A contemporary approach to AI fraud detection, actionable AI technology is designed to do two things:
This class of AI technology closes the gap between alerting and response by triggering safe mode controls or step-up verification in response to fraud attacks. Its functionality enables financial institutions and fintechs to not only detect and predict fraud but also act on findings in real-time, before losses occur.
Fraudsters no longer need technical skill to commit fraud. Thanks to GenAI advancements, they can access ready-made tools that automate the creation of realistic synthetic identities, fake documents, and convincing phishing campaigns.
Here are some of the most common ways financial criminals commit AI-driven fraud:
Fraudsters utilize AI to generate convincing fake personas, or synthetic identities, from a combination of stolen, falsified, and publicly available information. And they're automating the production of supporting documentation at scale, creating forgeries that accurately replicate fonts, layouts, and security features of legitimate documents.
What makes synthetic identity fraud particularly dangerous is how AI enables criminals to establish consistent digital footprints across multiple platforms — establishing coherent social media profiles, employment histories, and credit records that appear to be real across various systems.
Learn more about synthetic identities, and why fraudsters need imaginary friends.
Global deepfake fraud recently increased tenfold, driving up the success rate of fraud attempts. And this technology is highly accessible. It’s estimated that 95% of deepfakes in existence today were created using open-source software.
This technology is also extremely effective. According to cybersecurity provider McAfee, fraudsters need just three seconds of audio to create AI-powered voice clones with 85% similarity to the original speaker. Meanwhile, text-based large language models are also empowering fraudsters to mass-produce phishing messages that are free of traditional red flags, such as grammatical and punctuation errors. These activities aren’t just limited to organized crime rings; they’re accessible to almost anyone with a laptop and internet connection.
Financial institutions and fintechs overwhelmingly attribute a majority of fraud attacks to sophisticated crime rings. These criminal groups use AI-powered bots to automate high-velocity, coordinated attacks that systematically probe for weaknesses across multiple channels simultaneously. These bots have dramatically increased credential stuffing success rates by automating username and password testing across thousands of accounts.
Despite the widespread adoption of traditional AI among financial organizations, many financial institutions and fintechs still struggle to act on current fraud threats. These organizations are up against challenges like:
A major shortcoming of many AI fraud solutions is that they stop at detection. This means that although these systems issue alerts or flag suspicious events, they may lack the capability to trigger effective next steps. This can harm the organization's ability to shut down fraud attacks in real-time.
When a financial institution or fintech is unable to stop orchestrated fraud attacks, it may overcompensate with overly blunt responses, such as killing an entire onboarding funnel at the first sign of risk. This can disrupt the customer experience, lead to a loss in marketing spend, and create unnecessary operational overhead. The result is a persistent gap between insight and truly actionable, operational AI fraud defense.
AI is no silver bullet. Still, decision-makers may overestimate GenAI’s fraud prevention potential due to a misunderstanding of its capabilities and the requirements that machine learning (ML) models need to succeed. While these technologies have been around for a long time, the current hype over GenAI has changed how these capabilities are marketed, creating room for confusion.
The AI expectation gap becomes a threat when it leads to an underinvestment in layered defenses. After all, AI isn’t a magic bullet. Effective prevention requires a multifaceted approach that involves combining robust AI models with strong fundamentals, up-to-date controls, and an agile, multi-layered fraud strategy.
Recommended reading: Learn why AI is not the fraud lifeline banks think it is.
Building effective AI fraud detection systems is complex and resource-intensive. In-house solutions demand significant time, budget, and expertise — requirements that can strain even the largest financial organization. Meanwhile, off-the-shelf third-party tools often need lengthy customization to address each organization’s unique risks and workflows.
As a result, fraud teams are left managing fragmented systems that don’t communicate or build well with each other, making it difficult to coordinate a prompt response when new threats emerge.
AI models are only as strong as the data they're trained on. Many rely on historical patterns that may not reflect current or emerging fraud tactics, leading to blind spots. For example, while unsupervised machine learning models are useful in identifying novel fraud patterns, they can flood teams with false positives, creating more work than value. And while fraud tactics change quickly, traditional model update cycles often lag behind, allowing novel attack methods to slip through undetected.
