Borrow

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In an evolving mortgage landscape where algorithms are levelling the playing field, Australian mortgage brokers are finding that trust, rather than price or speed, is becoming their most valuable asset. As artificial intelligence (AI) accelerates the commoditisation of mortgage offerings, the ability to systematise trust could determine market leadership.

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The Mortgage & Finance Association of Australia (MFAA) reports that the broker channel now accounts for over 70% of new Australian home loans, underscoring the vital role brokers play in guiding borrowers through complex financial decisions. However, the challenge remains: how can brokers operationalise trust in an industry facing regulatory pressure, shifting demographics, and AI disruption?

The economics of trust

The growing dominance of the broker channel highlights a key market reality: borrowers are seeking trusted advocates. Despite this, trust in financial services remains fragile, as evidenced by the Edelman Trust Barometer, which consistently ranks the sector below others. For brokerages, this presents a strategic opportunity to transform trust from a mere promise into a measurable performance system.

"There’s solid economics behind it," explains an industry expert. "Bain & Company has shown that even modest increases in customer retention can lead to disproportionately large profit gains across industries. In broking, where lifetime value depends on refinancing, cross-selling, and referrals, a trust-led model can significantly enhance returns."

Operating model: Trust-by-design teams

The traditional model of the lone hero broker is giving way to specialised teams comprising advisers, loan processors, compliance officers, and marketers. This shift is not just about efficiency but also about reliability. Clear roles within these teams reduce errors, shorten cycle times, and create auditable trails that both regulators and clients can trust.

"Defined service levels and transparent file notes are crucial," says a brokerage leader. "Proactive client updates at set milestones and clear documentation of decisions and product comparisons strengthen the Best Interests Duty evidence trail."

To track the metrics that matter, brokerages are focusing on indicators such as pull-through rates, average days to decision, rework rates, complaint response times, and post-settlement referral rates. These metrics serve as reliability signals that clients appreciate and regulators respect.

AI: A double-edged sword

Artificial intelligence is reshaping back offices and client interfaces. Technologies like optical character recognition and natural language processing are used to classify statements, calculate income, and flag anomalies. Generative AI is being employed to draft lender cover notes, highlight policy nuances, and personalise client communications.

"McKinsey estimates that generative AI could deliver hundreds of billions in annual value to global banking through productivity gains and enhanced decision-making," notes a technology analyst. "Mortgage workflows are squarely in scope."

However, AI also presents risks. It is crucial to keep humans in the loop for credit-critical judgments, use AI for preparation rather than final decisions, and adopt model governance practices. "Explainable outputs are essential," the analyst adds. "Clients need to understand the basis for income calculations, expense categorisation, and policy matches."

Sustainable and ethical lending

As values-driven decisions become more prevalent, particularly among younger cohorts, brokers have the opportunity to differentiate themselves through sustainable and ethical lending options. Green home loans and energy-upgrade financing are becoming mainstream, supported by initiatives from the Clean Energy Finance Corporation and major banks.

"Brokers who can translate sustainability features into lender policy and pricing advantages convert purpose into performance," says an industry commentator. "Mapping lenders’ green products and disclosing trade-offs clearly reinforces trust."

Next-gen clients and regulatory compliance

Millennial and Gen Z borrowers expect real-time status visibility, digital documents, and transparent communication about costs. They seek the convenience of self-service combined with expert escalation when needed. This requires brokers to offer client portals with live application tracking, secure messaging, and consent management.

"Trust is a function of predictability and clarity," says a customer experience specialist. "Plain-English disclosures of broker remuneration and lender rebates, along with service personalisation, are key."

In the regulatory arena, Australia’s Best Interests Duty and Design & Distribution Obligations make advice quality and documentation non-negotiable. Leading firms are leveraging compliance as a proof point, using decision logs, product comparison matrices, and client-file artefacts to serve clients, regulators, and the business simultaneously.

The competitive landscape and future outlook

As brokers capture more market share, lenders are responding with faster direct channels and retention strategies. Aggregators and franchise groups are investing in proprietary CRMs, workflow engines, and data layers to secure scale advantages. The competitive frontier is evolving into a platform game, where the ability to orchestrate lenders, data, and client experience while maintaining trust will be crucial.

In the coming years, trust is expected to become increasingly quantifiable, with verified client reviews, auditable advice trails, and standardised product-comparison artefacts becoming standard. For brokers, the strategy is clear: make trust your operating system, not just your slogan.

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In an evolving mortgage landscape where algorithms are levelling the playing field, Australian mortgage brokers are finding that trust, rather than price or speed, is becoming their most valuable asset. As artificial intelligence (AI) accelerates the commoditisation of mortgage offerings, the ability to systematise trust could determine market leadership.

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Australians under cost‑of‑living pressure are sidestepping hardship help because they fear a permanent stain on their credit file. Arca’s CreditSmart initiative has thrust this misconception into the spotlight, forcing lenders, brokers and policymakers to rethink how hardship is communicated and delivered. This case study examines the decision-making, execution and measurable business impact behind a quieter but consequential shift in Australian credit practice. The prize for early movers: lower losses, stronger customer retention and regulatory goodwill.

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Context: Rising financial strain meets a trust deficit

Since May 2022, the Reserve Bank has lifted the cash rate by 425 basis points, dramatically increasing borrowing costs. Inflation peaked near 7–8% before moderating, but household budgets remain tight. In this environment, many borrowers who could benefit from temporary relief are not asking for it, largely due to misconceptions about how hardship help appears in their credit history.

Arca’s CreditSmart initiative spotlights a persistent issue: consumers believe seeking help automatically damages their credit standing. Under Australia’s credit reporting framework, financial hardship information (FHI) can be recorded when a borrower and lender agree to relief. Industry guidance indicates FHI is intended to contextualise repayment history during a support period rather than penalise credit scores. Yet the nuance is poorly understood, fuelling avoidable arrears and complaints.

