The banking and financial services sector is processing more transactions than ever. As volumes surge across digital channels, so do cyber threats.
Attackers once relied on phishing emails to trick customers into handing over card numbers and login details. Now they use automation, synthetic identities, and deepfakes. The traditional “detect and react” model no longer works. What was once a manual investigation now requires intelligent systems that learn and act in real time.
Customers expect seamless digital experiences, while regulators require transparency. Staying ahead of smarter and faster fraud means institutions have to do both at once.
The model choice should be based on more than the model’s novelty and most recent advancements. Among the core factors are latency needs, fraud risk, explainability requirements, data complexity, compliance obligations, and customer experience goals. A wrong model adds friction and cost across payments, onboarding, and compliance, while the right one turns fraud prevention into a source of speed and customer trust.
A research report by Coherent Solutions, Future of Finance: How AI Is Advancing Fraud Detection in Banking and Financial Services, describes real fraud scenarios institutions face today and maps each to the AI methods that address them. Building on that report, this article highlights the most common model options and the reasoning behind each choice.
How Financial Institutions Should Choose the Right Agentic AI Models
According to PYMNTS Intelligence, 71% of financial institutions now use AI and machine learning for fraud detection — up from 66% in 2023. AI is key to reducing fraud-related losses and detecting financial irregularities and suspicious transactions within milliseconds.
However, not all AI models for detecting fraud are the same. Banks and financial institutions should understand the critical business and technical factors that decide whether a model delivers or falls short.
- Latency — how quickly the model returns a decision, which matters most in real-time payment processing.
- Explainability — whether analysts, customers, and regulators can understand how a decision was reached.
- Data complexity — the type, volume, and structure of the transaction and customer data involved.
- Compliance — the transparency and governance needed to meet regulatory requirements.
- Customer experience — how well the model reduces false positives so legitimate transactions clear without delay.
By weighing these factors, institutions can get a financial solution that aligns with business objectives and delivers measurable outcomes. A poor model will work against a bank, adding false positives, friction, and costs. But the right model can close the digital value gap between deploying AI and getting real results.
The Most Common AI Model Options for Financial Institutions
AI can enable an all-in-one fraud detection solution, as long as it works alongside human-in-the-loop (HITL) monitoring. The combination balances speed, transparency, and accountability.
But choosing a reliable model is only half the task, as it has to map to a business outcome. Which model fits depends on the type of fraud being targeted and the regulations it needs to satisfy.
Gradient Boosted Machines (GBMs) — Real-time scoring at scale
A machine learning system that develops sequential AI models and corrects errors along the way to improve performance. Financial institutions should choose GBMs if they want real-time decision-making based on fast transaction scoring, alert prioritization, and credit risk modeling.
Deep Neural Networks (DNNs) — Catching complex, hidden fraud
A multi-layered neural network that can recognize highly complex fraud patterns in massive amounts of data, such as intricate, nonlinear transactions. They are particularly valuable for document verification and biometric authentication. Although DNNs generally require more computing power and are less transparent than other models, explainability tools can help organizations better understand their decisions with this model.
Graph Neural Networks (GNNs) — Exposing fraud rings
A type of neural network that can identify hidden relationships and patterns of fraud in financial accounts, devices, and transactions. GNNs do this by structuring data as graphs to visualize the fraud patterns and spot suspicious activity easily. The only problem is that the computer resources to run GNNs are quite costly, which is why they are usually applied in a layered decision-making process.
Unsupervised Models — Detecting fraud with no history
Unsupervised machine learning models can analyze unlabeled data to identify unusual behaviors and emerging fraud patterns that have not been previously identified. Instead of relying on historical fraud data, these models can discover hidden relationships and irregularities automatically. They are particularly valuable when financial institutions need to detect entirely new fraud attacks with no historical data available to identify them.
Autoencoders — Spotting insider and off-pattern activity
A type of neural network trained to detect anomalies by learning what normal transaction behavior looks like and identifying activities that fall outside of those patterns. They are frequently used to detect insider fraud and other unusual account activity that follows no particular pattern. However, since human intervention is still required to verify and interpret the findings, financial institutions typically pair autoencoders with human analysts to validate suspicious activity before taking action.
Of course, there is no perfect AI model. Institutions must develop and implement a comprehensive AI fraud-prevention lifecycle with supporting workflows to ensure the model works best for them.
Five phases of an AI fraud detection lifecycle
The lifecycle is a multi-layered defense. It combines both the AI fraud-prevention workflow with human-based interpretation and judgment.
- Data Collection – Gather data from devices, KYC, and transactions for accurate model development.
- Model Development – Train AI models on historical and simulated fraud scenarios based on available data.
- Approval & Compliance – Review the AI model results for fairness, regulatory alignment, bias mitigation, and explainability.
- Deployment – Integrate real-time scoring into authentication workflows, payments, and onboarding.
- Monitoring & Feedback – Continuously track the AI model performance for precision and potential false positives. Incorporate analyst feedback when retraining and improving AI models to detect the latest fraud patterns.
Strict regulatory requirements require financial institutions to keep humans in at least part of the fraud detection process. No fraud detection system can reject transactions entirely without a human reviewing them first. If they did, it would breach the accountability and transparency requirements that financial institutions must follow.
Turning Fraud Prevention into Digital Value
Banking and financial institutions are moving toward a proven approach to fraud detection, and it is human-in-the-loop. Institutions will continue to improve their AI models for better real-time transaction monitoring, stronger biometric authentication, and faster document fraud and synthetic identity detection during onboarding.
As the AI processes vast amounts of data in real-time, human analysts will oversee the fraud detection system, manage complex edge cases, and ensure regulatory accountability and transparency. They can ensure their AI fraud detection systems align with new and evolving threats safely and securely to satisfy both their customers and government regulators.
