Identity theft is a serious issue today with the advancement of technology. The rise of AI technology has made this problem worse as thieves no longer require physical access to your data to commit fraud. They can often do a lot of damage by jeopardising your identity and working from afar.
Technology has been employed to tackle this problem, and we are witnessing some significant successful improvements in this area, particularly from credit card firms and other market players. Artificial Intelligence has been at the forefront of the cybersecurity industry, and anybody concerned about their security should keep an eye on that field. Those working in the financial sector in any capacity, of course, have an even greater motivation to take note of how things function.
AI algorithms have shown to be an excellent tool for assisting human operators in this regard and ensuring that their operations are done effectively and according to requirements. It has the potential to even completely replace the role of a human operator in some cases, but this is still left to further discussion.
Here are a few ways AI technology helps to reinforce measures against identity theft and synthetic identity fraud:
1. Decrease False Acceptance
If your fraud detection tools are not receptive enough, you may risk turning away legitimate clients. This is the basis behind false rejection cases. Implementing a system that provides focused and relevant monitoring is critical to identity theft protection. Since criminal tactics are constantly evolving, security systems must adapt to meet these evolving threats.
False rejection – which essentially describes results wrongly indicating certain conditions or attributes to be present within a system, has plagued various tech-related industries for some time now. Financial institutions and e-commerce platforms often experience transactions being wrongly flagged as suspicious. In other cases, legitimate consumers are incorrectly identified as suspicious parties. On the other hand, false acceptance also means that fraudsters are able to bypass security measures and cause damage. The key factor lies in the sophistication of the technology applied to accurately identify and distinguish legitimate users.
Rule-based fraud detection is not enough, which is why machine learning has become the driving force behind the effectiveness of reducing false positive cases. Machine learning relies on intricate mathematical models gathered from base data and algorithms to make precise predictions – as opposed to being programmed to perform specific tasks. There’s some level of flexibility involved, where systems learn from data instead of carrying out simple and linear commands. This technology can ultimately be used to identify fraudulent and legitimate behaviour.
2. To Stop Account Opening Fraud
Account opening fraud is frequently linked to data breaches for financial institutions and FinTech companies. Banks that do not have the necessary data to discern between fraudulent customers and legitimate ones can prove to be difficult. As most personal information data is recorded traditionally, AI and machine learning play a critical role in preventing potential fraud cases. Banks may utilise machine learning and artificial Intelligence to better evaluate client behaviour which gives them greater insight into how legitimate consumers behave. This strategy depends on algorithms to identify users based on accessible data. The type of data that financial institutions may collect for fraud prevention is also important to the effectiveness of the AI system.
Such algorithms could also be used to better assess fraud risk before a customer’s application is approved or rejected. These techniques are frequently combined with eyeballing by banks, who perform final checks on potential users who have been marked as high-risk. These solutions offer banks and clients the benefits of a speedy and convenient process. Since users are frequently unaware of these identification checks, this eliminates the need for them to supply more information and reduces the time it takes to authenticate new accounts.
An example of such algorithms used is close related to biometric security protocols. Biometric security, which used to be a thing of fiction, is now an industry standard for ensuring that possible fraudsters are caught before they can commit any kind of fraudulent transaction or scam.
3. Analysing Telecom Networks
In the telecommunication industry, Artificial Intelligence is gaining much traction as well. AI can quickly process and analyse large amounts of data to extract relevant information. Furthermore, AI and machine learning make it easier to create algorithms that can detect fraudulent network activity. Fintechs and mobile network providers complement each other well with fintech companies bringing rapid innovation and flexibility, while network operators provide powerful access to the market with their marketing and distribution capabilities. Some Fintechs monitor user transaction data to detect fraud patterns. The more data they gather, the more accurate their fraud detection capabilities become. These algorithms figure out how to distinguish between normal and abnormal patterns. As a result, the AI system can easily detect and examine anomalies that could indicate potential signs of fraudulent activity.
AI-powered fraud detection not only safeguards the interests of telecom companies but also protects customers from potential harm. By quickly identifying and addressing fraudulent activities, AI helps in reducing losses incurred due to fraud and preserves the integrity of the telecom network.
However, it is essential to strike a balance between network security and customer privacy. Telecom companies must ensure that the data collected and used by AI systems comply with privacy regulations and is appropriately safeguarded against unauthorised access.
4. Improving Accuracy Over Time
Most AI systems rely heavily on data collection and analysis. Thus, it is crucial to pay attention to the data that financial institutions provide to their analytical systems. With the appropriate method, the precision of the results might improve considerably over time. However, not all data points may be equally valuable for training the AI system. Identifying and only including the most relevant data is crucial to avoid overfitting, where the AI system becomes too specialised in its predictions and performs poorly on new or unseen data. It is therefore recommended to remove any old data sets that could cause the analytical system to become clogged with irrelevant data.
Financial institutions must establish robust data governance practices to ensure data integrity and improve the precision of AI systems over time. This includes establishing clear data policies, implementing security measures to protect sensitive data, and conducting regular audits to monitor data quality.
5. Identifying Fraudulent Charges
The concept of “anomalous transactions” (ATs) differs from that of conventional banking exchanges which makes them difficult to spot. Remember that being different does not always imply that you are being deceived. Financial institutions, on the other hand, must safeguard against illicit activities such as money laundering. AI provides an improved approach for distinguishing between fake and real AT. When the AI identifies an AT, it sends the account owner a one-time password (OTP) prompt. This temporary password is only good for one digital device login per session.
6. Protecting Air Passengers
Protecting air passengers is a critical aspect of aviation security, and AI has emerged as a powerful tool in enhancing the efficiency and accuracy of identity verification processes. Traditional methods of passenger verification often involve manual checks by airport staff, which can be time-consuming and may lead to human errors. However, AI technology has proven to be more effective and precise in handling this task.
AI-assisted identity verification systems employ various advanced techniques to streamline the process and ensure the safety of passengers. One of the key advantages of using AI in this context is its ability to process vast amounts of data in real-time. By integrating with databases containing passenger information and cross-referencing it with other relevant data sources, AI systems can quickly verify a passenger’s identity, travel history, and any potential risks associated with them.
Bottom Line
Identity theft has become a significant concern in the modern technological age, and the rise of AI technology has further exacerbated the problem by enabling remote and sophisticated fraud. Fortunately, the same AI technology that poses challenges can also provide essential solutions in the fight against identity theft and synthetic identity fraud.
AI algorithms have proven to be valuable tools in assisting human operators — leading to effective fraud detection and prevention. Through machine learning, AI systems can reduce false positive cases and accurately distinguish between legitimate users and fraudsters. Additionally, financial institutionscan better evaluate client behavior and assess fraud risks with the help of AI. This helps them in streamlining processes and enhancing security in account opening procedures.
