Stealing debit or credit card information is something that has been happening since online transactions came into existence. Businesses and financial institutions are spending thousands every year on solutions to combat fraud.
The costs of fraud are constantly rising. As a result, businesses need to be aware of the various ways that fraud can occur in order to protect themselves. Hackers are constantly attempting to steal information so fraud detection is becoming a very important part of ecommerce and B2B businesses. Friendly frauds also need to be taken into consideration. In fact by 2023, 61% of chargebacks in North America will be cases of friendly fraud.
This article will give you a comprehensive guide on the different types of fraud, what fraud detection is, how it works and the use cases of fraud detection systems.
What Are The Types of Fraud Detection That Exist?
Before we get into answering this question, let us first highlight that new types of fraud are on the rise these days, so it is important to keep yourself updated.
Credit card fraud:
This fraud happens when an unauthorized user uses another person’s credit card to make a purchase. This type of fraud can happen online, over the phone or even in person. In order to prevent credit card fraud, businesses should take some security measures like verifying the identity of the customer, using only secure payment gateways, etc.
This type of fraud occurs when a customer requests an illegitimate chargeback and attempts to receive the products for free. It also happens when a credit card has been stolen and the original card owner requests a chargeback. In either case, it is a loss for the businesses. This article on chargeback fraud detection gives you a detailed insight into what chargebacks are and how to prevent chargeback frauds.
This type of fraud happens when an insurance policyholder attempts to claim benefits by providing false information. For example, a person might exaggerate the cost of the damage caused by an accident in order to receive a higher insurance payout.
Identity fraud happens when someone uses another person’s personal information like name, Social Security number, date of birth, etc. to commit fraud. This type of fraud can be used to open new credit card accounts, take out loans, etc. in someone else’s name.
Bank account takeover fraud:
This type of fraud happens when a hacker gains access to your bank account and uses it to transfer money to another account.
What is Fraud Detection?
Simply put, fraud detection is the process of identifying potential instances of unauthorized or illegal activity so that you can take measures to prevent them from occurring. It can be used in a variety of contexts, from detecting fraudulent financial transactions to identifying fake reviews or accounts.
How Does Fraud Detection Work?
Fraud detection systems are used to detect and prevent fraudulent activities such as identity theft, credit card fraud, and money laundering. Previously, the process was done using a rule-based method to analyze data to identify patterns and anomalies that may indicate fraud.
Nowadays, in addition to rule-based systems, the methods of fraud detection also include machine learning and predictive fraud analytics. Data mining is a process of analyzing large data sets to find hidden patterns and trends, whereas machine learning is a subset of artificial intelligence that can learn from data and make predictions.
Fraud detection typically relies on a combination of machine learning and human analysis. Machine learning algorithms are used to identify patterns that may indicate fraud, while human analysts then review these cases to confirm whether or not fraud has actually occurred.
With the availability of big data, the rate at which machine learning is developing has increased. In other words, the process of fraud detection has become efficient, scalable and fast. The typical way a machine learning fraud detection algorithm works is as follows:
- Feeding Data – The more data you feed into the model, the better the performance of the model. To get the best outcome from the model, you need to give data related to a particular business, so that it reciprocates and detects suspicious activities according to the history of your business.
- Extracting Features – This step is getting important information from each part of the transaction process. Data science classification is another important step as data needs to be segmented before extracting features. The different types of features in data sets can include:
- Network/Device: Checking out the mobile device or network used to complete transactions previously.
- Location: This makes use of the customer’s IP address and shipping address and the previous number of frauds that happened at that particular location.
- Identity: This involves checking the customer’s email address, mobile number, and other account details.
- Payment Method: This checks the payment method and what bank’s card was used and the history of the number of fraudulent transactions from that bank.
3. Training the Algorithm – This step involves providing details to the fraud detection algorithm that would enable them to distinguish between a genuine and fraudulent transaction. Once the algorithm is trained, it is able to comprehend very large sets of data.4. Creating a Model – The model is created using a specific dataset once all the rules are created. The model works by detecting which transactions are real and which might be a scam. It does so by predicting the probability of fraud.
Predictive Fraud Analytics
Predictive fraud analytics make use of data and analytics to help you predict an outcome that allows you to take a certain action. The process makes use of important metrics that help in the analysis:
- Capture Rate – The percentage of total fraud that is detected by the model resulting in risky transactions being declined.
- False Positive Rate (FPR) – Sometimes the number of fraud predicted will be greater than the actual fraudulent transactions that happen.
- Common Point of Purchase – There might be some merchant location which is the most common point from where information is being stolen.
What are the use cases and applications of fraud detection?
As technology is advancing so are the types of frauds. To prevent such frauds from occurring, fraud detection systems are now being used in a variety of industries such as banking, insurance, e-commerce, and government.
Some common use cases of fraud detection systems include detecting internal fraud, fraudulent financial transactions, identifying fake reviews or accounts, and combating click fraud.
For example, businesses can use fraud detection to screen customers before they are allowed to make a purchase. This can help to prevent fraudulent activity from occurring in the first place. Additionally, businesses can use fraud detection to investigate suspicious activity after it has occurred. This can help to identify the individuals responsible for the fraud and to recover any losses that have been incurred.
They are also used by organizations to detect ecommerce fraud. Ecommerce fraud can take many forms, such as carding, account takeover, and card testing fraud. DataDome can help prevent ecommerce fraud by managing bot traffic in real-time and blocking them from reaching your server.
Another use case is in the insurance industry where they use fraud detection to screen claims before they are paid out. Insurance fraud can take many forms, such as false or exaggerated claims, staged accidents, and even identity theft. Hence, insurance companies can save money in this manner.
Wrapping Up The Things We Learnt About Fraud Detection
Fraud detection is the process of identifying potential fraudulent activity which is done through a variety of means such as data analysis, manual review, and machine learning.
By using a fraud detection system, businesses can identify suspicious activity and take action to prevent it from occurring. It is an important tool for businesses of all sizes. By using fraud detection, businesses can protect themselves from losses and help to ensure that their customers are legitimate.
Fraud detection rule writing is a data-driven process that helps to identify where fraud is happening currently and places where it might occur. Fraud detection rules can be written manually, however, the process of manually creating rules to identify potential fraud is becoming redundant.
Nowadays Artificial Intelligence is being used to create efficient rules for fraud detection. AI’s subset, machine learning, is being used to provide more accurate predictions. Since machine learning is dependent on data, the more data you feed into the algorithm, the better the chance for the model to work.
Predictive analytics is also used for fraud detection which works by taking historical data and looking for patterns to predict future behavior. A very important key takeaway is that the right fraud detection software will help your business save thousands of dollars.
Osama is a technology content strategist who is passionate about content marketing and all things SEO. He has helped various companies in ranking their content on the first page of SERPs. In his free time, Osama loves to play football and travel.