Fraud schemes in the digital sphere and searching for the ways to prevent potential fraud will always be the hot topic. Unfortunately, not only businesses evolve with technological progress but so do cyber attacks and financial crime tactics. According to the statistics from the UK government, despite some decrease in numbers, the percentage of fraudery attempts and actions remain high.

Cyber threats hit hard: 43% of UK businesses faced breaches or attacks last year, down from 50% in 2024. Small businesses saw a drop in phishing incidents, but cyber crime still looms large with 20% affected. Medium and large businesses remain high-risk targets, with over 67% experiencing breaches.

As a result it leads to increasing investments in cyber security from organizations. Statista reveals that in 2024 cybercrime costs in the U.S. skyrocketed to an estimated $452.3 billion. This dramatic rise from 2017 levels is just the beginning, with projections suggesting a staggering surge to $1.82 trillion by 2028.

Although investments in cybersecurity are substantial, the impact of fraud loss can still overshadow these efforts, making it crucial to invest in the latest trends and AI fraud detection services continuously. In recent trends, the banking and financial services industries have been actively adopting artificial intelligence for fraud detection to safeguard transactions. Although AI can analyze vast amounts of data in real-time, identify complex patterns, and detect anomalies that are not obvious to humans, its adoption in other sectors remains limited.

In this article, we will demonstrate how AI improves security in general and can assist in fraud detection across various sectors, not just in finance.

Understanding AI and machine learning in fraud detection

It’s important to say that in fraud detection the main technology here is machine learning. It is one of the branches of artificial intelligence technology, empowering data science and dedicated to processing large data volumes and recognising repeating patterns among them. It helps identify fraud scenarios in content, behavior, activity, and transactions. And how does it actually work on fraud detection? There are three key types of machine learning:

Supervised learning

Supervised machine learning requires labeled data where each data point is tagged as normal or anomalous. The system learns from these examples to classify new data points. The method is effective for identifying known anomalies as its recognition is based on labeled examples and flag outliers. In reverse, it makes it less effective for detecting unknown anomalies.

Unsupervised learning

Unsupervised learning doesn’t require labeled data. Instead, it finds inherent structures or clusters in the data by analyzing similarities and differences. The AI system groups data points into clusters and identifies normal patterns and flags data points that do not fit well into any cluster as anomalies. Unlike supervised learning, it helps identify unknown anomalies and handle complex, unlabeled datasets. However, in this case, AI algorithms may mislabel anomalies, so some human monitoring is still needed.

Reinforcement learning

Reinforcement learning is a method where agents — which are AI-based software systems — learn to make better decisions by trying different actions and receiving feedback, which helps them adjust and improve over time. It's particularly effective at adapting anomaly detection systems to new and changing data patterns, as it uses feedback to fine-tune detection methods. Anomaly detection also benefits reinforcement learning by identifying and warning about unusual situations that could lead to mistakes. This type of learning is the key to training AI agents.

When it comes to implementing effective fraud detection systems, choosing the right technology — whether it's supervised, unsupervised, or reinforcement learning — can make a significant difference in accurately identifying fraudulent activities.

General reasons to implement AI

PwC’s early research on AI’s impact on fraud detection was conducted at a time when generative AI was already gaining significant attention but hadn't yet reached advanced technologies, such as AI agents. Back then, they outlined the possible areas of AI technology implementation and the reasons they believed it was worth implementing. They provided three main considerations:

1. Processing structured and unstructured dataset, advanced AI models will help more effectively identify fraud and scam activity, preventing threatening operations.
2. Suspicious activity monitoring conducted by AI, similar to how it responds to customer inquiries, will free up resources.
3. AI business integration will help combat AI by detecting voices, messages, and images that were generated by artificial intelligence.

The forecast turned out to be pretty accurate taking into account current advancements. After AI agents were introduced, dataset processing has become both more accessible and effective. Let’s see how AI agents can help with fraud detection and how to understand whether it’s a suitable solution.

AI agents in fraud detection

When a company decides to implement AI agents, it’s important to understand that it requires rethinking the workflows when rule-based and deterministic approaches don’t fit. How does it contribute to fraud detection capabilities?

