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.