Fraud detection techniques are numerous and vary by company and e-commerce. In this article we tell you which are the best currently.
On a daily basis, digital businesses and businesses are affected by constant fraud attempts. This makes the implementation of fraud detection strategies an increasingly common and important need among organizations. And not only companies, large E-commerces or Fintechs, it has also become important for SMEs and retail or local businesses; these are usually the entities with the highest rate of fraud. It is not efficient to apply any method or technique that is at hand, since said efficiency will depend on the business or company, its sector and its potential fraudsters.
In this you will discover how to detect fraud in a timely manner to reduce the losses of your business and provide a better user experience.
WHAT IS FRAUD DETECTION?
We can classify as a fraud detection strategy or technique those joint actions that are aimed at identifying fraud, whether they are being carried out or are about to be carried out. Often, this task is delegated to anti-fraud programs that use artificial intelligence in the data analysis and anomaly detection processes.
Many analysts also work in conjunction with programs to improve results and make decisions such as allowing or voiding a risky transaction. In other cases, detection consists of identifying internal fraud and other procedures are needed, usually deeper and with a more analytical approach.
In this type of process, internal audits and investigations carried out by specialists take center stage. However, in order for you to properly understand these processes, you need to know how they work, both in e-commerce and in companies.
HOW DOES FRAUD DETECTION WORKS?
For e-commerce, fraud detection tools are geared towards identifying phishing attempts just milliseconds before a purchase is made on the website. In short, the program is responsible for obtaining a series of data and determining, based on them, how likely it is that a transaction or user is fraudulent.
The decision to cancel or allow the transaction can be made by the tool or left to professional criteria. of an analyst. However, the ideal would be to rely on the criteria of the anti-fraud solution.
In companies it is not so different (as far as anti-fraud programs are concerned), since it is necessary to investigate past transactions to identify patterns and anomalies that can be repeated in future transactions. For example, constant diversion of funds to an unknown company or irregular transactions without justification.
Once the anomalies are detected, an additional investigation must be carried out on the employees involved to determine if it is a fraud or not. Even so, it is oneself who is more likely to allow a data and/or information leak by not taking the necessary security measures.
Although it is useful to mitigate fraud attempts, it is often difficult to stop new fraudulent activities, as they have a different pattern and are only identified after they have been completed.
This is, without a doubt, a problem for every business. In order to solve this problem, many companies and online businesses implement fraud prevention strategies, which helps reduce the number of attempts. However, detection and prevention are not the same, their benefits are different, and their procedures are different.
PREVENTION VS DETECTION
WHICH IS THE BEST?
Detection strategies are different from prevention strategies, especially in terms of procedures. Some clear differences are as follows:
Both strategies complement each other and maximize results to the point where it is common to see fraud prevention and detection programs for electronic businesses that combine both processes. Even so, there are several additional ways to detect fraud within companies and electronic businesses.
5 TECHNIQUESTO detect frauds In COMPANIEs & E-COMMERCES
The processes carried out to detect fraudulent activities in companies and businesses are usually different, especially because they are aimed at identifying different types of fraud: internal, external and electronic.
In the case of companies and organizations, fraud is usually perpetrated by members of the same organization and, depending on the medium they use, they are classified as internal and/or electronic. On the other hand, online businesses are often victims of outside fraudsters who carry out the crime by digital means; these frauds are classified as both external and electronic.
Below you will learn about some of the best techniques today to detect fraud.
This is perhaps one of the simplest and most creative methods for fraud detection; it is based on detection through hypotheses, so that a situation of vulnerability arises in which a subject takes advantage and commits fraud.
Through the hypothesis, frauds can be discovered "thinking like a fraudster", one could say. An investigation is then carried out to find out if the hypothetical situation was correct.
For example, the hypothesis could be that a fraudulent customer purchases a certain product from an online store and commits chargeback fraud.
Benford's Law has many uses and one of them is anomaly detection. Let us remember that data anomalies may be due to suspicious and, in this case, fraudulent activities.
Said law (applied to this case) consists of determining the rarity and frequency of certain numbers according to their first digit. In other words, it is more common to find numbers starting with 1 in the records, while it is more unlikely to find those starting with 9.
An example of this would be applying said law to the analysis of the database and thus discovering those purchases, transactions, amounts and other relevant numbers that are more frequent than Benford's Law indicates.
Even so, this only serves to start an investigation, since a confirmation process is necessary to determine whether or not it is a registered number from a fraudulent activity.
These tools are extremely useful for detecting and preventing fraud in e-commerce, although some can also be implemented in companies and organizations to make use of their advanced data mining and fraud detection through analysis of variables and anomalies.
They are very effective in identifying electronic fraud. , since they assess the risk level of the transaction before it is carried out and based on previously collected data.
This is known as “fraud detection through machine learning models”, since they have a system of AI-driven analytics and machine learning.
This work is usually carried out by a trained analyst or specialist and is based on the study and analysis of all the bank transactions and movements of the company to detect:
Once suspicious patterns are detected, the anti-fraud program or analyst identifies the pattern and then further investigation is conducted to delve into the specific anomaly. This can take weeks and even months to discover the fraudster, but it is necessary.
One of the benefits of this method is that, once the pattern is identified, it is added to the database of an anti-fraud tool so that it alerts when the behavior is repeated in a future transaction and can be prevented.
However, using exclusive anti-fraud tools is not very advantageous because its database and identified patterns are limited and are only referenced by information provided by the user and the merchant.
In contrast, a collective intelligence tool has a large database of data and effectively protects the entire e-commerce network from potential fraudsters.
In this way, when a fraudster attacks, the artificial intelligence will save the attack pattern and be alert to similar anomalies on all your other websites. This allows the level of security to increase without having previously suffered fraud.
Whether in a company or in an E-commerce, fraud attempts never end completely and you need to be prepared for when they happen. In addition, they are constantly evolving and the methods that currently serve to stop them may lose effectiveness in the future.
It is recommended that you continue analyzing data and trends, informing yourself about digital fraud, its patterns, how to detect it, how to prevent it and how to deal with it. Let us remember that proactivity is a key characteristic to adapt to sectors in constant change.
Detect fraud in time & don't let it repeat
Although the objective of fraud detection strategies is based on its mitigation, it is of little use to stop it if we do not prevent it from happening again. Therefore, it is necessary to implement prevention systems in conjunction with detection processes.
In this way, you would be maximizing results, since prevention is much more effective in reducing the risks of future fraud. It is also recommended that you investigate more about some of the fraud detection methods that you have learned about in this article and consider their implementation. You can mitigate and prevent fraud in your company or online business, you just need to get to work and implement a strategy that suits your sector and specific context.