AI in Fraud Detection: Real-World Use Cases Across Industries

Key takeaways

AI in fraud detection uses algorithms and models to identify patterns in large datasets to prevent fraudulent activity in real time.

• According to a 2025 Alloy survey, 99 percent of respondents reported already using AI as part of their fraud prevention system [1].

• AI technologies in fraud detection can range from using deep learning to detecting complex patterns in data to applying computer vision for identifying fraudulent documents.

• You can apply AI and machine learning for fraud detection in several fields, including fraud analysis, risk management, anti-money laundering, identity management, and access management.

Learn more about the different AI technologies used in fraud detection, and about the key roles, industries, and pros and cons of using this technology. If you’re ready to start building expertise in AI, enroll in IBM’s AI Foundations for Everyone Specialization. You’ll have the opportunity to learn about key AI technologies, such as deep learning, machine learning, and natural language processing, in as little as four weeks. Upon completion, you’ll earn a career certificate for your resume.

AI in fraud detection: Types of AI and their uses

The increased digitization of financial processes has expanded the attack surface for malicious actors, from credit card theft to AI-enabled phishing attacks. According to a survey by the Association for Financial Professionals, 79 percent of organizations experienced attempted or actual payment fraud in 2024 [2]. At the same time, Deloitte projects that GenAI-enabled fraud could lead to losses of $40 billion in the US by 2027 [3].

While banks and other organizations previously relied on rule-based systems to detect fraud, these methods are ineffective against complex and continuously evolving attack tactics. Using artificial intelligence (AI)-powered analytics and algorithms to detect anomalies, uncover suspicious patterns, and analyze vast amounts of data enables organizations to identify fraudulent activities and prevent financial crimes in real-time. In fact, in the 2025 Alloy State of Fraud report, 99 percent of respondents reported implementing AI in their fraud prevention systems, with 59 percent supplementing their rule-based systems with supervised machine learning models for anomaly detection [1].

From reducing false positives to speeding up manual reviews and learning from data to adapt to new fraud tactics, AI models can be invaluable tools for financial institutions. Some of the AI technologies in use for fraud detection include the following:

• Supervised and unsupervised learning: Supervised machine learning models learn from labeled data sets, while unsupervised models learn from unlabeled data sets without human intervention. In fraud detection, training supervised learning models on specific fraud tactics can allow them to identify patterns and differentiate between legitimate and fraudulent transactions, whereas unsupervised models excel at recognizing unknown patterns of fraud.

• Deep learning: Similar to the human brain, deep learning uses a multilayered neural network architecture for learning. Deep learning’s ability to process large amounts of unstructured data and detect complex patterns makes it helpful for flagging anomalies, such as potential fraud.

• Graph neural networks (GNNs): GNNs are a type of deep learning architecture that uses graph data to represent objects and their relationships. GNNs can process billions of complex transactions to track patterns in data and identify anomalies. 

• Generative AI (GenAI): GenAI learns from its training data, which can include text, images, and code, to generate content with similar characteristics. By training GenAI models on data containing fraudulent patterns, GenAI can generate synthetic data that mimics the training data. You can then use this data to diversify the data set for training your fraud detection model.

• Computer vision: Computer vision enables devices to analyze and extract insights from visual data like images and videos. This can help verify identity documents, such as passports and driver’s licenses, and detect fraudulent ones.

AI and fraud detection: The key players using this technology

Various teams can utilize AI in fraud detection, from enhancing the efficiency of fraud investigations to developing sophisticated models for fraud analysis. Discover the jobs and fields where you can expect to use AI in fraud detection below, along with their median US salaries.

Fraud analysts

Fraud analysts or investigators observe and analyze financial data to identify any fraudulent activity. AI technology can automate the process of data collection and quickly process large amounts of data to detect subtle patterns. This frees fraud analysts to focus on obtaining meaningful insights from the data previously undetectable to the human eye, refining fraud‑management strategies, and devoting more time to complex cases.