As regulatory requirements evolve, many financial organizations face new expectations around transparency and auditability. AI models with black-box processes can conflict with documentation and explainability standards, as systems lacking robust audit trails or clear documentation for automated decisions may fail to meet guidelines for model governance and oversight.
Fraud prevention has always been a moving target, with fraud enablement and prevention technologies influencing each other rapidly. While 93% of financial organizations believe artificial intelligence will revolutionize fraud detection, the same technology is also being used by bad actors to facilitate fraud easier and faster.
Here’s a look at how this game of cat and mouse has changed fraud detection over time:
AI isn’t new technology; it was first developed in the 1950s, with early chatbot programs like ELIZA launching in the 1960s.
Before widespread adoption of AI, or even the internet, fraud schemes were more rudimentary. Forging convincing fake IDs and other documents required specific knowledge and patience, and scaling sophisticated attacks required substantial effort. High barriers to entry kept organized fraud out of reach for less determined or resourceful criminals.
Financial organizations, for their part, depended on basic rule-based systems — rigid if/then logic designed to discern suspicious activity from legitimate transactions. These systems were limited in scope, but created new operational efficiencies that matched the relative capabilities of fraudsters.
The rapid digitization of banking in the 2010s fundamentally changed the fraud equation for both fraudsters and the organizations working to keep them out. Financial organizations transitioned from static, manually defined rules to dynamic pattern recognition powered by machine learning. New technology enabled financial institutions and fintechs to process enormous volumes of transaction data in real-time, identifying suspicious patterns that would have been invisible to human analysts. As these systems continually learned from emerging fraud tactics, anomaly detection became more sophisticated and accurate, making it possible to automate fraud detection at scale.
But as defenses advanced, so did the attackers. Fraud rings adapted quickly, probing for weaknesses and developing new strategies to circumvent traditional machine learning controls. The rise of coordinated, multi-vector attacks — across channels and geographies — meant that the pace and complexity of fraud outstripped what manual processes could counter. Major data breaches during this era also armed criminals with vast troves of personal information, fueling more targeted and organized fraud attempts.
The rise of generative AI has accelerated change in ways few risk leaders anticipated. Where the 2010s were defined by pattern detection and ever-improving anomaly spotting, the 2020s are all about scale and automation, both for financial organizations and for fraudsters. Fraud-as-a-service marketplaces and underground forums now circulate AI-powered toolkits, lowering the barrier for entry and making multilayered, coordinated scams a reality at scale. Sophisticated credential stuffing, deepfakes, and real-time social engineering are now commonplace, enabling bad actors to slip past legacy rules and basic machine learning defenses with ease.
But stronger data networks and machine learning algorithms have also brought forth new advancements in AI fraud prevention, such as empowering more financial institutions to spot fraudsters at onboarding. Because of these tools, many fraudsters are never able to bypass controls to enter financial systems in the first place. Meanwhile, advancements in actionable AI help financial organizations optimize their response to ongoing fraud attacks characterized by suspicious surges in applications and other behavioral anomalies.
With GenAI predicted to cost financial organizations and their customers US$40 billion by the year 2027, these solutions couldn’t be more timely.
As GenAI tools become more powerful, legislative bodies have moved to address emerging risks with stronger regulatory protections. The No AI Fraud Act, introduced in early 2024, is one such response to the rise of AI-generated fakes and deepfakes. This legislation establishes robust federal enforcement mechanisms and mandates new standards for verification protocols, aiming to curb synthetic identity fraud and empower individuals to control how their digital likeness is used.
For financial institutions and fintechs, the act serves as both a signal and a framework — guiding responsible AI deployment, requiring heightened transparency, and raising the bar for trust and security across digital financial services.
Alloy’s data shows that a majority of fraud events now involve organized groups rather than lone actors. These groups perpetuate high-velocity, automated attacks that overwhelm traditional detection systems, making it much harder to contain losses before they escalate.
Overall, 60% of financial institutions and fintechs reported an increase in fraud last year. As fraud techniques grow more sophisticated, back-office teams face a surge in manual review workloads. In some cases, organizations have been forced to shut down digital channels temporarily just to catch up.
