Illustration of a board of images; a robotic hand in pink points to a criminal's photo also in pink; demonstrating how advanced artificial intelligence and machine learning can perform fraud analytics and detect financial threats

Banks lost an incredible $485.6 billion to fraud and scams last year. This significant loss demonstrates that it’s more important than ever for banks to stay a step ahead of criminals. Fraud analytics is critical to helping banks shift away from reacting to fraud to preventing it in the first place.

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

What is Fraud Analytics?

Fraud analytics blends artificial intelligence (AI), machine learning, and predictive analytics for advanced data analysis. Combining these techniques empowers banks to review and draw insights from large volumes of data quickly.

Using both technological analytics and human-based insights offers fraud experts several benefits. These include detecting fraud, uncovering patterns invisible to the human eye, and predicting future threats. Perhaps most importantly, banks can respond to suspected fraud in real time.

infographic detailing 5 key fields of artificial intelligence (AI): artificial intelligence, machine learning, predictive analytics, deep learning, and Generative AI
infographic detailing 5 key fields of artificial intelligence (AI): artificial intelligence, machine learning, predictive analytics, deep learning, and Generative AI

Why Banks Need Fraud Analytics

Digital banking adoption has been steadily climbing for several years now. That trend only increased during the pandemic when millions of people could not visit bank branches in person. More people using digital banking services has resulted in the creation of large volumes of digital data.

At the same time, the digital banking shift has also created new openings for bad actors to commit fraud. With each new digital banking channel, criminals unleash a new wave of fraud and scam tactics. This has resulted in regulators in several global regions pressuring banks to compensate customers for fraud and scam losses.

In the age of big data, banks can’t rely solely on traditional rules-based systems to catch fraudulent transactions. Fraudsters quickly learn a bank’s rules and find ways to commit fraud undetected. Each new fraud tactic requires a new lesson, pushing banks into an endless cat-and-mouse game.

Fraud analytics proactively and rapidly review vast amounts of data. This reduces the need for time-consuming and cumbersome manual tasks. These solutions use data analytics to detect unusual patterns that would go unnoticed. Banks can use this information to calculate an accurate transaction risk score before approving it.

5 Key Benefits of Fraud Analytics

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

Predict Future Fraud Risk

Banks can use fraud analytics to move reactive measures by predicting future risks using risk scoring. Machine learning models tap into historical data to refine their ability to recognize new fraud patterns before they escalate. Adopting a forward-looking approach to fraud enables banks to stay one step ahead of fraudsters. More importantly, it allows them to shift from a reactive fraud detection approach to a proactive fraud prevention strategy.

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 swiftly. Real-time monitoring can immediately detect anomalies, minimizing the impact of fraudulent activity. 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

Financial institutions can optimize resource allocation using data-driven insights by directing attention to high-risk cases. This reduces the need for extensive manual investigations on every transaction, allowing teams to prioritize their efforts effectively. As a result, the organization can improve efficiency and cost savings by thwarting the most essential fraud cases.

Uncover More Fraudulent Activities

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

Credit Card Fraud

Let’s say a customer loses their credit card and doesn’t realize it’s missing. A thief can find the card and use it to make small online purchases to avoid suspicion. The thief eventually starts to make larger purchases over time.

  • Stopping Credit Card Fraud. Fraud analytics can flag unusual spending patterns, location changes, and transactions made with unfamiliar 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 information. The fraudsters then use this stolen data to make unauthorized purchases online or withdrawals from the victim’s account.

  • 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 unusual locations.
  • Benefit. Banks can protect their customers’ funds and businesses from potential chargebacks.
Account Takeover

A fraudster tricks a customer with phishing emails to steal their online banking details, change their password, and then steal their money.

