Ever since the payment systems existed, there were always people who would find sophisticated ways to get to someone’s finances illegally. It has become a major problem in the modern era when all transactions can easily be completed online with only entering your credit card information. Even in the 2010s quite a lot of American retail website users suffered from online transaction frauds until the two-step verification came into power while shopping online.
However, credit card frauds are still a challenge for money transaction platform owners or small to global retail services and hundreds of online frauds keep popping up daily as long as unauthorized card transactions hit the record amount of 16.7 million victims in 2017 (as reported by Javelin Strategy & Research). Additionally, according to the Federal Trade Commission (FTC), the number of credit card fraud claims in 2017 overcame the previous year’s number of frauds by 40%. There were around 13,000 reported cases in California and 8,000 for Florida, which is the most per capita credit card fraud claims). The future number of transactions will only increase and exceed approximately $7.2 million by 2020 where card-non-present (CNP) transactions online or with mobile phones will make the majority of that number.
Google and Amazon have their own Machine Learning solutions for Fraud Detection which deploy a Machine Learning model and an example dataset of credit card transactions for model training so that it can recognize fraud patterns in transactions. The advantage is that the model can self-learn, meaning it can adjust to new, unknown fraud traits.
Fraud Detection and Prevention
Fraud detection and prevention has become a major challenge with the increasing amount of identity thefts and unauthorized transaction through the Internet. The 2019 Internet Security Threat Report by Symantec says that Internet criminal groups attack Microsoft Office files and Supply Chain creating malicious PowerShell scripts the amount of which grew by 1,000% throughout the last years. IoT devices are attacked around 5,000 times per month, while routers and cameras being the main targets of such breaches. The common problem is also when cybercriminals hijack credit card data from online payment forms. Several options can be used to detect and prevent many fraud types, among them is the method of AI fraud detection, which is considered later in the article.
What is credit card fraud detection?
“Fraud detection is a set of activities undertaken to prevent money or property from being obtained through false pretenses.”
Fraud can be committed in a number of different ways and in a number of different settings. For example, fraud can be committed in the banking, insurance, government, and healthcare sectors.
Most fraud detection solutions combine a range of data components to form a connected view of both genuine and fraudulent payments to decide on the likelihood of a transaction being fraudulent. Increasingly, this view should consider IP address, geolocation, device identification, “BIN” data, global latitude/longitude, historic transaction patterns, and the actual transaction information. In practice, this means that merchants and issuers deploy analytically-based responses that use internal and external data to apply a set of business rules or analytical algorithms to detect fraud. Leading organizations are using machine learning and moving away from business rules to improve fraud detection and reduce customer friction.
Credit Card Fraud Detection with Machine Learning is a process of data samples (fraudulent and normal transactions provided by the customer) investigation by the Data Science team and development of a model that will show the best results in revealing and preventing abnormal transactions. This is achieved through bringing all meaningful features of card users’ together, such as Date, User Zone, Product Category, Amount, Provider, Client’s behavioral patterns, etc. and run through a subtly trained model that finds patterns and rules so that it can classify a transaction as following: fraudulent or not.
How does credit card fraud happen?
Credit card frauds can happen due to various reasons, caused either by card owner’s negligence with his data, either by the breach in a website’s security. Here are some of them:
- consumer reveals his credit card number to unfamiliar individuals;
- a card is lost or stolen and someone else uses it;
- when mail is stolen from the recipient and received by criminals
- business employees copy cards or card numbers of its owner;
- making a counterfeit credit card.
When your card is lost or stolen an unauthorized charge can happen, so that the person who finds it, uses it for a purchase. Criminals can also forge your name and use the card, or order some goods through a mobile phone or on the Internet. Also, there is a counterfeit credit card – fake cards that have real account information that was sneaked from holders. That is especially dangerous whereas the victims have their real cards, but do not know someone already copied their card. Such cards look absolutely valid, having the logos and encoded magnetic strips of the original one. Counterfeit cards are usually eliminated by the criminals after several successful payments, just before a victim can report the misuse.
Who is liable for data fraud?
Personal information theft is dangerous because, although not being liable for the losses, the victim may spend a few years to deal with all financial and credit problems done by criminals.
Credit Card Fraud types
Online Credit card Fraud types can be identified as the following:
- unauthorized card charge;
- counterfeit cards;
- identity theft;
Apart from unauthorized card charges and counterfeit credit cards there also such types of fraudulent activities as identity theft, skimming, and fishing.
