Bulma is the best AI tool for mortgage brokers who want lender-policy research and scenario planning. Ask a policy question in ordinary language and read the lender wording behind the answer. For a full client scenario, Bulma compares policy fit across its covered lenders and identifies the conditions to meet.
The seven tools below cover different broker jobs. Some help prepare an application, while others identify clients for a retention review. Client-facing chatbots need a separate setup because their answers and enquiries go directly to borrowers.
Top 7 AI Tools for Australian Mortgage Brokers
1. Bulma
Bulma ranks first for policy research because its answers include quoted lender policy and cross-lender comparisons. It covers more than 52 residential lenders and about 50 policy areas. A broker can compare the same income or deposit question across lenders without changing the client facts.
Scenario Planner checks a full scenario against covered lenders. It groups lenders by policy fit and lists the conditions and documents needed. Bulma also calculates borrowing power using each lender’s servicing inputs, so the research connects to the application you are preparing.
Choose Bulma when the policy question and the client scenario need to stay in one conversation. Review the quoted wording against the verified client facts. An exception or a point the policy does not address still needs lender confirmation, and the lender makes the credit decision.
2. Quickli
Quickli Pro suits brokers who want AI inside their serviceability workflow. Jiffi AI answers natural-language questions about lender policies and products. Doc Handler identifies documents, renames them and extracts information into a Quickli scenario.
Quickli Pro also generates loan submission notes from the scenario. That makes it useful when the same team handles document intake and prepares the application. Its specialist lender-matching tool is rules-based, so distinguish that function from Jiffi AI and document extraction.
Choose Quickli for the application workflow around the research. Review extracted amounts against the original documents and edit generated notes before submission. A completed scenario can still contain an incorrect field, even when the document has the correct name.
3. Sherlok
Sherlok suits an existing customer who wants a prioritised retention workload. Its machine-learning model scores loans and puts higher-risk clients into a weekly review queue. Factors include possible refinance savings, loan amount and time since settlement.
The broker controls the review process and can delay a client’s repricing or request it immediately. Use the score to order the work. A refinance recommendation still needs the client’s objectives, current finances, switching costs and available options.
Sherlok has paused onboarding for new customers and has a waitlist while it rolls InstantAPPLY out to existing customers. Existing customers receive its fact-find and application workflow within their subscription. New brokerages cannot treat Sherlok as an immediately available implementation.
4. AFG Smart
AFG Smart suits AFG brokers who want retention prompts within their marketing platform. The core program lists a Red Alert AI customer campaign. AFG describes Red Alert notifications and emails as a way to contact customers at risk of leaving.
The wider platform includes a branded website and scheduled campaigns, such as settlement and loan-review messages. Those scheduled messages are automation. Red Alert is the AI-labelled function, so assess its handling of customer information separately from the campaign calendar.
This option fits the AFG network. Assign a staff member to review the client record behind an alert and control the contact that follows. A campaign must use the brokerage’s consent records and give staff a way to stop an inappropriate message.
5. broQA.ai
broQA.ai is a policy-research option for Australian mortgage brokers. Its terms describe AI-generated answers drawn from lender policy documents and scenario analysis that helps identify lenders for complex situations.
It suits brokers who want to narrow a policy question before investigating the full client scenario. Keep the facts that change the answer in the question, then compare the response with the relevant lender document.
The terms leave professional judgement with the broker and require subscribers to maintain their own records of generated content. Save the answer you use with its source evidence. Team access also needs an owner who removes access when a staff member leaves.
6. CreditPolicy.ai
CreditPolicy.ai suits brokers who want sourced policy answers through a web app, a connected AI assistant or email. Its website describes policy chat alongside residential servicing and a branded client portal.
Email questions receive a sourced answer in the same thread. Connected assistants can search the policy library from the tools a broker already uses. This can reduce movement between applications when the brokerage approves that connection.
Each access method creates a data flow to assess. Decide which client fields the connected assistant can retrieve and who controls those permissions. Read the cited policy before applying an answer, and retain the evidence with the client file.
7. Mortgage Choice Policy Search
Mortgage Choice Policy Search suits brokers in the Mortgage Choice network. The tool searches residential policy pages in the group’s Lending Toolkit and returns answers with links to source documents.
A network broker can ask about a complex scenario and follow the answer back to the policy used. The built-in location makes it a practical research starting point for a team already working in that toolkit.
Access is tied to the Mortgage Choice network. Brokers outside it need another policy tool approved for their business. Within the network, the broker still checks the source and applies the client’s verified facts before recommending a loan.
Client-Facing Chatbots
Client-facing chatbots answer enquiries, collect contact details or book a meeting before a broker speaks to the client. AI receptionists and voice agents belong in this group. Their setup must control what they say and who receives the enquiry.
PropertyChat.ai
PropertyChat.ai is a property education assistant based on Jane Slack-Smith’s material. It accepts typed questions, photos and voice conversations about Australian property. Brokers can learn from its explanations and the questions borrowers ask.
Treat it as a public education resource. It gives general information and excludes current suburb prices and growth data. Use a brokerage-controlled enquiry tool when you need to capture leads into your own client records.