Market signals reinforce the urgency. Lenders report higher early‑stage delinquencies as fixed‑rate “cliffs” roll off, and consumer advocates note rising hardship‑related disputes. The regulatory vector is clear: ASIC and AFCA expect proactive, fair support for customers in difficulty, in line with responsible lending obligations and evolving expectations of good customer outcomes.

Decision: Standardise the story and simplify the journey

CreditSmart catalysed a sector‑wide decision to close the information gap. The strategy hinged on two choices:

• Reframe hardship as a stabilisation tool, not a stigma. Use plain language to explain that when relief is in place and payments are made as agreed, the credit record shows context, not simply missed payments. Remove emotive terms (“black mark”) from customer communications.
• Move from passive disclosure to proactive engagement. Rather than bury hardship links on websites, leading lenders and brokers are surfacing offers through mobile apps, outbound messaging, and broker scripts—before accounts roll 30+ days past due.

Not all institutions moved at the same pace. Some favoured highly controlled messaging to avoid over‑utilisation risk; others adopted a human‑centred design approach to reduce friction and stigma. Fintechs seized the gap with self‑serve hardship modules, affordability checks and consented data‑sharing to speed decisions.

Implementation: The operating model behind credible hardship support

Execution has been as much about governance and analytics as it is about words on a webpage:

• Plain‑English templates and journey mapping. Rewrite hardship pages, emails and app prompts at Year‑9 reading level. Test comprehension with customers who have never sought assistance.
• In‑app pre‑eligibility and fast lanes. Offer two‑to‑five question screening to route customers to the right option (payment pause, reduced instalment, term extension). Enable upload of basic evidence or consented data pulls to avoid paper chases.
• Contact centre and broker enablement. Micro‑training and talk‑tracks to explain FHI and set expectations about what happens on a credit file, supported by decision trees and escalation paths.
• Data controls and credit reporting accuracy. Tighten processes for recording the two common FHI categories (temporary arrangements and contract variations), ensure accurate start/stop dates, and reconcile with repayment history information.
• Early‑warning analytics. Use transaction data and behavioural triggers (missed utility bills, reduced discretionary spend, payment reversals) to prompt opt‑in outreach before arrears deepen.

Technical deep‑dive: How hardship appears in credit files

Two features matter for executives:

• Context not conjecture. FHI is displayed alongside repayment history for a limited period to indicate that payments during the arrangement were made under an agreed plan. It does not disclose sensitive personal reasons and is designed to prevent routine missed‑payment markers from misrepresenting the period of support.
• Scoring and decisioning. Industry guidance indicates credit reporting bodies do not design scores to penalise the mere presence of FHI. However, lenders may incorporate the information in underwriting or collections strategies. The operational takeaway: quality and timeliness of reporting are essential to avoid unintended decline logic.

For boards, the compliance‑risk equation is straightforward: clear disclosures, accurate credit reporting, and fair hardship assessment reduce AFCA exposure and reputational risk under heightened regulatory scrutiny, including the spirit of the UK‑style “fair outcomes” trend.

Results: The economics of getting hardship right

While sector‑wide outcome data is still emerging, the business case can be modelled conservatively to illustrate impact:

• Arrears containment. If proactive hardship outreach reduces roll rates from 30–59 days past due to 90+ days by just 15% across a $50bn mortgage book (assuming 1.5% of balances are at‑risk), that prevents roughly $112.5m from migrating to late‑stage delinquency at any point in time (0.225% × $50bn)—a material buffer to expected credit loss (ECL) and capital charges.
• Cost‑to‑serve reduction. Digitising hardship flows can cut assessment time from ~20–30 minutes per case to under 10, enabling the same workforce to handle 2–3× more cases during peak cycles, and lowering average call handling and back‑office costs.
• Customer lifetime value (CLV) protection. Retaining an otherwise viable borrower through a three‑month payment reduction preserves interest income. On a $600,000 mortgage, each percentage point of rate equates to ~$6,000 per year; avoiding forced exits protects margin and cross‑sell potential.
• Regulatory risk mitigation. Reduced hardship‑related disputes lower remediation liabilities and legal spend; even a small reduction in complaint volumes can save seven‑figure sums annually in complex cases.

Macro context underscores the need for speed. A borrower on a $600,000 variable mortgage has faced an annual interest increase of roughly $25,500 since the rate tightening cycle (425 bps), or about $2,125 per month before any principal effects—enough to push otherwise sound households into arrears without timely relief.

Market trends and competitive advantage

Three trends are taking shape:

• Consumer‑duty drift. Global regulatory momentum (e.g., the UK’s Consumer Duty) is influencing Australian expectations for proactive support and friction‑free assistance.
• Embedded hardship in digital banking. The best apps now embed hardship as a first‑class feature with eligibility checks, dynamic options and real‑time confirmation.
• Data‑driven triage. Institutions are pivoting from reactive collections to predictive support, using behavioural analytics to distinguish temporary cash‑flow shocks from structural distress.

First movers gain hard advantages: lower ECL, higher NPS, and differentiation with brokers who prefer partners that protect clients’ futures and their own reputations.

Implementation reality: What trips programs up

Common pitfalls include:

• Inconsistent language. Confusing messages across channels recreate the myths CreditSmart is trying to dispel.
• Operational bottlenecks. Manual evidence collection and slow decisioning deter applicants and increase abandonment.
• Data mismatches. Poorly synchronised credit reporting (e.g., late end‑dates on arrangements) can misstate repayment history and trigger avoidable declines.

Mitigations: centralised content governance, straight‑through processing for standard cases, and monthly reconciliations with credit reporting bodies.

