How Machine Learning Drives Advanced Payment Fraud Detection

We’ve witnessed high adoption of digital payment methods that offer convenience and speed to both customers and businesses.

With conveniences comes the risk of exploitation and we’ve also come across a high degree of fraud attempts. Making it much more critical for businesses to deploy robust fraud preventive measures that are immune to sophisticated fraudulent practices that cause financial losses.

This blog showcases everything you need to know about the role of machine learning in payment fraud detection and prevention.

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Understanding payment fraud and its impact on businesses

An attempt to manipulate or tarnish the legitimacy of an online transaction with the intention to either cause financial harm or steal sensitive information that can be used for financial advantage is known as payment fraud. Payment fraud is one of the many types of financial fraud that scammers resort to extort money.

Some of the examples of frauds specific to the payments industry include - credit card fraud, identity theft, account takeover, and friendly fraud. Criminal groups and networks are the masterminds of payment frauds where they find vulnerable systems and use tactics to benefit from them.

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Impact of payment fraud on businesses

Payment frauds do not just cause financial losses. There’s more for businesses that fall prey to fraud:

• Financial - first and foremost, but there are different types of losses impacting businesses. Beyond the loss of revenue, companies have to incur chargeback losses that occur when customers dispute a transaction, penalties, and fees like bank, legal, and more.
• Reputational - businesses that fall prey to frequent fraud attacks are deemed to have weak security systems. As a result, customers lose trust, the word starts spreading, and brand reputation takes a hit.
• Operational - managing and preventing a fraud attack is expensive. Plus, comes with temporary operational disruptions that demand manpower investment.

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Role of machine learning in detecting and preventing payment fraud

Machine learning algorithms play a massive role in detecting fraud attempts by analyzing huge data sets. They have the unique ability to read through tons of historical data sets and learn what’s normal pattern or behavior.

Based on the learning, they develop the ability to identify abnormal data behavior and deviation that are possible fraud attempts. And, they do it in real-time which helps prevent attempted fraud. Fraud detection algorithms immediately flag and block any attempt of fraud.

Furthermore, ML models evolve with more exposure to data over time, both legitimate as well as fraudulent data, helping them analyze and successfully predict fraudulent transactions from any future or new attempts.

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Advantages of using ML for fraud detection

Here’s why your business needs a robust ML fraud detection and prevention system:

• Real-time detection - ML models can detect, flag, and take action against an incoming threat in real-time, keeping your business clear from attacks.
• Fewer false positives - it is a hassle when legitimate transactions are flagged and blocked, tampering with customer experience. ML models have fewer possibilities of false alarms as compared to manual processes.  
• Adaptability - with continuous learning through data, ML models can adapt to new fraud patterns and develop the best response to new threats.

Key machine learning techniques for fraud detection

ML models use multiple techniques to detect fraud. Some of them are as follows:

Supervised learning

Here ML models are trained with prepared cluster data sets that are labeled as legit and fraudulent. Some examples of algorithms developed with supervised learning for fraud detection are decision trees, random forests, and logistic regression.

Neural networks

ML models here are trained to analyze and handle data similar to a human brain which makes it possible for the models to interpret large amounts of data and pick up sophisticated as well as non-linear patterns. Neural networks for fraud prevention depict high accuracy in fraud patterns like multi-account fraud or synthetic identity fraud.

Clustered detection

This approach groups transactions into improvised clusters based on similarity and behaviors to pick patterns that suggest fraud. ML models refine through these clusters to detect fraudulent behaviors apart from legitimate ones more efficiently. 

Anomaly detection

Payment processes have expected behaviors and any deviation from this pattern is picked up by ML models and flagged. This is anomaly detection in payments, where ML models report any new and inconsistent behavior that indicates fraud. Any data with no historical record is reported.

Text and network analysis

Frauds are rarely done in silos. A group of networks in partnership generally following the same intent come together and execute frauds. Certain ML techniques can hand-pick these networks based on their connections and behaviors such as users, accounts, and devices. Similarly, ML models can also read through texts from reviews, emails, and more, to identify suspicious networks from keywords.

