2020 was very challenging for business owners and ordinary people. We have all been forced to adapt to the “new normal” by rebuilding our business and life in accordance with the new, digital, and post-pandemic economy. And while the whole world was trying to protect themselves and their loved ones from the coronavirus, scammers found new ways to hack and take over data. In 2021, the issue of protecting health, business, reputation, and finances becomes especially relevant, and machine learning is one of the promising technologies for this purpose.
In this article, we’ll explore the potential of machine learning to prevent financial fraud and corresponding fraud detection techniques.
The Potential of Credit Card for Business and Personal Usage
The first credit card was issued in 1950, and since then, it has become a universal and convenient method of making payments accepted around the world. The use of credit cards provides a huge advantage for both businesses and ordinary users, allowing the former to accept payments online and achieve better transparency in financial reporting, and the latter to make convenient purchases regardless of time and place. In addition, under the influence of the pandemic, they have become the safest method of making payments by avoiding the use of cash, which can carry germs and viruses.
The following statistics reveal the even greater potential for credit card use in 2021.
- Currently, there are 511, 4 million credit cards issued in America only.
- There will be 1 115.83 million credit cards used by Americans by 2025.
- People aged 20-40 are the most active credit card users.
By the way, the latters are also the most frequent victims of credit card scams. Being the most active users of the Internet, vivid fans of online payments, and active social media users, these groups of people share their personal data with ease thereby posing themselves at risk. Also, the popularity of the use of credit cards, deep and widespread penetration, as well as the significant growth of e-commerce in 2020, has opened up new avenues for fraudsters. It is expected that in 2021, internet fraud will grow as well, which means that businesses and ordinary users need to protect themselves right now. Credit card fraud detection using machine learning is one of the ways to deal with financial fraud.
Why Fraud Detection With AI and Machine Learning Is Effective
At the moment, artificial intelligence and machine learning in banking are the most promising technologies for dealing with a credit card, loans, and insurance fraud, money laundering, and terrorist financing, as well as risk evaluation and prediction. The usage of artificial intelligence in banking is quite effective because of the following reasons:
- Credit card fraud detection using machine learning is fast. Since the machine learning model works with data in real-time, it can prevent fraudulent attempts even before harmful consequences occur.
- The case studies dwelling on artificial intelligence in banking usage prove that it is efficient. The potential for using artificial intelligence in banking is not limited to combating fraud. This technology is also effective in assessing risks, personalizing marketing, and improving user experience.
- The usage of machine learning in banking is secure both for the financial institution and ordinary users. The use of machine learning further enhances the security of user data, which in turn is used to detect anomalies and prevent fraudulent attempts.
How Does Machine Learning Detect Fraud?
If we temporarily omit the technical details, then the algorithm of machine learning is quite simple. This is a model that is in a constant process of data analysis, that is, it analyzes the flow of incoming data in real-time, constantly comparing new information with historical information.
As for credit card fraud detection using machine learning, the ML model analyzes data on user behavior in order to timely detect behavioral anomalies, for example, an attempt by a user to enter a banking application from an unknown IP address.
Taking into account the totality of other data, the machine learning system can either block this attempt and recognize it as illegal (for example, when the user does not only use an unknown IP address, but also enters the wrong password several times in a row), or take additional measures to make sure the user’s behavior is legal (for example, send a one-time password to his phone).
What Are the Best Credit Card Fraud Detection Techniques?
Even with the seeming simplicity of machine learning in banking, there are diverse credit card fraud detection techniques that allow you to work with data and detect fraudulent attempts in different ways.
According to the research, “The credit card fraud detection techniques are classified into two general categories: fraud analysis (misuse detection) and user behavior analysis (anomaly detection). The first group of techniques deals with supervised classification tasks at the transaction level. The second approach deals with unsupervised methodologies which are based on account behavior.”
Credit Card Fraud Detection Techniques Comparison
| Decision tree | This is a basic machine learning algorithm that works according to pre-programmed laws of logic, that is, “if the user performs action X, then it is legitimate” or “if the user performs action Y, then it is potentially fraudulent.” |
| Genetic Algorithms | This is another behavioral analysis method that is based on the user actions algorithm and analyzes this algorithm as a whole. |
| Clustering Techniques | This method is well suited for cluster analysis of behavior, that is, it helps to distinguish patterns of behavior in different groups of user accounts. |
| Neural Networks | This is a data mining method that allows you to find the most subtle anomalies. |
| Naive Bayes Classifiers | It is a machine learning method based on predictive analytics where assumptions are made based on a dataset with target classes. |
Difficulties of Credit Card Fraud Detection Using Machine Learning
Even with the versatility of machine learning in banking and the veracity of credit card fraud detection techniques, the use of this technology can be challenging for a number of reasons described below.
- Imbalanced data
- Different misclassification importance
- Overlapping data
- Lack of adaptability
- Fraud detection cost
- Lack of standard metrics.
Thus, in order for fraud detection using machine learning to be successful in banks and other organizations, it is necessary to carry out high-quality preparatory work with data, classify and structure it in the correct way, take into account the behavior of your users, and set the right tasks for the machine learning model.
How Can You Prevent Credit Card Fraud?
There are many ways to prevent financial fraud that are relevant to both businesses and ordinary users. The first and foremost method is common sense and caution. The second way is the use of machine learning and artificial intelligence in banking, the potential of which becomes even stronger when used with other techniques and instructions, as well as under the supervision of authorized human intelligence.
Conclusion
Thus, machine learning and artificial intelligence in banking are top technologies for combating financial fraud, as well as for boosting customer service, reducing costs, improving security, and personalizing marketing strategies. In this article, we covered the main credit card fraud detection techniques, and perhaps the practical implementation of these methodologies should be a top priority for financial institutions in 2021.