  • Stopping Account Takeover. Fraud analytics can use behavioral biometrics technology to detect login attempts from unusual devices or locations. Using multi-factor authentication and anomaly detection enables banks to prevent unauthorized access and transfers.
  • Benefit. Using advanced fraud analytics, banks can protect their customers’ financial information and prevent unauthorized transfers, securing both the bank’s reputation and strengthening 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 can identify unusual payment patterns, mismatched recipient names, and discrepancies in account information compared to legitimate payroll data.
  • Benefit. Banks can prevent financial losses for businesses and protect employees’ wages, maintaining their clients’ trust and economic stability.
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.
Infographic detailing 5 actionable steps banks can take to successfully implement fraud analytics
Infographic detailing 5 actionable steps banks can take to successfully implement fraud analytics

Feedzai’s Approach to Fraud Analytics

Several banks are embracing fraud analytics with varying degrees of success. Feedzai’s approach to fraud analytics yields strong results by succeeding across several key factors.

1. Protection Across the Customer Journey

Feedzai’s fraud analytics solution gathers insights based on how customers normally transact, pay for everyday items, or shop online. This data provides a holistic customer view, including their typical end-to-end journey. By understanding their usual behaviors, banks can easily spot suspicious activity and keep their customers safe at every interaction.

2. Leverage Collected Data

Feedzai uses data from both the bank and outside sources to understand customers’ spending habits. This delivers real-time insights quickly without requiring numerous complicated rules. It also saves time on maintenance and lets data scientists use this information for better results.

3. Real-time Metric Computation

Many banks use legacy systems with complex rules-based arrangements. However, these rules are challenging to maintain, as new scenarios require new rules and different teams for model adjustment and implementation. If someone writes a rule and leaves the bank, the remaining personnel will hesitate to change it for fear of causing problems.

Adding new metrics is effortless with Feedzai, requiring minimal coding and limited involvement for IT teams. This empowers analysts to act on data quickly and confidently, ensuring production models are always reliable.

4. Automated Data Profiling and Enrichment

Our fraud analytics solution automates anomaly detection by profiling customer behavior and identifying deviations. This simplifies model maintenance and reduces the need for manual rule creation. Of course, banks can still keep essential rules in place based on regulations, business needs, or safety nets.

5. Actionable Insights and Journey Adaptation

Feedzai provides risk scores and actionable plans, allowing banks to tailor the customer journey based on fraud risk. This includes prompting customers about potential scams, changing transaction confirmation methods, and providing analysts with specific scripts based on fraud types. 

Feedzai’s LightGBM algorithm delivers more accurate fraud detection and prevention by enhancing data profiling and enrichment. Focusing on data quality and labeled profiles rather than just the model itself strengthens fraud prevention efforts.

Major UK Bank Sees 30% Rise in Fraud Detection

How does the solution work in practice? A major UK bank is currently seeing the benefits.

Fraudulent activity and scams flooded the bank. Despite this surge, the bank only managed to catch half of the potentially fraudulent transactions. Through its Feedzai partnership, the bank improved its fraud detection rate by 30%, preventing millions in potential scam losses.

The bank enhanced its rules-based knowledge with Feedzai’s patented machine learning techniques to detect and prevent fraud. The model does most of the heavy lifting, while rules help stabilize the arrangement.

In addition, Feedzai’s fraud analytics capabilities provided benefits beyond minimizing financial losses. The collaboration also resulted in a 40% reduction in false positives. This allowed the bank to deliver a more seamless and friction-free customer experience.

EU Bank Improves Customer Friction

Meanwhile, an EU-based bank is also experiencing the benefits of Feedzai’s advanced fraud analytics solutions.

The bank was experiencing considerable losses from impersonation fraud and scams. Feedzai’s solution enabled the bank to reduce its impersonation-based losses by 29% within a year — the number of alerts dropped by half within the same timeframe.

Not only did the fraud analytics help the bank improve its bottom line, but it also improved its customer experience, with a 50% drop in false positives. Meanwhile, the share of customers who experienced fraud declined by 31%.

The collaboration enabled the bank to protect its revenue by reducing fraud losses. More importantly, the bank’s customers enjoyed a seamless journey and experienced less 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 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.

Two big banks found success using Feedzai’s fraud analytics, showing the real benefits of this approach. Certainly, more banks can benefit from fraud analytics to stay ahead of fraud instead of reacting to it.

Additional Fraud Analytics Resources

Here are additional resources for fraud analytics: