Fraud detection is defined as a process that detects scams and prevents fraudsters from obtaining money or property through false means. Fraud is a serious business risk that needs to be identified and mitigated in time. This article explains fraud detection in detail and shares some best practices that should be followed in 2021.
Table of Contents
What Is Fraud Detection?
Fraud detection is a process that detects and prevents fraudsters from obtaining money or property through false means. It is a set of activities undertaken to detect and block the attempt of fraudsters from obtaining money or property fraudulently. Fraud detection is prevalent across banking, insurance, medical, government, and public sectors, as well as in law enforcement agencies.Â
How Fraud Detection Works
Fraudulent activities include money laundering, cyberattacks, fraudulent banking claims, forged bank checks, identity theft, and many such illegal practices. As a result, organizations implement modern fraud detection and prevention technologies and risk management strategies to combat growing fraudulent transactions across diverse platforms.Â
These techniques apply adaptive and predictive analytics (i.e., machine learning) to create a fraud risk score along with real-time monitoring of fraudulent events. This allows continuous monitoring of transactions and crimes in real-time. It also helps decipher new and sophisticated preventive measures via automation.
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Types of Fraud Detection Techniques in Computers
Fraud detection generally involves data analysis-based techniques. These techniques are broadly categorized as statistical data analysis techniques and artificial intelligence or AI-based techniques. Let’s understand both in detail.
Types of Fraud Detection Techniques
Statistical data analysis techniques
Statistical data analysis for fraud detection performs various statistical operations such as fraud data collection, fraud detection, and fraud validation by conducting detailed investigations. These techniques are further subdivided into the following types:
1. Statistical parameter calculation
Statistical parameter calculation refers to the calculation of various statistical parameters such as averages, quantiles, performance metrics, and probability distributions for fraud-related data collected during the data capturing process.
2. Regression analysisÂ
Regression analysis allows you to examine the relationship between two or more variables of interest. It also estimates the relationship between independent and dependent variables. This helps understand and identify relationships between several fraud variables, which further helps in predicting future fraudulent activities. These predictions are based on the usage patterns of fraud variables in a potentially fraudulent use case.
3. Probability distributions and models
In this technique, models and probability distributions of various business fraudulent activities are mapped, either in terms of different parameters or probability distributions.
4. Data matching
Data matching is used to compare two sets of collected data (i.e., fraud data). The process can be carried out either based on algorithms or programmed loops. In addition, data matching is used to remove duplicate records and identify links between two data sets for marketing, security, or other purposes.Â
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AI-based techniques
Deploying AI for fraud prevention has helped companies enhance their internal security and streamline business processes. Through improved efficiency, AI has emerged as an essential technology to prevent fraud at financial institutions. AI-based fraud detection techniques include the following methods:
1. Data miningÂ
Data mining for fraud detection and prevention classifies, clusters, and segments the data and automatically finds associations and rules in the data that may signify interesting patterns, including those related to fraud.
2. Neural networksÂ
Neural networks under fraud detection perform classification, clustering, generalization, and forecasting of fraud-related data that can be compared against conclusions that are raised in internal audits or formal financial documents.
3. Machine learning (ML)
Fraud detection with machine learning becomes possible due to the ability of ML algorithms to learn from historical fraud patterns and recognize them in future transactions. Machine learning either uses supervised or unsupervised learning methods.Â
In supervised learning, a random subsample of all records is manually classified as either ‘fraudulent’ or ‘non-fraudulent’. In unsupervised learning, on the other hand, methods search for common patterns (i.e., fraudulent) and correlations in the raw data, and predictions are built without additional labeling.
4. Pattern recognitionÂ
Pattern recognition algorithms detect approximate classes, clusters, or patterns of suspicious behavior, either automatically (unsupervised) or manually (supervised).
Other techniques such as link analysis, Bayesian networks, decision theory, and sequence matching are also used for fraud detection purposes.
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Applications of Fraud Detection
Fraud detection is of paramount importance for banks and other companies that deal with a significant number of financial transactions and are therefore at higher risk of suffering from financial fraud. However, other sectors such as ecommerce companies, credit card companies, electronic payment platforms, and B2C fintech companies also need to employ fraud detection to prevent or limit financial fraud.
The most common applications of fraud detection include account-related fraud and payment and transaction fraud. Account fraud is further divided into new account fraud and account takeover fraud. In new account fraud, new accounts are created by using fake identities. Such frauds can be identified by using the patterns of various devices and session indicators for detecting fake identities.
Account theft frauds occur when a hacker obtains products and services by using another person’s existing account. In order to prevent this, session, device, and behavioral biometrics of the user can be computed and scored to verify an account. In addition, analyzing user journeys for behavioral patterns can help detect account takeovers before they cause any financial harm.
