What is Fraud Detection and Prevention: A Complete Guide

Banks are encountering a growing surge in financial fraud. Industry data indicate that more than 50% of banks experienced an increase in business fraud in 2024, and over two-thirds reported higher consumer fraud that year.

However, many banks are still relying on outdated fraud detection and prevention methods, like rigid rules and manual reviews.

The key to combating this rising threat lies in embracing modern fraud detection and prevention strategies.

In this guide, we'll highlight how banks are using advanced technologies to protect against the rising wave of financial crime.

Key takeaways:

• Fraud prevention and detection must work together: Combining prevention methods, such as multi-factor authentication, with detection tools like real-time monitoring ensures comprehensive protection against fraud.
• AI and machine learning improve detection: Machine learning models continually adapt to new fraud patterns, thereby improving fraud detection accuracy and enabling proactive responses.
• Dynamic funds availability policies boost security and convenience: Replacing fixed hold times with dynamic, risk-based rules provides quicker access to funds for low-risk transactions while protecting against fraud.
• Shared intelligence networks enhance fraud detection: Participating in fraud consortiums enables institutions to share data and to improve fraud detection by identifying patterns across multiple organizations.
• VALID Systems as a key solution: VALID Systems offers AI-driven fraud detection tools, including RTLA, and Instant Funds (IFA), to help banks proactively identify and protect assets from fraud in real-time.

What is fraud detection?

Fraud detection refers to the systems and processes that identify potentially fraudulent activity as it occurs or after it has happened.

In banking, this involves utilizing data analysis and automated rules to identify unusual or suspicious transactions.

For example, fraud detection software analyzes transactions, such as deposits or account openings, for potential red flags, including large withdrawals or mismatched signatures. Monitoring tools compare each event to typical behavior and known fraud patterns, triggering alerts when anomalies are detected.

What is fraud prevention?

While detection reacts to anomalies, fraud prevention uses proactive measures to prevent fraud before it occurs.

Prevention involves rules, policies, and technologies designed to make fraud difficult or unprofitable.

Examples include:

• Requiring strong identity verification.
• Implementing multi-factor authentication on logins.
• Using Positive Pay for check clearing.
• Placing strategic holds on transactions that look unusual.

Why fraud detection and prevention must work together

The importance of fraud prevention and detection is obvious, as Juniper Research forecasts an 86% market growth between 2024 and 2029, with spending expected to reach $32.2 billion by 2029.

Prevention methods, such as stricter controls and holds, reduce the likelihood of fraud; however, some attacks will still slip through. When they do, detection tools act as a safety net to identify and stop active fraud.

However, relying solely on detection can lead to constant losses, as it only identifies fraud after it has occurred. On the other hand, prevention without detection may miss evolving threats.

The key is combining both: prevention methods, such as identity checks and fraud scoring, stop basic attacks, while detection systems, like real-time monitoring, catch more sophisticated ones.

Top banks use both approaches. For example, they may implement multi-factor authentication for mobile logins (prevention) and run AI models on transactions to flag suspicious activity (detection).

Tools and technologies in fraud detection and prevention

Financial institutions use a wide range of tools to combat fraud. Modern solutions blend data analytics, AI, and fast decision-making:

Real-time transaction monitoring

Banks deploy software that continuously analyzes every transaction. These systems compare current activity against historical baselines and known indicators of fraud.

If a transaction deviates from a customer's typical behavior (e.g., an unusually large check deposit on a dormant account), it's flagged immediately.

Machine learning and AI

Leading systems use machine learning models to detect subtle fraud patterns that simple rules might miss. ML algorithms automatically learn from new data: every confirmed fraud case refines the model.

Over time, the AI improves at spotting sophisticated attacks (e.g., a network of linked accounts used for synthetic fraud).

For example, VALID System's tools retrain daily on global data to ensure models capture emerging schemes.

