As business grow and industries evolve, frauds grow too. The world is seeing an elevated number of advanced frauds and breaches in various walks of businesses like ecommerce, BSFI (Banking, Financial Services and Insurances), tourism and travel, healthcare etc. the shift towards online functioning and the concept of online customer services and retention has given way for a whole different spectrum of online frauds. The key to all these frauds being customer identity data that is PII (Personally Identifiable Information) has become a very vulnerable object and is what fraudsters need to commit their crimes. Businesses have been scuffling to find effective fraud detection and prevention solutions to incorporate in their working set.

Fraudsters look for ways to stealing crucial information of victims which is termed as PII (Personally Identifiable Information) in order to access services online and basically steal money or kind. The most common places where such frauds are seen are the banking industry, where fraudsters take loans in the names of others who are the victims or max out stolen credit cards, the ecommerce industry, the travel industry etc. there is another type of fraud known as synthetic identity fraud which is unique because of the techniques fraudsters use to create new identities which are hybrids of stolen original information and combined fake information. Such identities are very difficult to find and makes it very hard for businesses to blame anyone because of its unidentifiable nature.

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The first part of the equation being fraud detection and prevention is finished by its latter which is effective identity verification. There are various methods of identity verification which include knowledge-based Authentication (KBA), two factor authentication (2FA), Biometric verification, machine learning based identity verification etc. out of the above, machine learning and artificial intelligence has been proving to be the most effective tool to identity verification and thus reducing the turnouts of breach.

Knowledge based Authentication

Knowledge based authentication as the name suggests is a technique of identity verification where the customer is asked a few relevant questions relating to his identity and such specific security questions provide access to the customer’s online services. This type of identity verification is user friendly and is also much preferred by users. But the sad part is its high rate of failure. KBA is such a platform where fraudsters can easily ascertain answers to questions through common access sources like social media etc. and there is a very high chance of identity theft. This might also lead to users finding the system intrusive for questioning.

Two Factor Authentication (2FA)

Two factor authentication is a type of identity verification technique which not only uses the usual username and password duo but also an additional user-dependent piece of information like an OTP ( One Time Password) which is sent as an SMS to the registered mobile number or a confirmation mail etc. this technique is much efficient than KBA because of its two factor status, it also helps with effective verification for account opening and password resets. the problem with 2FA is that it becomes very vulnerable when an inattentive user does not overlook his payment gateway details and approves an attacker’s transaction request. 2FA is also prone to a lot email and SMS spoofing and man in the browser attacks.

Biometric Verification

Biometrics based identity verification and authentication is where a user’s biometrics are used for verifying the identity and giving access. This does not require a written password and is usually found very convenient by users. What makes it unsuccessful is the risk of biometric theft which comes with it. It is quite easy for scammers to just steal a picture of the user from social media or secretly record the user’s voice and misuse it to gain access to various sites.

Machine learning based Identity verification

Machine learning and artificial intelligence-based identity verification is a very strong and successful technique used by most businesses today for effective identity verification and efficient fraud detection and prevention. This type of identity verification technique obtains thousands of datasets from basic sources like phone numbers, emails, addresses, social media accounts, IP addresses etc. and applies machine learning in order to perform predictive and behavioral analysis on the data. This is a step further than traditional identity verification because it more successful in not only identifying fraudulent users and scammers but also predicting them in order to prevent breach.

When we look at the fintech industry or more specifically the BSFI (Banking, Financial Services and Insurances) industry, it is very important for the companies to have effective fraud detection and prevention systems to prevent money laundering, identity thefts, account and transaction frauds etc. verifying customer’s identities and creditworthiness becomes very important when it comes to lending because there are high changes of identity breaches leading to bad loans in the lending organization. Machine learning plays a very important role in preventing this because it uses a vast set of data to draw correlations and assess risk. It also incorporates itself quickly with business solutions and learns by itself thus improves accuracy and stays updated.

Thus, machine learning based identity verification is proving to be the best identity verification technique to be used by businesses because of its effectiveness and higher rates of success and satisfaction. Visit to book yourself a demo with us.

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