Payment fraud is any kind of false or illegal transaction that is carried out by a cybercriminal. The perpetrator cheats the victim of money, personal property, interest, or sensitive information. This category further includes unauthorized transactions fraud, stolen merchandise fraud, and false requests for refund fraud.
Let us now dive into industry-specific fraud detection.
1. Banking & financial services
As the digital trend has been gaining traction worldwide, frauds have been increasing with the rising number of online and ATM transactions. The most common types of banking frauds are:
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- API fraud: Payment Services Directive 2 (PSD2) mandates certain European financial institutions to open up their services via application programming interfaces (APIs). This creates a new attack surface.
- Stolen/fake credit card fraud: A fake card is generated based on a user’s card information that scammers manage to gain access to. There are numerous ways that scammers use to do this, with card skimming being the most common. Credit card skimming is a technique where the scammer attaches a small device to the transaction machine that cannot be easily noticed.
- Website cloning: Website cloning is one of the most popular methods among scammers to fleece people of their money. As the name suggests, cybercriminals first create a ‘clone’ site of the original website. Next, they create a trap intended to get unsuspecting victims to visit the cloned site. This is usually done via links shared through emails, text messages, or social media posts, asking unsuspecting users to click on them.
- ATM fraud: ATM fraud is described as a fraudulent activity where criminals use another person’s ATM card to withdraw money instantly from that account. Different types of ATM frauds include card shimming, card skimming, card trapping, and keyboard jamming, to name a few.
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2. Ecommerce & retail
With the ecommerce sector booming amid the COVID-19 pandemic, targeting users through ecommerce channels has become more frequent than ever. The most common methods include:
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- Promo abuse: Promo abuse or coupon fraud occurs when an individual customer, vendor, or partner agency takes advantage of a promotion, abusing the coupon policy. Fraudsters can benefit by redeeming coupons multiple times or simply by using them to gain money and other valuable items or services.Â
- Payment fraud: One of the most common fraudulent activities is ecommerce payment fraud, which is any kind of illegal online transaction performed by a cybercriminal. The victim is usually an online user who gets deprived of their money, interest, sensitive info, or personal property.
- Delivery fraud: The two types of delivery fraud include identity theft and friendly fraud. In identity theft, a fraudster attempts to obtain personal user data via malware, fake websites, emails, or short messages. The cybercriminal uses these to purchase goods on an invoice and have them sent to a different delivery address, with no intention of paying for the items. Friendly fraud is when the customer himself does not intend to pay for the ordered goods and claims that they never arrived.
3. Marketplaces & online ads
This kind of fraud is typically done through referral and promotion abuse as well as fake reviews.
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- Fake reviews: Many consumers rely on online reviews when making a purchase, signing up for services, or engaging with specific businesses. Fake reviews are shared to discredit brands, erode trust, and give examples of bad experiences that never actually occurred.
- Referral and promo abuse: Referral programs are one of the best ways to amplify word-of-mouth referrals for online stores. However, many customers try to take advantage of these programs to get better deals and discounts than what they deserve, leaving a business reeling with extra hidden costs.
In 2017, Tesla Motors discovered that people were buying keyword-based Google ads to promote their referral codes. This led interested customers to unknowingly click on an advertisement. This violated Tesla’s ‘Good Faith’ clause. Tesla then reserved the right to invalidate referrals that were made through abusive or fraudulent means.
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4. IT & telecom
These frauds are carried out through phone calls and other methods involving phones.
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- Phone fraud: Generally referred to as communications fraud, phone fraud uses telecommunication products or services intending to illegally acquire money from or failing to pay a telecommunications company or even its customers.
- Call forwarding fraud: A call forwarding hack is a common form of VoIP telecom fraud. In such a scenario, criminals gain unauthorized access to an enterprise private branch exchange system (PBX) or a voice mail system IVR. With this, they can configure call forwarding to an expensive long-distance destination and profit from a revenue-sharing deal therein.
- Multiple transfers fraud: In this fraudulent scenario, the call is transferred from the call source immediately after the destination answers the call. When the call is transferred, the fraudulent call is in progress with two high-cost destinations, and the call source hangs up. Once these calls have been transferred, they continue until and unless the carrier does not shut them down. Some customers have reported calls staying up for over 24 hours at a time.
- One-ring-and-cut (Wangiri) fraud: Wangiri, in Japanese, refers to ‘one and cut’, which simply means letting the phone ring only once and then disconnecting the call. The Wangiri phone fraud scheme works on the single-ring method to make some fast money. A fraudster will set up a computer to dial a large number of phones at random. Each call rings just once and then disconnects. This leaves a number as a missed call on the phones of numerous recipients.
