If you have ever watched the movie ‘Catch me if you can’, you must have realized how smartly the lead actor uses false checks to commit sensational frauds. Such movies perfectly picturise the concept of banking and application frauds. Note that fraud is an unlawful activity, and in India, the fraudster is liable to hefty fines or even years of imprisonment. Application Fraud is one of the several types of identity frauds where an applicant uses their own name but makes an application for an account, policy, service or insurance claim containing false information for the purpose of influencing the outcome of the application.
Today, banking fraud has developed into a sophisticated business that operates by manipulating the multiple loopholes prevalent within a financial institution. The digital world, with its anonymity and ease of database access, provides a perfect setup for a fraudster to commit the crime than ever before. The fraudster creates or robs an identity, establishes a legit credit history, wins the trust of the lenders and finally when the money is in, puts up their biggest disappearing act.
Due to the extreme competition and the fear of losing out on customers to competitors, many banks are accepting account opening and loan application online via digital devices, even exempting the applicant from actually being present at the bank office. This cuts short the time available for conducting thorough due diligence on a customer. While reduced paperwork has saved a lot of time (and even the trees), it has disabled financial institutions to verify applicants in person with the use of photo IDs such as drivers’ licenses, passports, and other documents. On the other hand, it is argued that the digital platform is more secure and concrete than paper because apparently, digital channels draw a more detailed and complex view of the applicant using checkpoints such as device fingerprint, IP address, geolocation and more.
Application fraud often starts with identity theft. There are three key types of frauds: identity theft, identity manipulation, and synthetic identity theft. Of these, synthetic theft is most difficult to identify. Synthetic identity theft is a type where the applicant creates either a completely new ‘faux identity’ or strips the identity off a deceased person. In this way, no one can raise a complaint against such theft which makes it so hard to spot.
How Do Fraudsters Give Personality to A Fraudulent Account?
Once a fraud identity is created, a fraudster uses various methods to add credibility to the account. For instance, the fraudster uses the newly formed identity to apply for credit cards and loans. The lender submits the information to the credit bureaus. If there is already a file for this account then a score is sent, if not, a new file is created. Whatever the case, this helps add substance to the identity and makes it look more real. The fraudster can also add themselves as an authorized user on an existing real account. Once a credit score due to the association is developed, the fake user can separate itself to form an individual file.
Measures to Detect & Prevent Application Fraud
In order to prevent fraud from happening, there should be a validation at multiple levels. Identity verification should be performed to confirm whether an applicant’s details match historical records derived from credit bureaus and other sources. It is important to KYC before you extend your contract to them. Conduct simple cross-checking techniques like PAN verification which also help link different aspects of an account to ascertain the identity. IDENCHECK is an application from CRIF that allows you and your branches to verify KYC details of your customers against many Government databases at a click of a button. Identity authentication helps verify further if the person is the actual owner by asking them secret questions only they would be able to answer. Finally, by conducting device verification, you can analyze whether the same device was used to apply for multiple accounts or whether the IP seems blacklisted, etc.
Identification and Anti-Fraud Solutions from CRIF
One of the more contemporary and advanced antifraud solutions can be to use the expertise of machine learning. Machine learning finds and learns from patterns in the data and can work on scenarios learned from past experience. ML can detect whether payments from the same source is being used to pay unrelated accounts? Whether the same device being used to access and/or make a payment on what appear to be unrelated accounts? Etc. Sherlock Lending, CRIF’s anti-fraud solution for lenders is empowered with features such as Intelligent Search leveraging on matching, Network and Machine Learning algorithms. With SHERLOCK you can:
- Control fraud rates better
- Lower the review time for suspicious cases
- Lower the operational cost with higher returns
- Configure and design ML rules yourself
- Enjoy connectivity to a vast & ever-expanding data network