Bank statement analysis involves summarizing cash inflows and outflows from statements and providing an overview of financial health of individuals. Businesses and NBFCs consider the financial history of borrowers during credit assessments and bank statement analysis software is being used by various industries for faster processing times, efficiency, and document processing purposes. In this post, we’ll discuss why corporations require bank statement analysis and what to look for when choosing these software solutions.
Businesses need bank statement analysis for the following reasons-
Banks and financial institutions do background checks on borrowers and assess their creditworthiness through underwriting. Underwriting is a process where a business gives out loans at an acceptable risk, in exchange for a premium. Bank statement software used for analyzing statements helps businesses figure out where bank deposits come from, what assets an individual holds, income verification, and if a person has the capacity to repay loans on time.
Borrowers who apply for loans and mortgages need to let their documents undergo financial audits. Intelligent document processing solutions are used to extract data from bank statements and do cash flow analysis of them. Banks are able to identify sources of income, total annual earnings, interests paid, and other liabilities through statement analysis. This helps them determine the amounts of loan borrowers are eligible for and disburse them accordingly. For borrowers who default on payments or have a poor credit history, bank statement analysis helps blacklist them and avoid making mistakes by giving out loans.
NBFCs and Fintech startups perform bank statement analysis for computing ITR filings and income tax returns. Income tax calculation helps businesses estimate how much taxes individuals pay every year, thus letting them know if the transactions occurring are genuine.
Bank statement processing using intelligent OCR can find patterns in recurring transactions and identify cases of financial fraud. Statement analysis models cross-check transactions shown on the business ledger versus what’s reflecting in account statements. Mismatching amounts help detect fraudulent activities in accounts and if the details are correct, the accounts get verified and validated.
Bank statement analysis works by pulling financial data from a statement and deriving insights from it based on the states of account activities. A bank statement analysis model aims to map cash inflows & outflows of accounts, penalty charges, vendor payments, and defaults (if any) for assessing the repayment capacity and creditworthiness of individuals.
It parses data from scanned images or PDF documents, structures it, and summarizes account activities of borrowers. Bank statement analysis gives an overview of one’s liabilities, recurring transactions, and monthly dues as well, which helps banks and financial institutions decide how much money they can afford lending to borrowers.
Bank statement analysis is not a one-size-fits-all solution since every bank is different and there are a variety of document formats for statements. If you are planning on investing in good bank statement analysis software, here are key features you need to look out for:
Every bank has its own unique format when it comes to printing statements. Good bank statement analysis software should be able to read unstructured data, organize it, and produce the information in more presentable formats. The ability to adapt to different document layouts and styles is a key characteristic of cutting-edge bank statement analysis software.
Bank statement analysis involves PDF to excel conversions where tabular data is read from scanned documents and entered into spreadsheets for cash flow analysis. Data extraction from PDF and parsing values are challenging problems faced by most OCR solutions. If you are investing in a bank statement analysis solution, make sure it yields a high-level of data accuracy when inputting information and is also capable of checking for fraudulent entries.
Financials in statements have to be compared with income tax figures, GST filings, and other documents to assess the financial health of borrowers. Good software programs are able to validate entered data, compare with different sources, and ensure that entries are legitimate.
Data extracted from bank statements after analysis should be reviewed by users. The software should flag anomalies and send alerts or notifications to users if it detects missing values or discrepancies during data extraction, requesting them for a manual review.
Automated bank statement analysis helps financial institutions in making faster decisions about credit profiles and offer high-end financial services to those whose applications get approved. Bank statement analysis software is capable of processing huge volumes of transaction data which boosts overall organizational efficiency.
Yes, it is possible to reduce costs in the long run if you invest in bank statement analysis or even an automated credit application processing system. This is because these programs are not prone to human error, very accurate, and eliminate data redundancy.
Docsumo uses smart machine learning algorithms to extract data from bank statements, detect fraud in real-time, and eliminate cases of forgeries. Docsumo can detect frauds such as cropped documents, photo on photo, B&W image, and formatting/layout differences of bank statements.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.