A bank statement is a document that shows all the transactions made on a bank account, including deposits, withdrawals, and payments. It’s commonly used by lenders as a way to verify an applicant’s income and financial stability before approving a loan or credit.
However, as technology has advanced, it has become easier for fraudsters to create fake bank statements that appear legitimate. These fake bank statements can be used to falsify an applicant’s income and financial stability, making them appear more creditworthy than they actually are.
The use of fake bank statements in loan applications can have serious consequences for lenders and borrowers. Fake statements can be used to inflate income, hide financial liabilities, or misrepresent the borrower’s financial situation. This can lead to lenders extending credit to borrowers who are not actually able to repay the loan. Additionally, lenders may be exposed to legal liability if they fail to properly verify the information contained in a fake bank statement. Borrowers may also be racked with debt and legal trouble.
Lenders are thus constantly looking for ways to detect fake bank statements and protect themselves from fraud.
This can include manual verification of statements, using data from the statement to check against other sources of information, which is time-consuming and error prone.
In this post, we’ll cover why fake bank statements are an important issue for lenders to solve, and how using AI and machine learning technologies such as Nanonets can help.
How do lenders spot fake bank statements?
Verifying bank statements can be a time-consuming and labor-intensive process, especially when dealing with a large number of records or statements. Typically done manually, the following steps are involved in spotting fake bank statements:
- Inconsistencies or irregularities in the information on the statement: One way to spot a fake bank statement is by looking for inconsistencies or irregularities in the information on the statement. For example, a statement that shows large or unusual transactions, has spelling mistakes, inconsistent font sizes and types, is a potential red flag.
- Compare the statement with other documents: Lenders may also compare the statement with other documents provided by the borrower such as ID or pay stubs, to ensure the information provided matches and the statement is not fake.
- Check for authenticity: Lenders may check for authenticity by contacting the bank listed on the statement and verify if the statement is genuine or not.
- Check for mismatches with the bank records: Lenders can also cross-reference the information on the statement with the bank’s records to ensure that the statement is legitimate.
- Use specialized software: There are also specialized software and services available that can help lenders detect fake bank statements by analyzing the document and comparing it to a database of known fake statements. Some of these methods involve:
- Data extraction and analysis: Lenders may use specialized software or services to automatically extract data from bank statements and analyze it for inconsistencies or irregularities.
- Fraud detection software: Some lenders use specialized fraud detection software to scan bank statements for patterns or characteristics that are commonly associated with fake statements.
It’s important to note that while these methods can be effective in verifying bank statements, they can be time-consuming and labor-intensive. This is where machine learning, coupled with human judgment, can be helpful.
Despite the above methods, fraudulent and tampered documents can be undetectable to the human eye. Manual reviews are also time-consuming, error-prone, and utilize company resources intensively.
This is where automation technologies such as Nanonets can help. Nanonets is an AI-based Optical Character Recognition (OCR) tool, which can help automate data extraction from various kinds of documents.
Nanonets can extract data from bank statements at scale, making it possible to quickly and accurately verify large numbers of statements. The platform can be used to model, identify and flag suspicious statements, and even to automatically check the information on the statement against other sources of information. This can save lenders a significant amount of time and effort, and help to protect their customers from fraud.
Automating bank statement data extraction using Nanonets provides many benefits, including:
- Increased accuracy and consistency of data extraction, as AI-powered technology can identify patterns in the data and extract it accurately.
- Reduced time, effort and costs as compared to manually extracting and verifying data, as the AI-powered technology can do it quicker and more accurately.
- Improved security, as automated processes and models can be taught to detect and alert any suspicious activity.
- Improved customer experience, as the AI-powered technology can quickly and accurately extract the data needed to provide customers with the best experience.
Takeaway
Fake bank statements are a growing problem for lenders, as they can be used to fraudulently obtain loans or credit. The sophistication of these fake bank statements is on the rise with more advanced technologies. The challenge for lenders is to quickly and accurately spot these fake statements, in order to prevent fraud and protect their customers.
Nanonets can be a valuable tool for lenders in the fight against fake bank statements. By quickly and accurately verifying large numbers of statements, lenders can protect their customers and prevent fraud.
