Can The Payments Industry Use AI To Detect Fraud In 2024?

Research predicted that hacks and data breaches will cost the global economy over $9.5 trillion by the end of 2024. This comes as fraud attempts and phishing attacks are increasing and getting more sophisticated, mainly due to the increase in online transactions. 

The payment industry facilitates the exchange of money for goods and services and is designed to enable secure and efficient transactions between customers, businesses, and financial institutions.

However, with the rise of digital banking and online transactions has transformed traditional institutions into digital entities. While this shift provides greater convenience to businesses and customers across the globe, it also presents new opportunities for fraudsters to create problems.

This is why over half of financial institutions have recently adopted artificial intelligence (AI) for fraud detection. AI offers various applications in banking and fintech, with fraud detection and prevention being the most critical. 

How Companies Use AI For Fraud Detection

As technological advancements give rise to online payment trends, many companies use AI to proffer solutions for detecting fraud. Here’s how it works:

Data Collection

In artificial intelligence (AI) and machine learning (ML), data collection involves accumulating raw information from various sources. Developers usually process this data to train and test AI models.

The process includes systematically gathering diverse datasets, which can be structured, semi-structured, or unstructured.

According to Transaction Trust, about 43% of all data compromises targeted financial institutions such as banks and other smaller financing firms. These datasets form the foundation for developers and researchers to train algorithms to recognize patterns, make predictions, or perform different cognitive tasks.

Feature Engineering 

Feature engineering is one of the best fraud detection techniques today. Companies can make detection easy by using a well-automated feature engineering tool to clean data and identify relevant variables specific to their business problem. 

Meanwhile, this AI technique offers numerous benefits, including efficiency, fraudulent pattern detection, and deep data exploration. By integrating automated feature engineering, organizations can bypass the complexities of manual feature engineering and swiftly utilize sophisticated, efficient machine-learning models.

Once identified, these features may need to be transformed or combined to enhance their utility for training a machine learning model, a process part of exploratory data analysis (EDA). Feature engineering is crucial for achieving good performance in fraud detection and other machine learning tasks.

A challenge in feature engineering for fraud detection is that many of the most valuable features must be manually crafted. While many features can be directly gotten from raw data, the most valuable ones often create a pattern.

Model Training

Fraud detection is often modeled as a supervised learning-based binary classification problem, with Gradient Boosting Decision Trees (GBDTs) frequently used as the model. GBDTs function by training AI on specific decisions made with the data used for fraud detection.

For fraud detection, the features used by GBDTs might include customer information and other behavioral data. This tool has training data labeled to indicate whether a transaction is fraudulent.

By training on a large dataset of known fraudulent and non-fraudulent transactions, the GBDT algorithm can learn to make highly accurate predictions about the likelihood of a given transaction being fraudulent.

Continuous Learning

The use of AI-generated audio and video clips to impersonate legitimate users is a growing concern. AI-based systems are being trained to detect subtle signs indicating that content might be synthetically generated.

These attacks use AI to craft and send personalized messages that are more convincing and harder to detect using traditional methods. AI systems counter these by analyzing communication for abnormal patterns and employing natural language processing to identify phishing attempts.

According to ScienceDirect, the increased premiums from insurance fraud cost the average American family between $400 and $700 per year. The continuous evolution of fraud necessitates equally advanced AI solutions that detect known fraud types and adapt to emerging threats.

However, it is essential to maintain and develop AI models for fraud detection. This development is needed for efficiency and consistency.

The Future of Cybersecurity

Consistent model maintenance is necessary for any big company to maintain the long-term efficiency of AI-driven fraud detection systems. These companies can regularly update this model and bolster reliable defenses against such financial threats in many ways.

In addition, cybersecurity jobs are likely to increase in the coming years as online fraud becomes even more concerning across many industries. While AI can do so much, humans need to fact-check and train the technology.