Algorithmic Risk or Risk Mitigated? A Comparative Study of Credit-risk Assessment Accuracy Pre- and Post-automation in Indian Commercial Lending

Evolution of Credit-risk Assessment in Banking

Credit-risk assessment has long been the cornerstone of financial intermediation, determining not only individual borrower outcomes but also the solvency and stability of banking institutions. Historically, credit decisions in commercial banks were underpinned by manual underwriting techniques, grounded in the ‘5 Cs’ of credit character, capacity, capital, conditions and collateral. These assessments were based on subjective judgement and qualitative borrower information, which introduced inconsistencies in default prediction, especially across heterogeneous borrower classes. Although these methods provided flexibility, they were vulnerable to personal bias, lacked reproducibility and exhibited limited predictive power in volatile credit environments.

To address these limitations, financial institutions progressively adopted quantitative models grounded in statistical inference, primarily during the post–Basel II era. Internal rating-based models emerged as regulatory-compliant tools to estimate probability of default (PD), loss given default and exposure at default, especially in the context of capital adequacy norms. Logistic regression and discriminant analysis became widely used techniques to classify borrowers based on historical repayment patterns, asset-quality indicators and macroeconomic proxies. These models improved objectivity and repeatability in credit assessment and allowed banks to align their credit portfolios with risk-weighted capital buffers. However, even with statistical formalization, traditional credit models remained constrained by linear assumptions and limited feature spaces. For instance, logistic regression-based scoring was often unable to account for non-linear borrower behaviours or the interaction effects between loan attributes and macroeconomic stressors (Kou et al., 2021). Moreover, such models were heavily reliant on structured financial data, which excluded informal borrowers, particularly in emerging economies like India, where credit penetration remains uneven (Ghosh, 2020). As a result, credit-risk assessment tools struggled to differentiate between transient delinquencies and chronic risk profiles, leading to suboptimal lending decisions and higher NPAs in certain borrower categories (Chatterjee & Sinha, 2022; Rajeev & Mahesh, 2021).

In response to these methodological limitations and growing data availability, banks began transitioning towards more adaptive and data-intensive approaches. Credit-scoring platforms integrated behavioural analytics, real-time transaction data and psychometric indicators to construct more granular borrower risk profiles. This marked the transition from rule-based systems to algorithmic credit-scoring frameworks capable of learning complex patterns through ML and AI methods (Baesens et al., 2016; Lessmann et al., 2015). This evolution not only promised improved accuracy but also offered scalability and speed, enabling banks to process vast loan volumes with limited human intervention while theoretically reducing model bias and error propagation.

Emergence of Credit Automation Technologies

The emergence of credit automation technologies marks a significant inflection point in the architecture of risk evaluation within the banking ecosystem. Unlike earlier decision-support tools that relied on pre-programmed scorecards, credit automation integrates algorithmic models capable of autonomously learning from large-scale, multidimensional data. This shift has been driven by the convergence of three forces: availability of granular borrower data, computational advances in model processing and a regulatory impetus towards efficient and transparent lending practices. Automation platforms now encompass a suite of tools from robotic process automation (RPA) for KYC to AI-driven underwriting engines, thereby transforming loan origination and risk evaluation into machine-led systems (Arner et al., 2020; Lessmann et al., 2015).

One of the most influential transformations has been the integration of ML and ensemble methods such as Random Forest, gradient boosting machines and support vector machines into credit-scoring algorithms. These techniques have been empirically shown to outperform classical statistical models in prediction tasks by capturing non-linear relationships and interaction effects that traditional logistic regression often overlooks (Kou et al., 2021). In particular, tree-based ensemble models like XGBoost and LightGBM have become popular due to their interpretability, computational efficiency and adaptability to class-imbalanced datasets often seen in default prediction (Beck et al., 2020; Mori & Kitagawa, 2022). These models also allow for continuous updates, enabling real-time credit decisions based on changing borrower behaviour and macroeconomic signals (Baesens et al., 2016). Beyond predictive accuracy, credit automation offers operational efficiency by reducing manual effort, documentation lag and credit officer subjectivity. For instance, RPA tools have streamlined income verification, document extraction and borrower onboarding, accelerating loan disbursal cycles significantly (Arner et al., 2020). AI-based credit-decision engines now integrate alternate data such as social media profiles, utility payment histories and mobile usage patterns, particularly valuable in emerging economies with large populations of ‘thin-file’ or informal borrowers (Gambacorta et al., 2020). These advancements have enabled broader financial inclusion while simultaneously improving credit-risk stratification, especially in retail and MSME segments that traditionally suffered from data opacity (Rajeev & Mahesh, 2021).