Effi
Effi combines a broker customer relationship management (CRM) system with chatbot and SMS automation. Its pricing page lists Voice & SMS AI Agent access and describes an AI SMS chatbot that contacts leads to book appointments.
Effi suits a brokerage that wants the conversation and follow-up within its CRM process. Selected AI functions can have usage charges, and SMS automation depends on the chosen plan. Include those terms in the implementation scope before assigning the agent a contact list.
Glide Solutions
Glide Solutions is a custom-app route when a standard enquiry workflow cannot meet the brokerage’s needs. Its managed service scopes the work, matches the project to an agency and supports rollout. Pricing follows a project quote.
For a chatbot project, put the conversation requirements and CRM hand-off in the written scope. Include access to source material, transcript storage, human escalation and ongoing maintenance. The scope must establish the exact chatbot being built and the integrations it needs.
Accuracy, Privacy and Ownership
Assign a named reviewer to transcripts and failed hand-offs. Test duplicate enquiries and an unavailable CRM, including a transfer that succeeds without assigning an owner. Give staff a queue for enquiries that automation cannot deliver.
Collect only the personal information needed for the enquiry. Keep a borrower’s documents out of a general website conversation. Any lending-policy answer needs broker review before a client relies on it, with a clear path to a person.
What Are AI Tools for Mortgage Brokers?
AI tools interpret natural-language questions, extract information from documents or predict patterns from data. Rules-based automation follows instructions such as sending a reminder after a task becomes overdue. A dashboard can calculate totals without making a prediction.
Ask a vendor which specific function uses AI and what information reaches that function. Identify the model provider and the records it retains. A broad AI label does not explain how a policy answer differs from an automated message.
For a general assistant, read the separate guide to using ChatGPT for broker tasks. For software across the whole brokerage, compare mortgage broker tools by job.
Understanding AI Tools for Mortgage Brokers
Pilot AI on one defined task and compare the result with the old process. Record the time spent and the errors that need correction before introducing the tool. Keep the same measures during the pilot, including the staff time needed to review outputs.
Map the input from the client or staff member through each processor to the stored result. Record field mapping, access permissions, retention and deletion. Every integration needs a data owner and a manual fallback.
Test missing values, duplicate clients, incorrect documents, withdrawn consent and failed transfers. A retry must preserve the record’s owner and avoid creating a second enquiry. Review the result in the destination system, where the next staff member works.
Australian privacy obligations apply to personal information handled through AI. Storage location, subprocessors and access controls belong in the assessment. The adoption checks below cover the input and output, with data security controls for client information alongside them.
AI does not take over a broker’s best interests duty or record-keeping. The Australian Securities and Investments Commission (ASIC) explains the obligations in RG 273 Mortgage brokers: Best interests duty. Retain the source, output, staff review and correction that support the recommendation.
Types of AI Tools for Mortgage Brokers
Workflow and Team Management
AI workflow tools can classify incoming work or draft a task summary. The brokerage owns the hand-off rules and assigns a person to failed or disputed outputs. A task must remain visible when the model or connection fails.
Document and Data Management
Document tools can extract a payslip amount or classify an uploaded file. The reviewer must compare the field with the source document before using it. A document request or file transfer can work without AI, so assess extraction separately.
Customer Relationship Management
A CRM holds contact history and assigns follow-up work. AI can draft a response or classify a message within that process. Keep client records accurate and preserve consent controls when an AI function receives access.
Lead Generation and Marketing Automation
AI marketing tools can draft copy or score client records for follow-up. Scheduled campaigns can run through ordinary automation. Staff must own the audience selection and approve communications under the brokerage’s consent rules.
AI Tools in Australia: Eligibility, Benefits and Considerations
A brokerage can adopt an AI tool when its systems permit the use, a named owner manages it and staff can review the outputs. Policy research can reduce searching, document extraction can reduce re-entry and retention scores can order review work. Measure each benefit against the previous process, including correction time.
The Office of the Australian Information Commissioner (OAIC) explains that privacy obligations cover commercially available AI products. Personal information entered into a system and personal information generated in its output both need controls. Public generative AI is unsuitable for client data without a controlled, lawful use.
How to Assess an AI Product
- Define one use case and the result that would justify adoption. Keep the pilot within that task.
- Use approved sample files that reflect the documents staff actually handle. Apply the brokerage’s permission and data-handling rules to the sample.
- Count extraction errors and false matches against the source files. Compare review time and corrected results with the old process.
- Read the privacy and model-training terms. Record which providers receive information, what they retain and whether they use it for training.
- Name a person who can reject or overturn outputs. Every high-impact use needs a human reviewer before the output affects client work.
- Repeat the review when the model changes. Reassess a changed provider, integration or data flow before using the altered function.
Budget for setup, staff review and failed integrations alongside the subscription. A time saving only counts when the corrected output is usable. Retain a process for a client to challenge or correct information inferred about them.
The Future of AI in Mortgage Broking
Broker software will keep adding summaries, document extraction and predictive scores. Each added function needs its own data-flow review and a person checking every output used in client work. Assess the function before enabling it across the brokerage.
The separate guide to whether AI will replace mortgage brokers examines the broker’s role. For the next software decision, define the job you want to improve and retain a named owner for the result.
