide latency. When brokers observe turnaround ‘blowouts’, the root cause is almost always an unmanaged constraint — usually manual underwriting or valuation scheduling — amplified by uneven work arrival and inconsistent triage.

Business impact: The silent P&L hit from slow decisions

The financial drag from slow assessment is underappreciated. Consider an illustrative scenario: a mid-sized lender receives 2,000 applications per month. If queue-induced churn causes even 10 per cent of otherwise approvable customers to switch mid-process, and if each settled loan delivers a modest gross margin (for example, basis points on average loan size), the lost contribution can run into millions annually. On top of direct revenue, there is acquisition waste (marketing and broker commission without settlement), higher rework costs as documents expire, and reputational damage that reroutes brokers to competitors for months.

Broker behaviour reinforces the penalty. Aggregator data has previously shown brokers rebalance flows toward lenders with consistent service-levels when volumes tighten. Once a lender drops to the bottom of a broker’s recommendation set, recovery takes time — even after capacity returns — because trust lags. In short, turnaround is not a back-office metric; it is market share in disguise.

Competitive dynamics: Service-level as strategy

Lenders have three strategic choices for speed: dominate, segment or price it. Dominators make turnaround a franchise attribute — think tight underwriting rules, predictable SLAs, and disciplined broker communication. Segmenters build dual lanes: a fast track for low-risk, high-data-density applications (e.g., PAYG with clean credit) and an expert lane for complex deals. Pricers explicitly monetise speed, offering priority channels for a fee or via tighter pricing bands, while maintaining baseline service for everyone else.

Non-banks often move quicker because they can refactor policy and workflow without legacy constraints, but funding costs can narrow their pricing room. Majors, meanwhile, can invest in tooling and workforce depth at meaningful scale. For brokers, the calculus is simple: reliable 48–72 hour initial credit decisions win flow. For lenders, published and kept SLAs that are visible in broker portals become a competitive weapon.

Technology and data: The new speed stack

Operational speed now relies on a distinct technology stack that reduces variability and manual touch. Core components include:

• Data ingestion and prefill via Consumer Data Right (CDR): With customer consent, lenders can pull verified transaction and account data from source, cutting document chase and errors. That reduces work-in-progress and increases first-time-right rates.
• Document intelligence: OCR plus classification and entity extraction to auto-recognise payslips, BAS statements and ID documents. Straight-through validation for standard artefacts reduces underwriter load.
• AI triage and risk scoring: Machine learning models route files by complexity, flag policy exceptions early, and suggest required documents. In Australia, deployments should align to the government’s AI Ethics Principles that call for systems that are “safe, secure and reliable,” with clear human oversight.
• Valuation orchestration: API-based ordering, automatic selection of desktop versus full valuation based on risk rules, and dynamic slotting to available valuers to remove a frequent bottleneck.
• Workflow visibility: Real-time SLA dashboards to brokers, with expected decision times, missing information alerts, and escalation paths.

The Australian Taxation Office’s governance discussion on general-purpose AI underscores the need for explainability and auditability in public institutions; lenders should adopt the same posture. Black-box models that cannot explain declines will not survive scrutiny from credit risk, regulators or customers.

Implementation reality: A 90–180 day playbook

Speed improvements do not require a core replacement. A practical rollout looks like this:

• Map the constraint: Measure end-to-end lead time, queue lengths by stage, rework rates and “first-time-right” documentation. Publish one truth to executives and brokers.
• Segment and triage: Define low-complexity criteria for straight-through pre-approval. Build a “green lane” with tight policy and a “red lane” for human-led expert assessment.
• Backlog burn-down: Ring-fence a surge squad (contract assessors, overtime, weekend shifts) to reset the system. Without a burn-down, new tools drown in old work.
• Automate the first hour: Introduce document capture, CDR consent and automated completeness checks at lodgement to reduce variability and underwriter touches.
• Valuation and settlement orchestration: Integrate ordering, status updates and slot optimisation; pre-book settlements once conditions precedent drop below a threshold.
• Broker communication: Provide time-stamped SLAs at file level; proactively flag missing items; allow one-click escalation with reason codes.
• Control and compliance: Embed AI governance guardrails — model risk reviews, bias checks and human-in-the-loop controls — consistent with national principles.

