Fraud analytics in banking is used to prevent digital and mobile banking fraud via big data analysis techniques. Banking fraud can pose a significant loss to financial institutions, and fraud only seems to be on the rise due to the rapid increase in online banking. This shift to online banking has led banks to implement artificial intelligence (AI) that can flag possible fraudulent behavior based on the user’s actions and mannerisms on the website.
With fraud analytics, your bank can predict fraudulent behavior in the future and quickly apply mitigation and detection of fraud in real time, allowing you to prevent fraud and save money.
How the Pandemic Has Affected Banking Fraud
The pandemic has led to more customers shifting to online banking for their financial activities. Unfortunately, this means online banking fraud has increased as well, including identity theft and a popular type of financial fraud known as account takeover. More than ever, financial institutions need to utilize comprehensive methods for fraud monitoring in banks and protection of customers’ accounts.
The adoption of AI-powered technology for financial services and fraud detection in banking has also accelerated due to the pandemic. Though monitoring customers’ behaviors and mannerisms online can help prevent fraud, there is growing concern about the use of AI in banking. A few U.S. agencies announced they were soliciting insights into how tech companies and banks were utilizing AI in financial services. Fortunately, the pandemic has also highlighted the benefits associated with data analytics for fraud detection in banking.
Challenges of Combating Banking Fraud
Banks are responsible for protecting their customers’ finances and data against theft and fraud. With the increasing accessibility of financial accounts via multiple channels, combatting banking fraud can be difficult. This increased vulnerability of sensitive financial data is why it’s essential to understand the challenges of combatting banking fraud and how you can overcome them. Some of the challenges banks may face include:
New services and channels: Customers can do their banking by using an online portal, accessing a mobile app, visiting the financial institution in person or contacting the call center. With the rise in online banking, banks are offering more services and payment channels, and this increased accessibility can present new challenges to banks when combatting fraud. Banks must prevent account takeovers and unauthorized transactions on these new channels.
Greater access to stolen credentials: While a teller may reasonably be able to verify a customer’s identity in person, it can be more difficult to verify a customer’s identity when they log into their bank account online. Fraudsters have greater access to stolen credentials than ever before.
Frequently changing regulatory landscape: For many banks, it can be challenging to monitor fraudulent activities and comply with the ever-evolving regulatory landscape. These regulations can lead to more hurdles in fraud management, requiring financial institutions to reveal which data management approach they’ve adopted. This can reveal confidential techniques for detecting banking frauds to fraudsters.
Difficult implementation of anti-fraud measures: Finding a permanent solution to prevent online banking fraud can be difficult. Many banks struggle to balance delivering a seamless user experience with stopping fraudsters and teaching their customers how to avoid fraud. If customers experience too much additional friction due to anti-fraud measures, your bank could lose customers. On the other hand, if anti-fraud measures are too lenient, hackers may find loopholes through which they can steal customers’ personal information or money. Striking a balance is essential for preventing banking fraud.
Tech-savvy fraudsters: Today, fraudsters are skilled at outwitting financial institutions’ data security systems. To perpetrate a cross-channel fraud, a fraudster leverages data analysis techniques that allow them to more easily analyze online transactions and mimic customer behavior.
Types of Banking Fraud
A fraudster uses legal transactions to disguise illegal activity. While some security systems flag transactions, systems can sometimes flag actual transactions from customers.
Types of banking fraud include:
Malware: A fraudster can use several methods to trick a victim into installing malicious software onto their devices. This malware can corrupt data, log keystrokes to access accounts and render the device unusable.
Check kiting: Kiting refers to the fraudulent use of financial instruments to get additional, unauthorized credit. With check kiting, a fraudster alters or issues a check when there are insufficient funds. Through this series of bad checks, a fraudster targets financial institutions, even sometimes drawing from multiple accounts.
Phishing: When a fraudster impersonates a legitimate financial institution or website via text or email to get a victim to transfer funds or share personal information, this is known as a phishing attack.
Employee fraud: Among financial institutions, employee fraud occurs when an employee intentionally steals, deceives or lies to the company with the intention of obtaining some type of compensation or benefits. These insider threats can take a few different forms, including physical theft, monetary theft or workers’ compensation fraud. Employee fraud can put the financial health of your bank at risk.
Social engineering fraud: To con customers, a fraudster may impersonate a customer service representative. Examples of social engineering fraud include the unwitting authorization of a fraudulent transaction and online links that redirect customers to a fake portal. Unfortunately, traditional methods for preventing fraud can fail to detect advanced social engineering attacks, which is why modern fraud analytics in banking is essential.
Card not present (CNP): CNP fraud involves an individual using a stolen credit card account for making transactions that don’t require a physical card. For instance, a fraudster may purchase an item from an e-commerce website.
Account takeover (ATO): ATO occurs when a fraudster accesses an existing online financial account by using stolen credentials.
Sim swapping: Sim swapping is a form of ATO in which a fraudster uses a victim’s sensitive information after it has been stolen via a data breach or from another information source like social media. The fraudster uses this information to convince the victim’s mobile company to port the phone number to the scammer’s phone.
Fraudulent loan applications: A fraudulent loan application can occur when someone knowingly provides false information on an application for a loan. Examples include falsifying documents, listing an incorrect salary, not reporting income and listing false employment.
Man in the Middle (MitM) attack: A MitM attack happens when an individual intercepts the communication between the customer and the online service for the purpose of hijacking an online session or stealing information.
