Let me get straight to it: AI is poised to upend the lending industry as we know it. And not in some distant, sci-fi future. It’s happening right now.
This is 2025. Unless you’ve been living off-grid without internet access, you’ve probably noticed that AI is now dangerously proficient. Scarily so. What was once a novelty for tech enthusiasts is now a deeply integrated part of everyday tools, and it’s getting disturbingly good at deception. If you’re a lender and you’re not losing sleep over this, you’re either uninformed or in denial.
Let’s rewind a bit. Remember when students actually wrote their essays? Just two years ago, ChatGPT made its grand entrance, and the world hasn’t been the same since. Today’s students may never understand the struggle of writing an essay from scratch, battling typos, or agonizing over tone. Back then, tone was a personal touch. Now, it’s just another setting in your AI tool. But this is merely the beginning. If AI only corrected grammar and composed love letters, we’d be fine. But no, it had to evolve further.
We’re now in a world where a tool like ChatGPT can write job applications, fabricate employment histories, and convincingly generate bank statements. The AI that helps you polish your emails is now helping fraudsters polish their lies. Although the productivity upside is huge. But for lenders, this same technology is turning into a nightmare.
Remember when a forged document looked like it was forged?
It used to be that you could easily spot a fake document. The font would be wrong, the formatting sloppy, the grammar laughable. You’d look at a suspicious payslip and instantly know something was off. Not anymore. These days, AI-generated documents are not only accurate but also hyper-realistic. They come with just enough variation to mimic a real-world scenario; misspellings in the right places, timestamps that match bank operating hours, salary numbers that align with market benchmarks. These aren’t the cheap forgeries of the past. These are professional-grade fakes that could fool a trained compliance officer, and they’re getting better by the month.
The scary part is how easy this has become. A quick prompt to an AI model and you have a fully fabricated three-month bank statement with perfect arithmetic, realistic merchant names, and plausible spending habits. Want to fake an offer letter? Just describe the company, job title, and salary range. Need a utility bill? That can be generated too. It’s not just that the fakes are good. It’s that they’re effortless. And when forgery becomes this easy, the default assumption in lending, that what a borrower submits is real, gets flipped on its head.
Lending is built on trust. AI is wiping that out.
The foundation of lending has always been trust backed by documentation. Whether you’re giving out a small personal loan or a multimillion-dollar mortgage, you’re relying on some form of evidence that the borrower can pay you back. This is where the famous “5 Cs of credit” come in: character, capacity, capital, collateral, and conditions. Some add two more Cs, like credit history and cash flow, but the principle is the same. You’re looking for proof that the borrower is both willing and able to repay.
All these Cs rely on some form of documentation. You want to verify income? Ask for a payslip. You want to assess cash flow? Ask for a bank statement. You want to confirm employment? Ask for an offer letter. The problem is that AI can now generate all of these with such convincing detail that lenders no longer know what’s real and what’s fiction. We’re entering a world where evidence can no longer be trusted at face value. And once that happens, the entire framework of credit decisioning starts to wobble.
I’ve seen the fakes, and they’re getting smarter
In the last year alone, through my interactions with Lendsqr lenders and the credit ecosystem in general, I’ve observed a noticeable spike in suspicious documentation submitted by borrowers, especially in markets where traditional credit bureaus are weak and lenders rely heavily on self-reported data. Many of these documents, bank statements, payslips, and offer letters arrive with formatting and language that appear flawless on the surface. But when verified against actual data sources, like payroll systems, the discrepancies become clear.
While I can’t say for sure AI was used in all of these forgeries, the quality and speed at which they’re produced point strongly in that direction. It’s no longer uncommon to see identical documents submitted across unrelated loan applications or to find income claims that don’t align with transactional patterns. These trends suggest borrowers are increasingly relying on tools that automate and enhance the forgery process, making manual review almost pointless.
And that’s not even the worst part. We’re now seeing attempts to spoof video verifications, with deepfakes and synthetic selfies passing rudimentary liveness checks. These aren’t people wearing masks or hiding in shadows. These are full-on facial overlays using open-source tools that can mimic blinking, head movements, and even speech.
And no, your “move-your-head-left-and-smile” liveness prompt isn’t saving you. These deepfakes are trained to mimic those exact cues. In fact, researchers from Sensity AI flagged over 1,000 deepfake identity attacks between 2022 and 2024, targeting financial services and crypto platforms specifically.
In other words, a fraudster can show up to your KYC process looking like a totally different person, and you won’t know unless you have military-grade detection tools.