In sum, credit automation represents a paradigmatic shift from rule-based, manual risk assessment towards data-driven, algorithmically governed lending infrastructures. While these technologies offer unprecedented speed, accuracy and scale, they also introduce new complexities related to model fairness, accountability and regulatory oversight. Consequently, their effectiveness must be evaluated not only through quantitative metrics but also through normative lenses of justice, inclusion and institutional governance (Brown et al., 2022; Gambacorta et al., 2020).

ML and Default Prediction

ML has increasingly become central to the predictive modelling of credit default risk due to its ability to uncover complex, non-linear interactions among variables and adapt dynamically to changing data environments. Unlike traditional linear models, ML approaches utilize large-scale, high-dimensional borrower data including behavioural, transactional and contextual features to enhance classification precision in identifying default-prone applicants (Lessmann et al., 2015). In contemporary commercial banking, especially in emerging markets, this capability is critical as customer heterogeneity and informal income sources introduce significant modelling challenges (Kou et al., 2021). By capturing hidden patterns that elude classical logistic regression, ML models have demonstrated significantly higher discriminatory power in credit-scoring applications.

Empirical studies validate the superior predictive performance of tree-based ensemble methods such as Random Forest, gradient boosting machines and extreme gradient boosting (XGBoost) in comparison to traditional credit-scoring techniques. These models offer advantages in handling class imbalance, which is a recurring issue in loan default datasets where default cases are typically a small fraction of the overall portfolio (Beck et al., 2020; Mori & Kitagawa, 2022). Moreover, techniques, such as synthetic minority over-sampling technique (SMOTE) and cost-sensitive learning, have been successfully integrated into ML workflows to further improve performance on skewed datasets (Kou et al., 2021). For instance, in a study of SME lending, models built using LightGBM outperformed traditional scorecards in both accuracy and recall, underscoring their ability to minimize false negatives, that is, wrongly classifying risky borrowers as safe (Baesens et al., 2016; Naik, 2023).

Key performance metrics used to evaluate credit-scoring models include the area under the receiver operating characteristic curve (AUC-ROC), precision, recall, F1-score and confusion matrices. These metrics provide a multidimensional view of model performance, balancing the trade-offs between Type I errors (false positives) and Type II errors (false negatives), which are both consequential in lending decisions (Arner et al., 2020). The AUC-ROC score, in particular, is widely adopted in banking contexts due to its robustness in evaluating classification quality under imbalanced datasets. ML models trained on hybrid data combining structured financial variables with unstructured behavioural and psychometric inputs have shown AUC improvements of 8%–15% over traditional methods (Brown et al., 2022).

In conclusion, ML holds immense promise for improving default prediction in commercial banking, particularly in environments with heterogeneous borrower profiles and dynamic risk conditions. However, its efficacy depends on careful model selection, validation and alignment with governance and fairness principles. The transition to ML-led credit assessment must therefore be accompanied by institutional mechanisms that balance efficiency gains with transparency and accountability (Lessmann et al., 2015).

Credit Scoring and NPAs in the Indian Context

India’s commercial banking sector has faced recurring crises linked to the proliferation of NPAs, particularly in the aftermath of credit booms driven by public sector lending expansion and underwritten infrastructure projects. Between FY2012 and FY2018, gross NPA ratios in public sector banks surged from 3.4% to over 14%, prompting concerns over inadequate risk assessment mechanisms and political interference in lending (Rajeev & Mahesh, 2021). Credit-scoring models deployed during this period largely relied on static borrower characteristics and legacy account data, often failing to flag early warning indicators of distress, especially in corporate and MSME segments (Ghosh, 2020). The lack of dynamic, real-time credit evaluation tools exacerbated asset-quality deterioration and delayed recovery actions, calling into question the robustness of traditional models in the Indian context (Chatterjee & Sinha, 2022).

