CREDIT CARD FRAUD DETECTION

 

• Language: R or Python
• Data set: Data on the transaction of credit cards is used here as a data set.

Credit Card Fraud is more common than you think, and lately, they’ve been on the rise. We’re on the path to cross a billion credit card users by the end of 2022. But thanks to the innovations in technologies like artificial intelligence, machine learning and data science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.

 

Simply put, the idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either R or Python with the customer’s transaction history as the data set and ingest it into , and . As you feed more data to your system, you should be able to increase its overall accuracy.

                

Introduction

Credit cards are now the most preferred way for customers to transact either offline or online. There are a number of reasons, as illustrated below, due to which consumers are slowly shifting from debit card transactions to credit cards, especially in developing countries like India.

• Lucrative cashback and reward point options are present for each credit card transaction. These are generally not offered by financial institutions for debit cards.
• Tie up credit cards with online and offline merchants, especially during festive seasons like Diwali, Eid, and Christmas, to offer further discounts on transactions. Several online merchants run their own promotional campaigns, which are tied up with credit cards—for example, Amazon Prime day, which happens at least once a year.
• Immediate needs can be fulfilled (for example, medical emergencies, lifetime events, etc.) quickly instead of having sufficient account balance for the same. Most credit cards offer 0% EMI options, so it makes it all the more worth pursuing this goal.
• Having a good credit history helps to build a good CIBIL score which then, in turn, helps customers to avail themselves better and competitive interest rates on longer-term needs like home loans or car loans.
• Credit cards are tailored to suit individual customer needs. For example, customers who want to use a credit card for daily usage are usually offered a card with no annual fees or joining fees (marketed as lifetime free credit cards). On the other hand, we have premium cards with annual fees or joining fees for affluent people who offer golf membership, airport lounge access, seamless transactions at international and domestic merchants with lower transaction transfer fees, 5x to 10x reward points, etc.

However, with all these advantages, we also have the additional advantage of the ease of usage without having to carry currency around, and we can get a record of all our digital transactions through credit card statements far more easily compared with cash transactions or bank statements. One downside that has been witnessed over the past few years of this increasing digital phenomenon is the rise of fraud on the credit card. Fraud can be of several types, as we will try to understand a bit later on in this blog. You can also take up a free online course and enhance your knowledge about the same.

Before going into details of credit , let us try to understand the size of the overall credit card industry, especially in the large western economies like in some of the European countries, the US, and the UK. Below are some of the numbers around the credit card industry in general at a worldwide level.

 

• There are 1.06 billion credit cards in use in America and 2.8 billion credit cards worldwide.
• A US citizen, on average, has four active credit cards.
• In the European Union (EU), the number of cards carried per person ranges from 0.8 to 3.9
• In the UK, there were 32.3 million people with credit cards or charge cards in 2016, which roughly translates to 6 in every ten adults. The numbers have only grown since then from 2016 to now.
• There were 368.92 billion card transactions worldwide in 2018. However, the average value per card payment is decreasing in most of the major economies, as a credit card is used more and more as a preferred financial product compared to other means. The average value per card payment drop indicates that customers are using a credit card more and more for daily use compared to one-off events like big purchases.

                     

Credit Card Fraud 

There is an explosion of demand for new payment methods. With new payment methods, we have an extremely complex backend which makes fraud detection all the more hard. We have nearly 1.8 billion Euros on average of fraudulent transactions detected in Europe every year.

Global fraud has increased by almost three times, from $9.84 billion to $32.39 billion in less than a decade (2011 to 2020). Fraud can be broadly categorized into the following types:

