How to Prevent Ghost Loans Using AI: Actionable Strategies (2024)

Ghost loans are the ones that are fake loans documented in the loan books and exist only on paper. The challenges of ghost loans can crash an individual’s credit report and erupt into the public domain when people realise that their credit scores have dropped. Their credit reports could contain inappropriate information about loans they had never taken when precisely analysed.

An overview of AI & bank fraud detection

With cybercrime accounting for damaging the global economy by about 0.8%, fraud is becoming a more serious threat. The best way to combat this is by utilizing AI and detecting fraud before it even happens. The ability of AI to analyze large amounts of data in order to uncover fraud trends. This data can then be used to detect frauds in real-time.

Machine Learning is the exact process that “learns” patterns. If it is executed properly, ML can be used to differentiate between fraudulent and legal transactions in milliseconds.

Some ghost loan victims who reported frauds are:

The victims of ghost loans include one of the most-searched celebrities, Sunny Leone. Sunny Leone’s credit score dropped to 20 percent for a trivial Rs 2,000 that showcased as an unpaid loan, by misuse of her PAN (permanent account number) and other personal information, directing the loan to some other person.

Sunny Leone is not the only one who has stated ghost loan frauds. Journalist Aditya Kalra tweeted about damaging revelations in his credit report previously. He saw a loan paid out by IVL Finance involving his PAN number, name, and addresses in Uttar Pradesh & Bihar, and he had no idea about the same.

So, relying on the repayment record, the penalties for the ghost loan target include a quick fall in credit score, reduced credit limits, and denial of credit if there is a non-payment on the fake loan.

Sunny Leone and Aditya Kalra’s examples show the lack of cross-checks in KYC procedures that fintech stakeholders should explicitly track.

Data privacy at every level is increasingly becoming significant

Aadhaar authentication is a prime example of data privacy and security. Aadhaar number and other facets comprising biometrics are submitted to the robust Aadhaar system for online verification through accessible documents.

Story of India Aadhar stack allows leveraging biometric matching to resolve issues with ghost loans for consumers in one shot. This scenario means that consumers come first in every identification process, which fintech players should prioritise. And the best option is to make sure you put advanced technology to work at full throttle.

Different kinds of frauds in banking and financial sector

Fraudsters find loopholes and invent new ways to deceive innocent customers constantly. Some of the most common kinds of frauds that customers fall prey to are-

Phishing – Phishing happens when a group of cyber criminals creates a fake bank website in order to deceive customers. Scam emails are sent that link users to false websites where they are asked for personal information and misuse it.

Identity theft – Fraudsters seek to acquire access to your bank account by obtaining crucial personal information such as your date of birth, passport number, aadhaar data, PAN numbers, and so on, and then carrying out fraudulent transactions.

Money Mule Scam – Victims are duped into laundering stolen or illegal money through their bank accounts in Money Mule scams. Customers are contacted via emails, chat rooms,etc and they are persuaded to deposit money into their bank accounts in exchange for tempting rewards.

Vishing – Imposters posing as bankers,insurance agents, government officials, etc call people on the phone and reveal a number of consumer details, such as the customer’s name or date of birth to gain their trust and ask for private information such as passwords, OTPs, PINs, and CVVs which are then used by the scammers to access the customer’s accounts.

Besides these methods, there are numerous other ways scammers trick people such as SMS spoofing, SIM swap frauds, Juice Jacking , Aadhar based payment system frauds etc which one must be aware of.

Here are some of the interesting statistics related to the impacts of financial frauds

Around 3 to 4 percent of digital onboarding will end up in ID theft if explicit KYC norms are not in consideration. On the other hand, the customer acquisition funnel drops by a fifth if higher accuracy on individuals’ names and addresses are taken into account, as quoted by the founder of an ID authentication solution provider company.

However, the same founder quoted earlier and admitted that their facial recognition algorithms could merely match the face across the PAN, Aadhaar, and selfie since the image quality diverges across these documents. He added that they don’t even attempt matching faces across IDs. Instead, they ensure the selfie captured is that of the borrower.

If someone’s credit score got reduced by 50 points at least, that is a 200-basis point effect. So, if they now request a home loan of Rs 1 crore ($13,387), they will have to pay out an interest rate of 12 percent rather than 10 percent. They will see a yearly loss of Rs 2 lakh ($2,678).

Read more: Fraud Impact Report in the US (2023)

How are banks using AI for fraud detection

Banks have already begun implementing approaches that try to prevent fraud before it happens rather than waiting till after fraud occurs to act. To effectively prevent fraud, AI can be used to group consumers, perform risk profiling, etc. Furthermore, there are some other ways banks can use AI for fraud detection as well:

• Know Your Customer (KYC): KYC done by using AI can verify ID, and perform biometric recognition in an instant.
• Fraud Investigation: With a well-trained AI model, the list of transactions that need manual investigation can be cut down to a manageable number.
• Building purchase profiles: Using machine learning to sift through data and build customer profiles is a useful way to provide an up-to-date snapshot of an account’s activity.
• Developing fraud scores: Using previous transaction data, future transactions can be given a fraud score. These fraud scores can be used to flag, reject or approve a transaction.

How HyperVerge’s technology can solve frauds like ghost loans

Our compliant Digital and Video KYC solutions comprise explicit ID digitalisation (OCR), face match, and liveness with accurate fraud checks.

Through HyperVerge Fintech Platform, enterprise companies can check the applicant and face data against millions of prevailing customers. So, companies with cutting-edge techniques and methodologies can instantly recognise fraudsters.

Furthermore, HyperVerge enables a face-based de-dupe system that halts fraudulent applications at the initial step of the application procedure right in seconds.

With KYC technology solutions and identity verification processes, HyperVerge has validated 600+ million identities. The facial recognition solutions by the company can match a face amongst hundreds of millions of data sets on a real-time basis.

Moving forward with advanced technology solutions resolving ghost loan issues

Given the complexity of financial and banking industry problems, a viable solution is to accept advanced technologies and progressive methodologies. With HyperVerge’s modern technology solutions, one can solve frauds, including fake and ghost loans.

With AI solutions and products driven by HyperVerge, the company empowers deep-learning networks to power applications for its clients in banking and financial services.

Would you like more information about the fintech products offered by HyperVerge and how you can set them in your workflow? If so, write to us at contact@hyperverge.co. Else to connect with us swiftly, fill out our request form right here.