Skip to Content
10 minutes read

Credit Card Fraud Detection: Machine Learning at its Best

By Robert Kazmi
By Robert Kazmi
10 minutes read

We live in a world where cash transactions are quickly being replaced by credit card transactions. This shift has created an opportunity for credit card fraud cases to evolve and get more sophisticated. In order to keep up, credit card fraud detection needs to evolve as well. Credit card fraud detection using Machine Learning is the next evolution in credit card fraud prevention. Machine Learning technology can analyze massive amounts of transaction data and identify anomalies in the data that otherwise humans may miss

Financial service providers lost more than 27 billion dollars to fraudulent charges in the year 2018, according to the most recent data available on credit card fraud. Experts predict that financial service providers will lose more than 40 billion dollars to fraudulent charges by the year 2027. Fraud is a big problem for credit card companies and other financial institutions. Machine Learning algorithms and other FinTech innovations can help reduce the amount of fraudulent credit card transactions and protect financial service providers and their customers. 

Let’s take a closer look at how Machine Learning works, what typical cases of credit card fraud look like, and how Machine Learning can help financial service providers detect fraud before they lose money. 

How Does Machine Learning Work?

Machine Learning technologies rely on advanced data analysis algorithms. One of the most common and popular algorithms used in Machine Learning applications is the decision tree model. Decision trees can get complex very quickly, especially when they are used to mine and analyze large amounts of data. 

In simple terms, the decision tree model follows a basic root question and branches off into more detailed and specific branches that eventually culminate in endpoints or the leaves of the tree. The more detailed and layered the decision tree becomes, the more information it is able to learn. A basic example of a decision tree would look something like this:

The decision tree above is very basic, but this is an illustration of the patterns that are followed to learn information. Complex decision trees are used in Machine Learning algorithms to help find and predict fraudulent credit card activity amongst a host of other potential applications

The decision tree model is a very powerful tool in the field of Machine Learning. A single decision tree can encompass the entire credit card fraud dataset of a financial service provider, but the accuracy and power of this tool can still be improved. What is better than a single tree? An entire forest.

A random forest is a collection of decision trees. Instead of utilizing a single decision tree for an entire dataset, a random forest creates random decision trees for specific data subsets and then averages the results together to make a final decision. The random forest model consistently returns more accurate results than the decision tree model. 

Machine Learning technologies combine a number of different algorithms and techniques to produce the most accurate and advanced models. Financial service providers can utilize the power of Machine Learning and data mining to help them identify fraud and predict where it could happen next. FinTech tools like this could help save companies billions of dollars in fraudulent losses

What Activities Constitute Credit Card Fraud?

The first thing you probably think of when you hear the term credit card fraud is stolen identity and credit card information. However, credit card fraud affects more than just cardholders. Credit card fraud has the potential to affect a number of different businesses and people, including:

  • Cardholders
  • Issuing banks
  • Acquiring banks 
  • Payment gateway providers 
  • Payment processing companies
  • Credit card payment systems

The most common form of credit card fraud results from stolen personal information or stolen credit card numbers, but money laundering is another emerging form of credit card fraud that can negatively impact businesses and financial institutions

Since there are so many transactions made online every single day, criminals have found that they are able to create a series of fake transactions through legitimate marketplaces to launder their money digitally. Online payment processing involves a number of different financial service providers and go-between companies that money launderers can often hide in plain sight. 

Fraudulent transactions resulting from stolen information and the fraudulent transactions created to launder money both have the potential to negatively affect online businesses. Most major credit card networks like AmEx, Visa, or Mastercard will cut off payment processing if a business shows a fraud rate that is too high. Each company has its own policy, but generally speaking, if a business’s fraud rate is greater than one percent, they risk losing the ability to process credit card payments entirely.

The methods used to steal credit card information, launder dirty money, and commit credit card fraud are always getting better and more advanced. Machine Learning technology enables financial service providers to keep up with the innovations of the criminal class, protect their customers, and reduce the amount of money they lose to fraud on an annual basis

How Does Machine Learning Help Combat Credit Card Fraud?

Credit card fraud detection relies primarily on identifying fraudulent credit card transactions and stopping them before they are accepted. However, credit card companies also need to make sure that they are not stopping too many real transactions in the process. Stopping too many normal transactions will anger and annoy customers. It could even lead them to change service providers if the issue becomes too frequent and debilitating to their daily life. 

