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AI Fraud Detection: How Technology is Modernizing Financial Fraud

Fraud Detection

AI fraud detection is revolutionizing the way businesses and individuals are protected from financial threats

Financial fraud is a growing problem in today’s digital world; in 2021, banks lost $756 million U.S. dollars in bank transfers or payment fraud alone. In addition, consumers reported losing 5.8 billion to fraud in the same year. These fraud schemes include phishing, fraudulent transactions, identity theft, payment fraud, and counterfeit checks. Financial fraud not only results in direct losses but can have long-lasting consequences for the individuals involved, damaging trust in online systems and damaging an individual or business’ reputation.

The rise of artificial intelligence (AI) has revolutionized the detection and prevention of financial fraud. Financial institutions can use AI fraud detection to quickly identify fraudulent activity such as fraudulent transactions, counterfeit checks, and identity theft, offering a proactive approach to stopping financial crime before it occurs. AI-driven algorithms can also detect patterns in payment fraud, using big data analytics to alert organizations when suspicious transactions are detected. Modernizing AI fraud detection helps reduce losses and safeguard individuals and businesses from potential financial loss.

Using Machine Learning Algorithms to Enable AI Fraud Detection

The traditional rules-based approaches to fraud detection use predetermined rules established and programmed by humans to detect suspicious activity. First, the transaction must meet these criteria for the algorithm to flag it as possibly fraudulent. And then, someone has to review the transactions to determine whether they are legitimate.

Conversely, organizations can use machine learning (ML) algorithms to detect patterns in data that may indicate financial fraud with greater accuracy than traditional methods. By learning from past data, ML algorithms can understand what constitutes normal behavior and identify anomalies that suggest potential fraud. These algorithms can also classify the risks so financial institutions can address the highest priority items first. Additionally, ML algorithms can react quickly and accurately to real-time changing conditions. As a result, ML can be used to detect and respond to fraudulent activities before losses occur.

There are three different ways of training AI: 

  • Supervised ML algorithms learn from human-labeled training data and can detect new fraudulent activity by recognizing similar patterns in incoming data streams. 
  • Unsupervised ML algorithms can identify potential transactions and customer behavior anomalies without requiring labels or examples. It then organizes these data into clusters or groups.
  • Self-supervised ML algorithms use unlabelled data to automatically create labels that the algorithm uses for improvement in subsequent iterations. Self-supervised ML works similarly to unsupervised learning but emphasizes extracting insights from the data rather than organizing it.

AI algorithms are only as accurate as the data used to train them. With access to more varied data, AI can become even more precise in interpreting complex problems and predicting outcomes. The larger the dataset, the better equipped an AI is to arrive at accurate decisions and offer meaningful insights.

By leveraging their data and machine learning algorithms, financial institutions can create highly effective methods to detect fraud quickly and accurately.

Harnessing AI for Effective Financial Fraud Detection

Here are some ways financial institutions use AI fraud detection.

Automating AML and KYC to Prevent Identity Theft

Financial institutions can use ML and AI to automate and improve the effectiveness of anti-money laundering (AML) and know-your-customer (KYC) processes. Having robust processes for AML and KYC can help reduce the risk of identity theft by providing organizations with a way to detect suspicious activities and identify fraudulent transactions. 

  • By using robotic process automation (RPA), or digital robots, to perform the investigation, companies can shorten the time needed for the analysis. 
  • Organizations can use AI to build a digital identity for each customer and gain an understanding of their financial behaviors. They can then use this data to construct individualized risk profiles, allowing better anticipation of potential threats. 
  • Financial institutions can dynamically adjust AML transaction monitoring thresholds and ensure compliance with the law by leveraging data from customer profiles.

By implementing more detailed processes, organizations can ensure that their systems are better equipped to flag any irregularities, thus helping prevent illegal activity. Additionally, having these in place means that organizations can quickly take action when needed, thereby reducing the chances of identity theft occurring in the first place.

Developing Fraud Scores for Transactions

Using AI and ML to develop transaction scores can be an invaluable tool in preventing fraudulent transactions. The AI tool determines transaction scores by analyzing the risk level associated with each transaction based on user information, device data, and location data (including IP and shipping addresses). During the transaction process, these algorithms can detect any suspicious activities or irregularities that could suggest a fraudulent transaction is attempting to take place.

Reducing Chargeback Fraud

By detecting fraudulent transactions in real time, companies can proactively guard against potential chargeback fraud, as identifying the issue prevents future chargebacks of that specific transaction. But transaction scoring cannot distinguish between legitimate and illegitimate chargeback dispute claims or those caused by friendly chargebacks. Friendly chargebacks occur when consumers file a dispute without a valid reason — basically, the consumer uses the chargeback process to dispute a credit card transaction. 

AI can expedite the chargeback resolution process by streamlining the dispute process and providing actionable insights. For example, these tools can provide businesses access to the underlying transaction data, allowing them to quickly monitor activity within their systems and flag any potential irregularities. Furthermore, AI tools can automate tedious tasks, such as document review and data entry, so financial institutions can resolve disputes faster.

Verifying Checks

AI can detect counterfeit checks by analyzing various check design patterns. For example, AI algorithms with advanced image analysis capabilities can identify irregularities in the pattern of a check, which could indicate that the check is not authentic. AI can also verify signatures, an essential step in counterfeit check detection. With AI, it is possible to identify falsified handwriting and signatures compared to authentic samples and quickly detect any forgeries.

Using AI for analysis, companies can easily identify potential counterfeit checks more quickly and accurately than with manual inspection methods. This automated analysis helps reduce both the cost and time associated with fraudulent checks, making it easier to combat counterfeiting attempts efficiently.

AI Fraud Detection – Wrap-up

AI and ML are invaluable tools for fighting fraud. By training AI systems on existing data, financial institutions can improve AML and KYC processes to maintain compliance, develop fraud scores for transactions to detect fraudulent activities in real time, reduce chargeback fraud to save money by proactively guarding against potential disputes, and verify checks through advanced image analysis capabilities to combat counterfeiting attempts efficiently. As a result, AI technology has dramatically impacted anti-fraud measures, making them more efficient and effective than ever before.

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