When an individual’s personal information, such as Social Security number or the secret question answer, date of birth is stolen by criminals, they can use this information to commit financial operations. A criminal can open new credit accounts or access the current credit and bank account, using a person’s name or information.
Credit card skimming means making an illegal copy of a credit or bank card with a device that reads and duplicates information from the original card. Fraudsters use machines named “skimmers” to extract card numbers and other credit card information, save it and resell to criminals.
Cardholders can be reached by their E-mail while someone sends the deceptive letters to users. The messages look pretty legitimate (very similar bank URLs and trustworthy logos) as if sent by the bank. In reality, such a message can be meant to steal the person’s information, bank account numbers and online passwords. If you follow the wrong link or provide valuable information in response to this message on a fake bank website – in a couple of hours your bank account will be drained by the criminals, while an electronic transfer of expenses is made to the account they hold.
How to protect yourself from credit card frauds?
As long as the Internet or Cybersecurity is a key factor for a safe user surfing of the Internet, security trends are longing towards AI solutions whereas more conventional methods are deemed less effective. Sophisticated modern fraud schemes demand a more subtle and smart approach to detect them, such as a Machine Learning model that can find new patterns in the user behavior and treat it as something malicious. Standard protective tools are programmed only to respond accordingly to previously known patterns and can never learn or understand if there is a new, unconventional behavior.
Google trends show us how searches for AI security change throughout the last years.
Banking and Online Market owners clearly understand that the safety and loyalty of their users’ transactions is their responsibility, and the best way to deal with it – is to arm yourself with the most efficient and innovative anti-fraud tools.
Credit Card Fraud Detection with Machine Learning
There are a few ways to implement automated fraud detection:
- Off-the-shelf fraud risk scores pulled from third-parties (e.g. from LexisNexis or MicroBilt)
- Predictive machine learning models that learn from prior data and estimate the probability the transaction is fraudulent
- Business rules that set conditions that the transaction must pass to be approved (e.g. no OFAC alert, SSN matches, below deposit/withdrawal limit, etc.)
Among these Fraud Detection and Prevention techniques, predictive Machine Learning models are the one that belongs to smart Internet security solutions. There are several steps within each AI Fraud detection and prevention development process. The first is Data Mining that implies classifying, grouping and segmenting data to make a search through millions of transactions to find patterns and detect fraud. After the patterns are discovered, there is a Pattern Recognition phase which can detect classes, clusters, and patterns of suspicious behavior. Machine Learning itself here represents the choice of a model/set of models that best fit to a certain business problem. For example, the Neural Networks approach helps to automatically identify characteristics found in fraud, which is the most effective if you have a lot of transaction samples.
Once the machine learning-driven Fraud Protection module is integrated into the E-commerce platform, it starts tracking the transactions. Whenever a user requests a transaction, it is being processed for some time and depending on the level of predicted fraud probability there are 3 kinds of possible outputs:
- If the probability is less than 10%, the transaction is allowed;
- If the probability is between 10% and 80%, additional authentication factor (one time SMS code, Fingerprint, Secret Question) should be applied;
- If the probability is more than 80%, the transaction is frozen, so it should be processed manually.
Using Machine Learning for fraud prevention also has its pros and cons as any other security means. Being a potent tool to identify and block frauds, and often one of the most accurate, it improves itself overtime when it gets additional data. It means that you will have a continually self-improving mechanism that spots frauds with newer patterns.
Although Fraud Detection with Machine Learning is surely an advantageous method, it still has some restrictions that should be paid attention to, before you implement it on a platform.
Firstly, training high-quality machine learning models requires significant internal historical data. That means if you do not have enough previous fraud and normal transactions, it would be hard to run a Machine Learning model on it because they are very demanding for information.
Secondly, models may be subject to bias based on the nature and quality of historical data. This statement means that if the platform maintainers did not collect and sort the data neatly and properly, or even mixed the information on fraud transactions with the information on normal ones, that is likely to cause a big bias in the results of the model.
But if you have enough data that is well-structured and not biased, if your business logic is paired nicely with the Machine Learning model, the chances are very high that fraud detection will work best for your safety.
Modern problems undeniably need modern solutions and approaches, and when it comes to Internet security on Retail platforms, payment services, and users’ safety, the ultimate way is to follow emerging AI trends. If to trust the statistics, 2019 and the next 5 years will be golden for Machine Learning and Artificial Intelligence era. Online card fraud cases with all their types and techniques can be reduced if treated with tools that are smart enough to extract new patterns and self-improve with time.