A large language model, which AI agents are, operates more like an experienced detective, analyzing context, recognizing subtle patterns, and identifying suspicious activities even if they don’t break the rules. Its ability to understand and process complex and detailed information helps it effectively handle complicated and unclear situations, such as those found in payment fraud detection. In contrast, a conventional rule-based system acts more like a checklist, flagging transactions according to predefined criteria — remember the difference between reinforcement learning and others that we discussed earlier.

Here’s a step-by-step list that can help you evaluate workflows and understand whether the process no longer needs a traditional approach and requires this generative AI technology adoption:

Evaluate complex decision-making needs

Determine if the fraud detection process involves nuanced judgment or context-sensitive decisions not easily captured by rigid rules.

Assess rule management challenges

Identify if current systems are overwhelmed by a large set of complex rules, making them difficult and costly to maintain or prone to errors.

Analyze unstructured data usage

Examine if the fraud detection system heavily relies on interpreting natural language, extracting information from documents, or requires conversational interaction.

Review automation history

Consider whether traditional methods have failed to automate the process effectively, indicating potential benefits from an AI agent.

Validate use case compatibility

Confirm that the use case clearly aligns with scenarios that benefit from AI’s nuanced reasoning capabilities to justify the investment in an AI agent.

If the evaluation meets these criteria, implementing an AI agent may enhance fraud detection processes. Otherwise, a rule-based system could be sufficient.

Does a new digital product need AI protection against fraudsters?

When developing a new custom software, it's essential to evaluate whether incorporating AI fraud risk prevention aligns with your business needs. Not every app development requires such extensive security measures. However, for certain applications, especially those dealing with financial transactions or sensitive personal data, AI-driven detection of fraud tactics can be a valuable component.

Consider what your app does and the kind of fraud risks it might face. If your app involves online shopping, banking, or handles a lot of user data, using AI tools for detecting fraud can be beneficial. These tools can keep an eye on operations and quickly spot anything unusual, helping to protect user data and prevent financial crime.

However, if your app doesn't deal with sensitive information or large transactions, you can start with basic security measures. Adding AI In software development can later enhance security features as your app grows or as new threats emerge.

By working with experienced developers in mobile and web app development, you can better understand the specific security needs of your project.

Improve fraud detection of your product with our custom AI agent development services.

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Industry-specific application of fraud detection with AI

We understand how AI integrations can improve management, speed up workflows, and save resources across industries. However, when discussing AI implementation for fraud prevention, it is typically in the context of the banking sector. In 2024, the banking industry led the way in AI investment, spending a whopping $45 billion in the financial sector. However, AI tools can be helpful in protecting data and fraud prevention in different fields. Let's outline the major areas, including resistance to financial crime in the banking sector.

Payment systems

Payment systems handle financial details, transaction amounts, and personal information of customers. This sensitivity makes it a prime target for cybercriminals. In what areas AI can be handy? First, in credit card fraud detection in in-person and online transactions — it can analyze vast amounts of transaction data in real-time to identify patterns and anomalies indicative of financial fraud. AI can effectively learn from historical fraud cases to predict and prevent unauthorized fraudulent transactions. It also can monitor anomalies in point-of-sale transactions, detecting internal payment fraud in case of unusual behavior.

E-commerce

AI agents can help in account takeover and identity theft prevention. For example, each time a customer visits an online store. AI can analyze multiple factors like IP addresses, device fingerprints, purchase history, and user behavior, and, as a result, disrupt a suspicious session and potentially prevent fraudulent activity, granting access to legitimate users only. It can as well monitor accounts for suspicious changes and help with the tricky issue of friendly fraud, often seen in chargeback cases where customers dispute legitimate purchases. AI steps in with checkout and real-time transaction monitoring, using sophisticated behavioral analytics to detect anomalies.

Banking and financial services

Every day, vast amounts of data flow through banking systems. To help financial fraud detection in banking, AI agents analyze vast volumes of transactional data in real time to identify deviations from typical spending behaviors, such as unusual purchase locations or amounts. They use supervised learning models trained on historical fraud cases to recognize known fraud patterns and unsupervised learning to detect new, evolving threats. AI continuously scores transaction fraud risk, enabling instant blocking or flagging of suspicious activities.