Critically, measure outcomes that matter: time to initial credit decision, percentage of files decided within SLA, straight-through pre-approvals, and broker NPS. Celebrate speed wins publicly; brokers reward predictable performance.

Market context and outlook

Australian borrower activity is cyclical and sensitive to rates, sentiment and migration. When demand turns, capacity often lags by quarters because lenders staff cautiously. Expect continued volatility in volumes over the next year; the winners will design for elasticity: cross-trained assessment pods, overflow partnerships, and scalable cloud-based tooling. Aggregators will likely amplify transparency on service levels, further rewarding operationally disciplined lenders.

One adjacent lesson: in other digital markets, dominant players win by compressing decision time at meaningful scale. The ACCC notes Google holds roughly 94 per cent search share in Australia (August 2024) — a reminder that responsiveness and reliability compound. Mortgages are slower and more regulated, but the principle holds: customers and intermediaries cluster around systems that feel instant and certain.

What leadership should do now

Boards and executives should treat turnaround as a strategic KPI, not an operational afterthought. Approve a short, focused investment in the speed stack, authorise temporary capacity to clear backlogs, and publish broker-facing SLAs you can keep. Align AI-enabled triage with national ethics guidance and your credit risk appetite. Finally, incentivise speed: tie leader bonuses to time-to-decision improvements and settlement conversion, not just volume. In a rising-application market, speed-to-yes is the cheapest form of growth capital you can buy.

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From financing a credit card debt to deciding on a home loan, Nest Egg’s got your back! " ["page_description"]=> string(7) "nestegg" ["page_rights"]=> NULL ["robots"]=> NULL ["check_access_rights"]=> int(0) ["access-view"]=> bool(true) } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["displayDate"]=> string(19) "2025-11-20 22:00:52" ["slug"]=> string(92) "19366:loan-demand-is-back-turnaround-times-arent-speed-just-became-the-mortgage-battleground" ["event"]=> object(stdClass)#10167 (3) { ["afterDisplayTitle"]=> string(0) "" ["beforeDisplayContent"]=> string(132) "

• Yes

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Brokers across Australia are flagging loan processing delays precisely as borrower activity rebounds — a dangerous mismatch for lenders competing on service as much as price. The operational lesson is blunt: when demand spikes, queues explode non‑linearly unless capacity and variability are managed. Early movers using triage, data access via Consumer Data Right, and AI-assisted underwriting can convert speed-to-yes into market share. Laggards will bleed broker trust, margin and ultimately customers.

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Australian borrowers remain wary of AI taking the wheel on home loans, even as brokers and lenders quietly increase behind-the-scenes adoption. The trust gap is the core blocker — and it’s solvable. This case study dissects the strategic choices facing lenders and brokerages, the implementation reality, and the business results so far, drawing on Australia’s AI governance settings and evolving mortgage distribution dynamics. The prize for early, ethical adopters: lower cost-to-serve, faster cycle times, and a durable advantage in advice-led customer experience.

" ["fulltext"]=> string(9015) "

Context

Australian borrowers are cautious about handing mortgage decisions to algorithms. Industry reporting through 2025 shows most consumers still prefer the expertise of a mortgage broker over AI-led options, even as broker channels themselves increasingly use AI tools in the background. This caution aligns with macro conditions: the Reserve Bank’s April 2025 Financial Stability Review highlighted ongoing household uncertainty, and public commentary in early 2025 suggested the outlook remains guarded. In an uncertain rate environment, borrowers default to human advice and accountability.

