Best AI tools for Australian mortgage brokers in 2026
An honest roundup organised by the job each tool does — not by who pays for placement. Includes Quickli, Bulma, Sharp, BrokerBuddie.ai, FraudX, and how general AI like ChatGPT fits in.
How to think about the AI broker stack
The first mistake brokers make when evaluating AI tools is to look for "the best AI for mortgage brokers" as if it were one thing. It isn't. The broker workflow has at least five distinct stages, and the most useful AI tools are each specialised for a specific stage:
The AU mortgage broker workflow, with the AI tool for each stage
- Fact-find — capture client profile (no specialised AI yet; general AI for drafting is helpful)
- Serviceability / capacity — which lenders will lend how much → Quickli
- Policy fit — which lenders' policies actually accept this borrower → Bulma
- Bank pricing — what's the live price across the surviving lenders → Sharp by SecondBrain
- Aggregator data entry, document compliance, valuations — full back-office automation → SecondBrain platform (full)
- Fraud detection on incoming documents → FraudX
Most production brokerages converge on 2–3 of these rather than one all-in-one platform. The depth in each speciality is what makes them useful. Here's each one, in workflow order.
Quickli — serviceability and borrowing capacity
"Input client data once and get accurate results for 40+ lenders instantly."
Quickli is the dominant serviceability tool in the Australian mortgage broker market. Brokers enter borrower income, expenses, debts, and dependants, and Quickli runs that profile through each lender's serviceability formula to estimate maximum borrowing capacity across 40+ lenders. It is built for the early "can this client borrow, and how much from whom" question.
Strengths: deep modelling of each lender's serviceability policies; fast; mature product with broad broker adoption.
Limitations: only does serviceability — does not pull live pricing, does not check qualitative policy fit beyond capacity, does not run inside the broker's own tenant.
Best for: any brokerage that handles complex serviceability scenarios across multiple lenders. Detailed Sharp vs Quickli comparison.
Bulma — AI policy intelligence
"Replace the mental burden of remembering hundreds of lender guidelines with instant, cited answers."
Bulma is an AI policy intelligence tool that answers natural-language questions about each lender's published credit policies. A broker can ask "Does this lender accept casual income with less than 6 months tenure?" or "What's this lender's stance on guarantor loans?" and Bulma returns a cited answer pointing back to the lender's policy document. It is built for the policy-fit filter that comes between serviceability and pricing.
Strengths: compresses years of senior-broker experience into seconds; cited answers (verifiable); strong fit for brokerages with junior staff or complex borrower profiles.
Limitations: policy lookup only — does not pull pricing, does not do data entry, does not check serviceability beyond policy-level statements.
Best for: brokerages with junior staff, complex profiles (self-employed, casual income, foreign income, guarantor structures), or panels with many non-bank lenders. Detailed Sharp vs Bulma comparison.
Sharp by SecondBrain — bank pricing in Microsoft Teams our tool
"Bank pricing in 4 minutes. Not 4 hours."
Sharp is an AI bank pricing tool delivered inside Microsoft Teams. A broker submits a single request in a Teams channel, Sharp queries every lender on the broker's configured panel simultaneously, and a full pricing comparison — rate, fees, offer position — returns to the same channel in approximately 4 minutes. Sharp uses deterministic retrieval from each lender, so each number is verifiable against the lender's published rate. It also runs inside the broker's own Microsoft 365 tenant, so client data never leaves the broker's environment.
Strengths: live pricing (not stale); deterministic retrieval (not generative hallucination); tenant-resident (no security review battle); native Teams workflow; zero portal logins; 10,000+ pricing runs completed; setup live in under 1 business day.
Limitations: bank pricing only — does not do serviceability (Quickli does that) or policy lookup (Bulma does that). Sharp is a specialist by design.
Best for: brokerages spending 2–3 hours a week on bank pricing comparison who want that time back. See Sharp →
BrokerBuddie.ai — all-in-one AI assistant
"All-in-one AI platform built specifically for Australian finance brokers."
