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Detecting Frauds with ML and AI

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  • Difficulty Level : Easy
  • Last Updated : 30 Sep, 2022
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We live in an era of technology, where news and information can travel much faster than we could think of! With the advancement of technology, the scammers are also smart enough to use this power and deviate huge masses. The fraudsters are using advanced technologies such as Artificial intelligence to play around the protective systems. 

Many cases have been there when scammers generate a similar voice to many authoritative people and scam the innocents. As we say, every coin has two sides, the technology is getting advanced day by day, and so are the fraudsters with the help of this advanced technology. Every technology has some of the other gaps which these people use for their undue advantage.

Is there a way to detect and prevent such frauds by using AI and ML?

Technically, there is no 100% guarantee that such scams can be prevented, as these people exploit the gaps of advanced technologies, so filling the gaps might be a better option for us. If we cannot prevent such scams, we can surely protect ourselves from such scams using AI and ML. Earlier ‘Rule-based detection method’ was used to detect such frauds, but with the increasing intelligence of such fraudsters, this method seems to be falling out. With the right use of advanced technology such as AI & ML, a much better preventive analysis could be done. To further understand the difference between machine learning and artificial intelligence, comparing them:

  • Accuracy: Rule-based method accuracy is around 30-50% since, in the rule-based method, the system does not learn from the attacks, it just checks for the conditions defined in the program. Out of 100 attacks, only 30-45 attacks were blocked, others were successful to breach the data. The accuracy of detecting frauds with machine learning is greater as, it is not conditioned to work on conditions, rather it searches for the similarities in these scams, and tries to find a common pattern.
  • Speed: Since in a rule-based method, the detection is done based on some rules and conditions, it takes generally a bit longer to detect fraud using this method, as compared to the ML & AI.
  • Effectiveness: The rule-based detection was effective as of the late 1990s and early 2000s, but with the evolution of technology, the attacks and scams have leveraged in their system and also quantity. The rule-based detection seems to be falling out as of this new decade, and more companies are using the ML & AI to protect themselves against such frauds.

Also, a very important thing to note here is that the rule-based method can only detect if any fraud/scam attack took place, but with machine learning and AI, we can predict the occurrence of such events based on tracking individual past user interactions and can surely prevent it in a much better way.

How ML and AI systems work to detect the frauds?

The system works on four different models, which can be used majorly to detect and prevent such attacks. The models are as follows: 

1. Unsupervised learning: This model is based on real-time data synchronization, where the system can learn and train itself, gaining more knowledge from the users. The model creates a pattern based on user interaction, and this makes it easy to detect the peculiarities associated with the model. This model is considered best for fighting financial and transactional frauds. 

2. Semi-supervised learning: The model is trained upon labeled as well as manual data. The system is fed with labeled data, which is generally in a smaller amount. The programmer then with the help of unsupervised learning collects similar data and feeds manually into the model. The patterns formed by this model are quite useful as they easily differentiate between the labeled ones and the manual ones. 

3. Supervised learning: The supervised model of learning is much similar to the rule-based method. The only difference is that we can have a larger number of inputs rules, as compared to the rule-based method of detection. This method of learning is used majorly in financial fraud detections. The only limitation is that the system checks cannot go beyond the previously available data in which the system is trained. 

4. Reinforcement learning: In this model of learning, the system is constantly learning and updating its algorithms based on user behavior and the conclusion it makes. This is a model in which the system is invariably trying to learn and find an ideal model and algorithm.

Two Prominent sectors associated with Frauds

1. Banking Sector: Scammers are generally looking for money to steal online, and they majorly target wealthy individuals or companies. Their main aim is to look for card numbers, CVV numbers and any sensitive details concerning a banking transaction. Use of machine learning to deal with such scams could be an ideal solution, as it may provide us the following details:

  • Check whether the money transferred, is in legitimate ways or not. To know the source, destination, and purpose of such transfers. Blacklisted accounts can be tracked in real-time, using the models of ML, which will thus help to catch such criminals, based on the patterns.
  • The models can also be used to check up on an individual’s credit score. Based on the previous loans/repayments/salaries/transactions, such a model can predict the amount of money to be given in loan, and whether it can be re-payed or not.
  • Prevent the banking sector to fall, as many banks have fallen due to no proper regulation and functioning of the funds. The companies can be analyzed based on previous data available before giving them loans if in any certain conditions they convert into an NPA (Non-Performing Asset). The models if trained properly can also check for the continuity and stocking of the flow of funds, which helps a nation to keep its economy running, by eliminating the stocking of funds.

2. E-Commerce: Scammers who are mostly interested in gaining user sensitive information are into E-commerce frauds. Since many of our sensitive information such as card number, CVV, address, phone number, etc., is required to place on such sites, scammers try to hack such information. For each of the advantages listed below, the ML model should be trained very accurately and should be scrutinized for all possible errors.

  • Using ML models, we can prevent fake buying attacks and block the blacklisted IP addresses. Also, a common user-specific pattern of purchasing can be found out, which can help the E-commerce companies in providing better buying options.
  • The fake reviews that are deliberately placed on any product can be erased and genuine reviews can be shown to the customers, which will, in turn, result in more purchasing power of the consumers.
  • Many fake products and services can be recognized and can be removed, which are placed to lure people by giving extra discounts, in turn asking their sensitive information.

Thus, with the advancement of technology, the scams and frauds have also taken a huge portion of the internet today. With many people trying to inject more frauds on the internet, very few are using advanced technology to fight such scams. Thus the right use of Machine learning models with Artificial intelligence can prevent major cyber scams and would create the online markets a better and safer place.

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