Disseminated on Behalf of: Pelican AI Corp.
All figures in Canadian dollars unless otherwise noted.
- Already profitable, Pelican AI is targeting roughly 65% revenue growth and continued positive adjusted EBITDA for 2027 as it scales its marketing and sales reach.
- Customer-reported results include a 96.8% straight-through processing rate at a global American bank and a reduction of more than 70% in false positives at a Big 5 Canadian bank.
- Its new Fraud Alerts Optimizer, launched Sept. 17, 2026, gives banks a way to filter out false fraud alarms, and Pelican is now adding bank pilot partners.
Banks are being squeezed from two directions. Payment volumes keep rising, and each payment must clear a widening set of regulatory checks before it reaches its destination.
The industry is increasingly turning to artificial intelligence to manage that load, using it to automate payment processing, anti-money laundering (AML) checks, sanctions screening, and fraud detection to lower operating costs. Grand View Research projects the global AI in banking market will reach US$143.56 billion by 2030, growing at roughly 31.8% a year.
Pelican AI Corp. (CSE: PEL) has been building this kind of software for 33 years, well before the trend had a name.
The Toronto-based company works across three core areas of a bank: a payments platform that moves money faster with fewer manual fixes, a compliance suite covering AML, sanctions screening, and fraud detection, and trade finance tools that scan global shipping deals for signs of money laundering.
Its clients include Wells Fargo, BMO, TD, Intesa Sanpaolo, Standard Chartered, and State Bank of India, spread across more than 55 countries, and the company began trading on the Canadian Securities Exchange on Sept. 1, 2026.
“Banks spend money on AI, regulatory compliance, and process automation,” said CEO Nino Di Teodoro. “We live at the intersection of all three of those.”
AI before it was called AI
Plenty of fintechs now describe themselves as AI companies. Pelican has been doing the work for 30 years, back when the industry still called it machine learning. Over this time, it has processed more than two billion transactions for the largest global banks.
That history matters because a large bank can always outspend a small software vendor on technology, but it cannot buy decades of real-world transaction data.
“They can buy the latest and greatest,” Di Teodoro said. “What they don’t have is the decades and literally billions of transactions that we’ve seen. That makes our models sharper and more accurate than anything they can produce.”
The hidden cost of a false alarm
To understand why banks pay for Pelican’s software, it helps to look at how a payment actually moves.
Payments are a high-volume, low-margin business, and the industry measures itself by straight-through processing, the share of transactions that travel end to end without anyone touching them. When a person has to step in, the bank usually ends up losing money on that payment.
False positives are the biggest cause of those interventions. A legitimate payment can stall over something as small as a missing space in a surname, or an address on a street that happens to share a name with a sanctioned individual. Large banks employ hundreds, sometimes thousands, of people to clear these exceptions every day, and most still fall behind.
“I don’t know of any bank that actually looks at every single alert that they get,” said Di Teodoro, who previously ran fraud and financial crimes at Symcor, the payments processor jointly owned by RBC, TD, and BMO.
Pelican’s software learns from those human reviewers. It watches how operators resolve each false alarm, then applies the same fix automatically the next time a similar case appears.
Clients say the difference shows up in their numbers. A global American bank using Pelican reports a 96.8% straight-through processing rate and says it handled 28% more volume without adding staff, saving $2.3 million to $2.6 million a year.
A British multinational bank reports $14 million to $17 million in annual benefit, while at a Big 5 Canadian bank, Pelican cut false positives by more than 70%.
Pointing the engine at fraud
Pelican’s newest product came directly out of conversations with those clients. The Fraud Alerts Optimizer (FAO), launched on Sept. 17, 2026, applies the same self-learning approach to fraud alerts, identifying the ones likely to be false before they land on an investigator’s desk.
FAO is designed to sit on top of a bank’s existing fraud systems, so clients avoid an expensive rip-and-replace project. Because it builds on technology Pelican had already developed, it was also relatively inexpensive to bring to market.
“By the time it comes back from us, they know it’s a real alert,” Di Teodoro said. “They don’t waste their time.”
The company is now recruiting bank partners for a pilot program. Di Teodoro prefers to let the product make its own case, giving banks a month or two to test it before they decide whether to subscribe, and he says that approach has served him well throughout his career.
“In all the times I’ve done this, not once has a client said no thank you,” he said.
Profitable, and built to sell
Unlike most newly listed fintechs, Pelican is already profitable on an adjusted EBITDA basis. In its most recent audited fiscal year, the company earned $5.0 million in adjusted EBITDA on $13.0 million in revenue, a 38% margin.
Management is now targeting significant year-over-year growth across revenue and adjusted EBITDA. These are management targets, not guidance.
Di Teodoro sees two main benefits from the public listing. It gives Pelican the same level of transparency as the large, publicly traded banks it sells to, and it opens the door to capital.
That capital is going toward growth rather than research, with marketing and awareness at the top of the list. By management’s estimate, roughly 40,000 financial institutions worldwide could use Pelican’s products, and the company wants far more of them to know its name.
“I need more boots on the ground,” Di Teodoro said. “I need more people to know about us.”
Existing clients are already deepening their relationships. In September, a leading United Arab Emirates financial institution selected Pelican’s trade-based money laundering module, broadening a partnership that was already in place.
Banks spend money on AI, regulatory compliance, and process automation. We live at the intersection of all three of those.
— Nino Di Teodoro, CEO, Pelican AI
Why investors are watching
Over the next year, investors can track Pelican’s progress through new bank clients, larger contracts with existing ones, and new products built on the company’s data and models. Conversions from the FAO pilot program will be one of the earliest signals.
Longer term, management has set an aspiration of $100 million in annual recurring revenue within five years, a target rather than guidance. Reaching it will depend on sales reach, tuck-in acquisitions, and new products rather than on proving the core technology.
With banks continuing to direct spending toward AI, compliance, and automation, Pelican sits right in the path of that demand. The company already has a working product, a roster of global banks, and a profitable business, and its public listing now gives it the capital to put all three in front of many more buyers.
About Pelican AI Corp.
Pelican AI Corp. (CSE: PEL) is a financial technology company providing artificial intelligence-powered solutions for payments, financial crime compliance and trade finance. With more than 30 years of proprietary AI expertise, Pelican’s technology is trusted by major financial institutions across more than 55 countries and has processed more than two billion transactions representing approximately $10 trillion in value.
Learn more at pelican.ai or their investor site at ir.pelican.ai.
