How AI and Machine Learning Improve Fraud Detection in Fintech

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Internet fraud is a threat in our various financial institutions, and many fintech companies have fallen victim to this scam. Detection of these attacks comes in two ways: through inconsistent traditional methods or through the use of ever-expanding artificial intelligence mechanisms.

Traditional methods, such as the rule-based method, are still widely used by most fintech companies in contrast to AI. At the same time, some are adapting to take advantage of machine learning and artificial intelligence, improving ways to detect fraud. Therefore, we come to the question below.

How has AI and machine learning improved fraud detection in the fintech industry? What specific applications does this technology touch, and what mechanisms does it complement? We have collected key areas where the application has become very beneficial.

Related: Fraud Detection in Fintech: How to Detect and Prevent Fraud in the Lending Industry

Fishes out identity thieves before they break into a server

Identity theft is common, but with the rise of AI, the effect on the fintech industry has been drastically reduced. Users are bound to become more vulnerable to fraud in this area as activities such as creating accounts, submitting applications or filing tax returns become more computerized. Digitized data is easier to access, giving identity thieves more opportunities to penetrate the server. For example, identity thieves can create accounts in someone else’s name, access that person’s benefits, or even steal their tax return using the stolen identification information. When it comes to mitigating these anomalies, AI comes to the rescue. AI-powered identification systems for identity theft such as pattern recognition are quite good at reducing the risk of such frauds and detecting them early. Depending on the circumstances, the models can identify suspicious transactions, behavior or information in the delivered documents that do not fit the customer’s usual behavior patterns, thus averting a possible danger.

Rapid detection of credit card fraud through identification of unusual transactions

Customers can secure their credit card and account information in various ways, such as using virtual private networks or virtual cards or checking website certifications. But with fraud tactics becoming more sophisticated, organizations handling credit card transactions and transfers need to scan them to avoid risk. AI methods such as data mining have been provided with a significant data set that includes both types of transactions (i.e. card transactions and transfers) to be trained to detect fraudulent behavior. By analyzing it, the model can detect fraud red flags. Are there possible ways that the illegal transaction can be flagged and detected in time? Yes, for example a rapid increase in the customer account’s weekly or monthly transaction values ​​or a purchase made at a store that does not ship to the account holder’s country of residence. All these can be quickly detected with the help of AI, and fraud can be reduced in time to avoid ongoing losses.

Related: How artificial intelligence is changing the cybersecurity landscape and preventing cyberattacks

Detection of money laundering among account activities

Fintech companies and banks use deep learning AI algorithms such as neural networks to uncover undetected connections between criminal behavior and account activity. Money laundering is difficult to identify with traditional approaches as the signs are often quite subtle. Nevertheless, since the rise of artificial intelligence, every action is carefully considered because such practices usually involve large sums of money and are carried out by organized criminal organizations or entities that appear to be real.

Despite a thorough mechanism in place, individuals are undoubtedly prone to error. It becomes challenging to detect money laundering-related actions among cover activities because they leave no room for suspicion, but AI has been at the forefront of detecting such. For example, an incorrect transfer of funds may be the key to uncovering a set of illegal activities. Additionally, there are situations where multiple transactions on a person’s account come together but do not appear legitimate when scrutinized. These patterns can be quickly identified by AI systems put in place, and fraudulent activity can be prevented in time.

Early detection of fraudulent loan and mortgage applications

In recent times, most fintech companies and banks rely heavily on fraud detection AI technologies to assess loan and mortgage applications from fraudsters. It is a crucial part of their risk assessment and helps the analysts in their daily work. Using machine language, they can extract relevant data from the applications and analyze it using a model developed through a data set that includes both legitimate applications and those flagged as fraudulent. The essence of AI in this area is to detect trends that are likely to lead to fraud, so that alarms can be quickly raised, whether they are accurate or not. This allows the responsible analyst to investigate further, which can either lead to an acquittal or the prevention of fraud. It also helps fintech companies predict the chance of a customer committing fraud, as it can help predict trends by examining consumer behavior data.

Related: Digital Twins: AI & ML Transforming the Fintech Landscape

Banks and fintech companies still sometimes believe that rules-based methods are safer and simpler. Traditional rule-based methods and AI tend to support each other, but are likely to change more quickly. This is due to the complexity of rules-based systems that have their limits and the fact that fraud efforts are becoming more sophisticated and dynamic than before. The rule-based method is a losing battle since it necessitates the creation of new rules every time new patterns emerge. Instead of constantly being one step behind, fintech companies can actively anticipate fraud by using AI and machine learning techniques to ensure their financial integrity.

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