Future outlook: From compliance to capability

Over the next 12–18 months, expect consolidation around three capabilities: (1) real‑time affordability engines using consented data to right‑size relief, (2) portfolio stress early‑warning dashboards that flag hardship propensity at segment level, and (3) standardised disclosures co‑created with industry bodies so borrowers across the market hear the same message about what hardship means for their credit file. Fintech partnerships will accelerate speed to value, but banks must own the risk decisions and reporting quality.

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Australians under cost‑of‑living pressure are sidestepping hardship help because they fear a permanent stain on their credit file. Arca’s CreditSmart initiative has thrust this misconception into the spotlight, forcing lenders, brokers and policymakers to rethink how hardship is communicated and delivered. This case study examines the decision-making, execution and measurable business impact behind a quieter but consequential shift in Australian credit practice. The prize for early movers: lower losses, stronger customer retention and regulatory goodwill.

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Forty‑year home loans are shifting from niche to feature in Australia, led by challenger banks and mutuals courting first‑home buyers. The headline promise—lower monthly repayments—masks a material increase in lifetime interest, slower equity build and fresh regulatory questions. For lenders, the product is a growth lever and a balance‑sheet duration bet; for regulators, it’s a test of consumer outcomes in a high‑rate era.

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Key implication: Extending mortgage terms to 40 years widens the front door to home ownership in a high‑price, high‑rate market—but it also stretches risk across the system. The winners will be lenders that pair longer terms with tight suitability rules, disciplined pricing and clear refinance pathways. The laggards will chase volume without guardrails and invite regulatory intervention.

The signal: Longer loans move from fringe to feature

Great Southern Bank’s move to offer a 40‑year term to first‑home buyers (with age limits) formalises a trend already visible among non‑banks and mutuals. Pepper Money, RACQ Bank and select customer‑owned banks have marketed 40‑year terms or variants for targeted segments, signalling a broader shift rather than a one‑off experiment. With mortgage brokers now originating more than 70% of Australian home loans, product design changes can scale quickly through broker channels.

Context matters. Australia’s outstanding housing credit sits around A$2.2 trillion (RBA), the cash rate is 4.35% (late 2024), and affordability is strained despite income growth. In other markets, longer terms became mainstream as prices outpaced wages: in the UK, the share of first‑time buyers taking 35‑plus‑year terms surged in 2023. Australia is now edging down a similar path.

Borrower economics: Lower repayments, higher lifetime cost

Longer terms reduce monthly repayments but increase total interest and slow the pace at which borrowers get below critical loan‑to‑value (LVR) thresholds.

• Illustrative math: On a A$600,000 variable loan at 6.5%, a 30‑year term implies about A$3,790 per month; a 40‑year term drops that to roughly A$3,510—around 7–8% lower. Over the life of the loan, total interest rises by approximately A$320,000 (+40% versus 30 years). At different rates, the monthly saving might be 6–10%, with lifetime interest up 30–50%.
• Equity build: Slower amortisation keeps borrowers above 80% LVR for longer, constraining refinance options and heightening negative‑equity risk if prices slip.
• Serviceability buffers: APRA expects banks to assess at least 3 percentage points above the rate. A longer term helps pass the buffer, but the underlying debt burden remains substantial.

Consumer researchers, including Finder’s Graham Cooke, have noted the trade‑off: a longer term can create cash‑flow headroom and borrowing capacity, but materially raises lifetime cost. The product suits borrowers with volatile near‑term cash flow who have realistic prospects to refinance or accelerate repayments—less so those at the limit with no plan beyond minimums.

Lender economics and balance‑sheet strategy

For lenders, 40‑year terms are a margin and duration play:

• Net interest income (NII): Lower monthly repayment doesn’t reduce outstanding balances as quickly, lifting interest income persistence, all else equal. Actual average life may still be 4–7 years due to refinancing churn.
• Risk‑weighted assets (RWA): Under APRA’s capital framework, mortgage risk weights hinge on LVR, borrower characteristics and underwriting quality more than contractual term. However, slower amortisation keeps LVRs higher for longer, potentially nudging modelled loss given default (LGD).
• Interest‑rate risk and optionality: Longer terms extend duration and prepayment optionality. In a falling‑rate cycle, prepayment speeds could spike, compressing realised returns; in a rising‑rate cycle, extension risk grows.
• Customer lifetime value (CLV): The hook is strategic: win first‑home buyers early, cross‑sell deposits and insurance, and retain through life events. CLV gains vanish if suitability is weak and arrears rise.

Competitive landscape: A new front in the mortgage rate war

Expect a two‑speed response. Challenger banks, non‑banks and mutuals will use 40‑year terms selectively to carve out first‑home buyer share in the A$2.2 trillion market. Major banks—already pulling back on cashbacks and sharpening risk pricing—may test variants with stricter guardrails (lower max LVRs, principal‑and‑interest only, tighter age caps) to protect brand and regulatory standing.

Product innovation will cluster around three levers: (1) term flexibility—automatic step‑downs from 40 to 30 years when income rises; (2) repayment accelerators—offsets/redraws and scheduled principal top‑ups; and (3) broker tooling—side‑by‑side lifetime cost disclosures embedded in suitability assessments. Distribution power matters: with brokers controlling the funnel, lenders that arm brokers with transparent, data‑rich comparisons will win share without racing to the bottom.

Risk and regulation: Consumer outcomes and system stability

Extended terms concentrate three risks:

• Consumer harm risk: Borrowers may anchor on lower monthly repayments and underestimate lifetime cost. ASIC’s credit guidance (RG 209) and the National Consumer Credit Protection regime require robust suitability testing; clearer lifetime cost disclosures are prudent.
• Macroprudential risk: Slower amortisation keeps system‑wide LVRs elevated. Combined with high debt‑to‑income (DTI) ratios, this could increase loss severity in a downturn. APRA’s 3‑point serviceability buffer remains a key backstop; a targeted macroprudential cap on very long terms for high‑DTI borrowers is conceivable if risks build.
• Lenders mortgage insurance (LMI): Higher LVR persistence shifts risk to LMI providers for longer. Expect LMIs to adjust pricing or impose tighter criteria on 40‑year terms.