Empirical research has shown that banks which implemented algorithmic or ML-based credit scoring witnessed significant reductions in loan defaults, particularly in consumer and SME portfolios. Studies covering lending patterns from 2017 to 2023 indicate that ML-enhanced scoring improved repayment rates by up to 12%, reduced first-year delinquencies by 15%, and enabled pre-default restructuring in high-risk accounts (Naik, 2023). These findings hold particularly true for private banks that invested in robust data infrastructure and embedded analytics within core lending operations. Conversely, the benefits were less pronounced in public sector banks with lower digital maturity and slower automation deployment (Kou et al., 2021).

Thus, while credit-scoring models in India have evolved considerably over the past decade and contributed meaningfully to NPA management, challenges remain in terms of model fairness, contextual adaptability and last-mile borrower inclusion. The integration of AI-enabled scoring must therefore be approached as a socio-technical system requiring not only technological precision but also regulatory oversight and ethical safeguards (Gambacorta et al., 2020; Sengupta et al., 2023).

Bias and Fairness in Algorithmic Lending

While credit automation offers notable gains in predictive accuracy and processing efficiency, it also introduces complex ethical challenges around algorithmic bias, fairness and inclusion. Scholars argue that ML models, when trained on historical data, risk perpetuating and amplifying the very biases embedded within prior lending decisions (Fuster et al., 2021; Veale & Binns, 2017). For instance, if past credit approvals favoured urban salaried men over rural or self-employed applicants, algorithmic systems may inadvertently learn to replicate these patterns, thereby entrenching historical inequities under the guise of objectivity (Barocas et al., 2019). This phenomenon, termed ‘automated discrimination’, becomes particularly problematic in regulated sectors like banking, where equitable access to credit is a public policy imperative (Sengupta et al., 2023).

The opacity of many ML systems further compounds fairness concerns. Complex models like deep neural networks or boosted ensemble trees are often ‘black boxes’, offering little transparency into how inputs are weighted or which features contribute most to predictions (Brown et al., 2022; Doshi-Velez & Kim, 2017). This lack of interpretability makes it difficult for borrowers to challenge adverse decisions and for regulators to audit model compliance with fairness norms. In response, researchers have developed post hoc interpretability tools, such as SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), which aim to explain model outputs and detect biased feature contributions (Beck et al., 2020). However, these tools offer approximations rather than complete transparency, and their effectiveness remains uneven across different model architectures and datasets (Raji & Buolamwini, 2019; Sengupta et al., 2023).

In sum, while algorithmic lending has the potential to enhance objectivity and scale, its deployment in credit ecosystems must be tempered with safeguards to ensure equity and accountability. A technocratic pursuit of efficiency, devoid of ethical scrutiny, risks undermining financial inclusion and public trust in digital credit infrastructures (Barocas et al., 2019; Kou et al., 2021).

Research Gaps and Theoretical Anchoring

Although extensive literature documents the rise of algorithmic models in credit-risk management, few empirical studies offer comparative analyses of model performance before and after credit automation, especially in emerging market contexts like India. Most extant work either focuses narrowly on model-building exercises using ML (e.g., comparing AUC scores of different classifiers) or offers theoretical critiques on bias and interpretability (Doshi-Velez & Kim, 2017; Lessmann et al., 2015). What remains underexplored is the real-world effectiveness of automated systems in improving credit-risk prediction accuracy, reducing NPAs and enhancing credit decisioning outcomes at the institutional level. Particularly scarce are longitudinal or quasi-experimental studies that examine the causal impact of credit automation across borrower types, product segments and banking institutions (Brown et al., 2022; Kou et al., 2021).

The Indian commercial banking context presents a compelling case for such research. Despite recent digitization efforts, public sector banks still exhibit significant variance in automation adoption, risk culture and model governance structures. Private banks, on the other hand, have implemented ML-based credit-decision systems with relatively greater agility and data integration (Ghosh, 2020; Rajeev & Mahesh, 2021). Yet, there is little academic documentation of whether these deployments have led to measurable improvements in borrower risk differentiation or mitigation of early-stage delinquencies. In addition, most studies fail to stratify results by borrower demography such as income group, gender or geographic origin, making it difficult to assess whether algorithmic credit-scoring promotes or inhibits equitable access (Raji & Buolamwini, 2019; Veale & Binns, 2017). This gap is critical, given India’s socio-economic diversity and its stated policy goal of financial inclusion.

