BenchCouncil Transactions on Benchmarks, Standards and

Evaluations, 2026

DOI: https://doi.org/10.66834/b5qtx140

Research Article

RESEARCH ARTICLE

A Hybrid MCDM Framework for Assessing Financial

Resilience and Trend Dynamics in Indian Commercial

Banks

Priya Das

1,∗

and Subir Kumar Sen

Research Scholar, Department of Commerce, Tripura University, Agartala, 799022, India and

Professor, Department of Commerce,

Tripura University, Agartala, 799022, India

∗

Corresponding author. priya568das@gmail.com

Received on 1 January 2026; Accepted on 20 May 2026

Abstract

Assessing ﬁnancial resilience in the banking sector requires an integrated framework that captures both cross-sectional

strength and long-term resilience dynamics. In this study, ﬁnancial resilience of Indian commercial banks during the

period 2013–2024 is assessed by using a hybrid MCDM and non-parametric trend analysis approach. Eleven ﬁnancial

indicators covering solvency, asset quality, eﬃciency, and proﬁtability are included in a composite resilience framework,

and criterion weights were objectively determined using the MEREC method. The technique RAM is used to calculate

annual composite resilience scores and ranks the 29 commercial banks. A Mann–Kendall time-series analysis is also

applied to the ﬁnal RAM scores to analyze long-term monotonic trends in bank-level and sector-wide resilience. The

results showed that RAM scores are tightly clustered across banks, suggesting structural convergence in resilience levels.

However, Kruskal-Wallis non-parametric test showed statistically signiﬁcant diﬀerences in the banks’ relative ﬁnancial

resilience across the study period. The MEREC-RAM ranking result showed Kotak Mahindra Bank Ltd. and Tamilnad

Mercantile Bank Ltd. consistently appeared among top at the rankings. While, the Mann-kendall trend test revealed

signiﬁcant Improvement in the resilience of CSB Bank Ltd. and Bank of Maharashtra over the study period. Overall,

combining year-wise relative rankings and monotonic resilience dynamics enables a comprehensive assessment of the

stability of the Indian banking sector, which can oﬀer key insights for regulators, policymakers, and bank management

in strengthening the long-term ﬁnancial resilience of the sector.

Key words: Banks, Financial Resilience, MCDM, MEREC, RAM, Mann-Kendall Trend Analysis

JEL codes: G21, G28, G32, C44, D81

1. Introduction

Financial resilience is the ability of an institution to manage,

respond to, and bounce back from ﬁnancial challenges such as

economic downturns, reduced earnings, or unforeseen costs. For

banks and other ﬁnancial institutions, the ﬁnancial resilience

refers to the ability to withstand shocks, adapt to changing

economic conditions, and maintain stability while continuing to

meet obligations and support economic activities[1, 2]. In the

context of emerging economies, where ﬁnancial systems often

face volatility, weak governance, and high exposure to non-

performing assets (NPAs), evaluating the resilience of banks

is particularly crucial[3, 4]. The resilience of ﬁnancial systems

is inﬂuenced by several factors, such as ﬁrm characteristics,

capital adequacy, liquidity management, risk governance, and

their ability to respond to external shocks[5]. Assessing ﬁnan-

cial resilience enables regulators and policymakers to identify

vulnerabilities and design regulatory frameworks that enhance

risk management and recovery mechanisms[2].

The Indian banking sector has experienced signiﬁcant struc-

tural changes in the past decades. The heavily regulated regime

of the 1980s constrained were limiting operational eﬃciency

of Indian banks and their ability to respond to a rapidly ex-

panding economy[6–8]. However, the economic reforms of the

early 1990s, which included deregulation, partial privatisa-

tion of public sector banks, and interest rate liberalisation,

brought improvements in productivity, competitiveness, and

risk management practices[6, 9–11] . Conversely, the post-

global ﬁnancial crisis period highlighted persistent weaknesses

in banking systems around the world, including India, in the

form of huge NPAs and governance issues[12, 13]. The evo-

lution of the banking sector in India has built upon previous

structural reforms and has moved towards a more risk sen-

sitive and resilient regulatory framework. This evolution has

P. Das and S. K. Sen

been guided by the adoption of Basel I and II capital ade-

quacy standards, the introduction of risk-based supervision,

and governance reforms in public sector banks aimed at enhanc-

ing asset quality, transparency, and prudential discipline[14].

These eﬀorts laid the groundwork for more speciﬁc post-crisis

reforms, such as the Asset Quality Review (AQR) and the

Insolvency and Bankruptcy Code (IBC) in 2015–2016 that en-

hanced balance-sheet recognition and codiﬁed mechanisms for

stress resolution[15, 16]. Although greater regulation has led

to better governance and operational eﬃciency, there is some

evidence that heavier compliance requirements can reduce prof-

itability and internal capital generation, particularly in times

of economic stress[17]. In 2018, the Punjab National Bank

(PNB) scam revealed serious gaps in internal controls, gov-

ernance mechanisms, and risk oversight in Indian banks. It

demonstrated the need for stronger processes, transparency,

and institutional resilience to avoid systemic vulnerabilities

[18]. At the same time, the digitalization of banking services

has brought new operational risks[19].

These developments point to some of the ongoing chal-

lenges faced by the Indian banking sector in sustaining ﬁnancial

resilience throughout cyclical stress, rising NPAs, and balance-

sheet pressures. Commercial banks, particularly public sector

banks, which account for a substantial share of total banking as-

sets, have remained vulnerable to capital adequacy constraints,

asset quality deterioration, and volatility in proﬁtability. Such

vulnerabilities underline the importance of continuously mon-

itoring the stability of banks and their capacity to absorb

shocks arising from macroeconomic ﬂuctuations and structural

adjustments.

Therefore, banking sector resilience should be systemati-

cally and multidimensionally assessed to explore how banks

cope with ﬁnancial stress and adapt to regulatory and eco-

nomic changes. In this context, this study aims to evaluate the

ﬁnancial resilience of Indian commercial banks over 12 years

(2013–2024) using a multi-criteria decision-making (MCDM)

framework that incorporates the Method based on the Removal

Eﬀects of Criteria (MEREC)-based objective weighting and the

Root Assessment Method (RAM), supported by non-parametric

trend analysis. The Method RAM is relatively new and ﬁrst ap-

plied in measuring banking sector performance. The MEREC-

RAM approach can be used to constructs composite resilience

scores based on deviations from reference performance levels. It

allows consistent aggregation of heterogeneous ﬁnancial indica-

tors without excessive sensitivity to scaling assumptions[20, 21].

This framework can be combined with the Mann–Kendall trend

test to compare the relative resilience rankings and their evo-

lution over time, extending MCDM applications beyond static

performance comparisons. The unique methodology of the cur-

rent study is the integration of the impact-driven weighting

scheme with a transparent composite scoring approach and a

dynamic time-series evaluation that does not rely on the sub-

jective judgments or correlation-based weights used in other

studies. Instead this model built on a method that assigns

weights by evaluating the contribution of each indicator to the

overall system performance[22].

Accordingly, the present study is organized into ﬁve major

sections. Section 1 presents a brief introduction of the study;

Section 2 outlines the background of the study, mentioning the

relevant literature, as well as hypotheses development based on

relevant theories. Section 3 presents the methodological steps

and procedures, Section 4 discusses the major ﬁndings using

tables and ﬁgures, and ﬁnally, Section 5 ends with the study

conclusions mentioning managerial and policy implications, and

future research directions.

2. Background of the Study

2.1. Financial Resilience Background

Financial resilience has become a key determinant in un-

derstanding how individuals, households, institutions, and

economies handle and recover from ﬁnancial shocks. It generally

refers to the ability to maintain stability and well-being during

disruptions by combining ﬁnancial resources, skills, and institu-

tional support. This idea encompasses proactive strategies, in-

cluding accumulating savings, diversifying income sources, and

utilizing eﬀective ﬁnancial tools, as well as having the capacity

to recover or even advance to a better ﬁnancial position[23].