International experience offers cautionary tales. Spain’s pre‑GFC 40‑year boom amplified losses when prices fell. The UK’s recent surge in >35‑year terms has triggered calls for clearer disclosures. Australia can get ahead of this with standardised lifetime‑cost and equity‑trajectory illustrations at point of sale.

Implementation reality: Underwriting, broker guidance, and product controls

Operational discipline will decide whether 40‑year loans are an affordability valve or a mis‑selling risk.

• Underwriting policy: Cap terms by borrower age and verified retirement plans; restrict high DTIs (e.g., >6x) from taking 40‑year terms unless strong mitigating factors exist; prefer principal‑and‑interest with no interest‑only periods.
• Product guardrails: Lower max LVRs on 40‑year terms; require offset or redraw features; auto‑convert to shorter terms when buffers exceed thresholds.
• Broker enablement: Mandate side‑by‑side 30‑ vs 40‑year comparisons showing (a) monthly savings, (b) total interest, (c) time to reach 80% LVR. Provide scripts and calculators; audit files for evidence of informed consent.
• Collections early‑warning: Use transaction analytics to spot stress early; offer repayment acceleration or refinance pathways as income rises.

Outlook and scenarios: What the next 24–36 months could look like

Base case (most likely): Penetration of 40‑year terms rises modestly among first‑home buyers via challengers and mutuals; majors adopt cautiously. Pricing remains disciplined; regulators monitor but do not intervene beyond disclosure expectations.

Upside case: If rates drift lower and prices keep climbing, longer terms become a mainstream first‑home buyer feature. Lenders package step‑down terms and automated accelerators; arrears stay contained; broker‑assisted education normalises lifetime‑cost trade‑offs.

Downside case: Price correction or unemployment shock exposes slow amortisation; negative equity rises in high‑LVR cohorts. APRA tightens macroprudential settings (e.g., caps on long terms for high‑DTI or high‑LVR loans); lenders pare back offerings.

Executive actions now:

• Lenders: Pilot with tight eligibility; embed lifetime‑cost disclosures; monitor cohort performance monthly; align LMI terms upfront.
• Brokers: Document suitability beyond repayment affordability; set default advice to “accelerate when able” and diarise check‑ins at 12/24 months.
• Regulators: Standardise disclosure templates for long‑term loans; stress‑test long‑term, high‑DTI segments; ready targeted, not blunt, macroprudential tools.
• Borrowers and employers: Encourage salary‑linked repayment boosts as incomes rise; treat 40 years as a starting term, not a destiny.

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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) "2025-09-17 06:00:37" ["slug"]=> string(100) "18944:australias-40year-mortgage-moment-affordability-optics-lifetime-cost-and-the-new-risk-calculus" ["event"]=> object(stdClass)#9109 (3) { ["afterDisplayTitle"]=> string(0) "" ["beforeDisplayContent"]=> string(132) "

• Yes

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Forty‑year home loans are shifting from niche to feature in Australia, led by challenger banks and mutuals courting first‑home buyers. The headline promise—lower monthly repayments—masks a material increase in lifetime interest, slower equity build and fresh regulatory questions. For lenders, the product is a growth lever and a balance‑sheet duration bet; for regulators, it’s a test of consumer outcomes in a high‑rate era.

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Mortgage regret has become a measurable market force, driving record refinancing, rising arrears off a low base, and a scramble by lenders and brokers to redesign the borrower journey. With the Reserve Bank’s rapid rate rises since 2022 and fixed-rate roll-offs exposing affordability gaps, mistakes made at origination are echoing through balance sheets. The result: a new competitive frontier built around advice quality, data-driven suitability, and post-settlement care. Business leaders that treat regret as a solvable design problem—not just a consumer sentiment—can capture share and de-risk portfolios.

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Key implication: Mortgage regret is no longer a soft consumer metric; it is a hard driver of churn, cost-to-serve, and credit risk. In Australia, the Reserve Bank’s fast cycle to a 4.35% cash rate (from 0.10% in 2022) coincided with record monthly refinancing above A$20bn in 2023, according to ABS figures, as borrowers sought to unwind earlier decisions. This shift is forcing lenders, brokers and fintechs to rebuild the home-loan journey around decision quality, not just price.

Business impact: A margin, churn and risk story

The borrower journey is leaking value at three points: pre-approval suitability, product configuration, and post-settlement management. Each leak shows up on P&L. First, poorly matched products (for example, high-fee packages or misused offset accounts) increase early churn and rebate costs. Second, serviceability stress raises collection costs and arrears risk—S&P Global Ratings has flagged rising arrears off their pandemic-era lows, particularly in the 2020–21 vintages that refinanced into higher rates. Third, complaints and remediation lift non-interest expense. Add it up and mortgage regret becomes a stealth tax on ROE.

For distribution, there’s a notable shift: brokers now originate roughly 70% of new Australian home loans, per industry bodies, making advice quality a systemic factor. Where brokers improve suitability and post-settlement coaching, lenders see lower churn and steadier NIM through the cycle. Where they don’t, portfolios suffer higher switching and loss of share-of-wallet.

Market trends: The regret-fuelled refinancing flywheel

Three macro undercurrents are amplifying borrower mistakes into market signals:

• Rate volatility and fixed-rate cliffs: As cheap fixed loans matured in 2023–24, many households discovered affordability gaps they hadn’t modelled. Refinancing surged as borrowers hunted for rate relief and cashbacks.
• Policy and prudential settings: APRA’s 3 percentage point serviceability buffer protected system stability, but it also steered some borrowers into shorter terms or features they later didn’t use effectively, setting up dissatisfaction.
• Digital discovery gap: Borrowers research online, but many tools optimise for lead capture, not comprehension. The result: confident clicks, fragile decisions.

