In consonance with the demand of a digitised market, a growing number of businesses are shifting toward a data-driven work culture. However, poor-quality data remains a pain point across industries.
A 2023 survey by McKinsey indicates that 60% of technology executives considered poor data quality as the main roadblock to scaling their data solutions.
The financial sector faces similar challenges, where key decisions must be made quickly and on short notice. These often impact millions of consumers and businesses.
In this blog, we explore the importance of adopting automated bank transaction data analysis by lending businesses to gain a competitive advantage in a crowded credit market.
What Is Bank Transaction Data Analysis?
Bank transaction data analysis is a process leveraged by financial businesses to evaluate the financial health of potential customers. For instance, lenders leverage this process to evaluate the creditworthiness of potential borrowers before approving a loan.
The rising demand for credit has nudged the rise of loan service providers, which include banks, Non-Banking Financial institutions, neo-banks, and payment apps. Loans are being offered at a much larger scale and with greater urgency.
Hence, the risks, such as rising non-performing assets (NPAs) and loan fraud, are much greater. These realities are nudging financial businesses to leverage the benefits of technology to extract more accurate data insights.
For instance, cloud-based bank transaction data analysis is helping lenders mitigate risks and deliver more value to customers.
How Automated Bank Transaction Data Analysis Works
Financial institutions invest a significant amount of time reviewing bank transaction documents of potential borrowers as part of the underwriting process. These documents, when analysed thoroughly, paint a realistic picture of a business’s financial health, cash flows, operational costs, assets, profits, and losses.
However, manual analysis interventions consume significant resources such as time and labour. There’s also a greater scope for errors, duplication of data, and omissions.
Superior financial analysis tools leverage a mix of technologies such as cloud computing, Artificial Intelligence (AI), automation, Machine Learning, and Optical Character Recognition (OCR) to automate processes.
For instance, bank transaction data analysis tools extract data from over 700 document formats, segregate all transactions into relevant categories, and analyse the data within minutes.
Benefits of Bank Transaction Data Analysis Software
Here’s a snapshot of how bank transaction data analysis tools are helping lenders enhance operational efficiency, deliver more value to their customers, and drive revenues.
1. Enhanced Risk Assessment
Lenders are always at risk of being unable to recover loans once they are disbursed. Loan recovery is an expensive process. Hence, making the right lending decisions is imperative for business success.
AI-driven bank transaction data analysis tools accurately predict a customer’s creditworthiness based on in-depth analysis of massive amounts of financial data.
Such tools paint a realistic picture of loan repayment capabilities by producing a creditworthiness score, thus reducing risk. For instance, it can capture past loan repayment behaviour and accumulated debt.
2. GSRT Cross-Analysis of Cash Flows
First-time borrowers often face a challenge in accessing credit. Cash flow lending is an opportunity for such cases where a borrower’s sales record can pave the way for determining lending decisions.
One of the most accurate ways to determine authentic sales is to review Goods & Service Tax Returns (GSTR). Lenders feed in GSTR data, and the software cross-references it with bank transactions.
Once authenticated, lenders are in a position to determine whether the cash flows of the business are generated due to revenues and then make a data-driven lending decision.
3. Superior Fraud Detection Capabilities
Lenders are continually at the risk of being duped by fraudulent players. These borrowers differ from those who intend to repay loans but may have fallen on hard times.
Hence, lenders need access to superior technology that can aid in helping them identify potential fraud. AI-powered bank transactions data analysis software has the capability to recognise minute distinctions in fake documents and irregular transaction patterns.
For instance, such software flags cases of circular transactions used by some bad actors to bolster revenues.
4. Customised Solutions
Off-the-shelf loan products are irrelevant to a wide range of borrowers with very specific credit needs. Hence, many do not qualify for loans.
Alternatively, they may overextend themselves and end up paying more for a loan than its actual value. Customisation of loan products is an attractive option but a challenging one due to the voluminous scale of loan applications.
However, automated bank transactions data analysis software can completely automate the process, recommending customised solutions for the individual customer based on real-time credit needs and cash flows.
This enables lenders to serve more customers and capture more market share.
5. Higher Efficiency
With automated workflows, the entire loan management cycle becomes more efficient. Using bank transactions data analysis software facilitates an end-to-end data governance process. Data is extracted, organised, analysed, and stored securely without any risk of exposing the sensitive financial data of borrowers.
6. Quicker Loan Approvals
As the demand for credit skyrockets, businesses must increase the pace at which they approve and disburse loans. Using automated bank transaction data analysis tools is helping lenders expedite processes and increase the rate at which they turn leads into customers.
7. Higher Business ROI
By leveraging technology, lenders bring more efficiency to their operations and reduce the incidence of NPAs. They also get to function with lean teams and reduce operational costs. They can also predict trends and respond to underserved markets, thus driving revenues and profitability.
A combination of these outcomes helps lenders increase business ROI and build a sustainable, profitable lending operation.
The Takeaway
By leveraging bank transaction data analysis tools, lending businesses and verticals address existing challenges and respond to market challenges faster.
Hence, lenders are advised to make early investments in technology, which transforms the way they operate. Access to insights and smart usage helps lenders respond to and capitalise on market opportunities faster.
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.
Request a free demo today!
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