Risk scoring

ML models designate risk scores to certain accounts, networks, and users based on their past behavior, location, devices, and more. The risk scoring method indicates businesses to be wary of certain users and accounts and ensure corrective verification is done before processing transactions. High-risk accounts indicate a higher probability of fraud.

Identity verification

ML can better verify the identity of the transactor based on the biometric data, proof submission, transaction history, and more. This ensures frauds like identity theft are kept at bay streamlining safe and secure access of accounts to original users.

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How are companies using ML for payments 

Based on their business nature and industries, companies have improvised to include ML models in their process for payment security.

Financial organizations like banks and payment processors who deal with financial transactions are using ML models to minimize chargebacks that result from fraudulent transactions, in real-time.

eCommerce organizations on the other hand are using ML and AI in payment security for a secure shopping experience that is free of account takeovers and data theft.

We are also seeing new-age technology start-ups emerge. These start-ups have mastered fraud detection and prevention with their advanced ML models developed in-house and offer their security services to the masses.

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Challenges and considerations in implementing ML for fraud detection

ML can do a lot of heavy lifting in fraud detection, but only when equipped with the right resources. Here are some of the considerations to ensure the output from ML models is at its best:

Privacy and security

ML models handle a lot of data and it is critical to ensure the safety and security of the sensitive data is preserved. Additionally, compliance with regulations like GDPR and CCPA must be maintained.

Data requirements

ML models learn from data, so the quality of output they produce will be equivalent to the data feeds provided to them. High-quality data must be severed to the models to ensure the learning, analysis, and accuracy are of high quality as well.

Model training

Fraud techniques have changed and they will continue to. While a lot more new techniques that can override ML algorithms will emerge, to prevent this from happening, ML models must be constantly trained to fight against new threats.

User experience with accuracy

False positives are a major blow to user experience and any vulnerability can lead to incoming fraud. So, it is critical to ensure the accuracy of fraud detection and enhanced user experience go hand in hand.

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Future trends in machine learning for payment fraud detection

While the progress we have made in machine learning for payment fraud detection is fascinating, the future has a lot more in store for this industry. Here are some of the trends we may see in the future:

(XAI) and model transparency

The classic ML models use complex modes of decision-making, making it difficult for organizations to understand the ML output. Using the Explainable AI (XAI) model fosters transparency in processes making it easy for fraud analysts and compliance teams to enhance the ML model's functionality.

Federated learning

This is a collaborative method of training machine learning models where multiple organizations come together and train the same ML model, without having to use the actual customer data that’s sensitive in nature. This safeguards the security of user data and facilitates utmost advancement in ML training.

Blockchain adoption

Transactions with blockchain technology become immutable, making it difficult for fraudsters to manipulate data. ML integration reinforces it through strict data analysis for better prevention.

Behavioral biometrics in action

This acts as an additional layer of security where ML models scan through biometric behaviors like typing speed in real-time, to detect anomalies and ultimately fraud.

ML-rule hybrid models

A match between traditional rule analysis systems and flexible and adaptive ML systems will strengthen how frauds are detected and prevented.

Deep learning for anomaly detection

Deep learning techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can process data at big scales and pick even the slightest indicators of attempted fraud.

Tailored learning with AI

ML models can self-learn and train with no manual updates, learning and adapting to new data behaviors and patterns. Human expertise can take it to the next level where experts make constant measures to refine and enhance the accuracy of the ML model's output.

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Conclusion

Machine learning models are a boon to the security of online transactions, but a hassle for businesses to keep up. That’s where choosing the right payment solution provider makes the difference.

Payment gateways like Payby use advanced technologies and techniques like ML and AI to ensure accuracy in fraud detection by reducing false positives, real-time fraud detection, and security against upcoming threats.

Get started with Payby to experience the difference.