Salignac et al.[1] proposed a foundational framework that ex-

plored the concept of ﬁnancial resilience at the household and

community levels in Australia. The study demonstrated the

role that resources, skills, and access to opportunities play in

resilience. This work was further extended to a global scale by

Klapper and Lusardi[24], who showed that ﬁnancial literacy is

a signiﬁcant predictor of ﬁnancial resilience at both the indi-

vidual and household levels, as well as over time[25]. Hamid

et al.[4] investigated the determinants of ﬁnancial resilience in

the context of an emerging economy in Malaysia, highlighting

the key role that savings, income diversiﬁcation, and access to

credit play in enabling individuals and households to build ﬁ-

nancial resilience. Salignac et al.[2] extended this to developing

economies, where institutional and policy supports for national

ﬁnancial resilience were found to contribute to inclusive eco-

nomic development. Jansson[26] linked ﬁnancial resilience to

ﬁrms, noting that ﬁnancial position, proﬁtability, and owner-

ship structures impact a ﬁrm’s ability to withstand downturns.

Sreenivasan and Suresh[27] examined start-ups and found that

good liquidity management, innovation, and preparedness help

new entrepreneurs to manage change. In a study of U.S. house-

holds during the COVID-19 pandemic, Clark and Mitchell[28]

found that ﬁscal assistance programs and savings buﬀers were

important for individual ﬁnancial resilience.

Financial resilience in banking and other ﬁnancial insti-

tutions has been studied extensively, including international

banks during the global ﬁnancial crisis[29], banking systems

around the world[30], the U.S. banking system[31], and a loan-

level analysis of ﬁnancial institutions in Mexico[32]. Alam et

al. [33] conducted research on corporate resilience among ﬁrms

in Bangladesh, speciﬁcally the relationship between corporate

ownership patterns and the resilience of ﬁrms, Daadmehr[34]

proposed a composite ﬁnancial resilience index for workplaces

and ﬁrms, which combines the multi-dimensional matrices (liq-

uidity, leverage, and sustainability practices). Chen and Sun[35]

presented an econometric approach to measuring ﬁnancial

resilience across institutions and economies that combine dy-

namic panel modelling and stress-testing indicators to capture

the temporal response of ﬁnancial entities to shocks.

2.2. MCDM in Bank Performance Evaluation

MCDM techniques have been extensively used for assess-

ing bank performance in diﬀerent institutional and regional

contexts[36]. Most empirical studies have grouped MCDM

approaches into criteria weighting methods and outranking

methods. Studies have combined the two types of MCDM

tools to get a robust performance ranking. AHP–TOPSIS and

IV–TOPSIS was used to evaluate listed private banks in India,

which consistently showed the superiority of HDFC Bank[37],

and a CRITIC-based TOPSIS framework was incorporated to

evaluate public sector banks in India, which showed persistent

performance diﬀerences[38]. Such hybrid frameworks have been

used internationally. Nguyen et al. [39] and Yazdi et al. [40]

showed that bank performance rankings during the COVID-

19 period are contingent on the weighting and aggregation

methods, and that objective weighting schemes are important.

Various alternative weighting and ranking methods have

been compared to improve methodological robustness (e.g.,

Unl¨u et al. [41],

Unvan & Ergen¸c [42], Wanke et al. [43], Sama et

al. [44]). Recent contributions have examined hybrid and inte-

grated models that combine subjective and objective factors to

increase discrimination power and ranking stability[45–47]. Mo-

han and Irfan[8] also explained how artiﬁcial intelligence with

MCDM tools was used to assess the performance of banks in

India.

The literature conﬁrms the eﬀectiveness of MCDM frame-

works in assessing bank performance; however, most studies

remain conﬁned to static, cross-sectional performance rankings.

Limited attention has been paid to integrating MCDM-based

composite indices with dynamic analyses that capture per-

formance evolution over time, particularly in the context of

ﬁnancial resilience. This gap motivates the present study’s use

of an objective weighting scheme and a composite assessment

framework that can support both ranking and temporal trend

analysis.

2.3. Hypotheses Development

Financial resilience can be conceptualized as a multi-

dimensional construct reﬂecting a bank’s ability to absorb

shocks, adapt to adverse conditions, and maintain core func-

tions over time[48, 49]. While the theory of resilience posits

that a bank’s solvency strength, asset quality, operational eﬃ-

ciency, and proﬁtability will result in resilience. However, the

theory does not suggest that resilience will improve in a lin-

ear or monotonic way, especially in banking systems subject to

regulatory reform, economic cycles, and changing risk proﬁles.

From a regulatory perspective, the Capital Buﬀer

Theory[50, 51], as formalized by the Basel framework, states

that capital adequacy and provisioning requirements are ﬁrst

and foremost intended to maintain minimum stability thresh-

olds rather than to continually improve performance. Stricter

asset classiﬁcation standards, supervisory reviews, and correc-

tive action frameworks are some of the regulatory interventions

that force banks to rebalance their capital structures and

portfolios[52]. These measures may increase system-wide stabil-

ity, but it is uncertain how they will ultimately aﬀect composite

resilience measures because improvements in capital or as-

set quality may put short-term pressure on proﬁtability and

eﬃciency.

Moreover, Dynamic Capability Theory[53, 54] provides a

strategic view, which states that resilience can only be im-

proved over the long term if a bank has the ability to perceive

upcoming threats and reorganize its resources in the face of

evolving circumstances[54]. Due to diﬀerences in ownership

structures, managerial skills, and strategic adaptability, not

all banks tend to react to environmental change or regulatory

compliance with higher overall resilience. Consequently, banks

may exhibit heterogeneous and non-uniform resilience trajecto-

ries over time. Combined, these theoretical perspectives suggest

that, at the aggregate level, bank ﬁnancial resilience may not

follow a systematic or statistically discernible long-term trend.

Accordingly, the following null hypothesis is formulated:

: There is no statistically signiﬁcant monotonic trend in the

composite RAM-based ﬁnancial resilience scores of Indian com-

mercial banks over the study period.

: There is no statistically signiﬁcant diﬀerence between

private and public sector banks in India over the study period.

3. Methodology

This study examines the ﬁnancial resilience of selected com-

mercial banks in India from 2013-2014 to 2024-2025.The study

employs an integrated MEREC-based RAM approach within

an MCDM framework to determine the weights of criteria,

rank banks based on their ﬁnancial resilience composite scores,

and then applies the Mann-Kendall trend analysis to iden-

tify the performance dynamics and resilience trends of banks

over the years. It also employed Welch two-sample t-test and

Kruskal–Wallis non-parametric test to examine bank speciﬁc

and year-wise signiﬁcant diﬀerences in resilience scores. The

study used equal weights and entropy-based weighting to check

for the sensitivity of weight change on the ranking outcomes. It

further employed TOPSIS, RATMI, and MARCOS techniques

to examine the robustness of the proposed MEREC-RAM

model. Finally, the Kendall’s tau coeﬃcients are determined to

analyse the concordance of ranking outcomes across the meth-

ods. Figure 1 presents the conceptual framework designed for

conducting the research.

Figure 1. Conceptual Framework of the Study. (Source: Authors’

Compilation)

Accordingly, the study integrated secondary data from the

CMIE database and banks’ annual reports from 2013 to 2024.

The data collected for a sample of 29 commercial banks out

of which 17 are private and 12 public sector counterparts. The

study performed robust data cleaning, accounting for any miss-

ing data. Companies with continuous missing data for multiple

variables were excluded from the analysis. To enhance the ac-

curacy of the data, no approximation or rounding has been

performed. Table 1 outlines the study sample, including bank

codes.