But simply having AI isn’t enough to combat these statistics. Meaningful results require a holistic, thoughtfully engineered approach that factors in new threats, evolving regulations, and the need for a rapid, coordinated response. Here’s how financial institutions and fintechs are making the most of AI fraud detection opportunities:
Today’s machine learning algorithms analyze millions of transactions instantly, using sophisticated pattern recognition to flag potential fraud indicators that static, rule-based systems often miss. These models learn from each transaction, constantly refining their ability to distinguish suspicious activity. With the capacity to process vast troves of historical and real-time data, ML helps financial institutions and fintechs reduce losses due to credit card fraud, payment fraud, and other financial crimes.
ML-driven automation has helped financial organizations dramatically improve efficiency. Advanced models can review thousands of alerts simultaneously, prioritize the riskiest cases, and learn from investigators’ decisions to improve future detection and routing. Automation now extends well beyond basic tasks; machine learning powers document verification, transaction monitoring, and other complex processes, letting fraud teams focus on strategy rather than routine reviews.
One of the biggest payoffs for modern ML in fraud prevention is a significant reduction in false positives. By building detailed customer behavior profiles and learning what “normal” patterns look like, models can spot subtle deviations that signal fraud risk — without inconveniencing legitimate users. The result: faster, more accurate detection and better overall user experience.
Although GenAI is most commonly used by fraudsters to enable more believable scams, it can also help financial organizations in select ways. For example, GenAI could help a financial organization enhance and complete training datasets, simulate new fraud scenarios for testing, and improve model documentation.
Still, its main impact to date has been making fraud more accessible and scalable to fraudsters.
Recommended reading: How has GenAI actually impacted fraud attacks?
An actionable AI strategy replaces slow alerts with automated responses that eliminate the critical delay that fraudsters exploit. Here’s how leading financial institutions and fintechs are building a truly layered, actionable approach:
Behind any attack, even an AI-enabled one, is an actual person with a real identity. That’s why effective fraud prevention starts with adaptable authentication protocols — ones that automatically increase security levels when activity seems off.
Starting with strong fundamentals involves verifying identity through multiple, overlapping techniques, not just a single check. Here are some of the core features your AI fraud detection solution should have:
Want to know the benefits of step-up verification? Click to find out.
Smart systems don’t rely on a single data source or method for risk management. Instead, they dynamically orchestrate (or route) verification checks according to real-time risk signals, triangulating data from multiple trusted sources to confirm identity. By running multiple verification methods in parallel, these systems minimize user friction while maximizing security. Continuous cross-checks across different providers make it far harder for fraudsters to slip through the cracks.
Learn why data orchestration is critical to banks and fintechs.
Machine learning, or ML, is crucial for identifying evolving fraud patterns at scale. You can add sophisticated ML capabilities to your existing fraud prevention in a couple of ways:
By incorporating machine learning models into your fraud prevention strategy, you can rapidly adapt to emerging fraud tactics, minimize manual reviews, and identify subtle anomalies that static rules may overlook. The result is a more adaptive, automated fraud defense that scales with both your ambitions and emerging threats.
Learn more about data and machine learning in financial fraud prevention.
Actionable AI means initiating the right defensive move as soon as a threat is detected, without waiting for human intervention. Risk-based authentication should escalate security requirements automatically when suspicious activity is identified.
Pre-defined automated workflows can quickly contain potential threats, close off vulnerabilities, or add layers of friction for risky users, while letting genuine customers proceed smoothly. And rapid-response tools ensure fraud teams can act immediately, closing the gap between detection and resolution.
By weaving together these layers, organizations can move from slow alerts to seamless, proactive fraud prevention.
Alloy uses actionable AI to bridge the gap by surfacing meaningful alerts and triggering instant next-step interventions. This is the differentiated value Alloy brings to financial organizations: not just fraud detection, but a system that acts, adapts, and scales as quickly as AI threats do.
Our platform brings together deep data partnerships, purpose-built tools, and intelligent orchestration to deliver actionable, layered fraud detection and response:
Alloy unites more than 250 data solutions and verification methods, allowing financial organizations to triangulate identity and risk signals from multiple trusted channels. We use advanced data orchestration to select the most appropriate verification methods, implement dynamic step-up authentication, and automate policy enforcement. This breadth enables rapid adoption of new data capabilities based on real-time and historic risk — helping Alloy clients stay ahead of emerging threats while maintaining reliability and speed.
Our data processes also help keep you compliant with anti-money laundering (AML), know-your-customer (KYC), and know-your-business (KYB) legislation, beginning with customer onboarding.
Alloy’s predictive risk analytics spot patterns and anomalies in data (including onboarding, transactional, and non-monetary signals), improving predictions about whether an entity might commit fraud. As Alloy’s ML models process more data, these algorithms adapt to detect new types of fraud even before your organization encounters them. Our backend analytics neutralize fraudsters’ advantage while leaving a clear audit trail.
Alloy goes beyond alerts to stop fraud in real time. To contain threats instantly, Alloy’s platform include built-in response tools and a “safe mode” for quick, friction-right containment when attacks strike. Fraud teams receive direct email and in-dashboard alerts and can instantly trigger a pre-approved policy, temporarily raising defenses long enough to contain the attack, but not long enough to erode your customer experience.
AI and machine learning have raised the bar for fraud detection, but more is required to truly protect today’s financial institutions and fintechs. In a world where fraudsters move at machine speed, organizations need AI that drives action — not just insight. Actionable AI means you don’t just see threats; you stop them as they happen, with automated responses that close gaps before losses occur.
That’s why Alloy is designed to bridge the gap between detection and prevention. Our platform goes beyond algorithms, pairing broad data partnerships, specialized tools, and orchestrated workflows so that defense is both seamless and immediate. The result: financial institutions and fintechs move from feeling exposed to being confidently proactive, with scalable prevention that works in real time — across every channel and every customer interaction.
Fraud threats won’t wait, and neither should your financial organization. Schedule a demo today to see how Alloy's actionable AI platform can strengthen your defenses while supporting growth.
Artificial intelligence (AI) refers to technology that enables computers and machines to mimic human abilities, including learning, understanding, problem-solving, decision-making, creativity, and autonomy.
A decision-making approach that relies on manually programmed if/then statements to flag suspicious activities. These systems use explicit rules created by experts and do not learn from new data. While transparent and easy to audit, they often miss complex or evolving fraud patterns.
A subset of artificial intelligence that enables computer systems to learn from data and improve over time without explicit reprogramming. Machine learning models can analyze massive datasets, identify patterns, and adapt to new fraud tactics by learning from historical and real-time inputs.
A field of AI that gives computers the ability to understand, interpret, and generate human language. In fraud prevention, NLP can be used to identify suspicious text or communications, though it is more commonly leveraged for customer service and document review.
A class of deep learning models where two neural networks compete to create realistic synthetic data, such as images or documents. While GANs can be used to generate fake documents or identities for fraudulent purposes, they also have legitimate use cases in data augmentation for model training.
Generative AI refers to artificial intelligence models, like large language models and diffusion models, that can automatically create content such as text, images, audio, or video closely resembling human-generated material. GenAI is used by both fraudsters (for scalable attack content) and for certain defensive tasks (like generating synthetic training data).
AI systems that go beyond detection — automatically triggering real-time, operational responses to identified threats. Actionable AI transforms insight into immediate defensive actions, closing the gap between spotting fraud and stopping it.
A subset of machine learning that uses multi-layered neural networks to model complex relationships in large datasets. Deep learning excels at processing unstructured data such as images and audio, but is often less interpretable than traditional ML models.
A technique that uses statistical or machine learning methods to identify deviations from normal patterns of behavior. In fraud prevention, anomalies include unusual activities that may suggest fraud, such as irregular transaction amounts or locations.
The use of historical data, statistical algorithms, and machine learning to predict future outcomes. In fraud, predictive analytics is used to assess the likelihood of fraudulent activity before it happens, enabling proactive responses.
For fraudsters, GenAI’s effectiveness lies in its ability to generate convincing phishing emails, social engineering scripts, and fake identities faster than ever before.
New AI fraud detection tool helps Alloy's clients stop fraud attacks before they escalate.
To understand how AI is impacting financial institutions, we must first examine it in the context of financial fraud prevention.
GenAI has also ushered in a new popular tool for bad actors: FraudGPT. With FraudGPT, not only can legacy fraudsters automate their attacks faster than ever before, but amateur fraudsters can join in the fun too and create sophisticated fraud attacks — also known as “DIY fraud” — that give financial institutions (FIs) and fintechs a run for their money.