Big Data: Using it to fight credit card fraud

Analytics | Data Science   |   
Published December 11, 2019   |   

In an age where information is becoming ever more valuable, many parties have taken it upon themselves to accumulate as much information on as many people as they possibly can. While the potential benefits of this data mining are well-known as it pertains to better understanding the consumer market, it nonetheless opens a raw nerve for many who see this as a massive encroachment on their privacy.
The issue of credit card fraud has become an increasingly pressing topic in recent years. With fraudulent transaction values going into the billions, many credit card companies are scrambling to find a solution to the problem of credit card fraud once and for all.

Big Data to the Rescue

Many credit card companies have turned to big data in hopes of finding a solution to credit card fraud. Big data is being used by many to tackle this problem because big data has the capacity to handle the data necessary to make a noticeable dent in the prevalence of credit card fraud.
Traditionally, credit card companies would have human employees look through transaction records in order to find suspicious activity. They would make that judgement call based largely on their intuition and past experiences. Unfortunately, that was not able to produce a satisfactory level of effectiveness.
That became even more apparent with the rapid growth in the volume and monetary value of credit card transactions. Human employees simply have not been able to keep up with the volume of data coming in to process them adequately. As such, credit card companies have turned increasingly to the use of data processing algorithms in order to supplement the skills of their human loss prevention departments.

Using the Algorithm

Each algorithm used by different credit card companies will certainly have some qualitative differences, but they all have the same basic general outline. The algorithm will first be given a sample data bank of prescreened normal transactions. This is in order to give the algorithm an initial baseline from which to use as a guide when sifting through real world unfiltered data.
Once the baseline has been established. The algorithm will then be thrown into the real world and start to receive real world transaction data. With the baseline, the algorithm will able to calculate the probability of any given series of transactions being fraudulent.
The algorithm uses nearly countless factors and takes them all into consideration. These can be factors such as the time a transaction took place, the monetary amount involved, the location where the transaction occurred, and even the store the transaction took place.
The algorithm will use the baseline as its North Star to determine the likelihood a given transaction is fraudulent. If the likelihood breaks a certain predetermined threshold, the algorithm will either launch an inquiry or send an alert to the human loss prevention department to determine whether the alert warrants further investigation.
Although human error will always be a factor considering humans are not perfect, this does not automatically mean that we should rely entirely on computer programs. This is because algorithms and computer programs in general have not yet reached the level of sophistication where they can learn and interpret new things without any human supervision whatsoever.  Although the former might be true for AI, advances in this innovative safekeeping method are ever advancing through machine learning (ML).  According to a recent Forbes article, ML screening tools can identify a fraudulent transaction before it’s completed.
These algorithms currently still require human input in order to introduce them to new scenarios and to program those scenarios and factors into them, but probably not for long. This symbiotic relationship between human and machine is what credit card companies are banking on to effectively tackle the issue of credit card fraud.

Challenges and Potential Pitfalls

Like with other new technological trends, while much progress has been made to the benefit of society as a whole, there is nonetheless plenty of room for improvement. One of the areas that are prime for improvement is the area of international transactions.
International transactions are a lot trickier to keep track of and are more prone to fraud at the same time. Tackling this specific issue will most likely require cross-border cooperation between the credit card company and payment processors in foreign countries so that the credit card company can have access to international transaction patterns.
Another challenge for credit card companies is that of actual changes in consumption patterns. When consumers end up changing their consumption patterns, many algorithms run the risk of interpreting that as a sign of fraud and will nonetheless send an alert which might end up negatively affecting the customer if the decision is made to temporarily freeze the card.
This means that algorithms as well as the loss prevention departments overseeing them need to be cognizant of this and reorient their operations accordingly. The human employees who oversee these algorithms will need to constantly update the algorithm in order to prepare them for this eventuality and to make the programs more flexible and fluid so that human employees and computers can complement each other’s specialties.

Man Teaming Up With Machine

The age of technology has seen an increase in the intimacy between humans and machines in various fields in order to produce a more optimal outcome. Very few places have demonstrated this more than in the field of credit card fraud prevention.
Increasingly sophisticated and capable computer programs have made it harder for unscrupulous third parties to enrich themselves at the expense of credit card customers. As the technological arms race continues to rage on, we can only hope that the good guys will eventually win out.