Financial resilience in banks refers to the ability to absorb

shocks, maintain core ﬁnancial functions, and adapt to adverse

economic and regulatory conditions. In line with the banking

resilience and ﬁnancial stability literature, resilience is concep-

tualised as a multidimensional construct encompassing solvency

P. Das and S. K. Sen

Code Name of Bank Ownership Code Name of Bank Ownership

CB1 Axis Bank Ltd. Private CB16 Indusind Bank Ltd. Private

CB2 Bank of Baroda Public CB17 Jammu & Kashmir Bank Ltd. Private

CB3 Bank of India Public CB18 Karnataka Bank Ltd. Private

CB4 Bank of Maharashtra Public CB19 Karur Vysya Bank Ltd. Private

CB5 CSB Bank Ltd. Private CB20 Kotak Mahindra Bank Ltd. Private

CB6 Canara Bank Public CB21 Punjab & Sind Bank Public

CB7 Central Bank of India Public CB22 Punjab National Bank Public

CB8 City Union Bank Ltd. Private CB23 RBL Bank Ltd. Private

CB9 DCB Bank Ltd. Private CB24 South Indian Bank Ltd. Private

CB10 Dhanlaxmi Bank Ltd. Private CB25 State Bank of India Public

CB11 Federal Bank Ltd. Private CB26 Tamilnad Mercantile Bank Ltd. Private

CB12 HDFC Bank Ltd. Private CB27 UCO Bank Public

CB13 ICICI Bank Ltd. Private CB28 Union Bank of India Public

CB14 Indian Bank Public CB29 Yes Bank Ltd. Private

CB15 Indian Overseas Bank Public

Table 1. Selected Commercial Banks (CB). (Source: Authors’ Compilation)

Resilience Code Attributes Descriptions Expected Outcome

Solvency

C1 CAR

Tier I Capital + Tier II Capital

Risk – Weighted Assets

Max

C2 Tier-1 CAP

Tier I Capital

Risk – Weighted Assets

Max

C3 D/E

Total debt

Shareholders’ Equity

Min

Assets Quality

C4 GNPA

Gross NPAs

Gross Advances

Min

C5 NNPA

Net NPAs

Net Advances

Min

C6 LGR

Total Advances

− Total Advances

t−1

Total Advances

t−1

Min

C7 PCR

Total Provisions

Gross NPAs

Max

Eﬃciency

C8 C-I

Operating Expenses

Total Income

Min

C9 C-D

Total Advances

Total Deposits

Min

Proﬁtability

C10 ROA

Net Proﬁt

Total Assets

Max

C11 ROE

Net Proﬁt

Shareholders’ Equity

Max

Table 2. Description of Selected Variables (Criteria). (Source: Authors’ Compilation)

strength, asset quality, operational eﬃciency, and proﬁtability.

Therefore, the criteria or resilience indicators are grouped into

four major dimensions: Solvency, Asset Quality, Eﬃciency, and

Proﬁtability, and treated as indicators of banks’ ﬁnancial re-

silience. Eleven ﬁnancial ratios are incorporated under the four

dimensions of the resilience framework. Table 2 presents the de-

scriptions of the resilience indicators (variables). The variables

are selected and established based on the available literature

and related concepts and theories on banks’ performance and

ﬁnancial resilience[3, 4, 37, 38, 43, 55–63].

3.1. MEREC Approach

Keshavarz et al.[22] proposed a new objective weighting

method, MEREC, which utilizes the removal eﬀect on alter-

natives to determine attribute weights. In contrast to previous

criteria weight calculation techniques, the MEREC method

relies on how the removal of conditions aﬀects the total ef-

fects of substitutes[64, 65]. Higher weights are assigned to

attributes with greater performance eﬀects, and smaller weights

are given to attributes with smaller performance eﬀects. It

gives an innovative approach to the criteria weight calculation

technique.

The method follows the following six steps[22, 64, 65]:

Step 1: Construct the decision/evaluation matrix. The

multi-criteria decision-making incorporates an m × n matrix

where m is the number of alternatives and n is the number of

criteria

X = x







··· x







(1)

The elements of the matrix are denoted by x

, where i =

1, . . . , m, and j = 1, . . . , n. The values in the matrix should

be greater than zero (x

> 0) as well as a positive integer.

Subsequently, any negative criteria values will be adjusted using

the proper method.

Step 2: Normalize the decision matrix n

Simple linear normalization procedure is applied to scale the

elements of the decision matrix.

max x

if j ∈ B (2)

min x

if j ∈ C (3)

Where B is the beneﬁt group of criteria (Max-type), and C

represents the cost criteria (Min-type)

Step 3: Calculate the overall performance P

In this step, a logarithmic aggregation measure with equal

weights for all criteria is employed to compute the overall per-

formance values P

. This measure is based on a non-linear

function. Based on the normalized decision matrix obtained

in the previous step, larger values of n

indicate better

performance of the alternatives.

= ln





1 +





j=1

|ln(n









(4)

Step 4: Determine the performance of alternatives by

removing each criterion

This step evaluates the eﬀect of each criterion on the over-

all performance of the alternatives by excluding one criterion

at a time. Let P

′

denote the overall performance of the i

alternative when the j

criterion is removed.

The performance value after removing the j

criterion is

computed as:

′

= ln





1 +





n − 1

k=1, k=j

|ln(n









(5)

Step 5: The summation of absolute deviations

In this step, we calculate the removal eﬀect of the j

crite-

rion based on the values obtained in steps 3 and 4. Let R

the removal eﬀect of the j

criterion

i=1

′

− P

| (6)

Step 6: Determine the ﬁnal weights of the criterion

j=1

(7)

3.2. RAM Method

The Root Assessment Method (RAM) aims to derive the utility

value of each alternative by aggregating its scores over deci-

sion criteria. The Method was ﬁrst applied to sustainability-

focused multi-criteria decision problems, addressing complex

evaluations with transparent ranking logic[20]. Subsequent

studies extended its application to energy technology selec-

tion, manufacturing systems, urban and cost-of-living assess-

ment, and comparative methodological analysis, including

fuzzy, spherical fuzzy, and neutrosophic environments to man-

age uncertainty[21, 66–68]. The RAM method is selected in

this bank resilience study, considering its low computational

complexity, stable rankings[20], and eﬀective integration with

MEREC-based objective weighting, which may outperform con-

ventional outranking MCDM techniques in complex ﬁnancial

evaluations.

The method involves the following major steps[20, 21, 68]:

Step 1: The ﬁrst step involves forming an initial matrix X.

This is preceded by deﬁning the m set of alternatives A

and a

set of n criteria C

D = x







··· x

. ···

··· x

m×n







(8)

Where alternatives i = 1, 2, . . . , m and criterion j = 1, 2,

. . . ,n canbe expressed based on the nature of the criteria, i.e.,

cost (C) or beneﬁt (B).

Step 2: Normalization of the initial matrix (x) using the

linear sum normalization

i=1

(9)

Where elements x

represents the elements of the matrix x.

Step 3: Determination of the weighted normalized matrix

= n

× w

(10)

Where n

represents the normalized values calculated in

step 2, and w

represents the MEREC weights.

Step 4: Calculate the aggregates of weighted normal-

ized values of beneﬁcial and non-beneﬁcial criteria for each

alternative using the following equations

j=1

+ij

(11)

−i

j=1

−ij

(12)

Whereas K

is the sum of weighted normalized values

of the beneﬁcial criteria and K

−i

is the sum of weighted

normalized values of the cost criteria.

Step 5: Determination of the aggregate relative resilience

scores of alternatives using the following function Q

2+K

−i

2 + K

(13)

The ranking of alternatives relies on the ﬁnal values Q

descending order.

3.3. Mann–Kendall Trend Analysis

To determine whether the ﬁnancial resilience of individual

banks exhibited a monotonic trend over the study period, the

univariate Mann–Kendall non-parametric trend test was ap-

plied to the composite RAM-based ﬁnancial resilience scores for

each bank from 2013 to 2024. The Mann–Kendall test is widely

used for trend detection in time-series data because it does not

require assumptions of normality and is robust to outliers.

P. Das and S. K. Sen

The test statistic is computed as:

S =

n−1

i=1

j=i+1

sgn(x

− x

) (14)

Where n is the number of years, x

, and x

denote the com-

posite RAM scores observed at time points i and j, respectively,

and the sign function sgn(·) is deﬁned as:

sgn(x

− x

) =











+1, if x

− x

> 0,

0, if x

− x

= 0,

−1, if x

− x

< 0.

For sample sizes greater than ten, the statistic S is stan-

dardized to a normal variate Z,

Where, Z =











S−1

√

Var(S)

if S > 0

0 if S = 0

S+1

√

Var(S)

if S < 0

Here,

Var(S) =

n(n − 1)(2n + 5)

It tests the following hypotheses:

: No monotonic trend exists in the series

: A monotonic trend exists in the series

A positive and statistically signiﬁcant Z-value indicates an

increasing trend in ﬁnancial resilience, whereas a negative and

statistically signiﬁcant Z-value indicates a decreasing trend.

Statistical signiﬁcance is evaluated at the 5% level (p < 0.05).

