10 min de lecture

Mastering fraud detection and prevention in banking and FinTech

Banking

FinTech

Imagine this: You’re on a video call with your CFO and a few colleagues. Everything seems normal until you realize none of them were actually there. And by then, you’ve already shared sensitive information or transferred money to a third party. That’s exactly what happened in January 2024, when a Hong Kong-based firm employee was tricked into wiring $25 million to fraudsters. The scammers used deepfake technology to mimic the CFO and others on the call, faces, voices, and all.

The reality is, this can happen to anyone now. Fraud in banking and FinTech has evolved far beyond stolen cards and phishing emails. We’re talking AI-generated voices, synthetic identities, and deepfake execs pulling off multi-million-dollar scams.

If that doesn’t make you rethink your fraud strategy, it should. In this guide, I’ll walk you through the top fraud types, how detection works today, and the best ways to stay one step ahead.

$40B

in fraud losses are projected in the U.S. by 2027, fueled by GenAI

Source : Deloitte

1 in 4

banks and FinTechs lost over $1M to fraud in 2023 alone

Source : Alloy

Common fraud types in banking and FinTech today

You can’t fight what you don’t fully understand. And while fraud is constantly evolving, let’s not forget some of the oldest tricks in the book are still in play. We’ve matured, but they’ve adapted too. So, before we dive into prevention, let’s look at the most common fraud techniques threatening banks and FinTechs today and why strong, adaptive fraud detection in financial services matters more than ever.

Credential theft & account takeover (ATO)

Credential theft and ATO happen when fraudsters use stolen credentials to log into user accounts. They use tricks like AI-powered phishing, credential stuffing, and malware to sneak past security. More advanced tactics like session hijacking, man-in-the-middle (MitM) attacks, and SIM swapping let them intercept authentication codes and drain accounts before anyone notices.

Synthetic identity fraud

Fraudsters mix real and fake personal data — often using AI — to create identities that don’t actually belong to anyone. These synthetic profiles slip through security checks, allowing criminals to open bank accounts, take out loans, and launder money. Without a real victim to report the fraud, fraudulent activity often goes undetected until it’s too late. Detecting this requires sophisticated AI and a strong fraud management system in banking.

Real-time payment fraud

With instant payment systems, fraudsters exploit the speed and irreversibility of transactions to move stolen funds before detection. Common tactics include authorized push payment (APP) fraud and mule networks that rapidly disperse illicit money. Once the money’s gone, there’s no chargeback, and banks need advanced banking fraud monitoring to catch threats before they escalate.

Credit card & card-not-present (CNP) fraud

Fraudsters swipe card details through skimming, data leaks, and phishing, and use them for shady online purchases where no physical card is needed. They pull off scams like chargeback fraud, credential stuffing, and bot-driven attacks, racking up charges before anyone catches on. With stolen card info flooding the dark web, banks and merchants are left dealing with the fallout.

API & open banking exploits

As banks and fintech businesses rely more on open banking APIs, fraudsters look for security gaps to steal data and hijack transactions. Weak authentication, misconfigured APIs, and exposed endpoints let attackers manipulate accounts, initiate unauthorized payments, or scrape sensitive financial data. With more third-party integrations than ever, a single weak link can open the door to large-scale fraud.

Malware & banking trojans

Fraudsters use malware and banking trojans to sneak into accounts, steal credentials, and mess with transactions. They spread through phishing emails, fake apps, and shady browser extensions, giving attackers full access to banking sessions. Some trojans are so advanced that they can even bypass multi-factor authentication (MFA), which makes them a nightmare for banks and users alike.

AI-driven fraud & Fraud-as-a-Service (FaaS)

AI helps criminals automate scams, bypass security checks, and generate deepfake voices and videos to trick banks and customers. Meanwhile, FaaS has turned cybercrime into a business, with ready-made phishing kits, credential stuffing tools, and AI-driven bots available for rent on the dark web. This lets even low-skill fraudsters launch advanced attacks, making financial fraud harder to catch and stop.

Crypto & DeFi fraud

As banks and FinTechs dive into crypto, fraud is evolving with them. We’re not just talking about the occasional rug pull — attackers are leveraging smart contract flaws, flash loans, and cross-chain tricks to move stolen assets before anyone notices. With transactions happening fast and anonymously, the pressure on institutions to detect and respond in real time is higher than ever.