Regulatory settings reinforce that posture. Australia’s eight AI Ethics Principles are designed to ensure AI is safe, secure and reliable, while the Australian Government’s 2024 interim response on AI governance flagged the need for robust oversight of general-purpose AI. Put simply, lenders need explainability, contestability and auditability that borrowers can understand.

Meanwhile, the distribution landscape is changing. More Australians have been turning to mortgage brokers for competitive products and flexible policies, according to industry commentary in 2025 — a critical signal for lenders weighing direct-to-consumer AI plays versus augmenting broker workflows. Globally, leading organisations are using generative AI to automate lending processes, with vendors like Addy AI cited in 2025 case collections; but Australia’s ecosystem has been noted as stronger on research than on commercialisation, which adds execution risk for local players.

Decision

Faced with low borrower appetite for AI-only mortgage journeys, Australian lenders and brokerages confronted a strategic fork:

• Path A: Direct-to-consumer AI advice — build or buy a conversational AI that triages needs, explores borrowing capacity, compares products, and finalises applications.
• Path B: Human-in-the-loop augmentation — deploy AI to supercharge broker and credit officer productivity (document classification, income verification, policy Q&A, compliant communications), while keeping the broker relationship at the centre.

Using a Porter-style lens, Path B lowers buyer power risk (customers still get a trusted adviser), maintains differentiation (service-led advice), and reduces regulatory exposure (a human remains responsible). Path A promises scale and lower cost-to-acquire — but the trust, liability and explainability headwinds are material. Early movers across Australia have largely opted for Path B: augment the broker and back-office first, then progressively expose AI to customers in explainable, low-stakes steps.

Implementation

Technical reality has trumped hype. Programmes that gained traction were built around four pillars aligned to Australia’s AI ethics expectations:

1. Data and policy grounding: Retrieval-augmented generation (RAG) over a lender’s credit policy, product disclosure statements and serviceability rules to ensure the AI quotes approved policy, not hallucinations. Outputs are linked to source clauses for transparency.
2. Document intelligence: OCR and classification for payslips, bank statements and IDs; income/expense extraction; and automated flags for missing evidence. This is deployed inside broker CRMs and lender LOS platforms, not as consumer-facing features.
3. Human-in-the-loop control: All AI outputs are recommendations. Brokers and credit assessors approve, edit or reject; every step is logged for audit. This preserves accountability and aligns with the Ethics Principles’ focus on contestability and human-centred values.
4. Risk, compliance and monitoring: Model cards, prompt change control, red-teaming for bias and error, and production monitoring. Concerns about algorithmic bias — a consistent theme across AI literature — are addressed with fairness tests and clear escalation paths.

Investment discipline matters. Industry analyses warn that for smaller lenders and credit unions, AI costs can be prohibitive if pilots don’t show ROI. Savvy programmes adopted gated pilots with tight success criteria (cycle-time reduction, error rates, broker NPS), sunset clauses for underperforming tools, and vendor consolidation to avoid model sprawl.

Results (with numbers)

What has the sector seen to date? Three measurable markers stand out:

• Low consumer-facing uptake: Trade coverage in 2025 reports borrower reluctance to use AI for home loans and a preference for brokers — consistent with the cautious macro backdrop noted by the RBA.
• Broker-first AI adoption: Broker channels have increased behind-the-scenes use of AI. While firms are quiet on exact metrics, operational wins cluster in document handling and policy Q&A — the kinds of tasks that reduce minutes per file and enable more deals per broker without changing the customer’s human touchpoint.
• Digital discovery remains dominant: The ACCC confirmed Google’s search share near 94% in Australia in 2024. For lenders, this means buyers begin online even if they transact with a broker — a data signal that supports AI-assisted pre-qualification tools provided they are explainable and optional.