Formerly, financial institutions used rule sets written by a team of financial experts to determine which transactions were fraudulent and which were normal. This methodology worked for many years, and for a long time, it was the best option available to banks and other financial service providers. Fraud cases have increased, and the methods used to perpetrate fraudulent transactions have gotten more sophisticated. Machine Learning has been an effective tool in combating fraudulent credit card transactions, and more financial institutions are using a Machine Learning approach to solidify their fraud prevention efforts

The main reasons why Machine Learning is quickly becoming the preferred method of fraud detection and prevention for financial institutions include:

  • Greater accuracy 
  • Less manual work
  • Fewer declines of normal transactions
  • Ability to adapt and evolve 

Greater Accuracy

Rule-based solutions offered a decent amount of accuracy when it came to fraud detection, but they weren’t able to keep up with the constantly evolving nature of credit card fraud. Machine Learning algorithms have a much higher accuracy rate at detecting fraud cases. Instead of following a set of rules, this technology analyzes a wide range of data points, behavior patterns, and specific account information to make a fraud determination

Machine Learning considers even the smallest details of behavior and transaction to help make the most accurate fraud predictions available. By taking a greater amount of information into account, Machine Learning solutions can help stop fraudulent credit card transactions before they are processed. This accuracy saves cardholders, banks, and payment processors time and money. 

Less Manual Work

Machine Learning is a very powerful tool, but it is not all-powerful. Human analysts will still be required to review a certain amount of transactions that could potentially be fraudulent. However, Machine Learning can help significantly reduce the number of transactions that need to be reviewed by human analysts. This benefits financial institutions and cardholders in two distinct ways. 

One, the majority of transactions will not require human review, so analysts will have more time to dedicate to the transactions that are truly suspicious and require the most amount of attention. 

Two, with fewer transactions to review and more time available to review them, analysts will be able to make better determinations about which transactions are fraudulent and which are normal. Once again, this ultimately helps save everyone time and money. 

The sheer amount of transaction data that can be accurately processed by Machine Learning algorithms will allow financial institutions to dedicate more resources to fighting fraudulent activity since they will have a lot less manual work to do.    

Fewer Declines of Normal Transactions

Having your credit card decline a transaction can be annoying. You’ve likely experienced this at least once in your life. Likely, you had to call your credit card company to assure them that the transaction was normal and not fraudulent. While being protected is nice, it is better to have the transactions that you want to make go through on the first attempt without a call to the credit card company.

Machine Learning helps cut down on the number of false declines. Since its algorithms are analyzing a large amount of data, they are able to make more accurate predictions about which transactions are normal too. That means when you make a purchase from a store you’ve never shopped at before or purchase a Nintendo Switch on a whim, your credit card likely won’t get declined because a Machine Learning algorithm was able to determine that purchase was made by you. 

It is hard to predict what you will want to buy tomorrow or where life will take you. Old rule-based sets of decision-making were not as accurate when it came to accounting for human behavior. Machine Learning takes human behavior into account and makes better decisions as a result

Ability to Adapt and Evolve

If criminals stayed still, there would be no need for Machine Learning or any other innovations in the fields of criminology and forensic science. This is not how the world works. As security measures evolve to keep people safe and criminals at bay, criminal methods evolve to circumvent the system and take advantage of people. 

Machine Learning is not a static innovation. This technology has the ability to learn and adapt to changes in behavior. New patterns of credit card fraud are always emerging. Machine Learning can recognize the changes in pattern and create new rules to help prevent and detect fraud cases before the people, and financial institutions are taken advantage of. 

The ability to adapt to new ways of committing fraud and new patterns of behavior is what makes Machine Learning technology a priority investment for many financial institutions. Many of the world’s largest banking institutions and credit card providers are utilizing the power of Machine Learning to reduce the number of fraudulent transactions, including Chase Bank, Visa, American Express, PayPal, and Amazon. 

Final Thoughts

Credit card fraud detection using Machine Learning is becoming the prevalent method of fraud prevention in the financial industry. This technology has the potential to help save financial institutions billions of dollars in fraud losses over the coming years. As its applications and different approaches improve, they will get more effective at preventing fraud, and they will save banks and credit card companies even more money in fraud loss. 

The exciting thing about Machine Learning technology is that it will learn how to get better on its own. Human analysts and programmers will still be essential for updating and monitoring rules and algorithms, but as this technology advances, it will be able to make more improvements on its own as it learns more detailed information about behavior and transaction patterns. 

Girl With Glasses

Want to Build an App?

Request a free app consultation with one of our experts

Contact Us