Many people instinctively return a missed call, even if it is from a mysterious international number. Once you call back, the call is routed to an expensive premium rate number. You are then coerced into staying on the line for as long as possible. The longer you stay talking on the line, the more money the fraudsters make.
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Top 8 Best Practices for Fraud Detection and Prevention in 2021
Advances in fraud detection technologies act as an accurate and efficient arsenal against fraudsters and cybercrimes. Let’s look at the top eight best practices for fraud detection and prevention in 2021.
Fraud Detection Best Practices
1.Create a potential fraud risk profile
Take a top-down approach to your risk assessment, listing the areas in which frauds are likely to occur in your business and the types of frauds that are possible in those areas. After doing this, qualify the risks based on the overall exposure that the organization might face. Develop fraud risk profiles that are a part of the overall risk assessment and include all stakeholders and decision-makers in the process.
2. Address the possible indicators of fraud
Organizations need to test 100% of their data rather than opting to test just random data samples. While sampling may be effective for detecting problems that are relatively consistent throughout data populations, that isn’t always the case for fraud involved. Fraudulent transactions, by nature, do not occur randomly. Transactions can fall within the boundaries of certain standard testing and still not be flagged.Â
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3. Implement continuous auditing and monitoring
Continuous auditing and monitoring can be implemented to test and validate the effectiveness of an organization’s controls over transaction authorizations. Continuous analysis can be employed by setting up scripts to identify anomalies as they occur over a period of time. This process can substantially improve the overall efficiency, consistency, and quality of an organization’s fraud detection process.Â
4. Increase organizational awareness of the monitoring activity
A significant part of fraud prevention is communicating the program across the organization. This can be especially helpful to avoid fraud within the organization. If everyone is aware of the prevention systems that have been put in place, employees will not indulge in fraudulent activities. This can act as a great preventive measure.
5. Deploy artificial intelligence
Machine learning is a powerful force for improving both the accuracy and efficiency of fraud detection. Through machine learning, systems can automatically perform the following tasks:
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- Create and update rules for detection and alert handling: Machine learning can examine masses of data to help establish rules and keep them up to date. Even something as simple as a decision tree can add some benefits (certainly in the segmentation approach) to more accurate rules.Â
- Select the most accurate detection models: A combination of machine learning techniques such as gradient boosting and support vector machines and neural networks can deliver the most accurate fraud detection rates.
- Automate investigation processes: On average, 60 to 70% of an investigator’s time is spent collecting data about a subject. Machine learning can guide systems to automatically search and retrieve data, run database queries, and collect information from third-party data providers without any human intervention.
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6. Encourage anti-money laundering and fraud suspicious activity reporting
The goal of suspicious activity reporting (SAR) and the resulting investigation is to identify customers involved in money laundering, fraud, or terrorist funding. SAR can cover most of the activity that is deemed to be out-of-the-ordinary. An activity may be included in SAR if it gives rise to a suspicion that the account holder is attempting to hide something or make an illegal transaction. Hence, organizations need to implement measures to report money laundering and related frauds.
7. Deploy intelligent case management
An advanced, analytics-driven, intelligent case management solution can automatically:
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- Prioritize cases, recommend investigative steps, and fast-track straightforward cases.
- Enrich alerts with details about the associated customers, accounts, or beneficiaries.
- Intelligently find and pull data from an internal database or even from a third-party data provider.
- Present data in easy-to-understand visualizations.
- Auto-populate and prepare SAR for electronic filing (if applicable).
As such, organizations can streamline their fraud investigations by deploying an intelligent case management solution to aid their fight against cybercrimes.
8. Learn, adapt, and repeat
Review, re-evaluate, and restructure your fraud profile, taking into account the most common fraud schemes and also those relating specifically to the risks that are unique to your organization, thereby moving your investigative lens accordingly.
Use data analytics to find out where controls are not working or are ineffective. Also, keep a watch on controls that application control settings cannot govern. You will need to investigate patterns and fraud indicators shown by fraud detection tests and continuous monitoring and auditing processes.
In conclusion
Fraud detection and prevention need to be a top priority for any business. A well-designed and implemented fraud detection system can significantly reduce the chances of fraud occurring within an organization. In addition, timely detection of fraud directly impacts the business in a positive way by reducing future potential losses.
Effective detection techniques such as AI and statistical data analysis serve as a deterrent to potential fraudsters. As regulatory requirements and compliance demands have grown, it has become extremely important to implement a robust fraud detection and prevention program.
Are you following the above best practices for better fraud detection and prevention? Comment below or let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you!