Don’t let fraud win — take control now!

How modern fraud detection works

Fraud isn’t always loud, obvious, or easy to catch — it can be subtle, adaptive, and often slips through where no one’s looking. That’s why modern fraud detection in banking isn’t just about spotting red flags. It’s about knowing how fraudsters think, where systems get weak, and when to act. So, how do the best systems stay in the game? Let’s take a closer look.

Behavioral analytics

AI-powered systems track typing speed, mouse movements, transaction habits, and location patterns to establish normal behavior. If an account suddenly behaves differently — for example, makes a high-value transfer from an unusual location — the system flags it and triggers security measures. This helps detect account takeovers, bot activity, and synthetic identity fraud.

Machine learning models

Supervised ML learns from past fraud cases to classify transactions, while unsupervised ML detects anomalies without predefined rules. These models spot sudden spending spikes, high-risk merchants, and login inconsistencies. Reinforcement learning helps refine detection by adapting to evolving fraud tactics.

Real-time transaction monitoring

Instead of catching fraud after it happens, modern systems analyze transactions as they occur. They check transaction frequency, amounts, and recipient history in milliseconds. Unusual activity, such as rapid withdrawals or inconsistent spending patterns, can trigger security measures before the transaction is completed.

Risk scoring & pattern analysis

Fraud detection engines assess multiple risk factors at once, including location, device history, past transactions, and login behavior. Instead of relying on a single alert, modern fraud management in banking uses multi-factor scoring to assess risk. Based on this risk score, businesses can apply extra authentication steps or block suspicious activity entirely.

Network-based fraud detection

Many fraud schemes involve coordinated efforts through mule accounts or stolen identities. By analyzing connections between accounts, devices, and transaction histories, fraud detection systems can uncover hidden relationships that indicate organized fraud. If multiple accounts share the same device or funnel money to the same recipient, they can be flagged as part of a larger fraud network.

Tools and technologies for fraud detection

Fraud detection isn’t about one magic solution — it’s about layering the right technologies to spot fraud before it spreads. Now that we’ve looked at how different detection methods work, let’s explore the tech that powers them in real-world banking environments.

Technologie	Comment cela fonctionne-t-il ?	Caractéristiques principales	Popular solutions
Fraud management systems (FMS)	Centralized platforms that aggregate fraud data, analyze transactions, and trigger alerts in real time	Transaction monitoring, case management, and real-time risk scoring	NICE Actimize, FICO Falcon, SAS Fraud Management
IA ET ML	Detects fraudulent activity by analyzing patterns, anomalies, and behavioral shifts	Predictive analytics, anomaly detection, adaptive learning models	Feedzai, Darktrace, IBM Trusteer, DataVisor.
Blockchain	Prevents fraud by providing immutable transaction records and decentralized identity verification	Cryptographic security, smart contracts, tamper-proof ledgers	Trust Stamp, Evernym, IBM Blockchain Fraud Prevention
Biometric & risk-based authentication (RBA)	Uses physical and behavioral biometrics to verify identities and assess risk dynamically	Fingerprint scanning, facial recognition, behavioral biometrics, dynamic risk scoring	BioCatch, Nuance Gatekeeper, Jumio, Onfido
Device intelligence & fingerprinting	Identifies fraudulent users by analyzing device characteristics, geolocation, and connection patterns	IP tracking, device binding, anomaly detection	ThreatMetrix, iovation, FingerprintJS
Synthetic identity detection	Uses AI to detect fabricated identities that combine real and fake data for fraud schemes	Identity clustering, AI-driven pattern recognition, document forgery detection	Socure, Sift, Experian CrossCore
Graph-based fraud detection	Maps relationships between accounts, devices, and transactions to uncover fraud rings and money mules	Social network analysis, entity link analysis, fraud ring detection	Quantexa, Linkurious, GraphAware
Dark web monitoring	Scans underground forums, marketplaces, and leaked databases for compromised credentials and fraud activity	AI-powered threat intelligence, credential leak alerts, real-time monitoring	Recorded Future, SpyCloud, CybelAngel

"The biggest misconception is treating fraud as a post-incident issue — detect, react, repeat. But by the time an alert fires, the damage is often done. Real protection means building systems that make fraud nearly impossible from the start. At Innowise, we help uncover hidden vulnerabilities and fine-tune your strategy before fraud ever has a chance to slip through."

