The fintech sector continues to expand at a rapid pace, bringing new tools and services that change how both businesses and consumers handle money. This ongoing growth opens doors but also exposes the industry to fresh risks. As digital transactions and financial apps become everyday tools, the need for constant vigilance grows.
Fintech professionals and financial institutions cannot afford to look away. Recent developments hint at threats that go far beyond anything seen before. Quynh Keiser, an accomplished Risk Management and Regulatory Compliance leader with extensive experience across financial institutions, discusses why the urgency to identify, prepare for, and defend against these new dangers should drive every decision in the industry.
The Next Big Threat: AI-Driven Financial Fraud
Artificial intelligence has begun to change the face of financial crime. Cybercrime groups now use machine learning to build smarter attacks that can fool even the most experienced security teams. No longer limited to simple phishing emails or fake invoices, these schemes now operate at a scale and complexity that makes them hard to catch and even harder to stop.
Several recent cases show how far scams have evolved. In 2023, a city staffer was tricked into transferring millions after a deepfake call mimicked the CFO’s voice. In Hong Kong, a finance worker sent $25 million to scammers after a fake video call featuring AI-generated likenesses of executives.
These attacks mark a sharp break from past low-tech scams. With AI, criminals now automate thousands of attacks, tailoring each to a target’s specific weaknesses. Even small groups or solo actors pose serious risks, creating a scale of threat that has both established and emerging fintech firms on high alert.
Artificial intelligence adds a powerful layer of danger to existing forms of financial fraud. In the past, scams relied on clumsy emails or crude impersonations. Today, machine learning can produce realistic fake documents, fake voices, or even video deepfakes. These tools can trick even those who think they know what red flags to watch for.
“Social engineering takes on new life when powered by AI,” says Quynh Keiser. “Algorithms scan social media, company filings, and transaction data to craft messages or scripts that sound real. Phishing emails now match the tone, timing, and style someone expects from a boss or business partner. Even trained staff may struggle to tell the difference.”
Fake transactions and synthetic identities have become everyday tools for criminals. Machine learning can generate data that fits normal transaction patterns, making it tough for old rule-based systems to spot fraud. Algorithms can also blend stolen information with fake elements, creating identities that pass background checks and KYC screening.
Automation makes the threat even sharper. AI can carry out endless attacks at a speed no human could match. If one tactic fails, algorithms can change the approach in seconds. Attackers learn from each attempt, improving with each cycle.
Deepfakes set a new high-water mark for impersonation. Criminals can create audio or video clips of real people that are nearly impossible to distinguish from the real thing. This new form of social engineering opens a door to wire fraud, blackmail, and insider access on a scale not seen before.
The consequences of AI-driven financial fraud hit all sides. For banks and fintech companies, the risks extend well beyond short-term losses. Repeated security breaches can destroy trust, wipe out years of reputation-building, and trigger customer flight. When customers lose money due to fraud, they blame the institution that held their funds, not just the criminals who stole them.
Legal and regulatory pressures mount fast after high-profile incidents. Regulators expect banks and fintech firms to keep up with new threats. They may impose fines or restrict business if defenses fall behind. Insurance costs rise, and in some cases, firms may struggle to get coverage at all after a severe breach.
Customers feel the effects of lost savings, threats to privacy, and increased anxiety around digital finance.
“When scams go public, users often rethink how much they trust online banking or payment apps,” notes Keiser. “Loss of user trust can drive customers to more traditional or less innovative competitors.”
Regulatory scrutiny grows with every major attack. Governments may respond by imposing new reporting rules, tighter controls, or demands for stronger verification. High levels of compliance risk can slow new product launches and create overhead that hits profit margins.
Building Resilience: Steps Fintech Leaders Should Take Now
Industry leaders now face a clear mandate. Preparation, speed, and smart choices must guide every move. No simple plug-and-play solution exists to counter AI threats. Instead, firms must pull together strong technology, well-trained teams, and smart alliances. Each layer in this defense must work together, catching mistakes and stopping unusual actions before damage spreads.
The stakes are high. Firms that prepare now can avoid major disasters and show customers they take security seriously. Those that wait risk joining the growing list of institutions caught off guard by smarter, more relentless attackers.
Firms must fight tech with tech. Anomaly detection now relies on machine learning, not outdated rules. Upgraded systems scan millions of transactions in real time, flagging suspicious patterns, even subtle ones that mimic normal behavior. Real-time risk scoring evaluates time, location, device, and history to catch fraud instantly.
AI learns from each event, growing stronger with human feedback. As scams evolve, so does the defense from them. Regular updates and analyst input help refine models. Sharing suspicious activity data within legal limits also boosts collective protection, giving firms a smarter, faster way to spot threats and stop fraud before it spreads.
Even the best tech can’t catch everything. People remain both the biggest risk and strongest defense. Firms reduce fraud by training staff and customers to spot threats early.
“Ongoing education, not one-time lessons, helps employees detect fake emails, links, or calls. Simulations build quick, confident responses,” says Keiser.
Clear messages via email or alerts keep customers informed. Rapid response teams aid recovery. Open communication builds trust. When safety is prioritized, users stay loyal and feel confident using digital tools.
Fighting AI-driven fraud requires collaboration. Fintechs, banks, regulators, and tech firms must share updates, spot trends, and build strong, shared defenses. Staying current on laws and standards helps firms act early and avoid costly fixes.
Regular policy reviews, audits, and compliance testing are essential. Working with regulators during product development prevents issues later. A dedicated team should track and apply new rules, advising leadership before problems arise and helping make the entire sector safer.
AI-driven financial fraud is a serious threat today, reshaping criminal tactics and testing even top-tier defenses. Standalone systems won’t suffice. Fintech leaders must act boldly, investing in AI-powered detection, promoting continuous learning, and collaborating with regulators and partners.
Firms that lead here will define trust and safety in digital finance. Caution, speed, and teamwork are essential. The next wave of attacks is coming. Those who prepare now will protect their customers and shape a safer financial future.
DISCLAIMER: No part of the article was written by The Signal editorial staff.