Context: A volume market meets a complexity problem

Connective has warned that the nature of complaints against mortgage and finance brokers is changing — and the timing matters. Brokers now intermediate the majority of home lending: industry analyses in 2023 placed broker-originated flows at roughly seven in ten mortgages, making the channel systemically significant. At the same time, industry commentary points to a pivot from simple service grievances (missed calls, delays) toward more complex issues: suitability of credit assistance, the handling of financially vulnerable customers, disclosures around fees and clawbacks, and the use of customer data and analytics in decisioning.

Two macro forces amplify the risk. First, digital visibility: the ACCC has noted Google held nearly 94 per cent of general search in Australia as recently as August 2024. One unresolved complaint can become the top search result for your brand overnight. Second, regulatory expectations: ASIC’s Internal Dispute Resolution regime (RG 271) tightened timeframes and standards, while its 2024 governance review warned of controls lagging technology adoption. As ASIC’s Report 798 put it, “The landscape of AI regulation in Australia is evolving.” In parallel, AFCA continues to receive tens of thousands of complaints across financial services annually, spotlighting systemic issues where they emerge.

The competitive context is shifting too. As rates and credit appetite changed, brokers increasingly placed customers with non-bank lenders — a rational response to policy niches and turnaround times, but one that introduced new complaint patterns (servicing, hardship and collections practices, and communications) that brokers must anticipate and manage.

Decision: From cost centre to risk intelligence capability

Leading aggregators and broker groups have reframed complaints handling from a reactive cost to a proactive, intelligence-led capability. The strategic decision had three planks:

• Re-architect IDR around root cause: Move beyond ticket closure to systemic analysis (product fit, remuneration conflicts, disclosure practices, lender selection logic).
• Adopt AI-enabled triage with strong guardrails: Use natural language processing (NLP) to classify complaints by conduct risk, consumer vulnerability and urgency, while implementing strict model governance aligned to ASIC’s guidance and public-sector exemplars such as the ATO’s AI governance approach.
• Tighten partner governance: Create standardised service-level and conduct scorecards for non-bank and bank partners, embedding complaint trends, hardship responsiveness and policy exceptions into placement decisions.

Implementation: The operating model that actually works

Execution was built on a pragmatic operating model — technology plus governance, not technology instead of governance.

• Data taxonomy and capture: Standard fields for complaint type, channel, customer cohort, lender, product, and outcome; structured flags for potential vulnerability and hardship; secure storage with data minimisation.
• AI triage, human-in-the-loop: An NLP model classifies inbound narratives into categories (e.g., suitability vs. service) and risk tiers. Confidence thresholds route low-risk items to frontline resolution and high-risk to compliance/legal. All models are subject to bias testing, version control and periodic validation, per the control themes highlighted in ASIC REP 798.
• Root-cause analytics: Monthly dashboards run Pareto analysis on complaint drivers (e.g., valuation delays, channel conflict, fee misunderstanding). Drill-through links to specific lender/product combinations and broker behaviours to inform coaching and product governance.
• Vulnerability playbooks: Scripts and pathways for hardship, domestic and family violence, and financial abuse, aligned with Australian Consumer Law guarantees and credit obligations, to ensure consistent, humane responses and faster resolution.
• Partner scorecards and escalation paths: Aggregated complaint metrics feed quarterly reviews with lenders (banks and non-banks), with clear thresholds for remediation plans and, if required, de-selection.
• Digital reputation hygiene: Given Google’s near-total share of search discovery, teams implemented consistent, timely responses on public platforms, structured FAQs, and schema-marked content to ensure authoritative pages rank above isolated grievances.

Results: Modelled ROI and measurable risk reduction

Because firms disclose little publicly, we model outcomes for a mid-sized broker group using conservative assumptions. For a network writing 25,000 loans annually with a 1 per cent complaint rate (250 cases):

• Faster resolution: AI-enabled triage and playbooks reduce average time-to-resolution from 12 days to 6–7 days, improving RG 271 on-time closure to 95–98 per cent.
• Fewer external escalations: Targeted coaching and partner remediation halve AFCA escalations from 10 per cent to 5 per cent of complaints (from 25 to ~12 cases annually). Even ignoring fee savings, the avoided management time is material.
• First-contact resolution (FCR): Structured scripts and authority matrices lift FCR from ~40 per cent to 60–65 per cent, freeing capacity without headcount growth.
• Conduct risk detection: Categorisation shifts reveal that 30–40 per cent of complaints now relate to suitability/disclosure rather than pure service, enabling targeted rectification (e.g., templated fee explanations and lender-selection rationale).
• Placement optimisation: Partner scorecards cut repeat issues with two underperforming products, shifting 5–8 per cent of flow to higher-performing alternatives without customer detriment.

These gains align with the direction set by ASIC’s governance emphasis and industry initiatives showcased during Australia’s AI Month in 2024: use AI to augment, not automate away, responsible decision-making.

Business impact and competitive advantage

For brokers and aggregators, the business case runs on three lines:

• Revenue protection: Lower churn and improved referral rates from FCR improvements and transparent remediation; reputation risk contained in an environment where search concentrates discovery.
• Cost-to-serve: Automation of intake and routing reduces swivel-chair effort; fewer AFCA matters mean less senior time and external adviser spend.
• Regulatory resilience: Evidenced root-cause remediation lowers the likelihood of systemic issue findings; model governance and data lineage show your work when ASIC or AFCA asks.

Implementation reality: What trips firms up

Three practical hurdles consistently emerge:

• Data quality: Free-text narratives without structure neuter analytics. Mandate fields and train staff.
• Model governance: AI without controls creates a new risk vector. Borrow from public-sector exemplars like the ATO’s AI governance framework and ASIC’s REP 798 control expectations.
• Partner alignment: Non-bank product strengths can be offset by post-settlement pain if service slips. Make complaint data a non-negotiable agenda item in quarterly business reviews, with consequence management.

Future outlook: Predict and prevent

Insurance leaders talk about a shift to “predict and prevent”; credit distribution is heading the same way. Expect more pre-issue suitability checks supported by explainable AI, enhanced vulnerability detection, and dynamic product governance that reacts to emerging complaint clusters within weeks, not quarters. With national initiatives like the National AI Centre spotlighting responsible adoption, the winners will be those who blend analytics with empathy and governance — turning a rising tide of complex complaints into a moat of trust.

Lessons

• Treat complaints as a strategic dataset, not a regulatory obligation. It’s the cleanest signal of product–market and process–customer friction.
• Invest in explainability so front-line staff can articulate reasoning to customers. Black boxes invite complaints.
• Scorecard your partners using the same rigour you apply to your own teams; shift flow where customer outcomes prove better.
• Design for vulnerability from the first script to the last email. It accelerates resolution and reduces escalation risk.
• Manage search reputation actively because discovery is concentrated; timely, factual responses and authoritative content protect brand equity.