Insurance

Insurance fraud detection relies on AI to analyze claims data, customer profiles, and historical fraud cases to identify suspicious patterns such as duplicate claims, inflated billing, or inconsistent documentation. AI agents use predictive data analytics and anomaly detection to assign risk scores to claims, prioritizing those that warrant deeper investigation.

Cryptocurrency

In cryptocurrency, AI focused on transaction network analysis and behavioral biometrics to detect fraud and money laundering. They scan blockchain data for unusual transaction flows, rapid fund movements, or attempts to obfuscate origins. Machine learning models predict fraudulent activity by recognizing patterns of suspicious wallet behavior. Backend AI integration into the cryptocurrency apps helps with identity verification and compliance by using biometric and natural language processing techniques, adapting quickly to the changing fraud scenarios in decentralized finance.

Healthcare

In healthcare, an AI fraud detection system analyzes billing records, medical claims, and patient data to spot inconsistencies such as duplicate claims, overbilling, or services not rendered. Natural language processing helps interpret unstructured medical notes to detect discrepancies. If implemented, AI agents can continuously learn from confirmed fraud cases to improve accuracy and reduce false positives, providing real-time flagging of suspicious claims and protecting both providers and payers from financial abuse.

Telecommunications

AI powered fraud detection in telecom monitors call patterns, SIM usage, and network traffic to identify scams like SIM box fraud or impersonation. Traditional rule-based systems are enhanced with AI’s ability to detect subtle anomalies in call routing or voice patterns, including those generated by deepfake technologies. Behavioral biometrics further strengthen identity verification, enabling telecom companies to respond quickly to emerging fraud threats.

Across these industries, AI implementations share core mechanisms — real-time data collection and ingestion, pattern and anomaly detection, risk scoring, and adaptive learning — but apply them to domain-specific data and fraud typologies. It means that the list of industries that deal with sensitive data can go further and not be limited by those listed.

Benefits and challenges

Despite its broad application across industries and big potential, the integration of AI in fraud detection presents both significant advantages and notable challenges.

Benefits

AI systems can prevent fraud by analyzing transactions instantaneously, effectively identifying and stopping fraudulent activities as they occur.

With an advanced algorithm, AI enhances the precision of fraud detection by minimizing false alarms, making sure a legitimate transaction proceeds smoothly.

By safeguarding customer data and compliance standards, AI builds trust and provides regulatory adherence.

AI driven fraud detection processes reduce reliance on manual interventions, resulting in substantial cost savings and faster operations. AI agents can be an ideal solution for startups aiming to effectively counteract fraudsters using artificial intelligence.

Challenges

AI's examination of sensitive information requires strict actions to handle data privacy and ethical concerns. It's best to use trustworthy and well-known solutions.

Effective AI models depend on high-quality, representative datasets, which can be difficult to acquire and manage.

Implementing robust security measures must be balanced with maintaining a positive customer experience.

The integration of AI in fraud prevention must navigate complex and ever-changing legal regulations.

How to deal with the challenges? One effective strategy to address the challenges of AI implementation is partnering with trusted experts. For example, the Ronas IT software development team has extensive experience working with applications that require strict adherence to compliance and regulations.

When integrating AI solutions into the apps we develop, we use pre-vetted technologies such as those from OpenAI and other reputable providers. Furthermore, we understand the importance of balancing fraud prevention with a customer experience. Our approach involves not just development but also collaboration with our team of analysts and designers so that the apps we create are both secure and user-friendly.

We believe that while the path to integrating AI into fraud detection might be fraught with challenges, continuous innovation and vigilance are still key to being confident that fraudsters remain one step behind.

Conclusion

As cyber threats continue to evolve and target businesses of all sizes, the need for advanced, intelligent solutions becomes ever more critical. AI's ability to process large volumes of data, identify complex patterns, and adjust to new fraud trends offers unparalleled protection against these threats. By embracing AI-driven solutions, businesses can not only safeguard their assets and customers but also remain competitive in an increasingly digital marketplace. Continuous innovation and vigilance will be essential to harness the full potential of AI in the ongoing battle against fraud. Today, using AI for fraud detection is essential for all industries to effectively fight threats and protect their business and customers.

We can help determine if your business needs AI integration for fraud detection and provide the most secure solution. Simply describe your issue using the form below.