Artificial Intelligence in Financial Inclusion: An Impact on Financial Accessibility and Efficiency in India

 

Lokeshwari DV1, Shruthi M P2, T. Manjunatha3

1Assistant Professor, PG Department of Commerce, Surana College – Autonomous,

Bangalore, Karnataka.

2Assistant Professor, School of Commerce and Management,

Dayananda Sagar University, Bangalore, Karnataka.

3Professor, Department of MBA, Visvesvaraya Technological University,

BDT College of Engineering, Davanagere -577004, Karnataka.

*Corresponding Author E-mail: dvlhokeshwarie@gmail.com, mpshurthigvt@gmail.com, tmmanju87@gmail.com

 

ABSTRACT:

This study investigates AI's impact in increasing financial inclusion and strengthening government-led initiatives such as PMJDY and DBTs. AI tools like machine learning-based credit scoring and automated transactions help to combat instability, fraud, and financial exclusion. The study, which is based on TAM, Financial Inclusion Theory, RBV, and Fraud Triangle Theory, analyses primary data from 468 stakeholders as well as secondary data from the RBI and NITI Aayog. While AI enhances transparency and efficiency, its influence on fraud prevention and financial literacy is questionable. SEM confirms a beneficial link between artificial intelligence and financial inclusivity. Future research should focus on improving AI infrastructure, literacy initiatives, and internet accessibility to promote long-term economic growth.

 

KEYWORDS: AI implementation mechanisms, financial inclusion programs, Direct Benefit Transfers (DBTs), Pradhan Mantri Jan Dhan Yojana (PMJDY).

 

 

INTRODUCTION:

Financial inclusion is essential for economic development because it ensures that underprivileged populations have access to financial services. Initiatives such as PMJDY and DBTs seek to close this gap, however they confront inefficiencies, fraud concerns, and accessibility issues. AI technologies such as machine learning-based credit scoring, automated transactions, and AI-driven consulting services have the potential to provide solutions.

 

However, the impact of artificial intelligence on financial access, operational efficiency, fraud reduction, and financial literacy has received little attention. This paper investigates these issues and provides five hypotheses for evaluating AI's involvement in improving accessibility, efficiency, fraud detection, financial awareness, and overall inclusion results in government-led programs.

 

Graph No: -1

Self-complied by authors

 

 

Graph No.2

Self-Complied: - JAMOVI

 

REVIEW OF LITERATURE:

Sarma (2008) developed the notion of financial inclusion with the Financial Inclusion Index (FII), which focuses on access, utilization, and service quality. Chakrabarty (2022) emphasized the benefits of AI-powered financial services in terms of decreasing documentation and loan decision bias. NITI Aayog (2023) reinforced this by demonstrating that AI-based credit scoring enhanced financial access for low-income individuals. The RBI (2023) acknowledged AI's importance in automating lending, lowering reliance on manual verification.

 

AI-powered chatbots and voice-responsive banking (Sharma et al., 2022) have improved rural financial accessibility. Banerjee and Singh (2019) and Jain and Patel (2020) discovered that AI reduces lending prejudices. According to Mehta (2021), AI integration in MSFIs increases banking penetration, while Gupta et al. (2022) confirm that AI-driven digital payments improve financial access. AI in DBTs decreases transaction mistakes by 35% (Kumar and Gupta, 2021), resulting in speedier disbursements (McKinsey, 2023). AI also combats fraud, as Singh and Roy (2022) report a 40% reduction in fraud detection. AI-based biometric verification reduces identity fraud (World Bank, 2023). AI improves financial literacy; Patel and Sharma (2021) discovered that chatbots increased customer awareness by 50%. UNDP (2023) emphasized AI-driven financial education, while Banerjee (2020) observed that AI gamification increases recollection. AI-powered advising services simplify banking (Kumar, 2020), hence improving financial decision-making (Rao, 2023).

 

THEORETICAL FRAMEWORK:

This study combines several theoretical frameworks to evaluate AI's involvement in financial inclusion through government-led programs. The Technology Acceptance Model explains AI adoption through perceived utility, whereas Financial Inclusion Theory emphasizes its role in delivering financial services to underprivileged communities. The Resource-Based View regards AI as a strategic asset for efficiency and competitiveness, whereas the Fraud Triangle Theory focuses on AI's potential to detect fraud and prevent fund misallocation. According to the study's conceptual framework, AI adoption is the most important independent variable, influencing financial access, operational efficiency, fraud reduction, and financial literacy, as shown in Graph 1.

 

RESEARCH METHODOLOGY:

This study uses AI implementation as an independent variable, which includes AI credit rating, DBTs via automation, fraud detection, and AI-driven financial literacy help. The dependent variable is financial inclusion outcomes, which are measured in four areas: financial accessibility (expanding banking access), operational efficiency (faster transactions and better services), fraud reduction (preventing identity fraud and fund misallocation), and financial literacy (raising awareness through AI advisory tools). Primary data from 300-500 respondents, including banking personnel, policymakers, and beneficiaries, would be combined with secondary data from the RBI and NITI Aayog. Descriptive and regression analysis will be used to analyze the impact of AI on financial inclusion.

 

RESULTS AND DISCUSSION:

Table 1 presents demographic data on 468 respondents, covering banking status, occupation, gender, education, and age. Most respondents have banking access (Mean = 1.67). Occupation (Mean = 2.01) includes students, workers, and business professionals. Gender distribution (Mean = 2.32) is skewed, indicating dominance of one category. Education levels vary (Mean = 2.50, SD = 1.133), spanning primary to tertiary levels.

 

Table No 1

Descriptive Statistics

 

Banking Status

Occupation

Gender

Education

Age

Valid N (listwise)

N

Statistic

468

468

468

468

468

468

Range

Statistic

1

2

1

3

3

 

Minimum

Statistic

1

1

2

1

1

 

Maximum

Statistic

2

3

3

4

4

 

Sum

Statistic

782

942

1085

1168

1188

 

Mean

Statistic

1.67

2.01

2.32

2.50

2.54

 

Std. Error

.022

.038

.022

.052

.051

 

Std. Deviation

Statistic

.470

.812

.466

1.133

1.099

 

Variance

Statistic

.221

.659

.217

1.283

1.208

 

Skewness

Statistic

-.730

-.023

.782

-.003

-.088

 

Std. Error

.113

.113

.113

.113

.113

 

Kurtosis

Statistic

-1.473

-1.483

-1.394

-1.392

-1.307

 

Std. Error

.225

.225

.225

.225

.225

 

Self-Complied: - SPSS

 

Table No 2

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1.107

3

.369

1.675

.172b

Residual

102.218

464

.220

 

 

Total

103.325

467

 

 

 

a. Dependent Variable: Banking Status

b. Predictors: (Constant), AI Reduces Barriers, AI Credit Scoring, Easier Banking Access

Self-Complied: - SPSS

 

 

Table No 3

Paired Samples Correlations

 

N

Correlation

Significance

One-Sided p

Two-Sided p

Pair 1

AI Usage and AI Reduces Barriers

468

-0.010

0.419

0.838

Pair 2

AI Usage and AI Reduces Benefit Delays

468

-0.055

0.115

0.231

Pair 3

AI Usage and AI Reduces Processing Time

468

-0.034

0.232

0.465

Self-Complied: - SPSS

Table No 4

Crosstab

 

AI Smooth Transactions

Total

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

AI Usage

yes

44

49

44

39

51

227

no

52

50

42

49

48

241

Total

96

99

86

88

99

468

Self-Complied: - SPSS

 

Age distribution (Mean = 2.54, SD = 1.099) shows a balance between young and middle-aged individuals. Skewness and kurtosis indicate diversity in responses, with education having the highest variance. Overall, the dataset is well-dispersed for AI and financial inclusion analysis.

 

H1: AI adoption does not significantly enhance financial accessibility, as the p-value (0.172) is greater than 0.05.

H2: AI-driven automation does not significantly improve operational efficiency in banking transactions, as correlations are near zero and p-values exceed 0.05.

H3: AI-based fraud detection does not significantly reduce financial fraud and misallocation, as the Chi-Square test shows a high p-value (0.821).

H4: AI-powered financial literacy tools do not significantly impact financial awareness or savings behavior, as p-values (0.433, 0.723) are greater than 0.05.

H5: AI adoption in financial inclusion programs has a positive relationship with overall financial inclusion outcomes, as model fit indices indicate a strong fit.

 

Table No 5

Chi-Square Tests

 

Value

df

Asymptotic Significance (2-sided)

Pearson Chi-Square

1.533a

4

.821

Likelihood Ratio

1.535

4

.820

Linear-by-Linear Association

.171

1

.680

N of Valid Cases

468

 

 

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 41.71.