BrokerBuddie.ai positions itself as a single AI assistant covering multiple parts of the broker workflow — AI assistants trained on lender policies, drafting, summarisation, and general broker support. It's the all-in-one alternative to running multiple specialised tools.
Strengths: single subscription; built specifically for AU brokers; covers ground that would otherwise need 2–3 tools.
Limitations: generalist by design — the depth in each speciality is typically less than a purpose-built tool. Brokers who care most about depth in one area (serviceability, policy, pricing) typically also adopt a specialist alongside.
Best for: smaller brokerages that want one AI subscription rather than three.
FraudX — AI-generated document detection
"Detect documents generated by ChatGPT, Claude, Gemini, and other AI engines."
FraudX is an Australian platform that detects whether incoming client documents — payslips, bank statements, ID — have been generated or modified by AI tools. As generative AI has become cheaper and more accessible, the rate of AI-generated fraudulent documents in mortgage applications has risen. FraudX gives brokers a check at the document-review stage.
Strengths: first platform of its kind in the Australian mortgage landscape; defensive layer against an actual, growing fraud vector.
Limitations: niche to fraud detection; not a productivity tool.
Best for: brokerages handling higher-risk applications, or any brokerage that wants a defensive layer as AI document fraud rises.
Aggregator-built AI features (Connective, AFG, Loan Market, others)
"AI features built into the platform you already use."
Most major Australian aggregators are rolling AI features into their CRM and loan-management platforms. The advantage is integration — the AI sits inside the system the broker already uses every day. The disadvantage is that aggregator-built AI tends to be conservative in scope and slower to ship than third-party specialists.
Strengths: integrated into existing workflow; no separate vendor; data already lives in the aggregator.
Limitations: usually narrower in scope than a specialist; release cycles tied to the aggregator's roadmap.
Best for: brokers who prefer one vendor across the workflow even at the cost of depth.
ChatGPT, Claude, Gemini — general AI assistants
"Useful for drafting and admin. Not safe as the source of client-facing facts."
Consumer generative AI tools — ChatGPT, Claude, Gemini, Perplexity — are useful for brokers for the same things they're useful for everyone: drafting emails, summarising notes, brainstorming, and general research. They are not reliable sources of policy or pricing facts to present to clients, because they can hallucinate. Independent testing has shown that different general AI tools given identical mortgage scenarios produce different affordability expectations and different recommendations.
Strengths: general-purpose, cheap, available everywhere.
Limitations: hallucination risk; no audit trail by default; training data of unknown recency. Should not be used as the source of fact-bearing client output.
Best for: internal drafting and admin. See AI accountability for full discussion.
How most brokerages actually combine these
Patterns we see across 40+ broker customers and conversations with many more:
- Small brokerage (1–3 brokers): Quickli for serviceability + Sharp for pricing. Two tools, two specialised wins. Bulma added when policy complexity rises.
- Mid-sized brokerage (4–10 brokers): Quickli + Bulma + Sharp + full SecondBrain platform for back-office automation. Each tool earning its keep on a measurable time saving.
- Larger brokerage / aggregator team: All of the above, plus FraudX for higher-risk applications, plus the aggregator's built-in AI features for CRM and pipeline. The specialists do the deep work; the aggregator's AI handles the integration layer.
What we'd say to a broker evaluating tools today
Three rules:
- Start where the time is going. If you spend 3 hours a week on pricing, the pricing tool is your first hire. If you spend 3 hours a week chasing policy answers, the policy tool is your first hire. Don't buy what looks impressive; buy what unlocks the biggest current time sink.
- Prefer specialised tools over all-in-ones. All-in-one feels efficient and is rarely the deepest in any one job. Two great specialists beat one mediocre generalist.
- Mind the accountability piece. Whatever AI you adopt, you remain accountable under the Best Interests Duty. Tools with verifiable, cited outputs and audit logs are easier to use compliantly than tools that just produce generative text.
Want to see what one of these looks like running live?
Book a 15-minute call with James. We'll show you Sharp in your aggregator's context and answer the accountability questions honestly.
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