What Is Fraud Analytics and How to Use Data for Fraud Detection?

Businesses lose $5 trillion to fraud each year.¹ That’s 5 percent of organizations’ revenue and the painful disappearance of wealth for everyday consumers. Let this statistic serve as a wake-up call for businesses and banks worldwide: getting proactive about fraud is essential. Fraud analytics is critical to helping organizations shift from reacting to fraud and scams to preventing them.

Discover how fraud analytics detects and prevents different types of fraud, helping organizations minimize financial losses and enhance customer satisfaction.

What is Fraud Analytics and How Does It Work?

Fraud analytics uses big data analysis to find patterns from massive amounts of transactions. Insights collected can help organizations catch and prevent suspicious activity by identifying essential trends. It combines artificial intelligence (AI), machine learning, and predictive analytics.

By combining tech analytics with human expertise, fraud experts can spot fraud more easily, find patterns that humans might miss, and prepare for new threats. Most importantly, businesses can react swiftly to potential risks in real time.

Why Businesses Need Fraud Analytics

People and businesses have been adopting digital tech for years. The pandemic accelerated things when millions couldn’t go to stores or banks in person. Digital banking and eCommerce surged, creating massive amounts of new digital data.

The downside of this digital shift is that it also created new openings for bad actors. Every new digital channel brings a wave of new fraud and scam tactics. This has led to regulators in some markets requiring banks to pay back customers for fraud and scam losses.

In today’s big data world, businesses can’t depend solely on old-school, rules-based systems to catch fraud. Fraudsters quickly learn how rules operate and find ways to commit fraud undetected. Each new tactic requires a new lesson, pushing organizations into an endless cat-and-mouse game.

Fraud analytics can quickly and proactively review large amounts of new digital data. This reduces the need for time-consuming manual tasks. These solutions use data analytics to identify unusual patterns that would go unnoticed. Businesses can use this information to calculate an accurate transaction risk score before approving it.

5 Key Benefits of Fraud Analytics

For banks, implementing fraud analytics solutions can result in several significant benefits. Here are some of the most notable benefits that fraud analytics offers.

Predict Future Fraud Risk

Fraud analytics helps banks get ahead by predicting future risks with risk scoring. Machine learning models use past data to get better at catching new fraud patterns early. This forward-thinking approach helps banks stay one step ahead of fraudsters and move from detecting fraud to preventing it.

Detect and Prevent Fraud in Real Time to Reduce Revenue Losses

A critical advantage of fraud analytics is its real-time detection capabilities. The system uses pattern recognition and data analytics techniques to identify potential fraud rapidly. Real-time monitoring immediately flags anomalies, reducing the impact of fraud. Earlier detection also empowers banks to prevent potential losses for the bank and customers.

Improved Customer Satisfaction and Trust

Proactive fraud prevention measures powered by advanced fraud analytics create a protective shield around customer assets. Banks reassure customers that their funds and sensitive information are safe, improving customer satisfaction and trust in their bank.

Resource Optimization

With fraud analytics, financial institutions can focus on the riskiest cases using data insights. This means less time spent on manual investigations, so teams can prioritize what matters most. This boosts efficiency and saves money by stopping the most significant fraud threats.

Uncover More Fraudulent Activities

Fraud analytics proactively detect and prevent a wide range of suspicious activities, including:

Credit Card Fraud

A fraudster steals a credit card and makes small online purchases to avoid suspicion. They eventually move on to bigger purchases.

• Stopping Credit Card Fraud. Fraud analytics can flag unusual spending patterns, location changes, and transactions with unknown merchants. Machine learning algorithms can also review historical data and user behavior to flag anomalies.
• Benefit. Early detection protects both the customer and the bank from financial losses.

Debit Card Fraud

Fraudsters use skimming technology to steal debit card information at ATMs or gas station card readers. When a customer swipes their debit card, the skimmer reads and steals the card’s info. 

• Stopping Debit Card Fraud. Fraud analytics can detect potentially fraudulent activity based on location, time, and amount compared to the customer’s usual spending habits. Meanwhile, geo-location tracking can identify attempted ATM withdrawals from unfamiliar places.
• Benefit. Banks can protect their customers’ funds and businesses from potential chargebacks.

Account Takeover

A fraudster uses phishing tactics to steal a customer’s login info, change their password, and transfer their money to an account they control.

• Stopping Account Takeover. Fraud analytics can use behavioral biometrics technology to detect login attempts from unrecognized devices or locations. Using multi-factor authentication and anomaly detection enables banks to prevent unauthorized access and transfers.
• Benefit. Banks protect customer data and prevent unauthorized transfers, building strong customer trust.