Behavioral analytics

By profiling typical customer behavior, banks can spot anomalies.

Banks that use AI predictive analytics experience a 60% reduction in fraud losses and 50% fewer false positives.

Analytics might track login locations, device types, or transaction timings. If a user logs in from a new country and immediately initiates a large transfer, the system raises an alert.

Behavioral scoring is also used in check processing: unusual payee names or deposit patterns trigger warnings. These behavioral tools operate across digital and physical channels.

Device and identity verification

Tools such as multi-factor authentication (MFA), biometric checks (including fingerprint or facial recognition), and device fingerprinting improve security.

When paired with fraud detection, they help confirm that the party initiating a transaction is indeed the legitimate customer.

For example, if a check deposit request comes from a suspicious device fingerprint, the system may require extra verification.

Fraud scoring engines

Instead of static "yes/no" rules, modern systems use fraud scoring.

Each transaction or deposit is assigned a numerical risk score based on multiple factors (amount, location, account history, payee, etc.).

High scores trigger automated holds or approvals only after manual review. The scoring models are "dynamic." For instance, a second check from the same suspicious account will compound the risk score.

This approach (sometimes called "dynamic hold rules") replaces legacy fixed hold times.

Fraud-orchestration platforms

These are integrated platforms that aggregate all risk signals into one decision framework. They can ingest data from teller systems, ATM networks, online banking, and external intelligence feeds.

The platform then applies complex logic (often via rule engines and AI) to make decisions in real-time.

This approach ensures a coordinated response: for example, an alert in mobile banking might inform how teller staff handle a related request.

Shared intelligence and consortium data

Some solutions use consortium data (shared networks of anonymized fraud incidents across many institutions). By comparing against a global fraud database, banks can catch repeat offenders or linked fraud rings.

A great example is the VALID Edge consortium, which analyzes over 300 million account records to identify duplicate or kited check patterns.

Forensic Image Analysis (for Checks)

Advanced image analysis can detect tampering.

According to the U.S. Postal Inspection Service, altered check fraud increased 385% from 2021 to 2024, making tools that scan check images for wash marks or misaligned elements essential.

Building a fraud-resilient organization: Best practices for fraud detection and prevention

Here are the most effective practices to strengthen your fraud defenses:

1. Implement real-time risk scoring for every transaction

Real-time risk scoring evaluates each transaction as it happens, minimizing the chances of fraud slipping through.

Action steps:

• Deploy real-time fraud scoring systems across all customer touchpoints: tellers, ATMs, mobile apps, and online banking platforms.
• Utilize dynamic risk thresholds that adjust according to factors such as transaction history, location, and device type.
• Prioritize flagged transactions for immediate review or hold based on their risk score.

Pro tip:

Integrating Real-Time Loss Alerts (RTLA)© into your fraud detection infrastructure improves real-time scoring by providing a broader context for transaction analysis.

This system assesses transactions in real-time and applies a dynamic risk score based on several data points, minimizing the risk of fraud-related losses.

2. Leverage machine learning to adapt to evolving fraud patterns

Machine learning helps detect emerging fraud tactics by constantly learning from new data, improving fraud detection over time.

Steps to implement:

• Implement machine learning algorithms to predict and identify fraud based on historical and real-time data.
• Continuously retrain models using updated data to ensure that detection methods adapt to evolving fraud schemes.
• Monitor model performance and make adjustments as necessary to improve accuracy.

Future forecasts:

AI and machine learning revolutionize fraud detection by enabling systems to adapt to new patterns. By 2025, over 60% of fraud detection systems are expected to use these technologies, thereby improving the accuracy of real-time prevention.

3. Enforce multi-layered authentication for high-risk transactions

Multi-layered authentication strengthens the security of high-risk transactions, preventing unauthorized access to customer accounts.

