A Bank Statement is the first item you receive when applying for a loan with any financial institution. Whether performed on a corporation or a person, Bank statement analysis provides lenders with a clear image of the borrower’s ability to repay the loan. The bank statement analyzer is mainly used to analyze bank statements, which will display the available and existing balances and deposits, withdrawals, and penalties owed, among other things. This statement can be analyzed to learn more about an account holder’s financial situation. When a person or a business requests a loan, the bank evaluates the prospective borrower’s creditworthiness. In this circumstance, a bank statement analyzer is a good option because it provides information about the borrower’s financial situation.
A Strong Bank Statement Analysis is required:
One of the most common methods for determining a borrower’s creditworthiness is to examine their bank statements. It is the practice of analyzing a borrower’s earnings and spending habits based on bank data. Before making loan judgments, all lending organizations conduct this type of study. Because bank statements are one of the most dependable documents for demonstrating a borrower’s financial situation, lending institutions usually conduct a detailed review of them as part of the borrower screening process. Manually reading through pages and pages of bank statements, on the other hand, can be time-consuming and prone to human error or even subjective prejudice. Automated bank statement analyzers will help you analyze bank statements easily, often known as BSA engines, come to the rescue in this situation. So here are some lists of key features of bank statement analysis that can help you ease the threat.
1) Analyzing Transaction Patterns:
Bank statements detail the borrower’s transactions during a specific period. It provides precise information on one’s expenses, income sources, checks, transfers, periodic ECS debits, EMIs, credit card payments, bill payments, default penalties, and cheque returns, among other things. As a result, banks are able to make better credit choices. The following are some key patterns that a competent BSA should recognize and emphasize.
Transactions with a High Value:
Based on a preset value limit, the bank statement analyzer should recognize and highlight the high-value transactions independently. The frequency and amount of outbound high-value transactions strongly indicate the borrower’s probable spending capability. Many high-value transactions are envisaged in the case of a high-net-worth borrower. However, if a typical borrower has a number of high-value transactions, the engine should raise an alert. The value limit should also be adjustable based on the loan’s value and net worth.
Accounting for Profits:
Credit transactions are not all considered revenue. A good BSA engine should have a clear rule for determining income on its own. This is particularly crucial when dealing with retail borrowers, often salaried employees.
Closing Balances Daily/Monthly:
Closing balances are important for determining a borrower’s financial stability. The Bank statement analyzer, for example, should be able to analyze daily closing balance data and identify how many days in a month the closing balance is more than two standard deviations from the mean. If the number of incidents beyond two standard deviations is less than two, it means the borrower is reliable with their money. This range of validation should also be customizable so that the engine can apply the rule with varying bounds for different borrowers.
Analyze Debit vs. Credit:
The number of credits and debits in a checking account is a strong measure of income and expenditure. The amount and frequency of credit show a clear picture of one’s various income streams. Based on the rules defined in the engine, the bank statement analyzer should be able to produce a clear overview of credits and debits and flag any anomalies.
2) Analysis of Charges and Returns:
The Bank statement analyzer should recognize all charges and returns from account statements. The following are some of the critical characteristics of parsing that a decent Bank statement analysis should spot.
Charges for not maintaining a minimum balance, charges on cheque returns, charges on forex conversions, charges on any pre-closing of deposits, charges on delayed payments, charges levied due to insufficient funds on recurring payments, and so on should all be identified and listed down by the statement analyzer.
Return of a Cheque:
Returning a cheque carries severe fines and might jeopardize the cheque issuer’s financial reputation. Cheque returns may be subject to legal action, depending on the amount involved and the impact on the recipient. To identify any returns, the BSA engine should be able to analyze all of the cheques issued and their realization details.
3) Fraud Analysis:
Based on the information provided in the statement, the bank statement analysis should identify and flag any fraudulent activity. The analyzer should verify the following crucial characteristics.
Circular transactions are artificial transactions that occur between organizations belonging to the same group or controller. Several countries consider this practice to be a swindle and have outlawed it. Especially in the case of business borrowers, the BSA engine should detect such transactions.
A specific set of blacklisted countries or institutions will be based on the lending institution’s geography. Any transmission of funds to such prohibited entities is considered criminal. Such questionable transactions should be detected by the Bank statement analyzer and brought to the reviewer’s attention.
Several Small Credits:
A potential money laundering activity is when an account contains a high number of minor credits from various and fresh sources. Such trends should be detectable by the Bank statement analyzer and highlighted for further research.
Additional Cash Deposits:
Frequent and larger cash deposits other than pay are a red flag for retail borrowers. The bank statement analyzer should recognize this pattern and highlight the examples that fall into it.
4) Predictive and Statistical Analysis:
Statistical analysis based on a predetermined set of rules and the engine should have real-time interaction with GST and credit bureaus to receive the most up-to-date information on borrowers’ credit histories. The bank statement analysis should combine data from various account statements and perform a comprehensive predictive. The output should be a guiding light in borrowers’ overall credit score.
Finally, the above are important features that can help determine a borrower’s creditworthiness. To provide good loans and reduce NPAs, they need strong bank statement analysis software that parses borrowers’ bank statements thoroughly and highlights all crucial information needed to make an informed choice.