4. Results and Discussions

4.1. Analysis using MEREC

The study applies a multi-stage MCDM-integrated Mann-

Kendall model. Initially, a 29 × 11 (m × n) decision matrix

is developed based on 11 criteria related to 29 banks (alterna-

tives), for each year from 2013-2014 to 2024-2025, using Eq. (1).

The relative importance of each criterion or ﬁnancial resilience

indicator is determined using the MEREC weighting method.

Then, the weights obtained are integrated into the RAM ap-

proach to generate the resilience index. The ranking of banks is

determined based on the aggregate function calculated at the

end of the RAM approach.

The values in the decision matrix (Eq. (1)) were normalized

for each period using Eq. (2) using the linear min-max normal-

ization method to convert all the cost-beneﬁt criteria into the

same measurement scale. Subsequently, a logarithmic aggrega-

tion measure based on equal criterion weights is applied to the

normalized values n

to compute the P

values in Eq. (4). The

values represent the initial overall performance scores of the

alternatives, taking into account all criteria. Accordingly, the

modiﬁed performance values P

′

are calculated using Eq. (5) by

removing one criterion at a time from the set of criteria associ-

ated with alternatives, i.e., column-wise elimination. Equation

(6) is then used to evaluate the criterion removal eﬀect R

measuring the absolute deviation between P

′

and P

for each

alternative i and criterion j. Finally, the criterion weights W

for each study period are determined using Eq. (7) and are

presented in Table 3.

The MEREC results, as shown in Table 3, reveals moderate

variation in criteria weights over time. CAR (C1) increases from

0.046 (2013) to 0.123 (2022), reﬂecting rising regulatory em-

phasis. Loan growth (C6) ﬂuctuates between 0.064 and 0.092,

indicating cyclical sensitivity. Cost eﬃciency (C8) also varies

(0.069–0.098), while ROA (C10) and ROE (C11) remain rela-

tively stable (0.088–0.102). This suggests limited diﬀerentiation

and a consistently lower role in resilience assessment.

Figure 2. Relative Importance of Financial Resilience Indicators

(2013-2024). (Source: Estimated by the authors)

Fig 2 highlights temporal shifts in criteria importance as well

as ﬂuctuations in loan growth and eﬃciency indicators. This

reﬂects impact of real-world events such as the AQR and IBC

phases. On the other hand relative weight stability in proﬁtabil-

ity indicators suggests consistent regulatory and operational

frameworks.

4.2. Analysis using RAM

The method RAM evaluated banking resilience by aggregat-

ing multiple ﬁnancial indicators in a structured manner. The

ﬁrst step is to build the initial decision matrix D (Eq. (8)),

which summarizes the performance of each banking alternative

over all selected resilience indicators. Next it applies linear sum

normalization (Eq. (9)) to enable logical comparison across in-

dicators measured in diﬀerent units. The normalized matrix

is then weighted (Eq. (10)) with the MEREC-derived weight

coeﬃcients shown in Table 3 to produce the weighted normal-

ized matrix V , which ensures that criteria with higher systemic

relevance have a stronger eﬀect on the evaluation.

RAM explicitly accounts for the asymmetric roles of beneﬁ-

cial and non-beneﬁcial criteria by aggregating them separately,

as expressed in Equations(11) and (12). K

is the sum of

weighted normalized values of the beneﬁcial criteria and K

−i

is the sum of weighted normalized values of the cost criteria

which are presented in Table 4. Beneﬁcial criteria contribute

positively to resilience, whereas non-beneﬁcial criteria capture

vulnerability channels. This separation enables the ﬁnal RAM

utility score Q

, computed using Eq. (13), to reﬂect the balance

between shock-absorption capacity and risk exposure.

Table 5 shows the RAM-based aggregate relative resilience

scores (Q

) of each bank from 2013 to 2024. The (Q

) scores

show a high level of consistency. The values generally fall

between about 1.41 and 1.42.

The Q

values are normalized and presented in Table 6

which shows the ﬁnancial resilience ranges between 0.995 and

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

2013 0.046 0.101 0.095 0.095 0.091 0.082 0.101 0.082 0.103 0.102 0.102

2014 0.060 0.100 0.083 0.093 0.090 0.079 0.097 0.098 0.101 0.100 0.100

2015 0.043 0.103 0.094 0.098 0.096 0.091 0.103 0.069 0.103 0.100 0.101

2016 0.073 0.099 0.084 0.091 0.089 0.077 0.099 0.096 0.098 0.097 0.097

2017 0.047 0.101 0.093 0.097 0.095 0.084 0.102 0.078 0.102 0.100 0.100

2018 0.041 0.103 0.096 0.098 0.097 0.092 0.087 0.080 0.102 0.102 0.102

2019 0.054 0.100 0.093 0.096 0.094 0.087 0.102 0.074 0.101 0.101 0.101

2020 0.090 0.096 0.092 0.089 0.087 0.064 0.098 0.093 0.097 0.096 0.097

2021 0.065 0.099 0.095 0.094 0.093 0.064 0.101 0.097 0.100 0.096 0.096

2022 0.123 0.091 0.078 0.086 0.085 0.084 0.095 0.088 0.093 0.088 0.090

2023 0.048 0.101 0.098 0.100 0.098 0.077 0.103 0.073 0.103 0.100 0.099

2024 0.100 0.093 0.080 0.090 0.090 0.080 0.097 0.091 0.096 0.092 0.091

Table 3. Year-Wise Obtained Criteria Weights using the MEREC Approach. (Source: Estimated by the authors)

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

−i

CB1 0.019 0.015 0.019 0.015 0.021 0.018 0.018 0.018 0.017 0.022 0.014 0.020 0.017 0.019 0.018 0.016 0.019 0.017 0.014 0.014 0.018 0.018 0.018 0.013

CB2 0.015 0.018 0.015 0.014 0.011 0.019 0.015 0.018 0.015 0.021 0.013 0.019 0.015 0.024 0.015 0.018 0.014 0.020 0.017 0.014 0.015 0.019 0.016 0.018

CB3 0.013 0.023 0.013 0.020 0.009 0.026 0.013 0.025 0.012 0.024 0.012 0.023 0.015 0.023 0.016 0.018 0.013 0.020 0.014 0.019 0.013 0.025 0.014 0.023

CB4 0.016 0.021 0.019 0.023 0.012 0.023 0.009 0.027 0.013 0.021 0.005 0.025 0.016 0.019 0.016 0.014 0.015 0.014 0.019 0.010 0.020 0.013 0.021 0.015

CB5 0.012 0.020 0.010 0.063 0.008 0.010 0.015 0.018 0.013 0.012 0.012 0.011 0.020 0.015 0.020 0.015 0.027 0.014 0.026 0.008 0.021 0.015 0.019 0.022

CB6 0.014 0.020 0.014 0.017 0.011 0.021 0.015 0.022 0.013 0.022 0.011 0.021 0.015 0.020 0.015 0.023 0.014 0.020 0.017 0.018 0.016 0.022 0.017 0.020

CB7 0.010 0.027 0.013 0.022 0.009 0.023 0.010 0.024 0.008 0.024 0.007 0.022 0.014 0.025 0.015 0.020 0.012 0.024 0.012 0.017 0.013 0.021 0.015 0.018

CB8 0.018 0.012 0.019 0.011 0.021 0.013 0.021 0.012 0.021 0.013 0.016 0.014 0.017 0.014 0.019 0.013 0.020 0.019 0.020 0.017 0.018 0.024 0.019 0.020

CB9 0.018 0.016 0.019 0.015 0.021 0.014 0.019 0.014 0.020 0.015 0.016 0.014 0.018 0.015 0.018 0.017 0.016 0.019 0.016 0.015 0.014 0.023 0.014 0.027

CB10 0.006 0.022 0.008 0.032 0.011 0.011 0.016 0.026 0.016 0.013 0.013 0.016 0.017 0.014 0.015 0.030 0.009 0.021 0.011 0.022 0.008 0.017 0.009 0.021

CB11 0.019 0.012 0.019 0.012 0.018 0.014 0.018 0.014 0.019 0.016 0.015 0.015 0.017 0.014 0.017 0.012 0.017 0.015 0.017 0.012 0.016 0.017 0.016 0.015