Next steps to take:

• Implement multi-factor authentication (MFA) for all user logins and transactions involving high risk.
• Employ biometric methods, such as fingerprint or facial recognition, to further authenticate customers before processing large transactions.
• Set triggers for additional verification on transactions that deviate from usual behavior patterns (e.g., a high-value transfer from a new device or location).

Tip:

Combine device intelligence with MFA. If a transaction originates from an unfamiliar device or IP address, require additional layers of verification to reduce the chances of fraud further.

4. Adopt dynamic funds availability policies

Dynamic funds availability policies improve both security and customer satisfaction by adapting to the risk level of each transaction.

Actionable measures:

• Replace fixed hold times with dynamic rules based on the transaction's risk level.
• Offer customers the option to access held funds early for a fee, striking a balance between the bank's need for fraud protection and customer convenience.
• Regularly assess and update your risk model to ensure that hold rules are accurate and efficient.

5. Integrate alerts into staff workflows

Fraud alerts empower staff to act swiftly against potential threats, reducing the risk of fraud-related losses.

Immediate action:

• Ensure fraud alerts are seamlessly integrated into the systems used by tellers, customer service, and fraud teams, offering instant visibility into suspicious transactions.
• Train staff to respond efficiently, including verifying identities and escalating flagged issues when needed.
• Equip staff with tools that help them quickly assess the risk level of flagged transactions to ensure timely interventions.

6. Participate in fraud consortiums for shared intelligence

Collaborating with other institutions through fraud consortiums helps you detect fraud patterns that individual institutions might overlook.

Action plan:

• Join a regional or national fraud consortium to access shared intelligence on fraudulent activities across institutions.
• Automatically flag transactions that match known fraud patterns shared by consortium members, such as compromised accounts or stolen card numbers.
• Use consortium data to refine your fraud detection models, enhancing their ability to anticipate emerging threats.

7. Conduct regular audits and system updates

Regular audits ensure that fraud detection systems remain effective and aligned with the evolving techniques used by fraudsters.

Action items:

• Schedule routine audits of your fraud detection systems to assess their performance and identify gaps.
• Perform "red team" exercises to simulate real-world fraud attempts and test the effectiveness of your fraud detection systems.
• Update fraud detection models and detection logic as new fraud trends emerge.

8. Ensure compliance with regulatory standards

Compliance with financial regulations not only prevents penalties but also strengthens your fraud detection and prevention efforts.

Steps to ensure compliance:

• Stay up to date with regulations like AML (Anti-Money Laundering) and KYC (Know Your Customer), ensuring that your fraud systems meet compliance requirements.
• Use fraud prevention systems to enforce KYC checks and integrate anti-money laundering processes seamlessly into your operations.
• Regularly review and update your compliance strategies to reflect the latest regulatory changes.

What is the VALID System's unique approach to fraud detection and prevention?

Your institution may already be facing a surge in fraud attempts, but are your current systems equipped to respond in real time?

VALID Systems offers specialized, AI-driven fraud detection and prevention solutions, with a focus on the banking industry.

Key elements of VALID's approach include:

Real-Time Loss Alerts (RTLA)©:

A real-time fraud scoring and alerting system for check deposits. RTLA assigns a risk score to every check at the teller or ATM and delivers immediate alerts on suspicious items.

In trials, RTLA flagged over 75% of loss-bound check items before posting. This preemptive scoring provides tellers and staff with a "heads-up" to verify identities or reject high-risk deposits.

Instant Funds / IFA:

Valid's Instant Funds feature (also known as IFA) works in conjunction with fraud detection to provide legitimate customers with immediate liquidity.

Using the same risk models, it offers instant availability on good funds for a small fee, while holding back checks that score poorly.

INclearing Loss Alerts:

This tool detects altered or out-of-pattern checks that may have been tampered with between the deposit and clearing stages. It monitors the clearing process and flags any items that deviate from earlier profiles.

Is your fraud detection and prevention strategy up to date?

Partner with VALID Systems and protect your organization before fraud strikes.