AI Slop Is Hitting Finance Content—Here’s the Fix

A single academic study recently quantified what a lot of teams already feel in their gut: once people start using generative AI, output volume jumps fast. Researchers analysing over one million preprint abstracts (2018–2024) found that after authors adopted AI tools, their publishing rate increased by 36.2% to 59.8% per month. Among non‑native English speakers, the lift was even larger—up to 89.3% in some groups.

That productivity spike sounds great—until you ask the uncomfortable follow-up: what happens to quality when everyone can produce polished text at scale? The same research points to a messy outcome: more fluent writing, but a growing risk that complex language becomes a mask for weak work.

For Australian banks, fintechs, lenders, insurers, and the vendors that sell into them, this isn’t just an academic problem. Finance is one of the most regulated, trust-sensitive categories in marketing. If your team starts shipping “AI slop” (high-volume, low-substance content), you don’t just waste budget—you erode credibility, invite compliance headaches, and train your audience to ignore you.

What “AI slop” looks like in finance and fintech marketing

AI slop is content that reads well but doesn’t hold up under scrutiny. It’s grammatically clean, confidently phrased, and often full of generic claims—yet light on evidence, specificity, or practical usefulness.

In AI in Finance and FinTech, slop tends to show up in predictable places:

• Thought leadership with no point of view (“AI will transform banking” + vague benefits, no trade-offs)
• Overstated product pages that imply outcomes you can’t substantiate (a compliance risk)
• Content that confuses complexity with expertise (dense wording, thin insight)
• SEO pages that repeat keywords but don’t answer real buyer questions
• Surface-level explainers that ignore Australian context (ASIC, APRA, OAIC, AUSTRAC, AML/CTF obligations)

Here’s the hard truth: in finance, readers don’t reward you for sounding smart. They reward you for being precise.

The science signal you should pay attention to: productivity up, quality harder to judge

The key finding for marketers is not “AI increases output.” We already know that. The key finding is: language quality becomes a less reliable proxy for substance.

The study (published in Science) evaluated whether AI use correlated with productivity and quality. Productivity was measured by the number of preprints produced; quality was approximated by whether papers were eventually published in journals.

Two results matter for business content teams:

1) AI use strongly correlates with more publishing

Once authors started using AI, monthly output rose 36.2%–59.8%, with the biggest gains among non-native English speakers (often 43%–89.3% depending on platform and group).

Marketing parallel: AI removes the “blank page tax.” Teams can ship more landing pages, email sequences, policy explainers, product updates, and enablement docs—especially when writing in English isn’t everyone’s strength.

That’s a genuine benefit. I’m strongly in favour of using AI to reduce friction.

2) Complex language stops being a quality indicator

The study found a twist: for content written without AI, more complex language correlated with higher odds of publication. But for content written with AI, that relationship flipped—the more complex the language, the lower the odds of publication.

Translation for finance marketing: “Sophisticated wording” can become camouflage.

If you’ve ever read a fintech blog post that sounds impressive yet leaves you unable to explain:

• what changed,
• why it matters,
• who it affects,
• what to do next,

…you’ve seen this effect in the wild.

Why AI slop is riskier in regulated industries (it’s not just a content problem)

Finance content isn’t only marketing—it’s a trust artefact. Customers, journalists, regulators, and partners treat what you publish as a window into how you operate.

Three concrete risks show up quickly:

Compliance drift

AI-generated text often produces plausible-sounding statements that are subtly wrong or overstated. In finance, that can mean:

• implying guaranteed returns
• misrepresenting fees or eligibility criteria
• oversimplifying responsible lending obligations
• making privacy/security claims that your controls don’t support

Even if you catch the big mistakes, “near-miss” inaccuracies can still cause brand damage.

Brand dilution through sameness

Generative AI pulls toward average. If your competitors are using the same tools with similar prompts, you can end up with a market full of indistinguishable articles about “the future of digital banking.”

Buyers don’t choose vendors because of generic optimism. They choose the ones who can explain trade-offs clearly.

Operational drag (the hidden cost)

Slop creates work.

Every low-value article still needs editing, approvals, design, CMS handling, internal review, distribution—and then it sits there underperforming. When teams chase volume, they also flood sales and customer success with content that doesn’t help them.

A better standard: use AI for speed, then prove substance

The fix isn’t “use less AI.” The fix is “stop treating writing quality as the finish line.”

In finance and fintech marketing, content needs to pass two tests:

1. Accuracy and compliance (can we stand behind every claim?)
2. Decision usefulness (does this help a reader choose, implement, or reduce risk?)