CB12 0.019 0.014 0.020 0.011 0.022 0.015 0.021 0.012 0.021 0.014 0.017 0.015 0.021 0.014 0.020 0.011 0.023 0.012 0.021 0.009 0.018 0.020 0.018 0.014

CB13 0.019 0.020 0.019 0.020 0.020 0.022 0.019 0.020 0.019 0.021 0.015 0.019 0.017 0.016 0.019 0.012 0.023 0.013 0.022 0.010 0.020 0.016 0.020 0.013

CB14 0.015 0.018 0.015 0.014 0.015 0.016 0.017 0.016 0.017 0.019 0.013 0.018 0.016 0.018 0.017 0.025 0.014 0.017 0.015 0.013 0.014 0.017 0.017 0.015

CB15 0.012 0.024 0.011 0.024 0.008 0.034 0.009 0.032 0.007 0.028 0.011 0.030 0.010 0.020 0.017 0.016 0.015 0.018 0.014 0.019 0.014 0.020 0.016 0.018

CB16 0.019 0.016 0.018 0.016 0.022 0.017 0.021 0.014 0.021 0.017 0.015 0.020 0.017 0.017 0.018 0.013 0.019 0.014 0.020 0.011 0.018 0.017 0.011 0.017

CB17 0.019 0.012 0.015 0.014 0.017 0.018 0.009 0.017 0.016 0.016 0.013 0.019 0.013 0.017 0.015 0.016 0.013 0.022 0.016 0.016 0.015 0.017 0.017 0.017

CB18 0.015 0.017 0.016 0.014 0.017 0.013 0.018 0.014 0.017 0.015 0.014 0.016 0.016 0.016 0.016 0.012 0.014 0.016 0.018 0.013 0.018 0.021 0.016 0.019

CB19 0.017 0.014 0.018 0.011 0.020 0.012 0.018 0.012 0.018 0.015 0.015 0.018 0.017 0.016 0.018 0.016 0.017 0.016 0.019 0.009 0.018 0.013 0.019 0.016

CB20 0.020 0.015 0.020 0.015 0.021 0.022 0.021 0.013 0.022 0.014 0.018 0.015 0.020 0.013 0.021 0.010 0.024 0.012 0.024 0.008 0.021 0.013 0.022 0.012

CB21 0.012 0.022 0.013 0.020 0.015 0.018 0.014 0.020 0.013 0.020 0.076 0.022 0.012 0.025 0.010 0.029 0.014 0.022 0.015 0.022 0.011 0.023 0.014 0.025

CB22 0.014 0.023 0.014 0.021 0.010 0.028 0.014 0.021 0.008 0.026 0.008 0.023 0.016 0.022 0.015 0.024 0.010 0.024 0.011 0.021 0.012 0.020 0.018 0.019

CB23 0.016 0.032 0.017 0.022 0.018 0.024 0.018 0.018 0.019 0.017 0.015 0.019 0.017 0.018 0.017 0.014 0.010 0.016 0.014 0.015 0.013 0.020 0.012 0.015

CB24 0.016 0.014 0.014 0.011 0.015 0.016 0.016 0.013 0.016 0.015 0.013 0.018 0.014 0.019 0.014 0.018 0.009 0.026 0.013 0.025 0.015 0.022 0.016 0.022

CB25 0.015 0.023 0.015 0.017 0.016 0.023 0.016 0.020 0.015 0.023 0.012 0.021 0.016 0.018 0.016 0.018 0.015 0.019 0.016 0.015 0.015 0.021 0.015 0.017

CB26 0.017 0.015 0.018 0.010 0.020 0.012 0.019 0.013 0.019 0.011 0.016 0.015 0.019 0.012 0.020 0.014 0.024 0.014 0.025 0.008 0.022 0.015 0.024 0.021

CB27 0.015 0.024 0.014 0.019 0.008 0.028 0.011 0.024 0.009 0.028 0.008 0.026 0.013 0.022 0.015 0.018 0.010 0.017 0.012 0.092 0.012 0.021 0.012 0.022

CB28 0.014 0.022 0.014 0.021 0.014 0.022 0.014 0.025 0.011 0.024 0.010 0.025 0.014 0.025 0.015 0.029 0.014 0.023 0.015 0.018 0.016 0.019 0.018 0.016

CB29 0.019 0.017 0.019 0.017 0.021 0.019 0.020 0.012 0.019 0.025 0.013 0.026 0.006 0.035 0.012 0.030 0.012 0.039 0.010 0.026 0.008 0.019 0.011 0.019

Table 4. Aggregates of Weighted Normalized Values of the Beneﬁcial (K

) and Cost (K

−i

) Criteria

1. This normalization simpliﬁed it to compare the scores across

banks and over the years. Most banks keep consistently high

normalized scores, reﬂecting strong and stable performance

throughout the study period. The limited spread in normal-

ized Q

values highlights the stability of the banking system,

while the observed diﬀerences give valuable insights into how

banks perform relative to each other. This supports the reliabil-

ity of the RAM-based evaluation and suggests that the banking

system exhibits consistent resilience with limited dispersion in

performance levels.

The banks such as CB8, CB11, CB12, and CB20 consistently

reﬂected higher Q

scores across multiple years. These banks

demonstrate minimal ﬂuctuations, reﬂecting strong adaptabil-

ity and sustained resilience under uncertainties. In contrast,

banks such as CB5, CB6, CB10, and CB15 demonstrates

slightly lower Q

values and maximum variation over time. Al-

though their scores remain within a high range, the observed

ﬂuctuations suggest relatively lower stability and sensitivity to

macro-level changes compared to the top-performing banks.

Accordingly, Table 7 presents the year-wise ranking results

obtained using the MEREC-RAM approach.

The annual RAM-based rankings provide additional insight

about banks’ relative ﬁnancial resilience over time. While the

changes in normalized Q

values between banks are almost

negligible, the modest variations discovered have a substantial

impact on year-over-year rankings. The narrow dispersion of

RAM scores reﬂects the standardized and highly regulated na-

ture of the Indian banking system under RBI and Basel norms.

This demonstrates that even slight changes in performance can

aﬀect how banks compare in a competitive market.

A set of banks frequently holds the top ranks. In For

instance, Kotak Mahindra Bank Ltd. (CB20) performed ex-

ceptionally well, frequently ranking among the top places

and achieving ﬁrst position multiple times, particularly in re-

cent years. Similarly, Tamilnad Mercantile Bank Ltd. (CB26),

HDFC Bank Ltd. (CB12) and ICICI Bank Ltd. (CB13) main-

tained stable ranks throughout the study period. City Union

Bank Ltd. (CB8) and Federal Bank Ltd. (CB11) also exhibited

relatively high and stable rankings, with minor ﬂuctuations.

These banks can be considered as consistently resilient per-

formers within the sample. On the other hand, CSB Bank Ltd.

(CB5) reﬂected signiﬁcant improvement in rankings from bot-

tom to top ten over the years. Bank of Maharashtra (CB4) also

demonstrated signiﬁcant improvement in rankings by reaching

to the second place during 2024.