How Analytics Can Help Prevent Digital Banking Fraud
Banking fraud is constantly evolving, and even as financial institutions implement remediation measures, new threats can arise. This means stagnant, traditional systems for preventing banking fraud can’t keep up. Fortunately, ample data is available to banks and can be used to detect and predict financial fraud and adjust to new digital banking threats.
Though collecting usernames and passwords used to be sufficient for guarding against fraudulent activity, now other data can be used to determine whether the customer or the transaction is legitimate, including the device, whether the device has been registered with the bank previously, whether the transaction is typical and whether the user can verify their identity via a fingerprint.
Banks need reliable systems that can accurately distinguish between legal and illegal transactions to better prevent digital banking fraud. Streamlined cyber intelligence software with a variety of tools like intelligent forms and mobile capabilities can make fraud cases easier to navigate. With analytics from that software, your bank can:
Find Patterns
Customers’ financial transactions like check deposits or bank withdrawals follow certain patterns that data analytics can help identify. With data analytics, you can analyze trends and compare them against indicators of fraud.
Analytics can study several transactions across customers and identify patterns. While sensitive information like a customer’s Social Security number, what street they grew up on or their mother’s maiden name may be vulnerable to theft and hacking, behaviors are more difficult to replicate or steal with precision.
For instance, if an unusually large transfer is made, the account may be flagged for investigation. Additionally, if typing and mouse movements suddenly deviate, a behavioral analytics system may flag this as potentially fraudulent activity. The software may even collect data on how a legitimate customer typically moves the mouse when the cursor disappears. When fraud is suspected, the software can make the cursor disappear to verify whether the user is legitimate based on their reaction.
Even a customer’s age can help indicate fraudulent activity. The speed at which a user types, moves the cursor across the screen and clicks the mouse slows as they age, so an older user who is suddenly typing with the speed of a younger person may result in a fraud flag.
Distinguishing between legitimate transactions and banking fraud relies on finding patterns that indicate illegal activity. For example, a legitimate customer may quickly type their Social Security number and name but will take more time to fill out their bank account number. A fraudster, on the other hand, may demonstrate the opposite behavior. Banking fraud patterns should be identified while the transaction is occurring and flagged in real time.
Many fraudsters follow familiar patterns of banking fraud. Tax-related scams, for instance, tend to happen during tax season. Since data analytics can study transaction patterns, it’s essential for flagging signs of fraud and helping to prevent banking fraud.
Make Predictions
Banks collect large amounts of transactional, device and behavioral data. By analyzing this data, the fraud detection system can help find and prevent financial fraud, but the quality of the analysis depends on the data available. As long as good data is available, a fraud analytics system can use big data analysis techniques to combat financial fraud. Predictive analytics specifically examine patterns to make predictions about the potential for future fraud.
With pattern recognition and the ability to identify events that fail to conform to the expected patterns, analysis algorithms can make predictions by learning from the data.
Digital channel unification — a visual analytics tool — aggregates and monitors transactions automatically for suspicious activity. Other visual analytics tools include fraud visualization tools that can rapidly identify the potentially fraudulent transaction’s source and web-based case management that allows fraud analysts to look into key fraud indicators and review cases.
The examination of a financial fraud event’s causes and consequences, known as forensic analysis, can also be strengthened by visual analytics data. This data can include IP addresses, locations, devices, relationships and users associated fraud cases. By analyzing the relationships and data, your financial institution can expose cooperation between fraudsters and identify potentially fraudulent activity.
Implement Faster Solutions
The costs of banking fraud activity can add up quickly, especially if it lasts for several months. When action is taken quickly to stop fraudulent activity, the savings for both your bank and your customers can be enormous. Additionally, you can boost your bank’s reputation because your customers can rest assured that their financial information is secure. Data analytics is an effective method for quickly detecting fraud due to analytics’ processing power.
More and more banks are turning to analytics solutions to reduce credit card fraud. Compared to conventional methods, data analytics can speed up banking fraud detection. Data analytics works in real time, and new techniques can be learned quickly. Since fraudulent transactions can be flagged in real time, you can catch them and resolve the issue as fast as possible. Resolving certain kinds of fraud is especially time-consuming, such as account takeover fraud.
With an analytics platform, your institution can review transactional records around the clock for probable fraud. This accelerates the resolution process and makes it easier to identify illegal activity that takes place in different time zones.
Fraud Analytics With Kaseware
Detect and manage fraud with our investigation software. Our platform is a suite of tools to document each step of crime investigation cases. What makes us unique is that we combine nearly all features that investigators need into one single platform. Our competitors typically offer one or two of the features we offer.
The value we provide is to combine everything into one place. This can save our clients time that would otherwise be spent managing multiple products, frustration and often quite a bit of money. Our tools include:
Case management with autofill smart forms to eliminate duplicate data entry.
Collaborative tools so you can securely work with your colleagues and even across agencies, which will help eliminate information silos.
Search features that allow you to search the “dark web” and social media live in real time directly in the platform to help research information related to your investigation.
Many analytic tools to help you visually “connect the dots,” including one of our more popular features, Link Analysis.
Automatic visual graphs so you can easily present information to project stakeholders.
A web portal so you can easily receive and manage tips from the public.
Schedule a free demo today to learn more about us, our support offerings and our incredible product features. Reach out to us at Kaseware to learn more about how we can help your financial institution detect and prevent fraud.