AI for Fraud Prevention: Top Pitfalls and How to Avoid Them
Fraud drained financial institutions of over $1 trillion last year, despite billions spent on defenses. AI delivers greater accuracy for more successful fraud prevention. But its effectiveness depends on how it’s used. In this post we summarize some of the pitfalls that often stand in the way of maximizing impact.
The need for AI-powered fraud prevention
Rules are still key in fraud prevention. They're effective when you know what fraud looks like and need to stop it fast. But rules alone aren't enough.
The $1 trillion in global fraud losses last year marks that reality.
Fraudsters have become students of rule systems, deliberately probing limits and operating within detection boundaries. For example, a fraudster realizes that a bank's fraud prevention system doesn’t flag peer-to-peer transfers under $200. Using stolen account credentials, they initiate a series of low value Zelle payments starting from $10 up to $199.99. Because the amounts stay just below the threshold the system doesn’t raise any alerts.
Rules can also create friction for genuine customers. For example, a night-shift nurse shopping online at 2 am between patient rounds often has her transactions flagged as suspicious, forcing her through verification steps that she can't complete during her brief breaks.
The answer isn't eliminating friction entirely. In fact, a little friction can actually build trust and reassure customers their money is protected. It's only when friction is applied too frequently or arbitrarily that it becomes frustrating.
This is where context matters. Effective fraud protection must recognize these nuanced patterns. Detecting complex behaviors across a range of evolving threat vectors is where AI comes in. AI is being widely used to detect known fraud typologies, spot anomalous customer behaviour, and reduce false positives, all with unprecedented precision.
But we often see three common pitfalls that can significantly limit the impact of AI in fraud prevention.
Pitfall #1: Not maximizing risk signals
One pitfall we see often in fraud detection is failing to fully leverage the transaction data your business already collects.
Many teams assume they need external data sources, expensive consortium access, or additional integrations to detect fraud more effectively. But the truth is you can unlock powerful insights from the data you already have.
All too often, customer data isn't fully integrated with transaction data. Building effective fraud prevention systems requires those dots to be connected. Transaction data shows the 'what' and customer data gives context to the 'who.'
Only when those pieces come together can you spot fraud effectively.
When you have rich, connected datasets of transaction histories, customer information and more, machine learning can learn from past transactions to spot abnormal transactions, atypical device usage, or location deviations.
AI models build an understanding of customer behavioral profiles and then detect deviations from these to uncover fraud, all while using signals to uncover subtle behavioral trends, anomalies, and correlations within your internal transaction data.
Take the example of when similar email addresses have been used to register 34 accounts — all using the same email domain and containing digits between '777'-'835'. Without AI, this indicator of bots and potential phishing could evade detection.
Pitfall #2: One-size-fits-all approach
One size fits none in fraud prevention. Out-of-the-box and plug-and-play models are great for capturing known fraud typologies at speed, but they can’t account for nuances in customer behaviors at specific institutions.
Industry or regionally generic models have limited efficacy since every consumer base acts slightly differently. That means high false positives and low true positives.
High-net-worth clients have different transaction patterns than small business owners or retirees. A model that considers a $10,000 transaction suspicious may catch fraud for a typical retail customer but might generate false alerts for a wealth management client.
Made-for-you AI models trained on individual organisations’ data provide high precision in detecting known fraud typologies, reducing false positives, and catching novel risk. The challenge is that building and training these models takes time, and when it comes to fraud, time lost means money lost.
This is the inflection point many FIs are now facing: How can we reap the benefits of AI, without the months-long development cycles?
Pitfall #3: Ignoring explainability
Too many financial institutions rely on black box models, which are the antithesis of transparency. These opaque systems create a nightmare for analysts. Black box models flag transactions without explanation, forcing analysts to triangulate at speed between customer data points to determine whether the flagged transaction is fraudulent or not.
With multiple alerts firing simultaneously, this inefficiency multiplies rapidly. While analysts conduct their investigation, payments sit frozen. Rather than reducing friction, black box AI amplifies it.
Auditability is another challenge with opaque AI fraud systems. Regulators are tightening the screws on algorithmic decision-making. When an AI system flags a transaction as fraudulent, regulators increasingly expect analysts to know what triggered the decision. Black box AI models make this nearly impossible. These systems flag transactions based on subtle patterns across hundreds of variables that even data scientists struggle to interpret.
When regulators arrive, they expect to see:
- Clear decision logic and thresholds
- Evidence that the system treats similar cases consistently
- Proof that the model isn't introducing bias or discrimination
- Documentation of model performance and validation
Interpretable models, on the other hand, provide readable explanations for every decision.
Rather than simply flagging transactions, they explain precisely what triggered the alert, such as detecting 30+ accounts with similar names. They analyze what constitutes normal behavior for specific customer groups, then explain how far a transaction deviates from that baseline. These narratives help fraud teams and auditors quickly understand why a decision was made.
Personalised fraud protection in days, not months
The bottom line is financial institutions need three critical capabilities: explainability, out-of-the-box speed, but with tailored accuracy. That's why Hawk has built Day One Defense Models, which provide personalized protection against common fraud typologies.
The models safeguard an institution’s customers from day one with the Hawk fraud platform, defending against typologies ranging from authorized push payment fraud and merchant fraud to money mule behavior and account takeover.
Hawk data scientists tailor model blueprints to each organization's specific needs at speed, delivering trained models within three days of initial data and solution setup.
This approach leverages highly automated feature selection and model pipeline that accelerate tuning and deliver precision prevention, without the months-long development cycles typical of custom-built AI models. FIs can stop high-impact threats specific to their business from day one, effectively halting financial crime in its tracks.
With Hawk’s self-service rule management, real-time prevention (150ms average response), and custom anomaly detection models, fraud teams are covered from every angle.
Get in touch with our team to learn more about Hawk's fraud solution.