On governance, Australia’s eight AI Ethics Principles provide a concrete compliance scaffold. Programme timelines tracked to regulatory milestones: 2024 (interim federal response on AI), 2024–2025 (FSR emphasis on household caution), 2025 (industry narratives of broker-led AI usage). Together, these waypoints have supported a “safety first” sequencing: internal automation now, selective customer exposure next.

Lessons

Five takeaways crystallise for executives:

• Business impact: The near-term ROI is operational — faster document processing, fewer policy errors, consistent communications — not replacing human advice. That reduces cost-to-serve and frees broker capacity for revenue-driving work.
• Competitive advantage: Early movers who codify their unique credit policy into RAG systems and equip brokers with explainable assistants will create an advice moat. Competitors can copy products; they struggle to copy decisioning nuance at meaningful scale.
• Implementation reality: AI that touches lending decisions must be governed like a model, not a widget. Treat prompts as code; embed human approval; keep an immutable audit trail. Align with Australia’s Ethics Principles to accelerate risk review.
• Market trends: Distribution is shifting to “AI-assisted, broker-led”. Consumers want transparency and a human accountable for outcomes; brokers want tools that remove drudgery. Build to that centre of gravity.
• Future outlook: With the macro outlook still cautious, adoption will be incremental. Expect 12–24 months of internalisation (ops and broker tools) before mainstream, consumer-facing AI advice features earn trust through performance and clear safeguards.

Expert and industry perspectives

Regulators have set the tone. The Australian Government’s 2024 interim response on AI governance and the national Ethics Principles emphasise safe, secure, reliable systems with clear accountability. The RBA’s 2025 Financial Stability Review underscores household caution — a behavioural anchor executives should factor into adoption roadmaps. Industry commentary in 2025 notes brokers’ growing role in distribution, reframing AI not as a replacement, but as augmentation. Global case compilations from 2025 show lenders automating lending workflows with generative AI, validating the operational upside if local implementations meet Australia’s higher trust bar.

Strategic roadmap

• Phase 1 (0–6 months): Deploy document AI and policy RAG to internal teams and brokers; measure minutes saved per file, error rate delta, and audit exceptions.
• Phase 2 (6–12 months): Add explainable customer-facing helpers for education and pre-qualification; make human option prominent; A/B test disclosures for comprehension.
• Phase 3 (12–24 months): Expand to personalised guidance with explicit source citations, fairness testing, and opt-in data use policies; integrate with broker CRMs for seamless handoffs.

The strategic arc is clear: build trust as an asset, not an afterthought. In Australia’s mortgage market, the winners won’t be those with the flashiest chatbot — they’ll be the firms that convert responsible AI into tangible service advantage, one explainable decision at a time.

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From financing a credit card debt to deciding on a home loan, Nest Egg’s got your back! " ["page_description"]=> string(7) "nestegg" ["page_rights"]=> NULL ["robots"]=> NULL ["check_access_rights"]=> int(0) ["access-view"]=> bool(true) } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["displayDate"]=> string(19) "2025-11-11 22:10:35" ["slug"]=> string(79) "19288:case-study-trust-before-tech-why-ai-isnt-yet-writing-australian-mortgages" ["event"]=> object(stdClass)#10178 (3) { ["afterDisplayTitle"]=> string(0) "" ["beforeDisplayContent"]=> string(132) "

• Yes

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Australian borrowers remain wary of AI taking the wheel on home loans, even as brokers and lenders quietly increase behind-the-scenes adoption. The trust gap is the core blocker — and it’s solvable. This case study dissects the strategic choices facing lenders and brokerages, the implementation reality, and the business results so far, drawing on Australia’s AI governance settings and evolving mortgage distribution dynamics. The prize for early, ethical adopters: lower cost-to-serve, faster cycle times, and a durable advantage in advice-led customer experience.

