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Credit Card Fraud Detection Using Machine Learning

In this digital era of mounting financial transactions, the number of fraudsters is also on the rise. According to a recent report by McAfee, cyber crimes in the form of credit card frauds have cost the global economy over $600 billion. This figure amounts to 0.8% of the global Gross Domestic Product. Intelligent credit card fraud detection and prevention technologies are thus becoming the necessity of the moment.

Large chunks of data gathered from customer financial transactions can be used to study and identify behaviors from the past to identify potential threats of fraudsters. Cybersecurity companies are working through artificial intelligent (AI) systems that can implement machine learning and deep learning techniques to recognize and prevent credit card fraud.

Machine Learning: Functionalities In Detecting And Preventing Frauds

  • Risk scoring and advanced in-memory capabilities have enhanced decision-making in corporate firms to deal with credit card fraud detection.
  • Large chunks of data are processed without human intervention or errors.
  • Cost reduction along with reduced manual reviews for fraud analysts due to rapid iterating machine models with machine learning.
  • Increased efficiency and productivity in workflows with machine learning algorithms that enhances the speed in which tasks are performed with 100 percent accuracy.
  • Business cycle times are considerably reduced with machine learning implementation. Unlike the traditional approach of batch processing, the data models can be trained with the same data sets.
  • Reduction of false positives made possible with machine learning by making use of behavior analytics.

Artificial Intelligence: Functionalities In Detecting And Preventing Frauds

  • Re-defined credit card fraud detection and prevention techniques are made possible with Artificial Intelligence systems.
  • Detecting fraudulent activities in real-time and at a rapid pace is a key role of AI systems.
  • Real-time risk scoring feature provides crucial insights that can help fraud analysts to set threshold scores and flag improper web crawlers.
  • Artificial Intelligence provides a more consistent and high-quality customer experience, round-the-clock.
  • Artificially intelligent systems can help in maximizing sales and minimizing fraud attacks.
  • Predictive Analytics techniques can help in preventing fraud attempts with the help of rule engines.
  • AI systems provide automatic updates of standard regulations. It helps corporate firms stay in compliance with both external and internal business policies. This helps in thwarting fraudster attacks.

Keys To Using Machine Learning And Artificial Intelligence To Prevent Frauds

Fraud-specific data science techniques help to understand how to create a properly architectured machine learning model to prevent fraudster attacks. Let us walk through the major methodologies involved:

COMBINING SUPERVISED AND UNSUPERVISED MACHINE LEARNING MODELS IN A COHESIVE MANNER

A supervised model is one that is trained with a rich set of ‘tagged’ transactions. A transaction is either tagged fraud or non-fraud. These models are built and tested with millions of testing models to learned patterns that identify potential threats of frauds. Unsupervised models are self-learning, AI designed to identify anomalous behavior. Both of these models are combined to combat new-generation fraud attacks.

APPLYING BEHAVIORAL ANALYTICS

This is used to analyze behaviors at a granular level by tracking profiles. Profiles could be that of both monetary and non-monetary, such as merchant, account or device. These profiles are tracked to predict future behavior.

SEPARATING SPECIALIZED AND GENERAL BEHAVIOR ANALYTICS

Domain knowledge is critical for analyzing the behaviors of fraudsters. Fraud-specific predictive analytics is performed to separate general from specialized fraudulent behaviors. This is performed by evaluating large chunks of data and deriving critical insights for patterns in behaviors.

TAKING ADVANTAGE OF LARGE DATA SETS TO DEVELOP DATA MODELS

The volume and type of data impact the data models. Fraud models are developed and trained based on a multitude of data sets. This huge number increases the accuracy of data models, compared to relatively thinner data sets, otherwise involved.

ADAPTIVE ANALYTICS AI IMPLEMENTATION

Transactions are scored from high to low, depending on the previous experiences. These are evaluated as either transaction that has scored too high or too low. Adaptive analytics brings about a clear picture of the distinction line between fraud and a non-fraud.

Role Of Artificial Intelligence And Machine Learning Technologies In Fraud Prevention

The major threat of fraudsters is towards the financial services sector. Let us walk through the various roles of Machine Learning and Artificial Intelligence in preventing frauds:

BIG DATA ANALYTICS: ENSURING ACCURACY IN TRANSACTIONS

Machine learning algorithms play a major role in analyzing large volumes of transaction data. Researches state that about 10 percent of payments that insurance companies are liable for is related to fraudulent claims. This is where Big Data Analytics has a huge potential for detecting and preventing fraudsters. For example, insurance companies are actively discovering ways to improve their ability to make real-time decisions more quickly and accurately than ever before to optimize claim processing for their customers. They use big data analytics alongside machine learning to best serve both the customer and employee.

The machine learning algorithms take into account critical contextual information such as the device used, the customer’s location, etc. This makes decision-making accurate and efficient.

EXTRACTING CROSS-CHANNEL DATA: REDUCING TASKS OF FRAUD ANALYSTS

Fraud analysts leverage machine learning algorithms to empower their decision-making and to generate informed insights to overcome fraudulent attacks. Additionally, fraud analysts can efficiently utilize resources as data models which are constantly modified with new features of data extraction. Machine learning enhances a fraud analyst’s task of implementing fraud strategies. This involves implementation cycles, data access, ability to scale as the industry grows, and also to stay aware of fraudulent attacks.

REDUCING FALSE POSITIVES

A false positive error is the major concern of cybersecurity these days. Misinterpretation of a non-malicious activity leads to a false positive. The pressing concern with this issue is that a false positive is an incorrect security alert that leads to monetary losses. For instance, there are scenarios where an e-commerce site can omit a particular online buyer. This buyer but could be a real customer. Such situations lead to a reduction in Internet visibility.

AI and machine learning can avoid genuine customers from getting rejected as fraudsters, from trained patterns in data sets. This also minimizes the labor cost and saves time considerably.

MITIGATING COMPLIANCE RISKS

According to the “Report To The Nations On Occupational Fraud”, 5% of a company’s revenue is lost to fraudsters. This figure amounts to $140,000 per year as a median loss, annually. This is where Artificial Intelligence plays a major role. AI systems can help companies stay compliant with the local, federal and state standards.

In addition to external regulations, AI systems prove to be useful for internal policies as well.

Artificial Intelligence and Machine Learning are keys to effective and accurate credit card fraud detection and prevention mechanism in corporate firms. To learn more on how to tackle fraud challenges in your organization, drop a call and our experts can help you with a step-by-step guide on strategies for the same!

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