Globally, the pattern rhymes. The UK and US saw similar rises in remortgaging/loan modifications as rates climbed, underscoring that regret is a structural response to volatile funding costs and information asymmetry—not a uniquely Australian issue.

Technical deep dive: Turning suitability into a data product

Fixing regret requires moving from brochureware to decisionware. Four capabilities stand out:

• Open Banking-driven affordability modelling: Using Consumer Data Right data to ingest real transaction histories yields more accurate expense and income estimates than self-reporting, reducing mis-sizing mistakes. Lenders using dynamic expense categorisation can cut variance between stated and actual living costs and provide realistic buffers.
• Scenario engines at pre-approval: Stress-testing repayments across multiple rate paths, offset usage patterns, and life events (parental leave, second car, rentvesting) makes trade-offs explicit. Think “what-if” modelling embedded in broker CRMs and bank apps.
• Post-settlement nudging: Behavioural prompts—auto-sweeping surplus cash into offsets, alerts on redraw vs offset tax consequences, and renewal price checks—convert intent into action and reduce bill shock.
• LLM-assisted guidance with guardrails: Generative AI can translate product fine print into plain English and compare structures (split loans, IO vs P&I), provided outputs are constrained by policy and validated by credit rules.

These are not just CX features; they are risk controls. Suitability that is modelled, monitored, and nudged becomes a protective moat.

Competitive advantage: Design for “no surprises” and win share

Early movers are competing on “no surprises” as a brand promise. The playbook:

• Advice-as-a-service: Brokers productising annual mortgage reviews with portfolio dashboards and proactive rate check-ins. A subscription-style care model reduces reactive churn and increases referrals.
• Feature utilisation guarantees: Lenders tying package fees to actual usage (for example, offset activation thresholds) and offering fee rebates if features go unused—pinching short-term fee income but lifting lifetime value.
• Education with teeth: Short, interactive modules at key milestones (pre-approval, settlement, year one) with comprehension checks. Completion can unlock fee discounts, aligning incentives.
• Transparent repricing policy: Publishing repricing schedules and automating best-rate moves for in-book customers curbs the perception that loyalty is penalised—trading some NIM for lower churn and higher advocacy.

Implementation reality: Operating model upgrades, not just UX

Adopting this agenda is non-trivial. Practical challenges include:

• Data plumbing: Integrating CDR data into credit and product systems requires lineage tracking and consent orchestration. Firms need a clear data minimisation stance to maintain trust.
• Broker enablement: With most flows via brokers, lenders should embed tools into broker CRMs, not bolt on portals. Co-branded scenario reports and a single source of truth for policy reduce rework.
• Incentives and governance: Shift scorecards from pure volume/yield to “quality of advice” metrics—feature utilisation, complaint rates, early churn, and arrears by channel. Link bonuses to long-term outcomes.
• Compliance by design: Documenting rationale for product choice (including options not chosen) creates an audit trail that satisfies conduct expectations and streamlines remediation if needed.

Composite case examples illustrate the value. A first-home buyer who selected a package loan for the credit card perks but left the offset dormant paid hundreds extra each month; a broker-led annual review that moved them to a simpler product with auto-sweep cut costs and prevented a refinance out. Another household split their loan 70/30 fixed-variable without modelling childcare leave—repricing plus a redraw misuse created cash-flow stress; a lender’s nudge programme reframed repayments and restored buffers.

Policy and industry lens: Closing the literacy and transparency gaps

Regulators have signalled ongoing focus on responsible lending and fair treatment for existing customers. Expect continued scrutiny of repricing practices and clarity of comparison rates, especially around fees and feature conditions. Industry bodies are also pushing financial literacy measures that go beyond brochures to interactive, life-stage coaching.

There’s a broader societal dividend: improved mortgage decision quality reduces systemic stress and supports more stable consumption. For banks, fewer distressed restructures; for households, fewer expensive course-corrections; for brokers, stronger repeat business.

Future outlook: From product to partnership

The next competitive era will measure success by outcome stability, not just approvals. Lenders and brokers that operationalise three disciplines—predictive suitability, proactive stewardship, and transparent value—will convert the mortgage-regret economy into a growth market. The technology is available; the differentiator is governance and incentives. In a world of persistent rate uncertainty, the most bankable asset is a customer who feels they made a good decision—and can prove it a year later.

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Mortgage regret has become a measurable market force, driving record refinancing, rising arrears off a low base, and a scramble by lenders and brokers to redesign the borrower journey. With the Reserve Bank’s rapid rate rises since 2022 and fixed-rate roll-offs exposing affordability gaps, mistakes made at origination are echoing through balance sheets. The result: a new competitive frontier built around advice quality, data-driven suitability, and post-settlement care. Business leaders that treat regret as a solvable design problem—not just a consumer sentiment—can capture share and de-risk portfolios.

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Mortgage broking is maturing, and the next growth curve is hiding in plain sight: small business credit. With Australia’s SME lending pool topping roughly $631 billion in early 2024 and growing at 9.4% CAGR since 2019, brokers and lenders who pivot now can build more resilient revenue and stickier client relationships. But the prize is contested — fintech speed, bank balance sheets, and embedded finance are reshaping distribution and underwriting. This case-led analysis distils what’s working, the technology underneath, and how to execute without blowing up risk.

" ["fulltext"]=> string(9226) "

Context: The demand spike meets a distribution shift

Small and medium enterprises power Australia’s economy, comprising about 98% of all businesses. Yet working capital and growth finance remain stubborn chokepoints. The International Finance Corporation has long estimated an annual global SME credit gap exceeding US$5 trillion, and local data echo the theme: Australia’s SME lending stock was estimated at approximately $631 billion in February 2024, expanding at a 9.4% compound annual rate since 2019. The fuel? A post-pandemic rebuild, inventory swings, supply-chain reconfiguration, and digitisation projects that can’t be funded off a credit card.

