Check fraud remains a serious challenge for banks, taking advantage of delays in the clearing process to manipulate balances and extract funds. Traditional systems rely on fixed rules and manual reviews – methods that are often too slow and easy to bypass.
Fraud detection and machine learning offer a smarter approach. By analyzing real-time behavior and adapting to evolving threats, machine learning helps banks catch fraud as it happens – not after the damage is done.
This article will break down how fraud detection and machine learning work together, why traditional methods fall short, and what banks can do to implement stronger, data-driven defenses today.
Why is machine learning and fraud detection a powerful combo?
Machine learning and fraud detection are a powerful combination because machine learning can analyze vast amounts of transaction data in real time, detect subtle anomalies, and adapt to evolving fraud patterns.
Unlike rule-based systems, it continuously learns from past behavior, allowing banks to identify and prevent new types of fraud with greater accuracy and speed.
In 2024, the Federal Reserve reported that check fraud accounted for 30% of all fraud losses in U.S. financial institutions, second only to debit card fraud, which accounted for 39%. Notably, the number of institutions experiencing attempted check fraud increased by 10% from the previous year.
Machine learning offers a dynamic solution to rising threats in check fraud. It learns from transaction history, detects anomalies as they form, and adapts to new fraud strategies without requiring constant manual updates.
Why static fraud detection falls short
Banks have long relied on rule-based systems and manual reviews to detect check fraud, utilizing tools such as deposit limits, hold policies, and velocity checks.
However, these rigid controls often miss more sophisticated schemes, such as check kiting.
The rise of mobile and remote deposits makes tracking timing even more challenging. Criminals can initiate transactions from anywhere, at any time, without ever walking into a branch.
Float time has become a tool for fraud, and static rules are insufficient to address it.
The role of machine learning in improving fraud detection
Machine learning (ML) has revolutionized the way financial institutions detect and prevent fraud, particularly in high-risk areas.
As fraud tactics evolve, ML continuously updates its understanding of what fraud looks like, giving banks a critical edge.
Here are the key roles machine learning plays in modern fraud prevention:
Behavioral pattern recognition
ML algorithms establish behavioral baselines for each account, flagging deviations that may signal fraud.
For example, if a business consistently deposits payroll checks in the $5,000 to $8,000 range and suddenly deposits two $25,000 checks from unrelated banks, the system immediately recognizes the anomaly.
Real-time transaction scoring
Rather than reviewing transactions after the fact, ML systems score each transaction in real time.
Analysts assess risk by examining factors such as transaction amount, timing, frequency, account history, device data, and counterparty behavior.
High-risk items are either flagged for review or halted automatically, minimizing potential losses.
Adaptive risk models
ML models evolve with new data. They retrain continuously, learning from emerging fraud behaviors.
For instance, when fraudsters shift timing or distribute activity across multiple institutions, ML systems can still detect the overarching pattern by analyzing behavior across the network.
Benefits of machine learning in check fraud detection
Here are the benefits of applying machine learning to detect and prevent check fraud:
- Early detection of float manipulation: ML models analyze the gap between check deposits and clearances to identify when an account is exploiting float time. This approach is crucial for catching kiting schemes that involve coordinated deposits and withdrawals between entities.
- Cross-account and cross-bank visibility: ML detects coordinated movements between accounts, even if they occur across different banking platforms, by linking patterns and behavioral signals.
- Fewer false positives, sharper focus: ML systems learn customer-specific behavior, allowing them to distinguish between legitimate high-value activity and structured fraud. This reduces unnecessary alerts and ensures fraud analysts can focus on genuine threats.
- Real-time risk scoring and intervention: ML models score check deposits at the moment of presentment, giving fraud teams the ability to place immediate holds or escalate risk before any loss occurs.
- Continuous learning and adaptation: As fraud tactics shift, ML models retrain using fresh data. Constant upgrading makes it harder for fraudsters to stay one step ahead and ensures your detection logic stays current.
- Structured for complex schemes: Machine learning tracks layered activity across dozens of accounts, even when fraudsters split transactions to avoid detection.
Implementing machine learning for fraud detection
Here is how to implement machine learning effectively:
Audit your data landscape
The strength of your fraud detection model depends on the depth and quality of your data.
Without clean, structured, and relevant data, even the most advanced algorithm will fail to detect meaningful patterns.
Action steps:
- Inventory all internal data sources: transaction logs, check presentment metadata, account history, customer risk profiles, and mobile deposit records.
- Identify external data points that can enrich your analysis, such as device fingerprints, IP geolocation, and customer behavior trends.
