Tuesday, June 22, 2021

6 Ways AI is Fighting Back against Identity Theft and Fraud

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Identity theft is still a serious issue today. Admittedly, the rise of contemporary technology and the myriad advancements it has brought to the table have probably made the situation much worse as thieves no longer require physical access to your data to commit fraud. They can often do a lot of damage by jeopardizing your identity and working from afar, and they can accomplish most of it without even speaking to you.

However, 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 some of the most significant recent events, 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 has shown to be an excellent tool for assisting human operators in this regard and ensuring that their jobs are completed correctly and according to all requirements. It may even be able to completely replace the task of a human operator in some cases, but this is still a work in progress. 

Here are a few ways AI helps to reinforce measures against identity theft:
 
 

1. Decrease False Acceptance 


If your fraud detection tools are not sensitive enough, you risk turning away legitimate clients. This is the basis behind false rejection. Installing a system that provides focused and relevant monitoring is critical. Since criminal tactics are constantly changing, security systems must adapt as well. This allows you to stay one step ahead of the bad guys.

False rejection, which essentially describes results wrongly indicating certain conditions or attributes to be present within a system, have plagued various tech-related industries for some time now. Financial institutions and e-commerce platforms often experience transactions being wrongly flagged as suspicious or legitimate consumers being incorrectly identified as suspicious parties. On the other hand, false acceptance also mean that fraudsters get past security measures and cause damage. The key is in the intelligence of the technology applied to accurately identify and distinguish legitimate users or cyber criminals.

Rule-based fraud detection is not enough, which is why machine learning has pushed the effectiveness of reducing false positives. Machine learning relies on intricate mathematical models from base data and algorithms to make precise predictions, as opposed to being programmed to perform specific tasks. There's flexibility involved, where systems learn from data instead of carrying out mindless commands. This technology can be used to identify fraudulent and legitimate behaviour.  
 
  

2. To Stop Account Opening Fraud


Considering account opening fraud is frequently linked to data breaches at other banks or FinTechs, banks that do not quite have the necessary data to distinguish fraudulent consumers can make it difficult to distinguish the situation. As most of the data are recorded in the traditional way (mostly on paper), this is where AI and machine learning can help. Banks may utilize machine learning and Artificial Intelligence to better evaluate and understand client behaviour, giving them a more complete picture of how legitimate consumers behave. This strategy depends on algorithms to identify users based on accessible data, and the amount and type of data that financial institutions may collect for fraud prevention are important to the effectiveness of these systems.

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 can give banks and clients advantages such as speed and convenience. Users are frequently unaware of these identification checks, which eliminate the need for them to supply more information and reduce the time it takes to authenticate new accounts.

An example of such algorithms used is related to biometric security. Biometric security, formerly a piece of science fiction, is now an industry standard for helping ensure that possible fraudsters are caught red-handed.
 
 

3. Analyzing Telecom Networks 


In the telecommunication industry, Artificial Intelligence is generating tremendous growth. AI can quickly process and analyze 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 as fintech brings rapid innovation and flexibility, while network operators provide a powerful access to market with their marketing and distribution capabilities that facilitate their partners deployments. Some Fintechs monitor user transaction data to detect fraud patterns. The more data they have, the more accurate their fraud detection engines. These algorithms figure out how to distinguish between normal and aberrant patterns. As a result, they can spot and examine anomalies that could indicate fraudulent activity.


4. Improving Accuracy Over Time


Most AI systems rely heavily on data collecting and analysis. Thus, it is crucial to pay attention to the data that financial institutions provide to their analytical engines. With the appropriate method, the precision of the results might improve dramatically over time. However, this can only happen if the system has enough data to learn from. It is also critical to ensure that the data is accurate and current, as well as to remove any old data sets that could cause the analytical engine to become confused.


5. Identifying Fraudulent Charges


The concept of "anomalous transactions" (ATs) differs from that of conventional banking exchanges. This 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). This dynamic password is only good for one digital device login session.


6. Protecting Air Passengers


AI can give a more precise and effective process than humans can. AI-assisted identity verification for flying passengers can make the procedure go more smoothly. Passenger data is kept safe, and the systems can do verifications at a faster rate.

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