The revenue growth and profitability of a lending business depend on several factors. A combination of superior risk assessment, fraud detection capabilities, and quick and accurate underwriting turnaround can transform a lender’s success rate with borrowers and reduce non-performing assets.
Access to superior data plays an important role in this process. For instance, the ability of lending teams to classify bank transactions helps lending teams understand the cash flows of a borrower in-depth. As businesses scale, it is impossible to sustain manual processes in transaction classification.
This is especially important in a scenario where digital transformation enables businesses to transact in bulk and at scale.
For example, the number of UPI transactions in India amounted to 118 billion in 2023, a 60% growth from 2022.
So, as the number of transactions keeps growing, lenders must be in a position to analyse financial data more minutely, comprehensively, and accurately.
This is where the automatic classification of bank transactions is making the process more efficient, error-free, and accurate. Let’s understand how it works, its benefits, and its impact on lending operations.
What Is Automatic Classification of Bank Transactions?
A borrower’s bank statements typically classify all borrower transactions into three basic categories – deposits, withdrawals, and transfers. However, the data, in its current format, is not enough to empower lenders to make accurate underwriting decisions.
To drive data-driven, real-time risk assessment, all transactions must be classified into various expense and income categories, thus indicating the state of a borrower’s daily cash flows and fiscal health. Such an in-depth classification can indicate whether the borrower has the capacity to repay the loan.
The adoption of financial analysis software solutions powers the automatic classification of bank transactions, which, in turn, paves the way for lenders to execute the underwriting process accurately, quickly and efficiently.
How Automatic Classification of Bank Transactions Works
Lending teams feed financial documents such as bank statements, balance sheets, and income statements into an AI-powered, cloud-based financial analysis solution.
All transactions are then automatically categorised into inflow and outflow transactions and further sub-categorised within these two broad categories. The more sub-categories, the more accurately a lender can understand a borrower’s finances.
Inflow categories
All incoming transactions are automatically categorised under inflow and further categorised into over 20 sub-categories. Examples of sub-categories include commissions, rental income, insurance benefits, commission, cash deposits, dividends, interest, tax refunds, and reverse sweeps.
Outflow categories
All outgoing transactions are classified under outflow categories and further categorised into over 30 sub-categories. Examples of sub-categories include staff salaries, marketing costs, upskilling costs, transportation expenses, fines and penalties, credit card bill payments, utilities, EMI payments, and fees paid to consultants.
Key Benefits of Automatic Classification of Bank Transactions
The automatic classification of bank transactions comes with several benefits, enabling lenders to increase the Return On Investment of their operations. They are as follows:
1. Highly Accurate Transaction Classifications
The ability of lenders to accurately break down and classify bank transactions is a crucial step in the analysis process of a borrower’s finances. AI-driven data processing can analyse transaction patterns and help lenders answer specific questions with transaction data.
For instance, dashboards can be customised to answer questions such as “What are the revenues of a business, and what are the profits?” and “How many previous loan EMI loan EMIs (Equated Monthly Installments) cheques have bounced?”
The sheer number of categories helps answer a wide range of questions, thus enabling lenders to make data-driven underwriting decisions that reduce risk.
2. Enhanced Fraud Detection Capabilities
Another major benefit of the automatic classification of bank transactions is the ability of AI-powered tools to recognise irregular patterns in a borrower’s bank-related data.
For instance, they can detect continuous occurrences of bounced EMI cheques, which show up in the penalties/ fines categories. Companies engaging in fraudulent practices, such as circular transactions, which are hard to detect, will be red-flagged.
This capability enables lenders to detect potential loan fraud early on. They can reduce the number of non-performing assets and loan fraud incidents, which can lead to investigations, penalties, and even shutdowns.
3. Higher Efficiency
Lenders have traditionally used manual processes to underwrite loan applications. Manually classifying transactions is a labour-intensive process that is prone to errors. On the other hand, automatic classification of bank transactions by cloud-based financial analysis software completes the process within minutes.
Processes such as data entry and manual data classification are eliminated, as the software can recognise and extract data presented in over 700 document formats. Lenders can function with lean teams and deliver quick turnaround on loan approvals.
4. Customised Solutions
Lenders, especially from the traditional banking sector, tend to offer standardised loan plans with fixed interest rates, tenures, and EMI brackets.
However, this strategy does not work for most businesses since they have such diverse needs. The automatic classification of bank transactions into such a wide range of categories gives lenders an in-depth understanding of a business’s specific credit needs.
The use of AI-powered financial analysis software also enables lenders to customise loan solutions to the individual needs of borrowers. They can deliver higher value by recommending relevant loan products, as opposed to typical cookie-clutter solutions.
5. Inclusive Lending
First-time borrowers with no credit history tend to be overlooked by the lending industry due to a lack of processes to evaluate creditworthiness.
However, AI-powered bank statement analyser software not only enables automatic classification of bank transactions. The transaction data can then be cross-analysed with a borrower’s Goods and Services Tax Returns information to cross-reference business revenue transactions.
Healthy business revenues demonstrate the potential ability of a business to repay its loans, even if they do not have a credit score. Hence, lenders can turn first-time loan applicants into customers, thus driving financial inclusion and revenues. They can capture a larger market share, especially in the MSME (micro, small, and medium-sized enterprise) sector.
Key Takeaway
The automatic classification of bank transactions enables lenders to have access to the right data and make accurate, expedited underwriting decisions. They can also offer customised products that are relevant to a wide range of borrower profiles. Adoption of cloud-based financial analysis systems is the way forward for lenders to run revenue-focused, profitable operations.
Presica’s comprehensive and seamless financial data analysis solution simplifies and speeds up the process through automation. The software provides actionable insights on a customisable dashboard, thus helping companies make informed business decisions.
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