• Card Not Present (CNP) fraud: These are mostly digital frauds for which the physical card need not be present at the point of transaction (POS). This usually means online payments. It is also known as “remote purchase” fraud.
• Card Present (CP) fraud: This is as expected and, as the name suggests, the opposite of the above, for which the physical card needs to be present at the POS site.
• Mail and telephone order (MOTO) fraud: Instances of stolen card details being used over the phone or mail (more popular in western countries like the US compared to India)
• First-party fraud: In this case, the customer itself is the fraudster. He or she might have run into a financial crisis like loss of job, medical emergency, or sometimes actual malicious intention of not paying back the credit card bank their dues.
• Identity fraud: In this case, the customer is the victim, and the fraudster is someone else. For example, says Shyam, the fraudster knows that Ram, the victim, has a very good credit score and other credentials. Shyam applies for the credit card in Ram’s name, and the bank, thinking that it is actually Ram who has applied, approves the credit card (this is more likely to happen in the US and other western countries as customers can apply for a credit card online without providing any documents, only a few personal information is required. In India, it is less likely as stringent screening processes like in-person verification, and salary verification, etc., are carried out). Shyam then intercepts the card through either change of address by setting up an online profile and starts transacting on the card. At the end of the month, Ram receives the statement in his email and refutes the charges, and this is when the bank knows that identity fraud has happened. This can occur either at the start of the credit card lifecycle, in which case it is known as a fraudulent application, or anytime in between the lifecycle, in which case it is known as account takeover. 
• Plastic fraud: In this scenario, it is generally one-off transactions or a few transactions that are fraudulent on the credit card instead of all the transactions. A popular example can be if Ram loses his Wifi enabled credit card at a shopping mall while having his lunch. Shyam happens to spot the credit card and, instead of volunteering to return to the rightful owner, goes on a shopping spree in the mall doing “tap and pay” or “contactless” transactions instead of providing pin (As per the latest VISA regulation in India, we can transact upto Rs 5,000 using “tap and pay “ or “contactless” without giving card pin). Ram, unfortunately, spots the transaction alerts later on his registered mobile or email and immediately blocks the card, but by then, the fraudster has done the damage. This is an example of a lost or stolen card scenario. Similarly, a counterfeit (CFT) credit card can also be created even though with current EMV chip technology, this has become tougher (it was much easier in older credit cards that did not have EMV chips and had only magnetic stripes). CNP, CP, CFT, and MOTO types of frauds belong under the larger umbrella of plastic frauds.

Credit Card Fraud in the United States

The United States has its own banking and finance system, which is different from the rest of the world. The United States is the world’s #1 in terms of the size of the economy, and it is observed that Americans have a particular affinity towards credit cards, or we can say towards credit in general. As a result of this, that country is a large target for external hackers, and credit card fraud is more likely to happen in the US than in any other part of the world.

• The US reports the largest credit card losses in the world, which is close to 38.6% of the whole world. Credit card fraud is the most common form of fraud that occurs in the United States.
• Credit card fraud has been on the rise year after year for the last five years. At the same time, total fraud and identity-based frauds have decreased.
• CNP fraud is 81% more likely to happen in the US compared to CP fraud.
• CNP fraud hit $4.57 billion in the US in 2016, rising about 34% year on year.
• Georgia, Nevada, Florida, and California are the states whose resident population is highly susceptible to fraud. Florida and California also have larger avenues to spend related to travel and entertainment like Casinos and sea sports.
• About 80% of the credit cards in the US have been compromised at some point in time or other.
• About three-quarters of Americans (~73%) are concerned that their financial account, email, or social profiles can be hacked.

Credit Card Fraud stats in the European Union (EU)

The European Union includes all European countries (except the UK after the infamous Brexit), the Nordics, and several other key countries like Switzerland, Monaco, and Liechtenstein. These countries have their own specific rules, especially where the common currency is not Euro. We will use a financial terminology known as bps instead of percentage (For example, 0.01% translates to 1 bps, you might have heard of this when RBI cuts repo rates, etc.)

• The fraud value for cards issued within Europe is estimated to be 1.8 billion Euro in 2016. ( as per European central bank)
• 73% of the above comes from CNP payments, 19% from transactions at POS (point of sale) terminals, and 8% from ATM (automated teller machines). It is worthwhile to note that CNP fraud has increased while card-present fraud has decreased. This suggests a migration from physical fraud to digital or online fraud, which is expected as financial institutions make digital access to credit cards easy for customer convenience. ( as per European central bank)
• Portugal is the only country which is an exception which has more point of sale or POS fraud than card not present or CNP fraud ( as per European central bank)
• The fraud level as a portion of the transaction value ranges from 0.5 bps (basis points) for cards issued in Poland to 7.3 bps for cards issued in the Netherlands in terms of value, and from 0.2 bps in Poland to 4.3 bps for credit cards issued in France in terms of volume. (as per European central bank)
• In general, countries with voluminous card markets (high volume of transactions and greater value per transaction like the UK and France) also experience a high level of card fraud (as per the European central bank)
• The Netherlands has 0.6 bps fraud, Denmark 1.3 bps, Norway 1.6 bps has the lowest ratio of fraud versus legitimate purchases, compared with 53 bps in France and 50 bps in the UK.

As per one of the reports, the Netherlands and Nordic countries are excellent examples of fraud control best practices in Europe due to well-managed fraud and risk prevention services thanks to their pan-European processors, which cover fraud prevention expertise across multiple country borders. In absolute contrast, the UK and France continue to experience higher card fraud losses, mainly from CNP fraud on internet purchases, lost or stolen card fraud, or fraud losses on domestic cards used across multiple country borders. 

• The share of fraud is much higher for credit cards versus debit cards, showing that fraudsters prefer to do credit card fraud than debit cards.
• Fraud share can further be broken down into region-wise components, which helps us observe that the majority of fraud is within the European Union itself.
  • 43% of fraud within the European Union outside the domestic country.
  • 35 % of fraud is within the domestic country of the card issuer.
  • 22% of fraud is outside the domestic country and European Union.