ACH Fraud

Criminals breach a company’s payroll system to initiate fraudulent ACH transfers, diverting employee salaries to the criminal’s accounts.

• Stopping ACH Fraud. Fraud analytics spots strange payment activity, mismatched names, and account info that doesn’t match payroll data. 
• Benefit. Banks prevent losses for businesses and protect employee payments, which keeps clients happy and the economy stable.

Synthetic Identity Fraud

Criminals create fake identities using stolen information from various sources and apply for credit cards and loans.

• Stopping Synthetic ID Fraud. Link analysis can identify suspicious connections between seemingly unrelated accounts and transactions. Additionally, fraud analytics can detect unusual patterns that are red flags for synthetic identities.
• Benefit. Fraudulent accounts and loan applications threaten the entire financial system. By implementing fraud analytics, banks take an active role in protecting the system, protecting banks themselves and legitimate borrowers.

Account Opening Fraud

Fraudsters use stolen personal information to open new bank accounts online, potentially for money laundering or other illegal activities.

• Stopping Account Opening Fraud. Fraud analytics can cross-reference applicant information with databases of known fraudulent activities and verify information with external sources to identify suspicious applications.
• Benefit. Banks can block the opening of fraudulent accounts and protect themselves from financial losses and potential involvement in illegal activities.

Scams

Fraudsters deceive people into making payments for goods and services that don’t exist, impersonate family, friends, or coworkers, or play with human emotions to lead victims into making money transfers, sometimes draining entire lives.

• Stopping Scams. Fraud analytics can help differentiate between different types of scams (e.g., CEO fraud and romance scams have very different modus operandi, and that’s reflected in the data)
• Benefit. Banks can tune their risk strategy to better fit the situation at hand. E.g., by having different user journeys depending on the flagged risk or by having different scripts for Fraud operators to communicate with the customer

Best Practices to Implement Fraud Analytics in an Organization

Implementing fraud analytics effectively requires a strategic approach and adherence to key best practices.

Establish Objectives

To start your fraud analytics journey, it’s recommended that organizations clearly define their priorities. Clarify the specific types of fraud to target (e.g., credit card fraud, account takeovers, scams, first-party fraud, or payment fraud). Make sure to align these objectives with your company’s broader business goals and fraud analytics strategy. Consider scalability, flexibility, and ease of use to ensure your selected platform can support these initiatives.

Adopt an Omnichannel View of Fraud

Successful fraud analytics must be able to access and analyze data from different sources. This includes transaction systems and customer databases to gather a holistic view of customer patterns. Implement robust fraud data governance policies to manage and protect your data assets.

Embrace Data Orchestration

Data orchestration streamlines fraud analytics by automating the collection, integration, transformation, and management of data from various sources into a single workflow. Think of it as a conductor for your data, coordinating different sources, formats, and workflows to deliver insights for fraud prevention. It also reduces manual workflows and helps overcome challenges like data silos and inconsistent formats, ultimately improving operational efficiency and data quality and enabling real-time insights.

Build Internal Collaboration

Adding fraud analytics is a team effort. Getting buy-in from essential stakeholders, including IT experts, fraud analysts, and business teams, is crucial to evaluate and enhance fraud analytics focus. Consider hiring training analysts with fraud detection and prevention expertise to ensure the investment goes smoothly post-launch.

Implement Robust Security Measures 

Keeping data secure is non-negotiable for fraud analytics. Use strong security measures to keep sensitive data safe, including encryption and access controls. Make sure your platform complies with all relevant data privacy regulations. 

Prioritize Ongoing Monitoring and Improvement

A fraud analytics platform’s performance must be monitored continuously to meet optimal performance standards. Regularly review your system’s effectiveness and update models and rules to adapt to new and emerging fraud trends. Use feedback loops to adjust your strategy and improve the platform’s accuracy.

Explainability and Transparency

Technology that isn’t trusted won’t be adopted. Transparency is critical to building trust in fraud analytics platforms. Ensure your system’s decisions and findings are explainable and the reason for fraud alerts is clear. Communicating the rationale behind decision-making will build confidence with customers and key stakeholders.

Testing and Validation

Thorough testing and validation are essential before deploying fraud analytics systems. This includes using diverse datasets and real-world scenarios to ensure the system functions as intended. Continuous validation of fraud detection models is necessary to maintain their effectiveness.

By following best practices, organizations can effectively implement fraud analytics, minimize financial losses, protect customers, and maintain trust in their operations. 