In summary, while the extant literature affirms the technical potential of credit automation, it falls short of empirically assessing its real-world impact on credit-risk outcomes, borrower stratification and institutional fairness. This study addresses these gaps by examining the differential effects of algorithmic lending on credit-risk assessment accuracy and NPA management across Indian banks, framed within the dual theoretical anchors of risk governance and algorithmic accountability (Fuster et al., 2021; Sengupta et al., 2023).

Descriptive Statistics

Post-automation analysis shows marked efficiency gains across borrower and credit metrics. Average credit scores increased from 685 to 714, with sanctioned loan amounts rising from ₹5.6 lakhs to ₹6.1 lakhs, suggesting improved risk differentiation. Default rates fell from 8.2% to 4.7% and NPAs declined from 6.5% to 3.1%, evidencing stronger predictive accuracy and portfolio control (Kou et al., 2021). Loan approvals rose from 72.3% to 79.8%, indicating broader credit access. Yet, algorithmic scoring may favour historically low-risk profiles, underscoring potential fairness concerns requiring further scrutiny.

Table 1 mentions the terms of financial health indicators, the average debt-to-income ratio reduced from 0.34 to 0.28, which indicates either improved borrower financial discipline or stricter model-driven lending filters post-automation. This metric also reflects a likely recalibration in underwriting thresholds, where risk-based pricing and tighter approval criteria may have resulted in more solvent borrower pools entering the lending pipeline.

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Table 1.

Descriptive Statistics.

Metric	Pre-automation (2015–2017)	Post-automation (2018–2024)	Change
Average credit score	685	714	29
Average loan amount (INR lakhs)	5.6	6.1	0.5
Default rate (%)	8.2	4.7	3.499999999999999
NPA rate (%)	6.5	3.1	3.4
Loan approval rate (%)	72.3	79.8	7.5
Average debt-to-income ratio	0.34	0.28	0.06

Collectively, these descriptive trends provide initial empirical support for the argument that credit automation may have enhanced both risk prediction and portfolio performance in Indian commercial banks. Nevertheless, these patterns require econometric validation through multivariate analysis to account for confounding effects such as economic cycles, regulatory shifts or changes in borrower mix. The next section undertakes ML model benchmarking to ascertain whether the observed improvements are attributable to the predictive performance of algorithmic systems or are artefacts of broader macro-financial trends.

Credit-risk Prediction Accuracy: ML Model Benchmarking

Across models, predictive performance improved markedly with algorithmic sophistication. The logistic regression baseline achieved an AUC-ROC of 0.78 and accuracy of 78.2%, correctly classifying most outcomes but struggling to detect high-risk borrowers (precision = 72.5%, recall = 69.3%, F1 = 0.71). Its linear structure limits recognition of non-linear borrower traits (Kou et al., 2021; Lessmann et al., 2015). The Random Forest model enhanced accuracy to 84.5% and AUC to 0.86, with balanced precision (81.2%) and recall (79.4%), effectively capturing income and collateral heterogeneity (Baesens et al., 2016; Beck et al., 2020). The XGBoost model outperformed both, with AUC = 0.91, accuracy = 89.7% and F1 = 0.86, demonstrating superior generalisability through regularized tree boosting (Naik, 2023; Mori & Kitagawa, 2022).

Table 2 provides the superior outcomes observed in XGBoost and Random Forest models also carry implications for credit policy execution. Higher recall reduces the likelihood of false negatives, ensuring high-risk borrowers are not incorrectly approved, a major contributor to NPAs. Similarly, higher precision reduces false positives, avoiding unnecessary credit denial to creditworthy individuals. Thus, the adoption of such models post-automation has likely contributed to improved default prediction, as reflected in the lower NPA rates and enhanced borrower profiling noted in earlier descriptive findings. However, these results also raise questions around transparency and explainability, particularly given the black-box nature of tree-based ensemble methods, concerns addressed in the following sections through SHAP-based interpretability and fairness audits (Doshi-Velez & Kim, 2017; Fuster et al., 2021).

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Table 2.

Credit-risk Prediction Model Performance.