Let’s deep dive into a few specific markets to understand how the underlying fraud trends differ from each other.

Credit fraud in France

France has its own system for cards known as CB (cartes bancaires). This means it has some different rules governing payments, on top of the standard rules for European countries.

It is very much possible and feasible that these rules make committing credit card fraud within France (that is, domestic fraud) far more difficult. Compared with the UK, the fraud rates in France are still lower and sizable.

• Domestic fraud losses on French cards have stabilized at about 3.2 bps. 
• On the other hand, the fraud rate on French cards used abroad outside European countries is 16 times higher than on domestic transactions in 2017, while for foreign cards used in France, the rate was 12.1 times higher.

• Identity theft of card details accounted for 66.1% of total domestic card fraud losses in France.
• The main methods of compromise responsible for fraud losses are as below:
  • Lost and stolen fraud (16.3%) 
  • CNP fraud (72.3%) based on theft of card credentials

 Together, the two categories accounted for 88.6% of losses as of 2017.

Credit Fraud in the Netherlands

The Netherlands brought in a new digital ID service (iDIN) in 2016. This collaboration between Dutch banks serves to increase online security, especially for domestic card usage.

Dutch shoppers rely far more on debit cards than credit cards, and the country also has a popular transfer service where customers can pay online from their bank account.

Overall, levels of credit card fraud in the Netherlands are low and have decreased substantially in recent years.

• Fraud levels had reduced significantly from €33.3 million in 2013 to €12.6 million in 2018
• 39% of card fraud losses in 2018 occurred on debit cards. This is down from 57% in 2017
• While debit card fraud has fallen substantially, internet banking fraud actually increased in 2018. This shows a similar trend in other western countries as well, where the fraudster prefers digital fraud over physical fraud. This was usually a result of phishing techniques, including “scam emails and text messages, fake apps, fake invoices, identity fraud, and deception of financial employees of companies (known as CEO fraud).” 

This may also be somewhat thanks to the popularity of iDEAL, the bank transfer system mentioned above. Fewer customers use credit cards in general, and bank accounts themselves may be a juicier target for fraudsters since the fraudster has direct access to a known amount that can be cashed immediately.

Regardless compared to other European countries, the Netherlands can be considered a success story in tackling credit card fraud to a large extent.

Credit Card Fraud in Denmark

As explained above, we have somewhat conflicting data about Danish rates of fraud (as a percentage of total card payment value). The European Central Bank (ECB) assigns Denmark the highest ratio of fraud to total payments. Meanwhile, other important sources like Nets.eu states that it has one of the lowest ratios.

Part of the cause for this may simply be related to the timing of the reports as the ECB report was published in 2016, while Nets.eu published its report later. 

• CNP losses rose significantly from 2014-16 and “show no signs of slowing as fraudulent attacks continue to migrate across Europe, away from France and the UK.” 
• Denmark also has an abnormally high level of lost and stolen fraud (52.7% of total losses), perhaps due to high credit limits. 
• In Q2 2018, contactless card fraud made up 65% of all fraudulent card payments, despite only 56% of all payments being contactless. In other words, contactless represents a disproportionate amount of card fraud in Denmark.

What makes credit card fraud detection hard?

After understanding the gravity of the fraud situation worldwide, particularly in the United States and some of the major European countries, the next automatic question that comes to mind is how we prevent this fraud and the damage it causes to the overall economy and especially to the customer sentiments and trust in financial institutions. Also, let us discuss some of the challenges that we face while dealing with credit card fraud as below.

• Data imbalance: The fraud and non fraud data are generally much skewed. To give an example in the sample open-source dataset that we will be dealing with here, we have 492 frauds out of a total of 2,84,807 transactions. This is roughly only 0.172% of all the transactions. So it is easy to achieve almost 99% accuracy with a naive model which just predicts all the transactions as non fraud.

• Customer friction: The most likely outcome if a model predicts a current transaction as fraud is to decline the transaction outright to prevent any financial loss. However, we will soon see that it sometimes proves to be a bone of contention with genuine customers, who might get declined if the model has too many false positives or Type 1 errors. Though we may never achieve 100% accuracy in a real-world scenario, it is desirable for the model to be as accurate as possible to minimize any real customer friction.
• Real-Time Detection: For most of the fraud detection models in practice they have to work under very stringent timing conditions. We can take an example of a transaction-level fraud detection model. This model has to run and give the decision as to whether the current transaction is fraud or not within a fraction of a second. If we employ a time-consuming but highly accurate model, we might irritate the customer who is waiting to do the transaction, and if we process too fast, we may improve on customer experience, but it might lose out on accuracy. So it is a very thin line that we have to tread on while developing such fraud detection models.

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