P. Das and S. K. Sen

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

CB1 1.417 1.417 1.417 1.416 1.415 1.414 1.416 1.417 1.417 1.416 1.416 1.417

CB2 1.415 1.416 1.414 1.415 1.414 1.414 1.414 1.415 1.414 1.416 1.415 1.415

CB3 1.413 1.414 1.411 1.413 1.413 1.413 1.414 1.415 1.414 1.414 1.413 1.414

CB4 1.415 1.415 1.413 1.411 1.413 1.410 1.415 1.416 1.416 1.419 1.418 1.418

CB5 1.414 1.403 1.414 1.415 1.416 1.416 1.418 1.418 1.420 1.421 1.418 1.416

CB6 1.414 1.415 1.413 1.414 1.413 1.413 1.414 1.414 1.414 1.416 1.415 1.415

CB7 1.411 1.413 1.412 1.412 1.411 1.411 1.413 1.414 1.413 1.414 1.413 1.415

CB8 1.418 1.418 1.418 1.419 1.418 1.417 1.417 1.418 1.417 1.417 1.415 1.416

CB9 1.417 1.417 1.418 1.417 1.418 1.416 1.417 1.416 1.415 1.416 1.413 1.413

CB10 1.411 1.409 1.415 1.413 1.417 1.415 1.417 1.412 1.412 1.413 1.413 1.412

CB11 1.418 1.418 1.417 1.417 1.417 1.416 1.417 1.417 1.417 1.417 1.416 1.416

CB12 1.417 1.418 1.418 1.419 1.418 1.417 1.418 1.419 1.419 1.420 1.416 1.417

CB13 1.416 1.416 1.416 1.416 1.416 1.415 1.416 1.418 1.419 1.420 1.417 1.418

CB14 1.415 1.416 1.416 1.416 1.416 1.414 1.415 1.414 1.415 1.416 1.415 1.417

CB15 1.413 1.412 1.409 1.409 1.410 1.411 1.413 1.416 1.415 1.415 1.414 1.415

CB16 1.417 1.417 1.418 1.418 1.417 1.415 1.416 1.417 1.417 1.418 1.416 1.414

CB17 1.418 1.416 1.416 1.413 1.416 1.414 1.415 1.416 1.413 1.416 1.415 1.416

CB18 1.415 1.416 1.417 1.417 1.416 1.415 1.416 1.417 1.415 1.417 1.415 1.415

CB19 1.417 1.418 1.418 1.418 1.417 1.415 1.416 1.416 1.417 1.419 1.417 1.417

CB20 1.417 1.418 1.416 1.418 1.419 1.417 1.418 1.419 1.420 1.421 1.418 1.419

CB21 1.413 1.414 1.415 1.414 1.414 1.435 1.413 1.411 1.414 1.414 1.412 1.413

CB22 1.414 1.414 1.411 1.414 1.411 1.411 1.414 1.414 1.412 1.413 1.413 1.416

CB23 1.412 1.415 1.415 1.416 1.417 1.415 1.416 1.417 1.414 1.415 1.414 1.415

CB24 1.416 1.416 1.416 1.417 1.416 1.414 1.415 1.415 1.411 1.413 1.414 1.414

CB25 1.414 1.415 1.414 1.415 1.414 1.413 1.415 1.415 1.415 1.416 1.414 1.415

CB26 1.417 1.418 1.418 1.418 1.418 1.416 1.418 1.418 1.419 1.421 1.418 1.417

CB27 1.414 1.414 1.410 1.412 1.411 1.411 1.414 1.415 1.413 1.397 1.413 1.413

CB28 1.414 1.414 1.414 1.413 1.412 1.412 1.413 1.412 1.414 1.415 1.415 1.417

CB29 1.417 1.417 1.417 1.418 1.415 1.412 1.408 1.411 1.409 1.412 1.412 1.413

Table 5. Aggregate resilience scores Q

of banks for each year from 2013-2024. (Source: Authors’ Computation)

In contrast, several banks show persistent lower rankings,

indicating relatively weaker resilience. For instance, Bank of

India (CB3), Central Bank of India (CB7) and Dhanlaxmi

Bank Ltd. (CB10), UCO Bank (CB27) frequently appeared in

the lower ranks. This indicates continuous structural or per-

formance challenges. These banks also exhibit higher ranking

divergence reﬂecting instability in their resilience over time. On

the other hand, Yes Bank Ltd. (CB29) experienced substantial

declines in their rankings especially from 2018, indicative of the

existence of underlying challenges and diﬀerent government’s

correction measures such as AQR and IBC implementations

during 2016-2017, as well as interventions with revised Prompt

Corrective Action (PCA).

Figure 3 presents the heatmap on normalized resilience score

of Qi across diﬀerent period. The colour gradient, ranging from

darker shades (lower scores) to lighter (higher scores) provides

a visual representation of the variation in ﬁnancial resilience

across banks and over time. The colour gradient, ranging from

darker shades (lower scores) to lighter shades (higher scores),

provides a visual representation of the variation in ﬁnancial

resilience across banks and over time. A noticeable deviation

is observed around 2018, where a darker vertical band appears

across a large number of banks. This indicates a sector-wide

decline in resilience during that year, pointing to the presence of

a systemic shock aﬀecting the entire banking system rather than

isolated bank-speciﬁc issues. This might be manifested from two

major events in the Indian ﬁnancial system e.g., PNB scam

and the IL&FS crisis during the year 2018 which signiﬁcantly

aﬀected the entire system.

Figure 3. Heatmap on banks year-wise composite ﬁnancial resilience

scores obtained using MEREC-RAM approach. (Source: Authors’

Computation)

Following this period, it shows a gradual return to normal

range, reﬂecting recovery and improved resilience in subsequent

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

CB1 0.999 0.999 0.999 0.998 0.997 0.985 0.998 0.998 0.997 0.996 0.998 0.999

CB2 0.998 0.998 0.997 0.998 0.997 0.985 0.997 0.997 0.996 0.997 0.998 0.997

CB3 0.997 0.997 0.995 0.996 0.996 0.984 0.997 0.997 0.995 0.995 0.996 0.996

CB4 0.997 0.998 0.996 0.995 0.996 0.983 0.998 0.998 0.997 0.998 1.000 0.999

CB5 0.997 0.989 0.997 0.997 0.998 0.986 0.999 0.999 1.000 1.000 1.000 0.998

CB6 0.997 0.998 0.996 0.997 0.996 0.985 0.997 0.996 0.996 0.996 0.997 0.997

CB7 0.995 0.996 0.995 0.995 0.995 0.983 0.996 0.997 0.995 0.995 0.997 0.997

CB8 1.000 1.000 1.000 1.000 1.000 0.987 0.999 0.999 0.997 0.997 0.997 0.998

CB9 0.999 0.999 1.000 0.999 0.999 0.987 0.999 0.998 0.996 0.996 0.996 0.996

CB10 0.995 0.994 0.998 0.996 0.999 0.986 0.999 0.995 0.994 0.994 0.996 0.995

CB11 1.000 1.000 0.999 0.999 0.999 0.987 0.999 0.999 0.997 0.997 0.998 0.998

CB12 0.999 1.000 1.000 1.000 1.000 0.987 1.000 0.999 0.999 0.999 0.998 0.999

CB13 0.998 0.998 0.998 0.998 0.998 0.986 0.999 0.999 0.999 0.999 0.999 0.999

CB14 0.998 0.998 0.998 0.998 0.998 0.985 0.998 0.996 0.996 0.996 0.998 0.998

CB15 0.996 0.996 0.993 0.994 0.994 0.983 0.996 0.998 0.996 0.995 0.997 0.998

CB16 0.999 0.999 1.000 1.000 0.999 0.986 0.998 0.999 0.998 0.998 0.999 0.996

CB17 1.000 0.998 0.998 0.996 0.998 0.985 0.997 0.997 0.995 0.996 0.998 0.998

CB18 0.998 0.999 0.999 0.999 0.999 0.986 0.998 0.998 0.996 0.997 0.998 0.997

CB19 0.999 0.999 1.000 0.999 0.999 0.986 0.999 0.998 0.997 0.998 0.999 0.999

CB20 0.999 0.999 0.998 1.000 1.000 0.987 1.000 1.000 1.000 0.999 1.000 1.000

CB21 0.996 0.997 0.998 0.997 0.997 1.000 0.996 0.994 0.995 0.995 0.996 0.996

CB22 0.997 0.997 0.995 0.997 0.994 0.983 0.997 0.996 0.994 0.994 0.997 0.998

CB23 0.996 0.997 0.997 0.998 0.999 0.986 0.998 0.998 0.996 0.996 0.997 0.997

CB24 0.999 0.999 0.998 0.999 0.998 0.985 0.997 0.997 0.993 0.994 0.997 0.997

CB25 0.997 0.998 0.997 0.998 0.997 0.985 0.998 0.997 0.996 0.996 0.997 0.997

CB26 0.999 1.000 1.000 0.999 1.000 0.987 1.000 0.999 0.999 1.000 1.000 0.999

CB27 0.997 0.997 0.994 0.996 0.995 0.983 0.997 0.997 0.995 0.983 0.996 0.996

CB28 0.997 0.997 0.997 0.996 0.995 0.984 0.996 0.995 0.995 0.996 0.998 0.998

CB29 0.999 0.999 0.999 1.000 0.997 0.984 0.993 0.994 0.992 0.993 0.996 0.996

Table 6. Normalized values of aggregate resilience scores (Q

). (Source: Estimated by the authors)

years. This pattern highlights the ability of banks to adapt and

regain stability after adverse conditions.