Model	AUC-ROC	Accuracy (%)	Precision (%)	Recall (%)	F1 Score
Logistic regression	0.78	78.2	72.5	69.3	0.71
Random Forest	0.86	84.5	81.2	79.4	0.8
XGBoost	0.91	89.7	87.9	85.1	0.86

Regression Results: DiD Estimation

To isolate the causal impact of credit automation on credit-risk evaluation and asset quality, a DiD estimation was applied to the panel dataset of Indian commercial banks spanning FY2015 to FY2024. This econometric technique leverages the staggered adoption of automation systems across banks, comparing changes in credit outcomes between automation adopters (treatment group) and non-adopters (control group) before and after the intervention period. The regression model includes interaction terms for automation and post-implementation timeframes, controlling for borrower- and bank-level variables. This section interprets the effects of automation on three key dependent variables: standardized credit score, PD and NPA classification.

Table 3 explaining the regression findings indicate robust causal gains from automation across all key outcomes. In the credit‐score model, the Automation × Time coefficient of 0.26 (SE = 0.08, p < .01) implies a 0.26-standard-deviation rise in borrower scores after adoption, reflecting improved profiling accuracy. In the default-probability model, the interaction term (−0.72, p < .01) corresponds to roughly a 72% decline in the log odds of default, confirming stronger predictive precision (Kou et al., 2021; Mori & Kitagawa, 2022). For NPA incidence, the coefficient (−0.61, p < .05) shows a 61% lower likelihood of loan misclassification as non-performing, consistent with post-disbursal monitoring gains (Ghosh, 2020; Rajeev & Mahesh, 2021).

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Table 3.

DiD Regression.

Dependent Variable	Automation	Time (Post-2018)	Automation × Time	Control Variables Included	R-squared
Credit score (standardized)	0.12	0.05	0.26	Yes	0.41
Probability of default (log odds)	−0.45	−0.1	−0.72	Yes	0.52
NPA classification (binary)	−0.38	−0.09	−0.61	Yes	0.47

Model R² values (0.41–0.52) denote moderate explanatory power typical of operational banking data. Directional consistency across credit scores, default risk and NPA ratios strengthens internal validity and substantiates a causal interpretation: algorithmic credit systems materially enhance borrower screening and asset-quality outcomes rather than merely coinciding with them (Lessmann et al., 2015; Naik, 2023).

Fairness and Bias Analysis: Introduction and Interpretation

While the preceding sections confirm the predictive and operational efficiency of credit automation, it is equally essential to examine whether these algorithmic systems distribute credit equitably across borrower categories. This section evaluates fairness and potential bias in credit outcomes based on demographic subgroup analysis. Drawing from recent scholarship in algorithmic accountability and responsible AI in finance, the analysis focuses on observable disparities in approval rates, default incidence and SHAP-based rank importance scores across urban–rural, gender and income-based segments. The inclusion of SHAP indicators allows for transparent assessment of whether certain groups are consistently under-ranked by the credit-scoring models, despite similar risk profiles (Barocas et al., 2019; Doshi-Velez & Kim, 2017).

Approval rate data reveal a stratified access pattern across borrower segments. Urban males received the highest loan approval rate at 82.1%, followed closely by urban females at 78.3%. In contrast, rural males and rural females had approval rates of 70.5% and 66.2%, respectively, while the low-income group saw the lowest rate at 62.4%. These disparities suggest that credit automation systems may favour borrower profiles with more structured financial footprints, such as salaried individuals in urban areas, over those with informal or variable income sources more common in rural and low-income cohorts. Although some of this variation may reflect real risk differentials, such patterns raise questions about data representativeness in training models and the design of risk features that indirectly encode structural disadvantage (Veale & Binns, 2017).

Table 4 explains the fairness analysis reveals persistent segmentation across income, gender and geography. Default rates remain highest among low-income borrowers (8.2%) and rural females (7.1%), compared with 4.2% for urban males and 3.1% for high-income groups. These disparities likely reflect structural limits—financial-literacy gaps, collateral bias and unequal access to data—rather than intrinsic risk (Hurley & Adebayo, 2017). Mean credit scores mirror this pattern: 745 for high-income and 725 for urban males versus 674 for rural females and 658 for low-income borrowers (Chatterjee & Sinha, 2022). SHAP-based rank disparity indicators confirm bias: 0.03–0.05 for advantaged groups but 0.16–0.18 for marginalized borrowers, implying systematic undervaluation of their features (Raji & Buolamwini, 2019; Sengupta et al., 2023). These results affirm that algorithmic credit systems, though efficient, require fairness constraints and regulatory oversight to ensure inclusive credit access.

