AI analysis of published AFCA determinations. Describe a complaint to see how AFCA decided similar ones, and why.
Describe a complaint in plain English. FincomplaintIQ uses AI to compare it with 1,397 published AFCA determinations and shows how similar complaints were decided, the likely outcomes and awards, and why.
Designed for complaints, IDR and legal teams at financial firms.
A customer disputes a storm claim declined as “wear and tear”.
Each analysis simulates AFCA deciding the complaint 100 times, using how similar determinations were actually decided and what AFCA actually awarded, with its compensation caps applied.
The site is in private preview while it's being tested, and access is by invitation. If you've been sent an access link, open it, or enter your code here.
Access is by invitation only while the site is in private preview.
Claude, an AI model made by Anthropic, read each published AFCA determination and summarised it into a record: the product, the issues, the outcome, the remedy, the deciding factors and key paragraphs quoted word for word. When a complaint is described, the site shortlists the closest determinations and the AI reads them against the facts to explain how AFCA approached similar complaints. The outcome ranges and simulation are then calculated from those determinations.
No. It's general information and research based on published determinations. Decisions on a complaint remain with the firm, under its own IDR process, with legal advice where needed.
No. It's an independent project and isn't operated or endorsed by the Australian Financial Complaints Authority. It works from the determinations AFCA publishes.
No. AFCA decides each complaint on what's fair in all the circumstances and isn't bound by earlier decisions. The finder shows how AFCA has approached similar complaints and the range of outcomes that suggests.
The description is sent to Anthropic's Claude AI to produce the analysis and isn't stored on this site's server. Saved analyses stay in the user's own browser. Customer names, account numbers and other personal information should be left out.
Include the product, what the firm did or didn't do, dates, amounts, what you've asked for, and anything the firm has said in response.
Each determination tested uses one analysis' worth of your Claude usage.
Pick a topic. The chart shows how the determinations in the library were decided each year. Then have Claude compare the reasoning in older and newer determinations, including FOS and CIO decisions from before AFCA began in November 2018.
Determinations relating to Dixon Advisory dated before 1 December 2025 have not been included, due to their volume and nature.
Which financial firms appear most in the library, and how often AFCA sided with the complainant in full or in part. Click a firm to open its profile.
Determinations relating to Dixon Advisory dated before 1 December 2025 have not been included, due to their volume and nature.
These figures only cover the determinations in the library, not every complaint made to AFCA. Most complaints are resolved before a determination, and bigger firms have more customers, so a higher count doesn't mean a firm treats customers worse. Rates based on only a few determinations can swing a lot; the tooltip shows a likely range. For official complaint volumes by firm, see AFCA's published complaint data.
Drop in determination PDFs, or a whole folder of them. There's no practical limit: the library holds tens of thousands. Each PDF is summarised into a searchable record with its product, issues, outcome, remedy, deciding factors and key paragraphs quoted word for word. Download PDFs from AFCA's published decisions search.
Each PDF uses some of your Claude usage, so very large batches may pause at your usage limit. Choose Resume to carry on. Files already in the library are skipped. Add files in one tab at a time.
Saving determinations from AFCA's site: open the determination, press Ctrl+P, and set the printer to Save as PDF, not “Microsoft Print to PDF”. That keeps the text, so it's faster and quoted paragraphs can be checked. PDFs saved as pictures still work: the page reads them itself, which takes a few extra seconds per page. You can also save the page with Ctrl+S (.html), or copy and paste it below.
Determinations relating to Dixon Advisory dated before 1 December 2025 have not been included, due to their volume and nature.
Anyone who opens this site can search the library and run analyses. Your own analyses and the facts you type are kept only in this browser.
FincomplaintIQ compares the facts of a financial complaint with published determinations of the Australian Financial Complaints Authority (AFCA), and summarises how AFCA has approached similar complaints: which way they went, the factors that decided them, and what AFCA ordered.
This site is an early version that's still being built and tested. That means:
While it's being tested, the tools are open by invitation only.
The Australian Financial Complaints Authority (AFCA) is the external dispute resolution scheme for financial services in Australia. It began on 1 November 2018, replacing the Financial Ombudsman Service (FOS), the Credit and Investments Ombudsman (CIO) and the Superannuation Complaints Tribunal (SCT).
Most complaints to AFCA are resolved along the way, by agreement between the parties, conciliation or a preliminary assessment. When a complaint isn't resolved, it can go to a determination: a written decision by an AFCA ombudsman, adjudicator or panel that sets out the facts, the issues, the outcome, the reasons and any remedy awarded.
AFCA decides what is fair in all the circumstances, considering legal principles, applicable industry codes and guidance, good industry practice and its previous relevant decisions. It isn't bound by earlier determinations, but they are the best public record of how it approaches a particular kind of complaint.
AFCA publishes its determinations on its website, where they can be searched and read in full. Complainants aren't named. Since late 2019, published determinations have named the financial firm, unless AFCA has a compelling reason not to. There are a lot of them: AFCA reported issuing 2,184 determinations in the six months from October 2019 to March 2020 alone.
For complaints, IDR and legal teams, past determinations show how AFCA is likely to approach a complaint before it gets there. They help to assess a complaint early, set fair offers based on what AFCA has actually awarded, write IDR responses that reflect AFCA's approach, spot recurring issues, and prepare for AFCA. But each determination is a long document, with the outcome, the amount awarded and the reasons written in prose, so comparing even a few dozen by hand is slow.
FincomplaintIQ is an AI tool for financial services complaints teams. It aggregates published AFCA determinations into a structured, searchable library, with each determination summarised into a record of the product, issues, outcome, remedy, deciding factors and key paragraphs. It then uses that library to analyse a described complaint: the closest determinations, likely outcomes and award ranges, the deciding factors and trends across products, issues and firms.
Sources: AFCA: the process we follow · AFCA: public reporting of determinations · ASIC: naming of firms in determinations · HSF Kramer: appealing AFCA determinations
Last updated 28 September 2026.