Introduction to Credit Models and GST Data
Credit risk modeling involves developing statistical models to predict the probability of loan default or other credit events using various borrower data. With the advent of GST and the wealth of transaction-level data it provides, GST data offers immense potential for enhancing credit risk models.
The granular and enhancing lending models with GST insights can lead to more informed and profitable lending decisions through better assessment of creditworthiness. This article explores the key factors to consider for optimizing GST data for credit assessment while building credit risk models.
Importance of Incorporating GST Data in Credit Modeling
The advent of Goods and Services Tax (GST) has brought about a transformational shift in the way businesses report their transactions. Under the GST regime, entities over 1.3 crore must file monthly and annual returns, including revenues, input taxes, output taxes, and more.
Real-time View of Business Health
One of the most significant advantages of GST data is that it provides recent information on a business’s financial health. GST returns must be filed monthly in a prescribed format containing different tables with transaction details.
For instance, the GSTR-1 formulated monthly list contains particulars of all outward supplies, bill-wise and customer-wise. This gives lenders near real-time visibility into revenues being generated. Any sudden dip in sales volumes or unusual fluctuations get captured here rather than waiting for outdated financial statements.
Similarly, GSTR-2A/2B returns provide inward supply data with details of purchases from suppliers. Trends reveal production capacities, inventory cycles, and working capital utilization over time.
In addition to outward and inward information, GSTR-3B provides a monthly consolidated summary of transactions and taxes paid. Together, these returns offer more periodic monitoring of business activities than was enabled through earlier financial or banking data.
Also Read: GST Data-Based Lending – Silver Bullet To MSME Credit Lending Challenges
Better Assessment of Working Capital Cycles
Working capital management is crucial for any company to ensure smooth business functioning. Assessing operating capital effectiveness requires insights into inventory cycles, turnover of receivables, credit terms to customers/vendors, etc.
GST returns provide rich invoice-level data that helps analyze working capital patterns better. GSTR-1 returns clarify the credit days offered to each customer and bill-wise details. This highlights debtor days accurately based on actual operational terms rather than broad sector benchmarks.
Benchmarking Against Industry Averages
Hitherto, financial data from borrowers was primarily suitable for standalone analysis with limited industry comparisons. All registered businesses file GST returns in a standard template stipulated by law.
This offers enormous potential for horizontal analytics – wherein a borrower’s GST metrics can be objectively stacked against aggregated industry data. Parameters such as revenue growth, input tax credits, and tax liability indicate business performance relative to the sector.
GST data and other financial details provide a wholesome view of working capital utilization, inventory turnover ratios, and sales growth trends for the entity vis-a-vis industry benchmarks. This allows lenders to compare the best credit modeling techniques with industry averages.
Authentication of Returns
One of the biggest risks in credit underwriting is a falsification of financial statements by borrowers to portray better health than actual. Data credibility is higher since GST returns flow directly from the GST portal based on statutory tax filings.
The returns submitted are signed digitally, prohibiting any subsequent modifications. Moreover, the inward-outward reconciliation in the GST portal reduces the probability of reporting fake transactions. This authenticity of financial information aids accurate credit assessment.
Tax Compliance Indicator
Tax policy provides vital insight into any organization’s standards and methods of governance. For example, late filings and payments of quotas or failure to report correctly are all signs that compliance vigor needs to be improved.
Returns for the goods and services tax (GST) are processed electronically with standardized data reporting to streamline GST processes. Looking at compliance history, from on-time filings to matching of returns and taxes paid, are key data for underwriters. Adverse flags could significantly raise risk perception for credit.
Hence, using GST data allows the development of the best credit modeling techniques with current and reliable borrower financials. This further helps in making sound lending decisions.
Also Read: How to Use Technology to Make Tax Compliance Easier?