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Table 4.

Fairness and Bias Analysis by Borrower Segment.

Borrower Group	Approval Rate (%)	Default Rate (%)	Average Credit Score	Automation Bias Indicator (SHAP Rank Disparity)
Urban male	82.1	4.2	725	0.05
Urban female	78.3	4.5	718	0.07
Rural male	70.5	6.8	682	0.14
Rural female	66.2	7.1	674	0.16
Low-income	62.4	8.2	658	0.18
High-income	88.7	3.1	745	0.03

Robustness and Sensitivity Checks

To ensure the internal validity and reliability of the causal estimates derived from the DiD framework, this section presents a set of robustness and sensitivity analyses. These tests were conducted to rule out spurious correlations, model misspecification and time-varying confounders that may bias the estimated impact of credit automation on default and NPA outcomes. Specifically, the following diagnostic tests were employed: (a) a placebo test on a pre-treatment period (2013–2015), (b) a falsification test on a credit segment unaffected by automation (agricultural loans) and (c) sensitivity analysis using varying post-loan assessment windows (6, 12 and 24 months). The results reaffirm the robustness of the study’s primary findings.

Robustness diagnostics confirm the reliability of the causal findings (Table 5). In the placebo test, where automation was hypothetically assigned to 2013–2015, the Automation × Time coefficient (0.02, p = 0.72) was insignificant, ruling out pre-treatment or anticipatory effects (Kou et al., 2021). The falsification test, applied to agricultural loans excluded from automation, yielded an insignificant interaction (−0.03, p =.65), confirming that unautomated segments were unaffected (Chatterjee & Sinha, 2022; Ghosh, 2020). The sensitivity analysis further validated temporal stability: at a 6-month window, the coefficient was −0.58 (p = 0.001), peaking at −0.72 (p =.000) for 12 months— the regulatory benchmark for NPA classification—and slightly moderating to −0.66 (p =.002) at 24 months. This attenuation reflects exogenous macro-financial influences and borrower attrition over longer horizons (Brown et al., 2022; Rajeev & Mahesh, 2021). Collectively, these tests demonstrate that automation’s impact on default and NPA outcomes is neither spurious nor time-bound. The null effects in placebo and falsification models confirm the absence of parallel-trend violations, while consistent significance across observation windows evidences the model’s robustness to temporal framing. These diagnostics substantially reduce concerns regarding reverse causality, omitted-variable bias or endogeneity, reinforcing the credibility of automation as a genuine driver of improved credit-risk performance (Baesens et al., 2016; Fuster et al., 2021).

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Table 5.

Robustness and Sensitivity Checks.

Test Type	Effect Size (Automation × Time)	p Value	Interpretation
Placebo test (2013–2015)	0.02	0.72	No pre-trend effect observed
Falsification test (agricultural loans)	−0.03	0.65	No effect in the control segment
Sensitivity: 6-month window	−0.58	0.001	Significant impact; moderate magnitude
Sensitivity: 12-month window	−0.72	0	Strongest observed effect
Sensitivity: 24-month window	−0.66	0.002	Slight attenuation, effect remains robust

Summary of Key Empirical Findings

Descriptive statistics reveal significant post-automation improvements in credit quality. Average borrower credit scores increased by 29 points, while default and NPA ratios declined by 3.5% and 3.4%, respectively. Loan approvals rose by 7.5%, and the average debt-to-income ratio fell from 0.34 to 0.28, signalling stronger borrower screening and improved financial stability. ML benchmarking confirmed automation’s predictive superiority: logistic regression achieved an AUC of 0.78 (F1 = 0.71), whereas Random Forest and XGBoost attained AUCs of 0.86 and 0.91, with XGBoost demonstrating the highest overall precision (Mori & Kitagawa, 2022; Naik, 2023). These results support H₁, affirming that automation enhances predictive accuracy.

Regression estimates using a DiD framework established causal effects. The Automation × Time term was positive for credit scores (β = 0.26) and negative for default (β = −0.72) and NPA outcomes (β = −0.61), confirming H₂ that automation significantly reduces asset-quality deterioration (Kou et al., 2021).

