Fraud Prevention in the Age of Fintech

• 7 December 2019 | 1119 Views | By Mint2Save

No matter how encrypted the money is. Fraudsters have always found there way in. The decade that has witnessed a drastic development in Fintech, is witnessing more smarter ways of getting scammed. This niche of Fintech is widely implemented and have found a thick arena of application.

Although the advancement of Fintech in safety, security, and privacy dimension is perfect, scams are seen to be still performing. Hence, Fintech has innovated itself with strategies and solutions that will prevent fraud and scams to a greater extent. New methods to commit fraud will always pop up, but with these precautionary measures, a lot is saved. 

Here are the ways that Fintech is implementing against the fraud attempts:

Unauthorized access denial with Rule-Based System

When fraud is attempted on the system, usually the fake or duplicate IDs and locations are created. At Rule-Based systems, it gets second to impossible to access any unauthenticated data. The rule-based system has 300 different algorithms just to check that only authenticated user is made to access. There are several factors that sum up to decide whether the person entering the information is the genuine one. Location, time, his past activities and the speed at which the information is entered. Everything is being keenly and thoroughly observed by the rule-based system. 

However, this method could be bypassed by fraudsters. So the implementation of some advance techniques, such as Machine Learning or Artificial Intelligence, should be considered. 

Machine Learning-Based Fraud detectors

It is found that machines that detect fraud human behaviors quicker than humans. With the proper programming, machine flawlessly detects unusual human behavior. The implementation of ML-based fraud detectors is increasing across the globe. In numerous prominent sectors, the ML-based detectors are already active. These detectors are very smart in detecting the location, time and purchase performed at the moment. On the time and activities that the user is performing, detectors can easily identify if the action was human. 

With the help of these two tools, many fraud attempts are already prevented. The algorithms are made smarter in identifying the fraudsters in real-time. ML-based detectors comprise of a wide range of programming and technologies. AI and deep learning are part of Machine learning. 

Discussed below are some common scenarios where fraud happens. And the same is prevented successfully with the help of the aforementioned strategies. 

Fraud Prevention in False Credentials and Claims

With respect to the various aspects of data feeding methods and the analysis of the same, ML detects the fraud attempt. The data is very systemically and sophisticatedly fed in the ML programming that it detects the unusual activity instantly. If the authorized agent or device is not used while feeding the information, the ML signals the concerned authority in real-time. The process is halted and doesn’t get completed unless the authorized member has approved. 

The claims for several insurances are also detected with the used rule-based systems and the ML detectors. People trying to feed the system with false information are detected based on the answers they provide. The pattern of the answer is not performed in a similar set of manner when the information is false. Also, the speed rate at which the answers are given, let the AI know the user’s authenticity. Answers regarding the assets and the health history are cross verified with the respective departments.

Any suspicious activity noticed by AI could spam the user’s profile. The real documents and the verification of user are performed by the respective in-charge members, manually. 

Fraud Prevention in KYC

During KYC your genuine and authenticated information is asked. When you are submitting your real papers there is a high chance of getting your data duplicated. Hence, in the KYC procedure, advanced ML with an AI program is used. The program identifies the false identity provider. Also, the duplication of the same data is strictly prevented. During the procedure, even bank side fraud attempts are made. Hence, logging into suspicious sites is protected from the antiviral sites too. Even if the site seems authentic, the data may be carried for a fraud attempt. To prevent fraud from both the side, ML-based programming is implemented. The keyword of KYC is checked for authentication on every possible site. 

Along with the AI, Human interpretation is carried in the most secured Fintech service providers. The suspicious sites and the duplicated sources are forwarded to the firewall database to prevent it by default. 

Fraud Prevention in Credit Card Usage

Based on the device’s IP address and the location, credit card access is immediately blocked, if found suspicious. Also, a confirmation message of 2-way verification is sent on every credit card transaction. The third-party platform that is accessing your credit card information for auto-debits, has the credit card credentials encrypted. The system itself doesn’t have the real value of credit card or any card for such matters. 

The ML-based programming and the rule-based system, both play vital in the case of credit card transactions. The verification before the payment is made is strict and is mandatory. It is tough for a fraudster to bypass the security. 

The third-party user’s credentials could stake the user to be fraudster’s prey. That remains the user’s responsibility to protect the passwords and other login data like personal questions and answers to the same. 

Fraud prevention in E-commerce

Mostly everyone shops online, which makes eCommerce category, the sought after websites for shopping and window shopping. Over such a trusted platform, fraudsters do show up. Either as a fraud customer or as a fraud seller. Both parties are seen to be attempting fraud in various ways. Sellers are asking for paid reviews and ratings. Whereas the customers are finding out ways to cheap on the payment gateways. 

ML-based programming sorts them out with their user behavior. The fake profile is easily identified by analyzing profile activities. Any suspicious activity spams the profile on the portal. The information that you fed in the profile is keenly observed and supervised. The seller is required to showcase the authenticate documents. Also, the consumer is required to complete his KYC. The verification is made to perform in such a manner that fraudsters couldn’t thrive. 

For fake customers, the payment gateway is made stronger with 2-way verification protection. The CVV number of the card is asked and OTP to the registered number is sent. The fake cards or users are out listed with this verification process. However, fraudsters may manipulate the real user for getting the OTP. Certain measures must be taken from the user’s side while entering the banking details online. 

Fraud Prevention in Loan Assessment

While claiming for a loan, you have to undergo the verification process that is quite strict. But these days, to generate the dummy KYC documents is not a big deal. All the information is available on social media sites. 

Hence the loan dealers are usually contacting your previous banker to cross-check your reliability. Even if the documents are created false, banking verification could not be done in a false manner. Only a real user could be able to fulfill all the requirements. In such a scenario rule-based systems play an important role in identifying the fraud. 

Anomaly Fraud Detection

This method is quite simple yet very effective. The algorithm is designed to categories the things as true transaction and the outliers. The activities that fall under outliers are re-verified and then processed further. These activities are checked with respect to the pace at which they are performed, repetition pattern, location pattern, and the consistency pattern. If any of these parameters fails to satisfy the condition of the transaction, the activity is considered as an outlier. 

This method is easier and quicker as the table of transactions and outliers is composed instantly. The real-time tracking and detecting take place efficiently. 

K-Nearest Neighbor Fraud Detection

This algorithm makes other nearest cluster remember the activities and stay responsible for the same. When suspicious activity is marked, the neighboring clusters are asked to generate the history of the activities. In the case of history is not supporting the current activity in any possible way, fraud activity is suspected immediately. This way, the history of the user allows the frauds to be spotted easily with ML-based and rule-based systems. The history of user behavior readily complies and the neighbor clusters are always active and updating. 

Bottom Line

Although some fraud attempts are still in practice, the Fintech domain has taken its responsibility to spam and block them. It is our duty as a user to take certain essential measures so that we can enjoy the perks of Fintech happily. Without the user’s data, at least primary, no fraud is easily possible. Being responsible and alert as much as the algorithms are, will save us from fraudsters. There are certain sites that seem as much authenticate as the real ones are. However, they are not secure and your device’s antivirus program will shut it down automatically. You will be asked to manually access such sites, on your own risks. However, without checking for the genuine site, users are advised, not to feed any data on the third-party site. Fintech has already extended its capabilities farther than the fraudsters. Only users can allow fraudsters to have eased by making their personal data easily accessible.