After this period, it indicates a gradual recovery back to

normal range, which shows that banks have returned to a

more stable situation in later years. Interestingly, while most

other banks’ resilience scores fell signiﬁcantly, as seen in Fig. 3,

the Punjab & Sind Bank (CB21) had the highest ranking in

terms of resilience in 2018 (Table 7), which was likely due to

a one-oﬀ balance-sheet adjustments. Overall, the RAM rank-

ings highlight meaningful improvements in relative resilience

and systemic stress, rather than dramatic shifts in the banks’

absolute strength.

4.3. Hypotheses Testing

The Mann–Kendall (MK) non-parametric trend test was em-

ployed using Eq. (14) to examine both bank-level and sector-

level dynamics in composite RAM-based ﬁnancial resilience

scores over the period 2013–2024. First, annual RAM scores for

29 banks were structured as a balanced time series, enabling

univariate MK tests for each bank to detect statistically sig-

niﬁcant monotonic trends. The test statistic S, Kendall’s tau

(τ ), and associated p-values were computed for all banks which

allow for classiﬁcation of resilience trajectories as increasing or

decreasing trend.

The Mann–Kendall trend analysis in Fig. 4 demonstrates

the upward (increasing) and downward (decreasing) trend in

ﬁnancial resilience, as well as banks with no signiﬁcant trend

or (stable over the period) highlighted from 2013 to 2024. The

Figure 4. Bank-speciﬁc Mann–Kendall Trend Test results (2013–2024).

(Source: Authors’ Computation)

MEREC–RAM rankings presented in Table 6 capture the rel-

ative resilience levels of banks in a particular year, while the

Mann–Kendall test captures the dynamics of resilience.

The results reveal that the majority of banks do not ex-

hibit statistically signiﬁcant trends in their resilience scores

P. Das and S. K. Sen

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

CB1 5 7 7 14 18 16 14 10 8 17 8 5

CB2 15 16 22 16 19 19 22 18 17 12 15 16

CB3 24 24 26 25 24 22 23 17 20 23 27 24

CB4 17 17 23 28 22 29 17 13 11 6 3 2

CB5 22 29 19 18 13 8 4 5 1 1 4 14

CB6 18 19 24 19 23 21 20 24 18 18 17 20

CB7 28 26 25 27 26 26 25 22 25 22 22 18

CB8 3 2 1 2 2 4 5 6 7 11 16 11

CB9 9 8 5 8 5 5 7 14 14 14 24 28

CB10 29 28 17 22 10 11 6 27 26 27 26 29

CB11 2 3 8 9 7 7 8 7 10 10 9 12

CB12 6 1 2 1 4 3 1 2 3 4 10 6

CB13 13 15 12 15 16 14 9 3 5 5 5 3

CB14 16 14 14 13 15 15 15 23 15 15 14 9

CB15 26 27 29 29 29 27 27 15 13 21 18 15

CB16 7 9 6 5 6 13 11 8 6 8 7 23

CB17 1 13 15 23 14 18 18 16 24 16 12 13

CB18 14 12 9 10 11 9 13 9 12 9 11 19

CB19 8 5 3 7 8 10 10 12 9 7 6 7

CB20 4 6 11 3 1 2 2 1 2 3 1 1

CB21 25 25 16 20 20 1 28 29 21 24 28 27

CB22 23 22 27 21 28 25 21 25 27 26 23 10

CB23 27 20 18 12 9 12 12 11 19 19 21 21

CB24 12 11 13 11 12 17 19 21 28 25 20 22

CB25 19 18 20 17 21 20 16 19 16 13 19 17

CB26 10 4 4 6 3 6 3 4 4 2 2 4

CB27 20 21 28 26 27 28 24 20 23 29 25 26

CB28 21 23 21 24 25 24 26 26 22 20 13 8

CB29 11 10 10 4 17 23 29 28 29 28 29 25

Table 7. Year-wise MEREC-based RAM rankings. (Source: Estimated by the authors)

over time. This suggests that ﬁnancial resilience remains rel-

atively stable for most institutions with no consistent upward

or downward trajectory.

However, a subset of banks demonstrates statistically signif-

icant trends. Speciﬁcally, banks such as CB4 and CB5 exhibit

signiﬁcant improving trends, indicating gradual improvements

in resilience over the study period. These results suggest that

these banks have strengthened their ﬁnancial position over

time. In contrast, several banks display signiﬁcant decreasing

trends in resilience. Notably, CB8, CB9, CB11, CB12, CB16,

and CB24 show statistically signiﬁcant downward trends, with

CB11 exhibiting the strongest decline (p < 0.01). This indicates

a functional decline in ﬁnancial resilience for these institutions,

which may warrant closer attention from the side of regulatory

bodies.

Banks CB4 and CB5 indicate statistically signiﬁcant up-

ward trends in resilience which align with Financial Resilience

Theory and show signs of improvement in solvency, asset qual-

ity, eﬃciency, and proﬁtability. Capital Buﬀer Theory focuses

on the need to maintain suﬃcient capital and Tier-1 buﬀers

to reduce bank fragility and promote long-term stability. This

reﬂected in the banks with positive trends as well as with

the banks showing stable ﬁnancial resilience throughout the

period. Meanwhile, banks that have experienced signiﬁcant de-

clines that correspond with the theory that weak buﬀers make

banks more vulnerable due to problems with NPAs, inadequate

provisioning, and earnings pressure. CB11 display marginally

signiﬁcant declining trend which reﬂect the lingering eﬀects of

capital erosion, governance challenges, and volatility in prof-

itability. The majority of large public and private sector banks

such as CB25, CB1, CB13, CB22, CB25, and so forth, showing

no statistically signiﬁcant trend implies relative stability over

the period but limited monotonic improvement in resilience.

The sector-level resilience is further evaluated by construct-

ing annual mean RAM scores, to which separate MK tests

are applied. To assess heterogeneity in resilience patterns, the

sample was stratiﬁed based on ownership structure (public vs.

private banks). Both parametric and non-parametric tests were

employed to determine whether resilience trajectories diﬀered

signiﬁcantly across subgroups.

Although the initial bank-level Mann–Kendall analysis pro-

vides signiﬁcant evidence on how some banks experienced

increasing and decreasing trends in their ﬁnancial resilience, the

Mann–Kendall test for sector mean resilience indicates no sta-

tistically signiﬁcant monotonic trend (Z = −1.71, p = 0.086).

However, the negative tau (−0.394) suggests a weak declining

tendency over the study period. Therefore, the null hypothesis

) of no monotonic trend cannot be rejected at the aggregate

level.

A Sen’s slope estimator is used to quantify the magni-

tude and direction of sector-wide change. The estimated slope

is visualised in Fig. 5. Initially, a Welch two-sample t-test

is conducted on average RAM scores over the study period

(2013–2024), which revealed a statistically signiﬁcant diﬀerence

between the two groups (t = 4.64, p < 0.001). A non-parametric

Kruskal–Wallis test is additionally performed to ensure robust-

ness. The results conﬁrm a statistically signiﬁcant diﬀerence

Figure 5. Ownership-Based Heterogeneity Analysis and Sectoral Trend in Financial Resilience. (Source: Authors’ Computation)

in resilience scores, especially between the public and private

sector banks (χ

= 71.096, p < 0.001). The null hypothe-

sis (H

) of no diﬀerence between public and private banks

is rejected, as Welch two-sample t-test and non-parametric

tests Kruskal–Wallis reveal statistically signiﬁcant diﬀerences

in resilience across ownership groups.

As can be seen in the boxplot (Fig. 5), private banks have

a more resilient recovery from the sharp drop around 2018,

which is a sign of greater systemic resilience. The diﬀerent re-

covery paths of public sector banks (PSBs) and private banks

since 2018 show the impact of diﬀerent governance structures,

support for recapitalization, and operational ﬂexibility. Public

sector banks received signiﬁcant recapitalization and gover-

nance changes through the EASE and Indradhanush programs.

However, they also had to manage a larger amount of legacy

non-performing assets. In contrast, private banks usually had

more managerial freedom and better proﬁt margin which al-

low them to recover and adapt more quickly. These diﬀerences

in structure might caused the varying resilience of each sector

after systemic shocks.