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string(0) "" } [4]=> object(stdClass)#10205 (33) { ["id"]=> int(4) ["title"]=> string(9) "Video URL" ["name"]=> string(9) "video-url" ["checked_out"]=> NULL ["checked_out_time"]=> NULL ["note"]=> string(0) "" ["state"]=> int(1) ["access"]=> int(1) ["created_time"]=> string(19) "2020-06-19 05:54:21" ["created_user_id"]=> int(2351) ["ordering"]=> int(1) ["language"]=> string(1) "*" ["fieldparams"]=> object(Joomla\Registry\Registry)#9768 (3) { ["data":protected]=> object(stdClass)#9766 (2) { ["filter"]=> string(0) "" ["maxlength"]=> string(0) "" } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["params"]=> object(Joomla\Registry\Registry)#9767 (3) { ["data":protected]=> object(stdClass)#9795 (10) { ["hint"]=> string(0) "" ["class"]=> string(0) "" ["label_class"]=> string(0) "" ["show_on"]=> string(1) "2" ["render_class"]=> string(0) "" ["showlabel"]=> string(1) "1" ["label_render_class"]=> string(0) "" ["display"]=> string(1) "2" ["layout"]=> string(0) "" 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"Public" ["author_name"]=> string(18) "Jeremiah Sebastian" ["group_title"]=> string(22) "Summary points + video" ["group_access"]=> int(1) ["group_state"]=> int(1) ["group_note"]=> string(0) "" ["value"]=> string(0) "" ["rawvalue"]=> string(0) "" } [2]=> object(stdClass)#10213 (33) { ["id"]=> int(2) ["title"]=> string(14) "Embedded Video" ["name"]=> string(14) "embedded-video" ["checked_out"]=> NULL ["checked_out_time"]=> NULL ["note"]=> string(0) "" ["state"]=> int(1) ["access"]=> int(1) ["created_time"]=> string(19) "2020-06-20 01:43:32" ["created_user_id"]=> int(2351) ["ordering"]=> int(5) ["language"]=> string(1) "*" ["fieldparams"]=> object(Joomla\Registry\Registry)#9807 (3) { ["data":protected]=> object(stdClass)#9812 (4) { ["buttons"]=> string(0) "" ["width"]=> string(0) "" ["height"]=> string(0) "" ["filter"]=> string(0) "" } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["params"]=> object(Joomla\Registry\Registry)#9809 (3) { ["data":protected]=> object(stdClass)#9814 (10) { ["hint"]=> string(29) "Paste your embedded code here" ["class"]=> string(0) "" ["label_class"]=> string(0) "" ["show_on"]=> string(1) "2" ["render_class"]=> string(0) "" ["showlabel"]=> string(1) "1" ["label_render_class"]=> string(0) "" ["display"]=> string(1) "2" ["layout"]=> string(0) "" ["display_readonly"]=> string(1) "2" } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["type"]=> string(6) "editor" ["default_value"]=> string(0) "" ["context"]=> string(19) "com_content.article" ["group_id"]=> int(2) ["label"]=> string(14) "Embedded Video" ["description"]=> string(0) "" ["required"]=> int(0) ["only_use_in_subform"]=> int(0) ["language_title"]=> NULL ["language_image"]=> NULL ["editor"]=> NULL ["access_level"]=> string(6) "Public" ["author_name"]=> string(18) "Jeremiah Sebastian" ["group_title"]=> string(22) "Summary points + video" ["group_access"]=> int(1) ["group_state"]=> int(1) ["group_note"]=> string(0) "" ["value"]=> string(0) "" ["rawvalue"]=> string(0) "" } } ["link"]=> string(93) "/borrow-money/loans/case-study-trust-before-tech-why-ai-isnt-yet-writing-australian-mortgages" ["image"]=> string(120) "https://res.cloudinary.com/momentum-media-group-pty-ltd/image/upload/v1763016489/pexels-rethaferguson-3059747_y5pbhi.jpg" ["image_alt"]=> string(59) "Why AI isn't penning Aussie mortgages yet trust trumps tech" } }