How do AI algorithms detect non

AI algorithms have attracted a lot of press, but what can they do in the world of payments? Since online payment systems were introduced, there have always been those who seek to unlawfully gain access to another’s finances. This has become a significant issue in the modern era, as all transactions can be conveniently conducted online by simply entering your information. Criminals managed to steal more than £1.2 billion through authorised and unauthorised fraud in 2022, equivalent to over £2,300 being stolen every minute.

The banking and finance industry successfully stopped an additional £1.2 billion of unauthorised fraud from falling into the hands of criminals.

Data breaches can jeopardise the security of organisations, consumers, banks, and merchants. They can also result in monetary theft and, ultimately, the loss of customer loyalty, and damage to the company’s reputation.

An AI algorithm is a series of instructions enabling a computer or a system to learn and operate autonomously. In our daily lives, we encounter various platforms such as e-commerce websites, trading platforms like TradingView, and video-sharing platforms like YouTube. These platforms leverage recommendation systems to gather user data and deliver personalised suggestions to enhance user engagement. For example, TradingView uses AI to provide users with insights and recommendations that can help them make better trading decisions.

AI programs are driven by a complex set of rules dictating their actions and learning capacity. AI wouldn’t exist if there wasn’t an algorithm.

At its core, an AI algorithm receives training data and uses that information to acquire and develop knowledge. After completing its tasks, it uses the training data as a foundation. Certain AI algorithms can learn autonomously and incorporate new data to improve operations. Others will require the assistance of a programmer to streamline their processes.

Fraud detection in financial transactions involves identifying abnormal activities that deviate from legitimate patterns. AI algorithms are highly skilled at identifying patterns and can be trained to categorise transactions as fraudulent or non-fraudulent using past data. AI algorithms have multiple applications in improving fraud detection in financial applications. Some of the commonly used methods and techniques include:

NLP techniques, like text-based conversations between customers and bank representatives, are applicable for analysing unstructured data. AI can analyse and understand these interactions to detect fraudulent conversations or requests.

AI can analyse extensive networks of transactions and detect interconnected patterns that indicate potential fraudulent activities. By analysing connections between various accounts, artificial intelligence can detect networks of individuals involved in fraudulent activities or money laundering schemes.

AI algorithms can constantly monitor real-time transactions, instantly detecting and flagging any potentially suspicious activity. Real-time monitoring allows instant action, such as blocking a transaction or contacting the customer to confirm the transaction’s legitimacy.

AI algorithms have the ability to learn patterns of normal behaviour by analysing historical transaction data. Any deviation from these patterns can be identified as potentially fraudulent.

Although AI algorithms can be applied in many ways to detect non-standard transactions, ML is at its heart. ML is a branch of AI that allows a machine or system to learn and enhance its performance through experience. Machine learning uses algorithms to analyse vast data, acquire knowledge from observations, and make informed decisions.

Machine learning algorithms enhance their performance over time through training, which involves exposure to additional data. Machine learning models result from applying an algorithm to a dataset used for training. The model will improve as more data is utilised. The financial services industry often deals with large amounts of data regarding daily transactions, bills, payments, vendors, and customers, making it ideal for machine learning.

For machine learning-based fraud detection to work, specific procedures must be followed. They are:

The data can be sourced from different places, such as transaction logs, client profiles, and other databases. The precision of the ML algorithm in detecting fraudulent activity increases with the availability of more data.

There are various machine learning algorithms available for fraud detection. Here are some of the common ones:

● Anomaly detection

Anomaly detection algorithms are employed to identify data points that exhibit notable differences from the remaining data. This can help identify fraudulent activity that deviates from usual patterns.

● Decision trees

Decision trees are a sort of algorithm that employs a hierarchical structure to make judgments depending on the input. The data is divided into branches based on specific criteria by the algorithm, and decisions are made at each branch.

● Support Vector Machines (SVMs)

Support vector machines (SVMs) are a specific type of algorithm that divides data into distinct classes using particular criteria. The algorithm maximises the distance between the data points and the boundary to create a boundary between the classes.

● Random Forest

Random Forest is a robust ensemble approach for making predictions by combining numerous decision trees. It is capable of effectively handling complex feature interactions and detecting anomalies.

● Gradient Boosting

Gradient Boosting is an algorithm that boosts weak models iteratively to create a powerful predictive model. It is especially beneficial for managing imbalanced datasets.

This process requires providing the algorithm with labelled data, which has been categorised as either fraudulent or legitimate. This allows the algorithm to learn and recognise patterns that indicate fraudulent activity.

Once the ML algorithm has been trained, it is crucial to evaluate its performance by testing it on fresh data. This includes providing the algorithm with new, unlabeled data and accurately assessing its ability to detect fraudulent activity.

To stay ahead of fraudsters, it is crucial to regularly update the machine learning algorithm to adapt to their evolving tactics and avoid detection. This requires providing the algorithm with fresh data and retraining it as necessary.

False positives can occur when an authentic action is mistakenly identified as fraudulent, which can harm the system if not recognised. In that sense, if a machine learning engine is poorly calibrated, it can lead to a negative loop where the increasing number of false positives being flagged results in less accurate future results.

Another disadvantage is the lack of human comprehension. It’s challenging to match good old psychology when comprehending why a user’s activity is questionable.

Detecting non-standard transactions in financial applications is extremely important for ensuring the security and reliability of financial systems. Without a doubt, advanced algorithms are essential in this process. These algorithms are designed to constantly learn and adapt to new fraud tactics. They serve as a strong defence for financial institutions, helping them identify and prevent fraudulent activities.