Key Factors for Building Credit Models with GST Data
While GST data unlocks new possibilities for credit risk assessment, there are certain crucial aspects lenders need to consider for effectively incorporating GST insights into underwriting. Evaluating the following four factors aids in the prudent integration of GST analytics into overall lending decisions:
Verify the Authenticity of the Borrower’s GST Identity
The 15-digit GSTIN is the unique ID code for every registered taxpayer. Just like checking the authenticity of a driver’s license, validating GSTIN provides preparatory signals. Check these for better credit models.
Check the Registration Status
This verifies active registration. De-registered entities warrant caution – revenues cannot be reliably gauged if they shut operations. I recently faced a case where the borrower shared three years of strong GST returns.
Also Read: How To Check GST Registration Status?
Analyze the Registration Type
Whether a trader, manufacturer, or service provider? Composition scheme or regular scheme taxpayer? These have varying compliance requirements and data richness. Clarity here helps establish the depth of GST data available from that entity.
Examine Date of Registration
When did GST registration commence? Was it taken only last year, or has it existed for over three-odd years? Newly registered entities pose challenges as records are limited. It also questions the credibility of previous tax regimes.
While inconclusive, these checks establish overall credibility even before further analyzing actual GST return data. After all, checking credentials comes first.
Ensure Authenticity of Return Copies
With digital authentication methods, obtaining accurate copies of returns filed by borrowers is essential. It thus ensures that actual tax return data, rather than details altered by hand and eye, would form the basis for credit decisions.
Lenders can verify authenticity through:
Mode of Obtaining
A copy of returns could be pulled directly from the GST Portal through API integration. In this way, pulled data from the government system matches filed details, so there is no risk of adulteration.
Consequently, alternative forms such as physical copies or Excel/PDFs shared by borrowers are still vulnerable to changes. Yet, matching up summary information with government statistics lends credibility.
Integrity Checks
A hashed signature is included in the JSON file downloaded through GSTN to guarantee data integrity. The filed returns also have a signed QR code imprinted to affirm the contents.
These measures demand changes about flags around data accuracy. They could be more foolproof, but they add a reasonable level of credibility and comfort.
Evaluate Overall Compliance Rigor
Merely accessing GST returns does not suffice; the discipline in making timely, accurate filings provides meaningful cues on compliance health:
Timeliness
Stop-filings or delays beyond stipulated deadlines attract fines and compound credit risks. Tracking late submissions validates diligence and accuracy in the borrower procedures.
Reconciliation Discipline
Non-reconciled filings with differences between input and output particulars are dangerous to accounting hygiene, raising tax risks. Combine a review of this with reasons for matches (or mismatches) to determine process compliance.
Pending Dues
Even if numbers reported in return are high, any outstanding tax liability or interest/late fees due warrants caution. Total dues position by return type will indicate the degree of seriousness about compliance.
- Assess Multi-period Filing Trends
- Review Historical Filing Trends
No one-time return snapshot conveys the complete picture. Volatilities across periods remain inherent across sectors like manufacturing, agriculture, etc. Let’s examine a few aspects through trend analysis:
- Revenue Trajectory – Growth or decline?
- Working Capital Days – Widening or narrowing?
- Seasonal Patterns – Peaks and troughs?
- Credit dependencies – Increasing reliance on vendor credit?
Such trend analysis provides contextual insights using the last 2-3 years of sequential GST returns. The more data, the more reliable conclusions are. We build perspective by accommodating quarterly versus monthly return mismatches or analyzing pre-GST tax records. After all, wisdom lies in interpretation.
Thus, while GST returns provide standardization, assessing historical trends across these filings is vital for prudence. No return filing or metric tells the whole story, requiring a holistic review.
Challenges in Building Credit Models Using GST Data
While GST data offers immense potential to enhance credit risk models, integrating these new datasets also poses specific challenges that lenders need to address:
Digital Consent Acquisition
The foremost prerequisite to accessing GST details is taking borrower consent explicitly permitting data share. Typically, this involves digital authorization by the taxpayer through a one-time password route.
However, such an additional consent layer for data access adds steps in loan origination. Educating borrowers beforehand on consent requirements and providing guided authentication during the application process is vital. This ensures lead drop-offs are minimized.
