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How AI and ML are used in payment fraud detection (16 use cases)

This article is written by Nomentia Artificial Intelligence (AI) and Machine Learning (ML) are two technologies that have been widely discussed in recent years. They offer a range of tools that are gradually being integrated into our personal and professional lives. These technologies are particularly useful in situations where there are many time-consuming and manual tasks. Or where there is a large amount of data to analyze. One such application is payment fraud detection. Before diving into the use of AI and ML in fraud detection, it is important to address them separately. What is AI exactly? AI systems are designed to integrate vast amounts of data with intelligent algorithms. They mimic or simulate human-like actions and decision-making processes. AI can be utilized for various techniques like problem-solving, natural language processing, image recognition, reasoning, learning, and more. The technology functions in a way that collects data, processes it, and selects an algorithm. It trains a specific model, modifies the model’s parameters, and evaluates the results. This can then potentially be deployed in the real world. As it operates, AI typically uses feedback loops from its users to improve over time. What is machine learning exactly? Machine learning is a form of AI that teaches a system to think in similar ways to humans, as it learns and improves based on previous experiences. Machine learning algorithms typically need little supervision because they are learning by themselves. We usually distinguish between three techniques of machine learning:  Supervised learning Supervised ML algorithms are taught to make predictions or decisions primarily based on previous data patterns. Here, the ML learns based on already labeled data. So, input data needs to be categorized beforehand. So that the algorithm will use it as a benchmark to come to conclusions when analyzing new data. Unsupervised learning In unsupervised ML, there is no need for labeled or categorized data. Instead, the algorithm tries to find patterns or relationships in the data without explicit guidance. Typically, the goal of unsupervised ML is to explore data and find clusters or relationships. Reinforcement learning With reinforcement learning, you train the algorithms to make sequential decisions. In an environment with a reward or penalty feedback mechanism that it learns from to improve its subsequent decisions. Usually, no labeled data is available. The algorithm needs to learn from its own experiences and the feedback it receives. The difference between AI and machine learning AI refers to the simulation of human intelligence in machines that are programmed to mimic human-like actions and decision-making processes. AI encompasses as a wide range of techniques, including problem-solving, natural language processing, image recognition, and expert systems, among others. It aims to create systems that can perform tasks that typically require human intelligence. Tasks such as reasoning, learning, perception, and problem-solving. Machine Learning is a subset of AI that focuses on developing algorithms. This enables computers to learn from and make predictions or decisions based on data. ML algorithms allow machines to improve their performance on a task through experience without being explicitly programmed for that task. It does so by using statistical techniques to enable computers to learn patterns and relationships from data and make decisions or predictions. Why are these topics relevant to payment fraud? Recent research by PwC showed that 51% of organizations have experienced fraud in the past two years. A 20-year high compared to previous survey results. Fraud mainly impacted organizations in terms of financial losses. Respondents highlighted that many of them will require new, advanced technologies to tackle the issue. Some respondents also mentioned that fraud had more considerable consequences. Like operational disruptions or damage to the brand or customer loyalty. Ultimately, AI has the potential to assist businesses in maintaining a secure payment environment. Thus safeguarding a company’s customers, revenue, and reputation. RESEARCH BY PWC SHOWED THAT 51% OF ORGANIZATIONS HAVE EXPERIENCED FRAUD IN THE PAST TWO YEARS — A 20-YEAR HIGH COMPARED TO PREVIOUS SURVEY RESULTS PWC’S GLOBAL ECONOMIC CRIME AND FRAUD SURVEY 2022 With the rise in cybercrime and the evolving sophistication of financial threats, we’ve come to an era where humans cannot keep up with processing an abundance of data efficiently and securely. We can by no means compete with the speed and thoroughness of data interrogation that AI and ML can deliver today. As a result, we need to embrace and team up with such technologies. To support this view, a recent study by the Association of Certified Fraud Examiners revealed that 17% of organizations already leverage AI and ML to detect and prevent fraud, and 26% of organizations are actively planning to adopt fraud detection AI or ML in the next two years. On top of that, technology providers are now heavily investing in developing practical AI-driven solutions to tackle payment fraud. How can AI and Machine Learning be used in fraud detection? These days, ML and AI can help you with fraud detection in various ways. Let’s focus on some of the main ways how organizations currently leverage the technologies and what the future may bring: For example, AI and ML can streamline payment processes and enable faster risk identification in payables, receivables, and reporting. They can help manage exceptions or spot anomalies in large data sets based on previous patterns it has studied. AI can analyze large datasets much faster than human beings, and it provides good insights and points to pay attention to. Faster analysis will also help speed up decision-making. Especially with more data than ever and little time to analyze, it will become essential to save time while deriving insights by leveraging tools like AI and ML. AI can help automate essential but manual tasks such as data entry, reconciling payments, or generating reports. Minimizing manual processes, in turn, reduces the room for errors and fraud. Reconciliation of payments is essentially comparing two data sets with each other and finding matches, which AI and ML are incredibly good at. Even when anomalies arise, you can train AI to handle them in pre-set ways. Machine learning…