Distribution is changing just as fast as demand. Fintechs have redesigned origination and underwriting for speed, banks are modernising legacy processes, and embedded finance has turned software platforms into lenders. Industry analysts, including Riverview Finserve, call out three reinforcing trends for 2025: the rise of alternative lenders, embedded finance inside business platforms, and hyper-personalised offers powered by AI and richer data.

Decision: Two paths that define the playbook

This case study focuses on two real-world strategies that leaders are using to win SME lending profitably:

Case A — Specialist bank scaling relationship-led SME lending (Australia): Judo Bank built an SME-only franchise from scratch, pairing experienced bankers with modern tooling. The decision was to reject universal banking economics (high-volume, commoditised credit) and instead compete on speed, access, and human judgement where the data are messier than consumer credit — and the margins better.

Case B — Embedded lenders inside business platforms (global): Shopify Capital and Square Loans (Block) integrated lending into merchants’ daily workflows, using checkout, sales, and settlement data to pre-score offers. The strategic decision was to prioritise distribution and data moats over storefront marketing. Offers appear contextually; repayment aligns with cash flow via revenue share.

Implementation: The technology and operating model that make it work

Data advantage: SME underwriting hinges on cash-flow visibility rather than collateral alone. Leaders ingest bank feeds (via Open Banking/Consumer Data Right in Australia), accounting ledgers, e-commerce carts, and payment processors. Signal-rich features include sales volatility, invoice ageing, seasonality, and supplier concentration. The goal: continuous, permissioned telemetry to turn lumpy SME risk into a managed, dynamic portfolio.

Decisioning stack: Successful lenders combine:

• Rules for eligibility and policy guardrails (sector exposure, loan-to-revenue thresholds, director checks).
• Machine learning models for probability of default and loss given default, trained on transaction-level histories to reduce thin-file penalties.
• Graph/network analytics to detect related-entity risk and fraud rings.
• Human-in-the-loop workflows for exceptions, complex financials, and covenant setting.

Orchestration: Digital onboarding (eKYC/KYB), document ingestion with OCR, and straight-through processing for lower-risk tranches. Cloud-native core lending systems and API gateways connect brokers, bank RMs, and platform partners. For brokers, lender marketplaces and decisioning portals standardise product comparison and term sheets — compressing time-to-yes from weeks to hours in many cases.

Compliance and capital: Banks align with APRA prudential standards and Basel risk weights; all lenders must meet AML/CTF obligations and Privacy/CDR rules. Non-bank fintechs compete with speed and UX, then secure scalable funding through warehouse lines, securitisation, or bank partnerships to lower cost of funds — a key determinant of sustainable pricing.

Results: What the leaders achieved (with numbers)

Market outcomes: Australia’s SME lending pool is estimated at ~$631 billion (Feb 2024), growing at about 9.4% CAGR since 2019 — a strong tailwind for brokers diversifying beyond residential mortgages. Globally, the SME financing shortfall remains above US$5 trillion annually, underscoring runway for alternative models.

Case A — Judo Bank (Australia): By focusing exclusively on SMEs, pairing relationship bankers with modern credit tools, and competing on time-to-credit, Judo scaled its loan book to around the A$10 billion mark by 2024. The bank’s proposition — banker access, cash-flow lending, and faster decisions — translated into durable customer acquisition without mass-market marketing spend.

Case B — Embedded lenders (global): Shopify Capital and Square Loans have each originated billions of dollars to SMEs, with approvals often delivered within minutes and funds landing as fast as the next business day. Repayments tied to daily sales reduce friction and improve affordability, helping sustain repeat borrowing and lower acquisition costs through native distribution.

Operational KPIs seen in high performers:

• Time-to-yes routinely compressed from 10–20 business days to under 24–72 hours for standard cases.
• Acquisition costs fall materially when distribution is embedded (e.g., within commerce/payments platforms) versus direct marketing.
• Portfolio monitoring moves from monthly to near real-time, enabling faster risk interventions and loss containment.

Market context and competitive dynamics

Porter’s view: Threat of new entrants stays elevated as cloud cores and Banking-as-a-Service lower setup costs. Buyer power (SMEs) is rising because switching costs fall when offers are instant and portable. Substitutes — including BNPL-for-business and merchant cash advances — keep pricing disciplined. Incumbent banks’ advantage remains cost of funds and trust; fintechs win on speed, data, and UX. Expect more partnerships, white-labelling, and co-origination.

Trendlines to monitor:

• Embedded finance: Software platforms, marketplaces, and PSPs continue integrating credit at the point of need.
• AI underwriting: From scorecards to adaptive models and explainability tooling that satisfies regulators and auditors.
• Open Banking/CDR maturity: Better data granularity and consent flows unlock richer cash-flow analytics and faster drawdowns.

Implementation reality: How brokers and lenders execute

For mortgage brokers diversifying into SME:

• Build a segmented product shelf (working capital, asset finance, invoice finance, merchant cash advances) and map to industries you already serve.
• Invest in business financial literacy — train advisers to read cash-flow statements and spot lending triggers (e.g., inventory bulges, receivables ageing).
• Use multi-lender SME platforms with real-time data pulls (bank feeds, accounting packages) to pre-qualify clients in the first meeting.
• Create post-settlement programmes: quarterly check-ins, covenant monitoring alerts, and proactive top-up offers.

For lenders:

• Prioritise data partnerships (accounting, commerce, payments) to reduce thin-file risk and enable pre-approved offers.
• Engineer an explainable AI pipeline: feature stores, bias monitoring, challenger models, and human sign-off tiers.
• Lower cost of funds early via committed warehouses and bank partnerships; unit economics depend on spread minus expected loss minus acquisition cost.
• Institutionalise “second line” risk: model risk management, stress testing by sector, and early-warning systems using anomaly detection.