Here’s a practical framework I’ve found works when teams want the productivity benefits without the slop.

The 5-layer “anti-slop” workflow for finance content

Layer 1: Start with a claim, not a topic

Instead of “AI in credit scoring,” start with something testable:

• “Using alternative data can reduce thin-file declines, but increases explainability burden.”
• “RAG lowers hallucination risk in customer comms, but you must log sources for audit.”

This forces specificity before the model writes anything.

Layer 2: Require verifiable inputs

Don’t prompt from vibes. Prompt from:

• product docs and approved messaging
• internal policies (privacy, retention, disclosures)
• validated metrics (support tickets, onboarding drop-off rates)
• approved case studies

If the model can’t cite an internal source, treat the output as a draft hypothesis, not publishable text.

Layer 3: Constrain the style to reduce fake complexity

Complex language is where slop hides. Add rules:

• short paragraphs (3–5 sentences)
• ban vague phrases (“robust,” “enhanced,” “next-gen”)
• require numbered steps, examples, or decision trees
• define acronyms once, then use consistently

Layer 4: Add a “red team” review pass

Before compliance signs off, have someone play attacker:

• Which statements could be interpreted as guarantees?
• What would a competitor challenge?
• What would a regulator ask you to evidence?
• What’s missing for an Australian reader?

This is fast and brutally effective.

Layer 5: Publish with proof, not polish

Finance audiences respond to:

• constraints (“This approach works when…”)
• trade-offs (“You’ll gain X, but you’ll pay Y”)
• operational detail (“Here’s how to implement it in 30 days”)

If the draft can’t support those, it’s not ready.

Snippet-worthy rule: If your content can’t survive a compliance lawyer and a cynical CFO reading it, it’s probably AI slop.

What AI search is getting right (and what marketers should copy)

The study also compared traffic patterns from Google vs Microsoft Bing after Bing introduced an AI chat experience (Feb 2023). Users coming via Bing were exposed to a wider variety of sources and more recent publications.

This matters because it pushes back on a common fear: that AI-driven discovery only repeats old, popular sources.

What finance marketers can learn: modern AI experiences reward content that is:

• well-structured (clear headings, direct answers)
• recent (updated references, current regulatory posture)
• specific (numbers, constraints, decision criteria)

If your fintech blog posts are vague, AI summaries will compress them into nothing.

Practical checks to spot AI slop before it ships

You can catch most slop with a 10-minute checklist. Use this on any AI-assisted draft—blog posts, emails, product pages, even investor updates.

1. What’s the one-sentence claim? If you can’t state it simply, the piece isn’t clear.
2. Where are the numbers? Finance readers expect thresholds, timelines, ranges, or measurable outcomes.
3. What’s uniquely Australian here? Mention the local environment where relevant (consumer expectations, privacy posture, regulatory landscape).
4. What would we remove if we had to cut 30%? If nothing is essential, the content is fluff.
5. Is any sentence “confident but unprovable”? Rewrite or delete.
6. Does it help someone make a decision this week? If not, re-scope.

Example: turning a slop paragraph into a useful one

Slop version: “AI improves fraud detection by analysing large datasets to identify patterns and anomalies, helping financial institutions reduce risk and protect customers.”

Useful version: “AI-based fraud detection works best when you combine rules (for known fraud patterns) with models (for novel behaviour) and measure outcomes in false positives per 1,000 transactions. If your false-positive rate is high, customers feel punished—so tune the model against chargeback outcomes, not just ‘suspiciousness.’”

Same topic. Completely different value.

Where this fits in the AI in Finance and FinTech story

Most people talk about AI in finance as models, data, and automation: fraud detection, credit risk, personalised banking, algorithmic trading. That’s the operational side.

But the market is now dealing with the communication side: how AI changes what gets written, what gets believed, and what gets approved. If content becomes cheap, trust becomes expensive.

The teams that win in 2026 won’t be the ones producing the most content. They’ll be the ones producing the most defensible content—clear, accurate, and actually helpful.

What to do next (if you want speed without slop)

If you’re using generative AI for marketing in Australian finance or fintech, set a higher bar than “it reads well.” Build a workflow that treats AI output as a draft, and evidence as the real product.

If you want help choosing the right AI marketing tools (and setting up guardrails so your team doesn’t ship polished nonsense), that’s exactly what we do at AI Marketing Tools Australia.

The question worth sitting with is this: when AI can write anything, what will you publish that’s worth trusting?