- Assess data quality and structure. Fix missing fields, normalize timestamps, and label known fraud cases clearly.
- Implement data governance standards to ensure the ongoing reliability of data.
Tip:
VALID Systems' Real-Time Loss Alerts (RTLA)© improve traditional transaction data with real-time presentment channel information, helping to detect high-risk check activity before it's visible through internal data alone.
Invest in feature engineering
Raw data doesn't make fraud visible – features do.
Feature engineering converts raw transaction behavior into specific, trackable fraud signals that machine learning models can interpret and act upon.
Action steps:
- Work with fraud analysts to identify high-risk behaviors: sudden increases in check amounts, float-time exploitation, new accounts funneling large deposits, etc.
- Build time-based features (e.g., average deposit intervals), behavioral trends (e.g., deviation from past check patterns), and graph-based relationships (e.g., network of counterparties).
- Use aggregation features: daily check volume, weekly return ratio, or month-over-month changes in deposit source or amount.
Choose a model architecture that fits
There's no universal "best model."
The right approach depends on your data volume, operational capacity, and need for explainability. Starting simple allows for iteration and trust-building.
Action steps:
- Begin with models that are easy to explain and audit: decision trees or logistic regression.
- Progress to more complex models like gradient-boosted trees or neural networks when your team is ready to handle model tuning, interpretability, and drift monitoring.
- Evaluate model performance across precision, recall, and ROC-AUC, not just accuracy.
Tip:
VALID's InteliFUNDS platform uses highly optimized ML models that strike a balance between speed and accuracy, allowing sub-second decisions that deliver immediate liquidity to trusted users while holding suspect checks for further analysis.
Integrate with core banking systems
Model output only has value if it results in action. For ML to prevent fraud, it must be tightly integrated with operational systems where real-time decisions happen.
Action steps:
- Connect ML scoring outputs to key decision points in deposit processing and fraud case management systems.
- Design workflows where high-risk items automatically trigger holds, alerts, or secondary verification.
- Build API connections for real-time scoring of check deposits, especially during remote or mobile transactions.
- Include audit trails and risk score explanations for compliance and operational transparency.
Set up human-in-the-loop feedback
Even the best machine-learning models require human oversight. Analyst review helps refine false positives, flag new fraud strategies, and reinforce institutional expertise in the system.
Action steps:
- Create workflows for analysts to review flagged items, especially borderline scores.
- Capture feedback consistently – was the alert a false positive? A missed pattern?
- Utilize human review outcomes to retrain the model with updated labels and case resolutions continually.
Tip:
VALID's systems INclearing Loss Alerts step in where rules and image analysis fail – using behavioral analytics and machine learning to identify fraud with high accuracy without disrupting legitimate transactions. This keeps the risk low and ensures good volume is flowing.
Monitor and retrain regularly
ML models degrade over time if left unchecked.
Fraudsters adapt quickly, and models must evolve to keep pace with them. Regular retraining and drift monitoring are non-negotiable.
Action steps:
- Monitor key performance indicators (KPIs) such as precision, recall, alert volumes, and drift indicators.
- Track changes in fraud behavior patterns, false positive rates, and alert fatigue among fraud analysts.
- Schedule model retraining using recent labeled data, at least quarterly or sooner if fraud tactics shift.
Tip:
VALID leverages AI to recognize subtle patterns in transactional behavior that human analysts or legacy systems might overlook.
Collaborate with peers and vendors
Fraud rings don't respect bank boundaries.
Collaboration expands your perspective, helps you detect fraud earlier, and makes your models smarter through the sharing of intelligence.
Action steps:
- Join fraud-sharing networks, industry consortiums, or real-time watchlists.
- Work with vendors who aggregate multi-bank data to uncover patterns you can't detect on your own.
- Leverage benchmark reports and fraud trend analyses to compare your performance against peers.
How VALID Systems helps banks to implement the benefits of machine learning
Check fraud continues to evolve, and so should your fraud defense strategy.
If your institution is ready to move beyond outdated controls and take a proactive stance against check fraud, VALID Systems offers proven, scalable solutions that deliver real-time results.
VALID Systems translates machine learning into real-world fraud prevention with two key solutions: InteliFUNDS and RTLA.
InteliFUNDS makes real-time deposit decisions, approving 99% of checks instantly while isolating the small fraction that poses a risk.
RTLA (Real-Time Loss Alerts) monitors every check presentment and flags over 75% of items that would result in charge-offs.
Banks using VALID don't just detect check fraud – they stay ahead of it.
Want smarter check fraud protection without slowing down operations?
Contact VALID Systems today and see how our ML-powered solutions can protect your institution.