Lessons: What decision-makers should take away

First, SME lending is no side hustle: the addressable pool is large and growing, and the economics can be superior to commoditised home loans if you solve for speed, insight, and service. Second, distribution is destiny: embedded and broker-led channels outperform cold direct acquisition. Third, data beats collateral when it comes to underwriting underserved businesses; cash-flow telemetry plus explainable models are now table stakes. Fourth, partnership beats purity — banks, fintechs, and platforms will increasingly co-originate to balance speed, risk, and cost of funds. Finally, governance matters: get AML/CTF, Privacy/CDR, and model risk right early to avoid growth-without-control — the fastest route to value destruction.

In short: the lenders and brokers who treat SME credit as a core business, not a bolt-on, will compound advantage as the cycle turns. Those who hesitate will watch their customer relationships — and margin — migrate to whoever shows up first with capital precisely when it’s needed.

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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) "2025-09-11 00:30:02" ["slug"]=> string(82) "18900:sme-lending-becomes-the-new-gold-rush-as-banks-shift-focus-beyond-home-loans" ["event"]=> object(stdClass)#9131 (3) { ["afterDisplayTitle"]=> string(0) "" ["beforeDisplayContent"]=> string(132) "

• Yes

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Mortgage broking is maturing, and the next growth curve is hiding in plain sight: small business credit. With Australia’s SME lending pool topping roughly $631 billion in early 2024 and growing at 9.4% CAGR since 2019, brokers and lenders who pivot now can build more resilient revenue and stickier client relationships. But the prize is contested — fintech speed, bank balance sheets, and embedded finance are reshaping distribution and underwriting. This case-led analysis distils what’s working, the technology underneath, and how to execute without blowing up risk.

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Australia’s $11.6 trillion housing market is booming again, but so are regrets among first-time borrowers who moved too quickly. A new wave of early refinances, broker-led interventions and lender transparency pushes is rewriting mortgage distribution economics. This case study quantifies the cost of missteps, the savings from course correction, and the strategic opportunities for lenders, brokers and fintechs. The message for business leaders: speed still sells, but clarity, comparability and cashflow tools win loyalty.

" ["fulltext"]=> string(8437) "

Context: A hot market, fast approvals, and costly oversights

Australia’s residential property market reached an estimated $11.6 trillion in value in the June quarter of 2025, up $213 billion from March — roughly five times annual GDP. Rate cuts earlier in the year and outsized growth in mid-tier capitals have pushed lending volumes back to “relatively high” levels. In that rush, many first-time borrowers prioritised speed over fit. A recent Money.com.au survey identified the same regret drivers again and again: paying excessive ongoing fees, accepting uncompetitive headline rates, and missing features that materially improve cashflow — particularly offset accounts.

Industry practitioners echo the pattern. Broker groups report a rise in first-year refinances by first-home buyers, consistent with borrowers realising they could have achieved sharper pricing or better features. “Comparison fatigue and urgency are leading to costly trade-offs,” notes Money.com.au mortgage expert Debbie Hays, pointing to insufficient product comparison and weak understanding of fee structures and offsets as the chief culprits.

Decision: The rushed loan that locked in structural cost

Consider a composite, representative scenario drawn from recent broker casework. A first-home buyer in Brisbane purchases an $880,000 townhouse with a 90 per cent loan-to-value ratio (LVR), borrowing $792,000. Keen to exchange quickly, they select a lender advertising rapid approval at 6.19 per cent variable with a $395 annual package fee, but with no genuine 100 per cent offset. Lenders Mortgage Insurance (LMI) is capitalised into the loan because of the 90 per cent LVR. The buyer forgoes a broker and does minimal comparison shopping.

Three structural issues are baked in on day one: a rate roughly 40 basis points above competitive alternatives, ongoing fees that erode the pricing headline, and the absence of an offset — limiting day-to-day interest savings and reducing financial resilience as rates move.

Implementation: Broker-led triage and a switch to feature-rich value

Nine months in, rising repayments and cashflow strain prompt the buyer to seek a broker. The process combines product discovery with open banking data to model spending patterns and likely offset balances. The broker identifies a mainstream lender offering 5.79 per cent with a true offset and zero annual package fee, subject to serviceability checks. The refinance includes a modest principal paydown to move from 90 per cent toward 88 per cent LVR, trimming LMI exposure and opening a sharper pricing tier. Switching costs (discharge, settlement, application, government fees) are estimated at $2,500.

Results: What the numbers say over three years

Assumptions for comparability: remaining principal circa $800,000 at refinance; 30-year term; borrower maintains an average $25,000 cash balance in their offset after switching.

• Rate differential: 6.19 per cent to 5.79 per cent yields a repayment reduction of ~A$206 per month per $800,000 — roughly A$2,477 per year.
• Offset-driven interest savings: A$25,000 average balance at 5.79 per cent avoids ~A$1,448 in annual interest that would otherwise accrue without an offset.
• Fee elimination: Avoiding a A$395 package fee saves A$395 per year.
• Total annual run-rate improvement: ~A$4,320; switching costs of ~A$2,500 are typically recovered within 7 months.

Three-year net effect: ~A$12,960 cumulative savings less ~A$2,500 switching costs = ~A$10,460. That’s the recoverable upside. The avoidable downside locked in by the initial choice is larger: over the first three years, the borrower’s all-in “cost of poor choice” (excess interest from the higher rate ~A$7,430; lost offset savings ~A$4,350; package fees ~A$1,185; plus LMI structuring inefficiency and assorted fees that could have been at least A$17,000 lower with an 88–90 per cent LVR strategy or guarantor structure) totals in the order of A$31,000. Some of that is sunk (LMI), but the refinance recoups a meaningful share.

For banks, these movements matter. Early refinances compress lifetime value (LTV) and inflate acquisition costs, while broker-led rescues shift distribution power. For brokers, file quality and scenario modelling turn into measurable client savings and stickier relationships.