Using AI & Machine Learning for Fraud Analytics

Traditional rule-based systems have limitations in detecting new and complex fraud patterns. By analyzing extensive datasets, artificial intelligence and machine learning algorithms can detect minute irregularities that human analysis might miss.

Here’s how integrating AI and machine learning has revolutionized fraud analytics and delivered powerful tools to combat evolving fraud tactics.

AI and Machine Learning: Key Fraud Analytics Capabilities

AI and machine learning offer several key capabilities for fraud analytics. These include:

• Pattern recognition: AI algorithms can recognize complex patterns and relationships in data to detect fraudulent activities. 
• Anomaly detection: Machine learning models identify potential fraud by detecting anomalies in behavior.
• Predictive analytics: AI can predict future fraud risks by analyzing historical data and trends.
• Real-time analysis: Real-time analysis enables immediate fraud detection and prevention without delay. 
• Automation: Automatically streamlines fraud detection processes, reducing the need for manual review.

AI and Machine Learning Techniques Used in Fraud Analytics

Various AI and machine learning techniques are employed in fraud analytics. These include:

• Supervised learning: Supervised learning algorithms are trained using labeled data to classify transactions as either fraudulent or legitimate.
• Unsupervised learning: Unsupervised learning algorithms identify patterns and anomalies in unlabeled data.
• Deep learning: Using neural networks, deep learning can analyze complex data to detect sophisticated fraud schemes. 
• Natural language processing (NLP): NLP can analyze text data, such as customer communications, to detect fraud indicators.

Key Benefits of Fraud Analytics 

The benefits for businesses that use AI and machine learning in fraud analytics include:

• Improved accuracy: Using AI and machine learning models, fraud analytics platforms can detect fraud with greater accuracy than traditional methods
• Increased efficiency: Automated fraud detection enhances efficiency by decreasing the time and resources needed.
• Enhanced real-time detection: Real-time detection enables the immediate prevention of fraudulent activities.
• Adaptability: Machine learning models can detect evolving patterns and quickly adapt to new fraud tactics.
• Scalability: AI solutions make scalability more achievable to reviewing large volumes of data and transactions.

Benefits of Effective Fraud Analytics for Banks and Businesses 

The adoption of fraud analytics is becoming increasingly common among banks and businesses. A well-executed fraud analytics strategy can deliver significant benefits to any organization.

1. Protection Across the Customer Journey

Fraud analytics solutions deliver insights into how customers typically interact, such as when they buy goods or use online services. This data creates a complete picture of their usual habits. Banks and businesses can easily spot anything abnormal that might signal fraud and keep customers safe.

2. Leverage Collected Data

Sound fraud analytics systems let you use data from your own systems and outside sources to understand how customers behave clearly. This helps you get real-time insights and rely less on rule-based systems alone. It also streamlines maintenance and lets your data analysts pull the most valuable information.

3. Real-time Metric Computation

Many banks operate with legacy systems that depend on intricate, rules-based configurations. These rules can be challenging to maintain and update, often requiring significant resources for adjustments and implementation. If someone who knows those rules leaves the business or bank, no one will want to touch them for fear of breaking something.

Fraud analytics solutions that simplify the addition of new metrics can reduce the coding effort and IT involvement required. This can enable analysts to work with data more efficiently and confidently, which helps ensure the reliability of production models.

4. Automated Data Profiling and Enrichment

Fraud analytics systems can automatically find unusual activity by profiling customer behavior and spotting anything out of the ordinary. This automation can simplify model maintenance and decrease manual rule creation while allowing banks to incorporate essential rules based on regulatory requirements, business needs, or risk mitigation strategies.

5. Actionable Insights and Journey Adaptation

Fraud analytics gives banks risk scores and clear recommendations so they can customize the customer experience based on how risky a transaction seems. This could mean sending customers alerts about potential scams, changing how they verify transactions, or giving analysts specific guidance for different kinds of fraud.

Securing the Digital Banking Future

The digital banking era has arrived, enabling customers to access their accounts and conduct business quickly. Unfortunately, criminals are eager to exploit the advantages of digital banking by committing fraud and scams at scale.

Reacting to fraud after it happens is an insufficient approach to digital fraud threats. Banks and other businesses need AI and machine learning to detect and prevent fraud in real time. Not only does fraud analytics reduce potential fraud losses, but it also strengthens customers’ trust in their banks.

Here are additional resources for fraud analytics:

• Article: Fraud Prevention Solutions: How to Proactively Stop Fraud
• Resource: Best Enterprise Fraud Solution
• Solution Guide: Prevent and Detect Payments Fraud with Feedzai
• Solution: Transaction Fraud