We can’t but agree that we stand at the beginning of a new digital payment era that payment fraud is the most common fraud type in online business. As for the online business owner payment fraud detection is now becoming one of the main issues. Many financial industries have to face such problem and suffer from losses connected with fraud every year. Clearly digitalization allows to conquer new heights, gain access to new customers and reduce operating costs. However such situation also creates rather rich environment for fraudsters. In this article we are going to find out how to detect payment fraud and will take more detailed look into the problem of payment fraud detection and automatization.

The past decade has seen a series of technological breakthroughs which opened many opportunities for the financial industry and took it to new heights. However the problem of payment fraud detection has also occurred, particularly, due to the Covid-19 pandemic, which has had a significant impact on the financial sector. No surprise that many scammers have become more active and, as a result, payment fraud detection has become one of the most significant problems.

What is payment fraud?

According to Merchantsavvy, global losses from payment fraud tripled from $9.84 Billion in 2011 to $32.39 in 2020. Payment fraud is expected to continue increasing and projected to cost $40.62 billion in 2027 –  25% higher than in 2020. And according to the Juniper Research forecast, by 2024 eCommerce transaction fraud loss will reach up to almost 50.5 billion dollars.

The so-called payment fraud or fraud with payments in remote banking systems is one of the types of fraud with bank cards, when a fraudster makes payments by a card without the awareness of the bank credentials owner. So practically any type of illegal transaction completed by a cybercriminal when someone's property, money or sensitive information is stolen can be regarded as a payment fraud.

Payment fraud detection may be rather challenging problem, however there are no such problems a specialist can not solve. So let us explain what are the most effective ways of payment fraud detection.

Payment fraud detection issues

Let's take a closer look at the specific methods that scammers use. The most common type which practically very person faced at least once in a lifetime is phishing. Phishing is practically such type of online fraud, when a fraudster has intention to steal the access to personal and confidential user data or, in other words, users credentials. For example, a fraudster uses such instrument as bulk mailing, often introducing himself as a company representative and adds the malicious link to the letter or sometimes fraudster may also use social media to send out the links allegedly from various banks, which looks similar to the real bank web-site. When a user goes to that fake page, enters the password and login and thus a scammer get access to the users online bank account. After that it is easy to enter to a carefree user's bank account in order to steal the money (transfer it to a scammer's bank account or to the third parties accounts and so on down the line in order not to het caught). Usually it's not so easy to detect payment fraud, however, luckily, there are many solutions, that help to detect payment fraud rather easily.

Identity theft is a type of fraud when a crime uses someones's personal data illegally to obtain material benefits. It is connected with an increase in the number of remote services that do not require the user's personal presence, such as online-payment using bank cards or payment systems. The second important factor is the spread of social networks, where confidential information is posted for everyone to see.

Credit card fraud also knows as carding is a type of fraud when an operation is performed using a payment card or its details, and such operation is not initiated or confirmed by its holder. Payment card details are usually taken from hacked servers of online stores, payment systems, as well as from personal computers (either directly or through remote access programs, "trojans" or bots).

Page hijacking is a type of fraud when hackers reroute traffic from company's page (part of it) and redirect the clients who visit company's site to another page, where they collect clients' personal data such as card numbers and cardholders' names.

Advance-fee scam is a widespread form of fraud that has received the greatest development with the advent of mass mailings by e-mail (spam). A fraudster promises a significant sum of money to the victim but but requires an advance payment in return which will allegedly be used to transfer the full sum to the victim's account.

Speaking about the fraud sources, the most common and the easiest way to reach more audience is email spam.

Payment fraud detection methods

Machine Learning can help to detect payment fraud in such industries as Fintech, Healthcare, and eCommerce. Let's find out how to detect payment fraud what is the most popular way that modern companies usually use. There are two main approaches that help to detect payment fraud: the first one is so-called rulebased approach. Online fraud can be detected through some explicit and implicit factors, for example there is no need to explain why enormously large transaction made from uncommon location usually requires really close attention from the chief risk officers or risk manager of every bank or financial institution. Fraud analysts that are usually responsible for payment fraud detection (fraud analyst is a person who investigates any type of fraudulent activities such online theft of customers' credentials (login or password) and transactions on behalf of a bank or a financial institution) write the algorithms that run a few payment fraud detection schemes. Nowadays legacy systems apply approximately up to 250-300 different rules to approve a transaction. Some experts have the opinion that released approach turn out to be rather simple not in a good way. It is difficult for these algorithms to detect implicit correlations as well as to identify payment fraud. Moreover a lot of released systems can not process the real time data which plays crucial role in modern digital world. Another disadvantage of such method is that payment fraud detection it heavily relies on the human labour which costs a lot.

The second way to detect payment fraud is machine learning approach. This method allows to create algorithms that find unevident correlations in huge amount of data. Machine learning (ML) is one of fundamental areas of artificial intelligence, the idea of which is to find a pattern in the available data, and then to spread it to new objects. In other words, it is a certain set or sample of values, which used to “train” the algorithm, in order to further apply for solving various types of problems, for example, forecasting, classification and are direct related payment fraud detection.

Another advantage of machine learning over the released model in payment fraud detection is that using this approach data is processed much faster and it also eliminates manual data processing. Nevertheless, there are often hidden and non-obvious things in user behavior that machine learning helps to reveal, finding a correlation between things that are not related at first glance. Algorithms process large amounts of data and help to find hidden markers that may indicate probable fraudulent actions as well as payment fraud.

An easy way to detect payment fraud

Being one of risk assessment and payment fraud detection and prevention solutions, JuicyScore uses deep machine learning algorithms to develop variables. Examples of such variables are Index variables (or variables of the IDX type in our standard data vector), which, on the one hand, extract useful information value from the factors underlying these Indexes, and, on the other hand, make it possible to level out data collection related issues and the insufficiency of useful values of each of these factors. Indexes allow using the synergy of many such factors that can be used as separate variables, reflecting the anomalies of one aspect of the Internet connection, for further research.

We developed an easy solution for detecting payment fraud, which includes:

  • Full-scale antifraud protection: we provide device ID, antifraud scoring, generic antifraud scoring and custom antifraud scoring with consulting (we filter out 20% of application flow on average, which amount to 75% of fraudulent applications in the flow). It will help to detect payment fraud in your business really easily.
  • Significant improvement of score models. According to different cases, on average our clients get 10+ ROI;
  • Credit risk reduction. We provide various risk factors, device behaviour markers, combination of device and Internet connection used to reduce level of credit risk. Improved risk technologies allow assigning better product parameters and settings for a customer and to detect payment fraud;
  • Approval rate increase. Wider audience evaluation tools which are not available via traditional offline channels as well as low risk segments definition and disposable income evaluation to improve approval rate in respective segments and to increase credit line;
  • Fully compliant. JuicyScore is compliant to GDPR, current and prospective regulating rules, browsers’ and operational systems’ security policy.

Steps for implementing payment fraud detection

JuicyScore is really easy to use - first of all you need to install JuicyScore script call on the web site or in the mobile application. After that you need to set data collection and transmission parameters sent to our service and finally you will get the response from the service, including the score, probabilistic device ID, probabilistic user ID, and a broad profile of predictors used for the decision-making strategy, antifraud policy, and increasing the approval rate for loan or insurance applications. Detect payment fraud and let your business grow fast without risks.

It is also really important to take a closer look to those antifraud solutions, which help online business and consult them on decision making system in order to detect payment fraud. JuicyScore help online business to build custom scoring as well as to adapt solutions for a certain client.