These validate that the observed variations in resilience

are statistically signiﬁcant and not an artifact of the MCDM-

based ranking framework. The results are also highly consistent

with Dynamic Capability Theory, which asserts that ﬁrms

can maintain superior performance by sensing risks, seiz-

ing opportunities, and reconﬁguring resources under changing

environments[53]. The gradual recovery of the Indian banking

sector after the structural change around 2018 also shows a

positive adaptive response through a series of balance-sheet

adjustments, capital augmentation, and operational restructur-

ing.

4.4. Sensitivity Analysis and Robustness Test

To examine the eﬀect of changes in criteria weighting, a

sensitivity analysis is conducted by comparing the baseline

MEREC–RAM results with alternative equal-weighting and

Entropy-based weighting scenarios. The equal-weighting was

implemented by applying 1/11, in which all ﬁnancial resilience

indicators were assigned identical importance 0.0909 (i.e., for

11 criteria), thereby eliminating any data-driven or subjective

prioritisation among criteria. Correspondingly, the method en-

tropy is applied to determine objectives which then followed by

entropy-RAM rankings.

The study applied additional outranking techniques such as

TOPSIS, RATMI, and MARCOS for a robustness test incor-

porating the MEREC weights. The Kendall’s tau coeﬃcient

is employed to assess the degree of rank concordance among

the ranking outcomes obtained from variations in weight-

ing schemes and multiple outranking models. Fig. 6 presents

the year-wise p-values of Kendall’s tau coeﬃcient for rank

agreement in both the sensitivity analysis and robustness tests.

The consistently high Kendall’s tau coeﬃcients across all

years (Fig. 5) indicate that the integrated approach produces

stable and reliable rankings. Entropy and MEREC shows near-

perfect agreement (τ = 0.96 − 1.00), while Equal Weighting

and MEREC ranges from 0.71 to 0.94. This demonstrates that

the results are not driven by a speciﬁc weighting technique,

but rather reﬂect inherent data structure. Under the robustness

test, RAM and TOPSIS (0.79–0.95) show strongest agreement,

followed by RAM-MARCOS (0.67–0.89) and RAM-RATMI

(0.67–0.81). The absence of signiﬁcant rank reversals across

methods and time reinforces the robustness of the framework.

5. Conclusions

Assessing banks’ ﬁnancial resilience is vital in emerging

economies, where ﬁnancial systems are characterized by volatil-

ity, governance constraints, and heightened exposure to NPAs.

Thus, in order to assess the ﬁnancial resilience Indian com-

mercial banks between 2013 and 2024, this study developed

an integrated analytical framework that combines the MEREC

objective weighting approach, the RAM-based composite re-

silience index, , and Mann-Kendall trend analysis.

The results showed that the RAM-based resilience rankings

are narrowly clustered among banks, indicating the eﬀect of

structural convergence brought about by regulatory standard-

ization. The statistical signiﬁcance tests Kruskal–Wallis verify

that the observed diﬀerences are meaningful and signiﬁcant.

However, the trend analysis shows that resilience trajecto-

ries at the bank level vary signiﬁcantly. A limited number

of banks have statistically signiﬁcant monotonic trends, but

cross-sectional rankings show year-to-year variability for most

banks. The lack of a noticeable trend at the sector-level indi-

cates that the Indian banking sector resilience develops through

periodic adjustments rather than steady directional improve-

ment. Moreover, while Private sector banks showed greater

adaptive capacity and quicker recovery from systemic shocks,

public banks were more sensitive to regulatory interventions.

The MEREC-RAM ranking result showed Kotak Mahindra

Bank Ltd. and Tamilnad Mercantile Bank Ltd. consistently

showed strong resilience across the study period. Meanwhile the

CSB Bank Ltd. reﬂected signiﬁcant improvement in rankings

P. Das and S. K. Sen

Figure 6. Heatmaps on Kendall’s tau (τ ) across methods. (Source: Authors’ Computation)

from bottom to top ten over the years. Bank of Maharash-

tra also demonstrated signiﬁcant improvement in rankings by

reaching to the second place during 2024. Conversely, Bank

of India, Central Bank of India and Dhanlaxmi Bank Ltd.,

UCO Bank constantly positioned at the bottom in the rankings.

Interestingly, the Mann-kendall trend test showed signiﬁcant

improvement in the resilience of Bank of Maharashtra and CSB

Bank Ltd. during the study period. Whereas, City Union Bank

Ltd., DCB Bank Ltd., Federal Bank Ltd., HDFC Bank Ltd.,

Indusind Bank Ltd., and South Indian Bank Ltd. reﬂected

signiﬁcant decline in their ﬁnancial resilience. However, other

banks demonstrated stable or no signiﬁcant improvement or

deterioration.

This study contributes to Financial Resilience Theory in

showing that resilience is non-linear, heterogeneous, and con-

vergent rather than monotonic. These ﬁndings are also in line

with Institutional Adaptation Theory, which contends that

resilience outcomes depend on banks’ capacity to adapt to shift-

ing macroeconomic and regulatory conditions. The results are

consistent with a convergence-diﬀerentiation approach to bank-

ing resilience, in which statistically signiﬁcant performance

diﬀerences resulting from diﬀerences in banks’ capacity to

adapt, manage risks, and react to changing conditions are con-

trasted with structural similarity brought about by regulatory

frameworks.

The Mann-Kendall analysis shows that banks exhibit dis-

tinct resilience trajectories, which have signiﬁcant managerial

and policy signiﬁcance. From a managerial perspective, the

ﬁndings suggest that persistent adaptive capability is neces-

sary in addition to maintaining high relative rankings. To

achieve long-term resilience, banks must place the utmost im-

portance on proactive risk management, ongoing balance-sheet

reconﬁguration, and operational eﬀectiveness. Banks with in-

creasing trends demonstrate great adaptive capacity and can

serve as benchmarks for best practices, with a continuous em-

phasis on risk management and capital strengthening. Banks

showing deteriorating trends (e.g., CB8, CB9, CB11, CB12,

CB16, and CB24) should prioritize strengthening asset qual-

ity through stricter credit appraisal, early warning systems,

and faster resolution of non-performing assets. Management

should improve capital adequacy, liquidity buﬀers, and risk gov-

ernance while adopting data-driven monitoring of proﬁtability

and operational eﬃciency. Diversiﬁcation of loan portfolios and

digital transformation can reduce concentration and operating

risks. Regulators may encourage periodic stress testing, en-

hanced disclosure standards, and corrective action frameworks

for vulnerable banks. From a policy perspective, the lack of a

broad sector-wide trend indicates that while regulatory changes

have stabilized the system, they have not consistently increased

resilience.

Although the study makes important contributions, it is

limited by its analysis to ﬁnancial indicators without incorpo-

rating macroeconomic or market-based variables. Although the

Mann–Kendall test captures monotonic trends but it unable to

conduct a complete structural break or regime shifts. Further

research could expand on this framework by including macro-

ﬁnancial variables, structural break or panel-based approaches,

and identifying causal links between regulatory interventions,

adaptive capacities, and banking system resilience.

Ethical Statement

No ethical approval was required for this study, as it did not in-

volve human or animal subjects. The study relied exclusively on

secondary data obtained from publicly available and authorized

databases and annual reports.

Funding

This research received no speciﬁc grant from any funding

agency in the public, commercial, or not-for-proﬁt sectors.

Declaration of competing interests

The authors declare that they have no known competing ﬁnan-

cial interests or personal relationships that could have appeared

to inﬂuence the work reported in this paper.

Data Availability Statements

The data supporting the ﬁndings of this study are available

from the corresponding author upon reasonable request. How-

ever, the data are not publicly available due to access restric-

tions associated with the Prowess-IQ database and institutional

data usage policies. Data were obtained from authorized sources

including Prowess-IQ (https://prowess.cmie.com/) and annual

reports of banks.

Credit authorship contribution statement

Priya Das: Conceptualization; Methodology; Investigation;

Data Curation; Formal Analysis; Software; Visualization; Writ-

ing – Original Draft Preparation. Subir Kumar Sen: Concep-

tualization; Supervision; Project Administration; Validation;

Visualization; Resources; Writing – Review & Editing.

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