Dzianis Kryvitski

Delivery Manager dans la Fintech

The building blocks of FinTech fraud prevention

Catching fraud is good. Stopping it before it starts? Even better. True fraud prevention in the banking industry begins long before a transaction is flagged — it starts at access, intent, and risk. And it takes a solid strategy to connect those dots. Here’s how forward-thinking teams stay ahead.

Regulatory compliance & anti-fraud frameworks

Regulatory compliance is a key pillar of fraud prevention. KYC makes sure users are who they say they are, AML keeps an eye on shady transactions, PSD2 and SCA add extra security layers for online payments, and PCI DSS locks down payment data. By following these regulations, businesses reduce vulnerabilities, strengthen security, and proactively prevent fraud.

Risk-based user access controls

Preventing fraud starts with who gets access. Instead of treating all users the same, risk-based access controls evaluate factors like location, device history, and login behavior before granting access. Suspicious logins get extra verification. Trusted users enjoy seamless access. That’s smart banking fraud detection in action.

AI-driven transaction pre-approval

AI doesn’t just detect fraud — it prevents it by blocking high-risk transactions before they are processed. AI models assess transaction legitimacy in real time, analyzing factors like spending patterns, geolocation, and merchant reputation. If a transaction appears suspicious, it can be declined before funds leave the account.

Biometric & behavioral authentication

Passwords are easily stolen, but biometric and behavioral authentication make fraud prevention more secure. That’s why anti-fraud software is increasingly layered with fingerprint scans, facial recognition, and behavioral cues like keystroke rhythm and screen pressure.

Payment tokenization & encryption

One of the best ways to prevent fraud is to never expose sensitive payment data in the first place. Tokenization replaces card details with a secure, one-time-use token, which makes it useless to hackers. Encryption ensures that even if data is intercepted, it can’t be used.

Consortium data sharing & real-time fraud alerts

Fraudsters often reuse stolen credentials across different companies. Consortium data sharing allows banks, payment providers, and merchants to share fraud intelligence, blocking fraudulent activity before it spreads. Businesses can also subscribe to real-time fraud alert networks to block transactions using compromised credentials.

Preemptive transaction limits & velocity rules

Fraudsters often start with small test transactions before making a bigger attack. Preemptive limits and velocity rules restrict certain high-risk transactions before fraudsters can take full control. This includes limits on rapid withdrawals, multiple login attempts, or cross-border transfers.

Secure APIs & multi-layered payment security

API security is a growing priority as fraudsters increasingly target payment integrations and financial services APIs. Secure APIs use authentication, encryption, and fraud detection layers to prevent unauthorized access before data breaches occur.

Lock down your defenses with top fraud management strategies.

Real-world challenges in fraud management

Fraud management is a moving target. Every time defenses get smarter, fraudsters get sneakier. Add in shifting regulations, UX trade-offs, and resource constraints, and it’s easy to see why even top institutions struggle. Here’s what today’s fraud detection and management in banks is really up against.

Navigating compliance requirements

Constantly changing laws like KYC, AML, and PSD2 make it challenging to stay compliant while preventing fraud. Businesses must implement flexible, automated compliance solutions to adapt quickly and meet regulatory demands.

Balancing security with user experience

Tight security is crucial, but too many authentication steps can frustrate real customers and drive them away. The key is using smart, risk-based authentication that adds extra layers only when something seems off.

Limited resources & budget constraints

Investing in advanced fraud prevention can be tough with tight budgets and small teams. Prioritizing AI-driven automation and scalable solutions helps maximize protection without overspending.

Managing global transactions

Cross-border payments come with higher fraud risks due to different regulations, currency challenges, and evolving fraud tactics. Using AI-driven monitoring and region-specific fraud controls helps detect threats without slowing transactions.

Data privacy & sharing limits

GDPR and CCPA limit how fraud data can be shared, even across trusted systems. That’s why many institutions now rely on gestion sécurisée des données to protect sensitive data while enabling safe, compliant fraud detection.

