Written byArtificial intelligence (AI) is technology that lets computer systems learn from data, recognise patterns and make decisions that would traditionally require human judgement, including credit decisions in lending.
Also known as: AI, algorithmic decision-making
Key points
- Lenders use AI for credit scoring beyond the traditional credit score, affordability checks from bank data, fraud detection and document processing.
- For borrowers it can mean faster decisions and broader access, but also less transparency about why an application was declined.
- Brokers use AI tools for lender matching, serviceability modelling and compliance checks, while human judgement still carries complex or non-standard deals.
- ASIC and APRA expect transparency, bias testing and human oversight of AI in credit decisions; the Privacy Act governs the data behind it.
How AI is used in lending
Credit decisioning is the biggest use. Machine learning, the branch of AI most lenders actually use, assesses creditworthiness and predicts the likelihood of default using more than bureau data: bank transaction patterns, spending behaviour and business cash flow trends. Some lenders use this to decide personal loans, car loans and small business loans automatically. On consumer credit the same models can read open banking data or bank statements to support the responsible lending assessment required under the NCCP Act, which does not cover business purpose finance.
Beyond approvals, machine learning flags suspicious applications, such as inconsistent identity documents or fabricated income statements, which helps lenders meet AML/CTF obligations. AI tools extract data from payslips, tax returns and financial statements, chatbots handle routine enquiries, and after settlement models watch for early signs of financial stress before a loan falls into arrears. Some lenders also use AI for risk-based pricing, where the rate offered reflects the individual borrower's risk profile.
What AI means for borrowers
AI can work for or against you. On the plus side, automated assessment can return a decision on a straightforward application much faster than a manual review, and models that use alternative data may approve self-employed borrowers, small business owners or people with a thin credit file who would fail traditional scoring. Applying the same criteria to every application also reduces inconsistent or biased human decisions.
The downside is opacity. If an algorithm declines you, it can be hard to learn which factor caused it or what to change. And if the data used to train a model reflects historical bias, the model can repeat it, which ASIC has flagged as an area of regulatory focus. You can always ask a lender for the reasons for a decline.
AI and the broker's role
AI does not replace brokers; it changes how they work. Aggregator platforms use algorithms to match a borrower's profile with suitable lenders on the panel, serviceability tools calculate borrowing capacity across many lenders' policies in one pass, AI-assisted file review checks applications against the best interests duty and responsible lending obligations, and automated updates keep customers informed on application status and outstanding documents.
The value a broker adds sits where automated models are weakest: interpreting results, handling exceptions, structuring complex deals and advocating for borrowers whose circumstances do not fit neatly into a model. Regulators are moving the same way, expecting lenders to explain automated decisions, monitor for bias and keep human oversight of AI systems.
Example
A sole trader applies online for a $30,000 loan for a delivery van. The lender's AI reads her open banking data, checks the identity documents for inconsistencies and returns its decision automatically. It declines: the model reads her seasonal income as instability. Her broker packages the same application with a BAS history showing the seasonal pattern repeats every year, and takes it to a lender on the panel whose credit assessor reviews non-standard income manually. That is the division of labour: the model handles the routine, the human handles the exception.
Not to be confused with
- Open banking
- open banking is the regulated framework for sharing your financial data; AI is the technology that may analyse that data once it is shared
- Credit rating
- a credit score is a single number from a credit bureau; AI models can go beyond it by using bank transaction and cash flow data
Frequently asked questions
Can AI approve or decline my loan application?
Yes. Many lenders use AI models to make or support credit decisions, especially for straightforward consumer finance such as personal and car loans. For more complex lending, AI usually informs the decision but a human credit assessor makes the final call on the application.
If I'm declined by an AI system, can I find out why?
You can ask the lender for the reasons behind any decline. Regulators increasingly expect lenders to give meaningful explanations of automated decisions, although the level of detail varies. If the reasons are unclear, a broker can often work out which factor caused the problem and whether another lender's policy would treat it differently.
Does AI make lending decisions fairer?
It can cut inconsistency, because the same criteria apply to every application, and alternative data can open the door for borrowers who fail traditional scoring. But a model trained on biased historical data can repeat that bias, which is why regulators push lenders to test for and address algorithmic bias.
Will AI replace mortgage and finance brokers?
Unlikely. AI handles routine processing and data analysis well, but it is less effective with complex borrower situations, lender negotiations and deal structuring. Brokers who use AI tools for matching, serviceability and compliance, and keep the human judgement for the hard cases, remain valuable.
Is my data safe when a lender uses AI?
Lenders must comply with the Privacy Act and their data security obligations whether or not they use AI. Data fed into AI models has the same protections as any other personal information a lender holds, and open banking data is shared under the regulated Consumer Data Right framework.
Related terms
Credit rating
A credit rating is an independent assessment of how likely a government, company or debt issue is to meet its obligations on time, graded from AAA down to D.
Read definitionOpen banking
Open banking is the regulated framework under Australia's Consumer Data Right (CDR) that lets you authorise accredited third parties to access specific financial data held by your bank.
Read definitionFraud
Fraud is deliberate deception or misrepresentation intended to secure an unfair or unlawful gain or cause loss, such as false documents on a loan application.
Read definitionNCCP Act
The NCCP Act is Australia's National Consumer Credit Protection Act 2009, the law that licenses credit providers and brokers and sets responsible lending and disclosure rules for consumer credit.
Read definitionAnti-money laundering (AML)
Anti-money laundering (AML) is the set of laws, controls and processes designed to stop criminals turning the proceeds of crime into apparently legitimate funds, enforced in Australia by AUSTRAC.
Read definitionASIC
ASIC is the Australian Securities and Investments Commission, the regulator for companies, markets, financial services and consumer credit, which licenses providers, keeps public registers and enforces conduct laws.
Read definitionGo deeper
Sources
This article is general information only and is not financial advice.