Customized tele-calling, how-to guides, and in-app tutorials are some ways to enable consent acquisition. AI-powered tools to auto-read SMS OTPs also help fast-track consents through minimal manual intervention.
Novel Data Source Limitations
Unlike financial statement spans, GST returns lack extensive sectoral benchmarks and historical performance records as new data inputs. This constraints trend analysis, peer comparisons, and back-testing analytics.
Building heuristics through experimental modeling remains the approach to garner insights from GST. Collaboration opportunities with industry associations and GSTN exist to compile aggregated sector statistics.
Data Quality Challenges
Although data is generally returned to a standard format, time-period variance, and related party reconciliations are two major current issues that need massive cleaning up to make the raw material suitable for modeling use.
Data refinement areas include:
- Correct treatment of input tax credits.
- Aggregated reporting for associated entities.
- Treatments differ between quarterly returns versus monthly ones.
Analytics Complexities
The granular details on invoice-level transactions lead to reporting structure and data modeling complexities. In other words, more information doesn’t necessarily mean a greater understanding.
Investments in data architecture, strict transformation protocol, and analytical talent are essential for the plethora of GST analytics. Higher cost and capability-building requirements are included.
Scope for Evasion
Inadequate cross-validation checks permit transactions to be suppressed in unreported sales or purchases. Bogus invoices for input credits distort financial position. But cash sales remain outside the scope.
Vital items are custom reconciliation checks, filing average reviews, and diligence on duty exemptions. Statistical techniques can pinpoint outliers, while trend analysis reveals unusual patterns to be pursued further.
Conclusion
Using GST data helps lenders develop credit risk analysis techniques that can lead them to make better-informed loan decisions. The key is to account for GST inputs in the credit assessment process rather than just an isolated indicator.
With its richness in borrowers’ volumes, revenues, and compliance, the best credit modeling techniques maximize creditworthiness using GST information. However, some things need consideration, such as how to use GST data effectively and guard against its shortcomings.
FAQs
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What is GST data, and how can it be used for credit modeling?
GST data refers to the detailed transaction-level indirect tax information businesses file in their GST returns. This data can be used to assess creditworthiness and build statistical credit risk models.
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What are the key advantages of using GST data for credit assessment?
Key advantages include real-time business financials, the authenticity of filings, a granular invoice-level view of revenues and purchases, insights on working capital cycles, and tax compliance.
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What are the four main factors when building credit models using GST data?
The four factors are – verifying GSTIN authenticity, ensuring return copies’ credibility, assessing compliance rigor, and analyzing multi-period filing trends.
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How can lenders verify the authenticity of a borrower’s GST number?
Check the registration status, registration type, and registration date in the GST common portal to validate the legitimacy of the provided GSTIN.
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Why should lenders directly pull GST returns data from the portal rather than rely on borrower-submitted copies?
To guarantee the credibility of the tax return data used for underwriting and avoid potential data tampering risks by borrowers hiding factual information.
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How does a borrower’s past GST compliance record provide insights for credit decisions?
Timely filings, reporting accuracy, and pending dues position help assess the strength of processes and governance rigor – crucial credit risk indicators.
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How does trend analysis of historic GST returns aid in credit appraisal?
Comparing sequential filings helps accurately evaluate revenue growth trajectories, working capital cycles, and seasonality patterns – key factors underlying risk profiles.
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What are some challenges in integrating GST data analytics for credit risk models?
Key challenges include – consent acquisition, benchmarking new datasets, data quality issues, analytical complexities, and reconciling exceptions/deviations.
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How can lenders overcome limitations around the need for comparative industry benchmarks in GST data?
Collaborate with industry associations to compile aggregated sectoral performance statistics from the GST filings of various players.
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What are some of the best practices lenders should adopt for prudent GST data-based analysis?
Practices like accounts aggregation, metrics normalization through adjustments, enriching data from other sources, and adding sectoral perspective through judgemental overlays.