Internal fraud & insider threats

Fraud doesn’t always come from the outside; insiders can misuse access for personal gain or collude with external criminals. Regular employee training, real-time activity monitoring, and strict role-based access controls help reduce the fraud risk.

Sophisticated fraud tactics

Fraudsters are always a step ahead, using AI, deepfakes, and synthetic identities to bypass security. Staying proactive with AI-driven threat detection and continuous fraud model updates helps keep defenses strong.

Emerging payment tech

Real-time payments, crypto, and digital wallets create security gaps before regulations catch up. Implementing fraud prevention tools with real-time monitoring and adaptive risk controls helps stay ahead of new threats.

Bringing fraud automation to life with Innowise

We know the tools worth using and how to make them work for you. With deep expertise in platforms like Samsub and SDK.finance, we help turn great platforms into seamless, scalable fraud management solutions for banks and FinTechs alike.

Samsub: identity verification & compliance

Fraudsters are fast, but regulations are even faster. Between KYC, KYB, and AML, compliance can feel like a full-time job. Samsub gets the job done with AI-powered risk scoring that makes identity checks faster and smarter. But where things usually fall apart is integration — that messy middle ground where the tool doesn’t quite fit your system, your workflows, or your team. That’s where we come in. At Innowise, we don’t just “implement” Samsub; we get in the weeds with you. We tailor it, connect it, and make sure it works in the real world, not just on paper.

SDK.finance: core banking & payment security

SDK.finance gives you the infrastructure to build serious financial products. It’s stacked with features like fraud detection, KYC/AML modules, and real-time analytics, all designed to support secure, high-volume transactions. But even the best platform doesn’t plug itself in. Innowise helps teams go from base product to fully functional ecosystem, connecting SDK.finance with third-party tools, shaping custom workflows, embedding security measures, and adapting everything to meet both regulatory and business demands.

What’s next in fraud detection and prevention

2025 is making one thing clear: the future of fraud prevention is collaborative, adaptive, and real-time. It’s no longer about reacting after the fact—it’s about anticipating the move before it happens. For fintechs and banks, that means aligning tech, teams, and partners around strategies that are always learning, always evolving, and always one step ahead.

Decentralized identity verification (DID)

DID is gaining real traction, especially in high-trust spaces like cross-border banking and DeFi. Powered by blockchain and frameworks like W3C verifiable credentials and eIDAS 2.0, it gives users control over their identity and makes fraud like phishing and impersonation harder to pull off. As adoption grows, businesses will use DID to simplify onboarding, reduce KYC friction, and build trust without compromising privacy.

Real-time controls for fraud containment

Proactive security features are gaining ground, shifting fraud prevention from passive alerts to real-time action. Tools like ANZ’s “digital padlock” let users lock accounts instantly, stopping fraud before it escalates. As adoption grows, more businesses will embed user-triggered controls into apps and platforms to make customers part of the defense system.

Cloud-based transformer models

As transaction volumes surge, fraud teams are turning to transformer-based AI models running in the cloud to keep up. These models spot subtle fraud patterns across multiple channels in real time without overwhelming systems or frustrating users. As performance proves out, more businesses will adopt them to scale fraud detection without sacrificing speed or accuracy.

Shared fraud intelligence networks

Collaboration is gaining momentum, with banks, FinTechs, and even telcos beginning to share real-time fraud signals through secure, privacy-preserving tech like federated learning. These networks are still emerging, but they’re reshaping how fraud is detected, turning isolated insights into collective defense. As adoption grows, businesses will use them to close gaps faster, stop coordinated attacks sooner, and make fraud response a team effort.

Dernières réflexions

Let’s be honest — fraud isn’t going anywhere. It’s evolving, getting bolder, and learning just as fast as we are. The question is: are your systems adapting, or are they just reacting? From AI-powered fraud detection systems to decentralized identity and shared intelligence, the tools are out there. But they’re only as good as the strategy behind them. If you’re serious about staying ahead, it’s time to stop thinking in silos and start building fraud prevention as a living, evolving part of your business.

Siarhei Sukhadolski Expert FinTech

Date: Apr 11, 2025

Siarhei Sukhadolski Expert FinTech

Date: Apr 11, 2025

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