And I wish I could say this is a niche problem, but it’s not. From Lagos to Copenhangen, from Sao Paulo to Kuala Lumpur, we’re seeing a pattern. AI isn’t just making fraud easier. It’s making it scalable.
The traditional defenses are no longer enough
It’s tempting to believe that better fraud teams or more document reviewers will solve the problem. But that’s like bringing a water pistol to a forest fire. This isn’t about human error anymore. It’s about systemic failure. The old model of “submit your documents and we’ll review them” is collapsing. In the face of AI-powered forgery, trusting user-submitted documents is becoming a liability.
This means the only way forward is to remove the borrower from the equation, at least when it comes to submitting proof. Allow me to elaborate. Consider bank statements. Instead of requesting a borrower to send a downloaded PDF (which AI can forge), it’s preferable to connect directly to their bank. If the bank confirms a monthly income of $3,000, that’s credible information. Banks have no incentive to lie.
Payslips? Let’s dig deeper. Soon, lenders will only accept payroll information that comes directly from either the employer, an HR SaaS platform, or the government. If you received payment, let’s examine your tax records or pension contributions.
This is already happening through open banking frameworks in countries like Australia, Brazil, and the UK. The lender accesses real-time financial data through regulated APIs, removing the guesswork and eliminating the potential for fraud. In this new world, if I can’t trace it to the source, I won’t believe it.
No more PDFs. Only verified data.
This is the new normal. If your underwriting system still relies on PDF uploads, you’re building on sand. In this new world, we only trust what can be verified at source. Open banking is the clearest path forward, but it’s not the only one. Employers can be looped in through payroll APIs. Government databases can verify identity, address history, and tax records. Telecoms can provide behavioral credit scoring. In short, we are moving from a document-based system to a data-based system. And in a data-based system, forgery becomes nearly impossible, because the data is verified in real time and signed digitally.
It won’t be perfect. Fraudsters will try to spoof integrations, intercept API calls, or manipulate phone numbers. But these are harder to scale, and much easier to detect, than a well-crafted PDF forgery. With the right audit trails and cryptographic verification, we can catch these attempts before they do damage.
The war on AI fraud will be fought at the hardware level
And what about video verification? If AI can fake faces, are we doomed? Not necessarily. The next frontier of trust will happen at the hardware level. Just like Apple uses its Secure Enclave to store biometrics, we’ll soon need secure chips that can vouch for the authenticity of camera input. In other words, the device itself will sign off on whether a selfie or video came from a real user in real time, without being tampered with.
This is already being explored in the mobile security space. Trusted Execution Environments (TEEs) and Secure Elements can confirm that the image you’re seeing is unaltered, captured from a real device, and not replayed or synthesized. It’s not cheap tech, and it won’t roll out overnight, but it’s inevitable. And frankly, it’s the only way to stop the flood of synthetic identities before it becomes a full-blown crisis.
If you’re still using 2021 tools in 2025, you’re already cooked
Let’s be honest. The tools most lenders are using today were built for a pre-AI world. They were never designed to detect generative forgeries or deepfakes. If your fraud detection system hasn’t been updated since the pandemic, you’re already obsolete. This is not the time to be complacent or nostalgic about how things used to work.
At Lendsqr, we’ve been investing aggressively in fraud detection, real-time data verification, and AI counter measures. Not because it’s trendy, but because we don’t have a choice. We work with lenders across geographies where fraud levels are not just high, they’re innovative. The fraudsters are evolving faster than most regulators, and definitely faster than most banks. We can’t afford to wait.
AI won’t kill lending. But it will kill lazy lenders.
AI isn’t evil. It’s just powerful. And like any powerful tool, it can be used for good or bad. The responsibility lies with us: the builders, the lenders, the operators. If we sit back and wait for someone else to solve this, we’ll be wiped out. Not just in reputation, but in losses, defaults, and regulatory blowback.
But if we act now, build the right verification infrastructure, and stay a step ahead, we can not only survive, we can thrive. Lending will always be a business of trust. The difference now is that trust must be verified, not assumed.
I’ve staked my career, my company, and my sanity on helping lenders succeed. I’m not going to let generative AI wipe out the progress we’ve made. Not on my watch. Because I still believe in the power of credit to change lives. But I also believe in meeting challenges head-on, not pretending they don’t exist.
Let’s fight smart, build resilient systems, and ensure that the future of credit is still human, just a bit more skeptical, and a lot more secure.