Fairness analysis introduced a critical caveat. Urban, high-income and male borrowers benefited most, while rural, female and low-income groups exhibited higher misclassification and lower approval rates. SHAP-based disparity ranged from 0.05 (urban males) to 0.18 (low-income applicants), evidencing systemic underweighting of vulnerable profiles (Raji & Buolamwini, 2019; Sengupta et al., 2023), thus validating H₃.

Robustness tests, including placebo and falsification models, showed no spurious effects and sensitivity checks confirmed stable results across time horizons. Overall, automation enhances credit precision and efficiency but introduces fairness asymmetries, warranting algorithmic audits, transparency mandates and inclusive data governance frameworks.

Theoretical Synthesis

The findings of this study represent not only operational improvements but a deeper institutional transformation in how creditworthiness is conceptualized and governed. Interpreted through risk governance theory and the technology acceptance model (TAM), the results reveal credit automation as both a technological and epistemic reconfiguration of financial decision-making.

Risk governance theory (Power, 2009) reframes credit-risk evaluation as a socially embedded practice, where institutional norms and accountability systems shape perceptions of uncertainty. The transition from manual underwriting to algorithmic scoring redefines risk knowledge—from subjective judgement to probabilistic modelling—thereby enhancing calculability and reducing ambiguity (Arner et al., 2020; Brown et al., 2022). The study’s findings of reduced defaults and NPAs illustrate how automation rationalizes financial governance through consistency and repeatability. Yet, the fairness audits reveal new opacity: algorithmic exclusion of rural, low-income and female borrowers demonstrates how automation can reproduce structural inequities (Sengupta et al., 2023; Veale & Binns, 2017). Risk, once institutional, is now partly embedded in model architecture, demanding algorithmic governance anchored in transparency and accountability.

TAM explains institutional adoption patterns. Private banks, perceiving higher utility and possessing stronger digital capacity, embraced automation faster, realizing measurable efficiency gains (Kou et al., 2021; Venkatesh et al., 2003). Public sector banks, constrained by legacy systems and regulatory ambiguity, exhibited slower uptake. Beyond perceived usefulness, fairness and trust emerge as key determinants of sustained adoption. Ethical and interpretability concerns directly influence institutional willingness to scale algorithmic systems.

In synthesis, risk governance theory highlights automation’s shift in risk logic—from discretion to data—while TAM underscores how institutional readiness and ethical trust shape adoption. Together, they frame algorithmic lending as both a technological innovation and a socio-institutional evolution demanding balanced governance between efficiency and equity.

Interpretation of Key Findings

At the predictive level, the marked improvement in model accuracy—especially in ensemble methods such as XGBoost and Random Forest—signals a decisive shift from rule-based credit scoring to adaptive, data-driven analytics. Superior AUC-ROC, precision, recall and F1-scores demonstrate these algorithms’ ability to model non-linear borrower behaviours that traditional regressions overlook. These technical gains translate directly into practical reliability: banks can now differentiate more sharply between high- and low-risk borrowers, contributing to the documented decline in defaults and NPAs where automation was integrated within mature data infrastructures (Lessmann et al., 2015; Mori & Kitagawa, 2022).

The DiD regressions reinforce this causal mechanism. Significant interaction terms (−0.72 for default risk; −0.61 for NPA status) confirm that automation materially improved risk outcomes rather than merely correlating with them. The impact was strongest in private banks possessing advanced data pipelines and internal analytics capacity—highlighting institutional readiness as a key moderator of automation’s success (Arner et al., 2020; Ghosh, 2020).

Yet these gains are not uniformly shared. SHAP-based fairness audits reveal higher rejection rates and rank disparities for rural, low-income and female borrowers, suggesting that algorithmic precision can coexist with demographic inequity. Historical bias, data sparsity and unrepresentative training sets risk converting predictive accuracy into structural exclusion (Hurley & Adebayo, 2017; Veale & Binns, 2017).

Robustness diagnostics—placebo, falsification and multi-window sensitivity tests—confirmed that automation’s impact is genuine, temporally stable and most pronounced over the 12-month regulatory horizon, consistent with NPA classification norms (RBI, 2022; Sengupta et al., 2023).

Collectively, the findings indicate a structural recalibration of India’s lending ecosystem. When institutionally supported, credit automation enhances risk differentiation and asset quality. However, its long-term legitimacy hinges on balancing efficiency with fairness through hybrid decision architectures, transparent model design and inclusive data governance.