The salary information below is the median total pay from Glassdoor as of January 2026. These figures include both base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other forms of compensation.

• Fraud analyst: $64,000 [4]

• Fraud investigation manager: $92,000 [5]

• Fraud prevention specialist: $52,000 [6]

Risk managers

Risk management professionals often need to assess organizational risks by reviewing financial statements and monitoring data for any discrepancies. AI tools can rapidly analyze large data sets and identify high-risk fraud areas that humans might miss, helping organizations to prepare timely fraud prevention strategies. 

• Risk analyst: $99,000 [7]

• Risk manager: $163,000 [8]

• Operational risk coordinator: $134,000 [9]

Read more: Machine Learning for Risk Management

Anti-money laundering compliance teams

Anti-money laundering (AML) professionals can utilize AI tools to continuously monitor transactions, identifying anomalous patterns and flagging potential money laundering or fraudulent activities. AI tools can also streamline the review of evidence in flagged cases by reducing false positives and automating the escalation of high-risk cases.

• Anti-money laundering analyst: $91,000 [10]

• Compliance analyst: $94,000 [11]

• Forensic accountant: $105,000 [12]

Machine learning engineers and data scientists

Machine learning engineers and data scientists develop novel AI algorithms for fraud detection purposes. These professionals experiment with various machine learning methods and design models that combine supervised and unsupervised learning to create more robust fraud-detection systems.

• Machine learning engineer: $159,000 [13]

• Data scientist: $154,000 [14]

• AI specialist: $112,000 [15]

Identity and access management specialists

Identity and access management (IAM) professionals are increasingly using AI-powered document verification systems that can analyze and validate identity documents, from government IDs to business licenses, while verifying them against watchlists and sanctions data. These AI systems can build dynamic profiles for users by continuously monitoring and learning from their behaviors and flag any deviations that occur.

• IAM architect: $195,000 [16]

• IAM specialist: $100,000 [17]

• IAM administrator: $98,000 [18]

How does generative AI detect fraud?

In addition to generating synthetic data to augment AI fraud detection models, GenAI can process and learn from historical data in real time to uncover suspicious patterns, predict potential fraud, and identify connections between possible suspects, helping anti-fraud professionals with fraud investigation and prevention. For example, Mastercard leverages a combination of GenAI and other algorithms to predict the 16-digit card numbers of stolen or at-risk cards, as well as the probability of criminal use [19].  

AI in banking fraud and other real-world artificial intelligence examples 

AI is revolutionizing fraud detection in the banking and financial services industry, but several organizations in other sectors are also benefiting from implementing AI for fraud detection. Explore examples of how various industries are utilizing AI for fraud detection to gain a deeper understanding of its potential.

Banking

JPMorgan has reduced fraud levels while enhancing the customer experience by utilizing large language models to screen payments [20]. American Express uses a machine learning model to instantly monitor card transactions and generate a fraud decision [21]. Capital One has adopted several machine learning models in partnership with AWS to analyze large amounts of data and detect suspicious activities in real-time [22].

Payment networks

Payment platform PayPal offers an AI-powered fraud control solution called Fraud Protection Advanced (FPA), which automates risk scoring for transactions and offers intelligent insights into fraud trends and transaction patterns [23]. Mastercard’s GenAI-based fraud prediction technology monitors large amounts of transaction data from cards and merchants to detect compromised cards faster, reduce false positives by up to 200 percent, and identify at-risk merchants 300 percent faster [24].

E-commerce marketplaces

Amazon has invested in advanced AI-powered controls for its counterfeit detection system, allowing it to block over 99 percent of suspected copyright infringement listings proactively [25]. Meanwhile, Shopify employs a supervised machine learning model trained on billions of transactions to predict and identify fraudulent activity across merchants, achieving 99.7 percent accuracy in approving legitimate merchants [26].

