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.

What is payment fraud detection?

Payment fraud detection is a complex of measures directed at spotting of various payment fraud risks, which may cause serious damage to online business. One of the most effective instruments for this is a machine learning, since it helps to achieve better accuracy and can simply adjust to fraudsters’ techniques and tools and also such method has better prediction rate, especially in case if the databases grow. However we are going to tell you more about the exact methods of payment fraud detection, it is just worth to mention that taken at the right time, such measures will yield good results and may help reduce operational costs very easily.

An easy way to detect payment fraud

JuicyScore is an easy solution to detect payment fraud. In JuicyScore you can reduce risks in your online business easily and effectively. We do not utilize sensitive personal data or direct consumer identifiers. We analyze more that 50.000 data points and by using machine learning we provide an anti-fraud score along with a data vector about 200 predictors important for anti-fraud and credit scoring via our API. JuicyScore is compliant with GDPR, current and perspective regulating rules and security policies of browsers and operational systems.

On top of the data vector provided by the API we help clients to build customized score and provide consulting on score modelling. If you implement our solution, you can expect a 10X ROI and a Gini increase of 5-20 points. Unlock your business and revenue growth with minimal risks by leveraging JuicyScore solution. You can book a demo to learn more in order to get the best payment fraud detection solution.

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 so many industries. To name a few, it may significantly reduce risks in such industries as Fintech, Healthcare, E-commerce, Travelling, Dating, Gambling, Betteing. Let's find out how to detect payment fraud what is the most popular way that modern companies usually use.

It does not take a crystal ball to tell that Machine learning methods are the main tools in terms of payment fraud detection. There are plenty of solutions which may help to detect payment fraud, basing on big data and algorithms training. The major benefits and advantages of such method are quite obvious - the more pattern and regularities machine can detect, the more data value may be obtained as a result of calculation. The other way to deal with fraud detection methods is deep machine learning, which has way more predictive power and requires a lot of technical skills. JuicyScore is based on the principles of deep machine learning, this is a subtype of machine learning with several significant differences. Deep machine learning’s main distinctive feature is that the problems, which neural networks need to solve, are quite similar to such problems, which may be solved mostly by a human. With deep machine learning methods we can find more deep connections between various factors, which are not related at the first sight.

However generally 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.

Steps for implementing fraud detection

In this section, we will tell you what needs to be done for the payment fraud detection system to work properly.

With the development of online services and the emerge of numerous financial products on the market, the number of companies ready to render these services also increases dramatically every year. Industry leaders give high priority to product parameters and characteristics, customer experience, clients' base expanding and keeping the loyal customers. The reverse side of this process is increasing complexity of clients risk assessment via online channels as well as rather large proportion of high risk users, who sometimes may come to the website of a financial institution with malevolent intentions. Thus, companies have to divert a significant share of resources (data collection, technology, staff) to reduce risks and to detect fraudulent users.

Along with the development of the companies doing business online, a large number of different vendor companies have entered the market. They provide solutions designed for reduction of the risk of fraud. Many of these solutions provide a set of risk markers that can be used to block some types of applications in application flow. However, this approach has several flaws.

JuicyScore team believes that apart from providing the markers themselves, it is also necessary to pay great attention to the information value and methodology of data usage regarding the risk assessment process. We have developed a number of methodologies and approaches that allow us not only to identify high-risk segments, but also to perform segmentation and risk assessment throughout the entire application flow.

What makes machine learning techniques so useful for fraud detection? First of all, no matter how smart and sophisticated fraudsters’ tools and techniques are, fraud detection tools for immediately.

The core of our approach within this methodology is careful and accurate device authentication taking into account device randomization and virtualization. This methodology is based on the developed set of instruments/ technologies for device metrics - a stable, probabilistic approach to device authentication.

  1. First of all you need to define what fraud exactly means for your business - it can be toxic customers with multiple accounts, unpaid loans without a single payment, unreasonable online chargebacks, user data substitution, hacking of personal accounts, etc.;
  2. Determine the “tolerable” level of fraud for your business. An important point: in many cases, completely getting rid of fraud of all kinds can be either a very expensive or very difficult task;
  3. Appoint a responsible digital risk management professional to continually reduce fraud. Involve external experts if necessary;
  4. Choose a technology solution, which suits best for your type of business.

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.