Technical deep dive: Why features and fee design trump the headline rate

• Offset vs redraw: An offset reduces daily interest by netting deposit balances against the principal; redraw simply accesses prepaid principal. For salaried households who can hold even A$15–30k in working cash, a true offset can save A$870–A$1,740 per year at current rates, with superior liquidity.
• Comparison rate discipline: Australia’s comparison rate attempts to bundle fees into a single metric. For first-home buyers, the gap between the advertised rate and comparison rate is a practical red flag for fee drag and product complexity.
• LVR breakpoints and LMI: Pricing and LMI step up sharply above 80 per cent, with additional jumps at 85, 88 and 90 per cent. A targeted paydown to sit just below a threshold, or use of a parental guarantee, can shave thousands off LMI and open cheaper pricing tiers.
• Rate-lock and timing: In volatile markets, a short rate-lock can avoid last-minute repricing, but it must be weighed against lock fees and approval timelines.

Market trends: The refinancing reflex and lender transparency

Two visible shifts are under way. First, a refinancing reflex among first-home buyers — many within the first 12 months — as regret drivers translate to action. Second, lenders are responding with clearer fee disclosures, side-by-side feature matrices and offset-by-default bundles on mainstream products. Broker associations continue to hammer the message: shop widely, model cashflow, and don’t conflate approval speed with overall value.

At the macro level, higher absolute rates magnify every misstep. A 40 basis-point gap at today’s rate environment costs materially more per year than in a 2–3 per cent world. That makes product-fit and fee design a board-level issue for lenders, not just a marketing line.

Lessons: Strategic implications for decision-makers

For lenders:

• Design for net benefit: Bundle genuine offsets and low-fee structures; shift pricing to value the offset behaviour you want to encourage.
• Transparency as a moat: Standardise fee schedules and surface a clear “all-in cost” comparator in digital journeys. It reduces adverse selection and early churn.
• Early-life retention: Proactive first-90-day reviews (pricing checks, offset coaching) lower first-year refinance rates and preserve customer lifetime value.

For brokers and fintechs:

• Scenario modelling: Build templates that show rate, feature and fee trade-offs at multiple LVR points; quantify offset savings in dollars per month to drive better decisions.
• Data-led triage: Use open banking to size likely offset balances and recommend the right product architecture upfront.

For employers and policymakers:

• Financial literacy at point-of-need: Short, modular education on offsets, LMI thresholds and comparison rates has outsized ROI for first-time buyers. Workplace programs and lender microsites are effective channels.
• Disclosure consistency: Continued focus on comparable, plain-language product information will reduce regret-driven churn and market friction.

The bottom line: in a high-rate environment and a fast-moving property market, the edge goes to institutions that help first-home buyers optimise for total cost of ownership, not just speed to yes. Those who quantify and communicate that value — in dollars, not just percentages — will win the next refinancing cycle.

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• Yes

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Australia’s $11.6 trillion housing market is booming again, but so are regrets among first-time borrowers who moved too quickly. A new wave of early refinances, broker-led interventions and lender transparency pushes is rewriting mortgage distribution economics. This case study quantifies the cost of missteps, the savings from course correction, and the strategic opportunities for lenders, brokers and fintechs. The message for business leaders: speed still sells, but clarity, comparability and cashflow tools win loyalty.

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string(0) "" } [4]=> object(stdClass)#9169 (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)#8676 (3) { ["data":protected]=> object(stdClass)#8674 (2) { ["filter"]=> string(0) "" ["maxlength"]=> string(0) "" } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["params"]=> object(Joomla\Registry\Registry)#8675 (3) { ["data":protected]=> object(stdClass)#8703 (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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object(stdClass)#8710 (2) { ["options0"]=> object(stdClass)#8709 (2) { ["name"]=> string(8) "Inactive" ["value"]=> string(1) "0" } ["options1"]=> object(stdClass)#8711 (2) { ["name"]=> string(6) "Active" ["value"]=> string(1) "1" } } } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["params"]=> object(Joomla\Registry\Registry)#8706 (3) { ["data":protected]=> object(stdClass)#8714 (10) { ["hint"]=> string(0) "" ["class"]=> string(9) "btn-group" ["label_class"]=> string(0) "" ["show_on"]=> string(0) "" ["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(5) "radio" ["default_value"]=> string(0) "" ["context"]=> string(19) "com_content.article" ["group_id"]=> int(2) ["label"]=> string(6) "Status" ["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) "" } [5]=> object(stdClass)#9073 (33) { ["id"]=> int(5) ["title"]=> string(22) " Essential information" ["name"]=> string(21) "essential-information" ["checked_out"]=> NULL ["checked_out_time"]=> NULL ["note"]=> string(0) "" ["state"]=> int(1) ["access"]=> int(1) ["created_time"]=> string(19) "2020-06-25 06:10:20" ["created_user_id"]=> int(2351) ["ordering"]=> int(4) ["language"]=> string(1) "*" ["fieldparams"]=> object(Joomla\Registry\Registry)#8708 (3) { ["data":protected]=> object(stdClass)#8716 (4) { ["buttons"]=> string(0) "" ["width"]=> string(0) "" ["height"]=> 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)#9177 (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)#8715 (3) { ["data":protected]=> object(stdClass)#8720 (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)#8717 (3) { ["data":protected]=> object(stdClass)#8722 (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(125) "/borrow-money/loans/the-31k-mortgage-mistake-how-first-home-buyers-rushed-decisions-are-reshaping-australias-lending-playbook" ["image"]=> string(111) "https://res.cloudinary.com/momentum-media-group-pty-ltd/image/upload/v1757478709/pexels-rdne-8293743_mc3ysj.jpg" ["image_alt"]=> string(66) "First-home buyers' $31k blunder reshapes Australia's mortgage game" } }