Insurance companies

Auto insurance company Geico has partnered with CCC Intelligent Solutions to implement the CCC Smart Red Flag Detection solution, which uses AI to assess claim discrepancies, flag inconsistent photos, and detect duplicate filings, helping reduce the risk of fraudulent payouts [27]. Meanwhile, commercial property insurer CNA has partnered with Shift Technology to leverage its AI-powered fraud detection solution FORCE, which analyzes data from multiple sources to detect suspicious claims while providing contextual insights for investigation [28].

Government agencies

The US Department of the Treasury uses an advanced AI-powered fraud detection process to prevent check fraud in near-real time and reclaim fraudulent payments from financial institutions. This technology has not only helped them prevent and recover a combined total of $4 billion in fiscal year 2024 but also aided in recovering $1 billion in Treasury check fraud cases [29].

Pros and cons of AI in fraud detection

Implementing AI technology for fraud detection can benefit organizations in several ways, including timely detection that helps aid in preventative measures. Explore the benefits and drawbacks in more detail, beginning with the advantages of using AI in banking fraud detection and across various sectors. 

• Real-time detection: AI systems can detect and respond to fraudulent activities instantly, which helps prevent financial losses and contain any damage before it spreads.

• Scalability: AI automation can process billions of transactions in real-time, far more than humans can manually, allowing businesses to rapidly scale their fraud detection systems with increasing traffic.

• Accuracy: AI tools can recognize both minor irregularities and broader data trends, thereby improving fraud detection accuracy by reducing false positives.

• Adaptability: AI systems can learn from historical and emerging patterns, adapting their tactics to detect even new forms of fraud.

Despite the advantages, you may also encounter some challenges when implementing AI in fraud detection. These include the following: 

• Data availability: AI models require vast amounts of good-quality data for training and learning to maintain accuracy.

• Bias: AI models can be inherently biased towards specific genders, races, or religions.

• Compliance: It’s necessary to ensure that AI implementation does not violate any data privacy laws and maintains compliance with evolving regulations.

How accurate is AI fraud detection?   

AI fraud detection solutions are typically more accurate than traditional rule-based methods since they analyze large data sets to identify suspicious patterns instead of looking at individual transactions. By reducing false positives and ensuring legitimate transactions aren’t flagged, AI-powered solutions can offer businesses a 40 percent increase in fraud detection accuracy compared to traditional methods [30].

How to get started with AI and fraud detection

Working with AI in fraud detection requires a multidisciplinary skill set that includes knowledge of machine learning, cybersecurity, finance, and more. While you might be able to enter an anti-fraud profession through various career paths, the following sections will give you an idea about what steps you can take to begin in this field.

Education

Most anti-fraud professions will require you to have a bachelor’s degree in accounting, criminal justice, auditing, business administration, fraud management, or a related field. However, you might be able to get started with a high school diploma or an associate degree. Make sure you take relevant courses to gain the necessary skills, like forensic accounting or criminal justice. You can also take online courses to supplement your education, especially if you wish to work with AI in fraud detection. A good place to start could be learning AI fundamentals through a course like the Google AI Essentials Specialization.

Experience

You will most likely need one to three years of experience or advanced certification if you want to pursue a role in this field. Getting an internship or a part-time job during or right after your education can help you gain hands-on experience for a full-time job in the industry. You might even be able to get a paid internship position at a public, private, or government organization.

Certifications

Certification is optional for a job in this field, but it may improve your chances of getting hired. A popular certification for anti-fraud professionals is the Certified Fraud Examiner (CFE) offered by the Association of Certified Fraud Examiners (ACFE). To gain expertise in specialized anti-fraud technology, like AI, you may also consider the Certified AI Security & Fraud Detection Specialist (CAISFDS) from the National Initiative for Cybersecurity Careers and Studies (NICCS), or the Certified Fraud Analyst (Using AI) from the American Institute of Business and Management (AIBM).

Resources to support every stage of your journey

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