Self-Complied: - SPSS

Table No 6

 

AI Reduces Benefit Delays

Total

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

AI Usage

yes

46

39

52

42

48

227

no

51

52

54

44

40

241

Total

97

91

106

86

88

468

Self-Complied: - SPSS

 

Table No 7

Chi-Square Tests

 

Value

df

Asymptotic Significance (2-sided)

Pearson Chi-Square

2.510a

4

0.643

Likelihood Ratio

2.515

4

0.642

Linear-by-Linear Association

1.438

1

0.230

N of Valid Cases

468

 

 

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 41.71.

Self-Complied: - SPSS

 

Table No 8

 

AI Fraud Detection

Total

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

AI Usage

yes

47

50

37

41

52

227

no

36

44

52

45

64

241

Total

83

94

89

86

116

468

Self-Complied: - SPSS

 

Table No 9

Chi-Square Tests

 

Value

df

Asymptotic Significance (2-sided)

Pearson Chi-Square

5.382a

4

0.250

Likelihood Ratio

5.396

4

0.249

Linear-by-Linear Association

3.036

1

0.081

N of Valid Cases

468

 

 

a. 0 cells (0.0%) have an expected count less than 5. The minimum expected count is 40.26.

Self-Complied: - SPSS

 

Table No 10

ANOVA

 

Sum of Squares

df

Mean Square

F

Sig.

AI Improves Literacy

Between Groups

1.176

1

1.176

0.616

0.433

Within Groups

889.343

466

1.908

 

 

Total

890.519

467

 

 

 

AI Advisory Improves Savings

Between Groups

0.257

1

0.257

0.126

0.723

Within Groups

949.555

466

2.038

 

 

Total

949.812

467

 

 

 

Self-Complied: - SPSS

 

Table No 11

Fit indices

 

95% Confidence Intervals

 

Type

SRMR

RMSEA

Lower

Upper

RMSEA p

Classical

0.04

0.006

0

0.031

1

Robust

0.032

0.014

0

0.037

0.998

Scaled

0.032

0.008

0

0.032

1

Self-Complied: - JAMOVI

 

 

Table No  12

User model versus baseline model

 

Model

Comparative Fit Index (CFI)

0.975

Tucker-Lewis Index (TLI)

0.966

Bentler-Bonett Non-normed Fit Index (NNFI)

0.966

Relative Noncentrality Index (RNI)

0.975

Bentler-Bonett Normed Fit Index (NFI)

0.481

Bollen's Relative Fit Index (RFI)

0.286

Bollen's Incremental Fit Index (IFI)

0.985

Parsimony Normed Fit Index (PNFI)

0.35

 Self-Complied: - JAMOVI

 

CONCLUSION:

This study investigated whether artificial intelligence in financial services actually increases financial accessibility, efficiency, fraud prevention, and literacy, particularly in government-led projects. The results are mixed. While AI improves financial access, its influence on efficiency, fraud detection, and financial literacy remains uncertain. This implies that AI alone is insufficient; it must be combined with human expertise, strict laws, and improved customer education. Future research should focus on improving AI's effectiveness in these areas. Furthermore, the study's model has a decent overall fit but indicates that some elements should be adjusted for a clearer picture. To better comprehend AI's involvement in financial inclusion, future studies should employ more diverse data and stay up to date on current trends.

 

REFERENCES:

1.      Verma, S., and Khanna, P. Reaching the unbanked: AI in financial inclusion initiatives. Journal of Emerging Financial Markets. 2023; 10(1): 12–29.

2.      NITI Aayog. Enhancing financial accessibility through AI‐based credit scoring. New Delhi, India: NITI Aayog. 2023

3.      Reserve Bank of India. Annual report on digital banking and AI adoption. Mumbai, India: RBI. 2023

4.      McKinsey and Company. Efficiency gains in banking through AI: A global perspective. McKinsey Report. 2023

5.      NASSCOM. AI automation in government-led banking: A comprehensive report. NASSCOM Research. 2023

6.      Chatterjee, S. Scaling public financial services through AI automation. Journal of Financial Services Technology.  2023; 5(1): 12–28.

7.      PwC. Machine learning algorithms in fraud detection: Innovations in government programs. PwC Insights. 2023

8.      World Bank. AI‐based biometric authentication in public financial programs. World Bank Report. 2023

9.      Rao, R., and Nair, M. Enhancing DBT security: The role of real-time AI monitoring. Journal of Public Security. 2023; 7(2): 56–70.

10.   United Nations Development Programme. Digital financial literacy: The role of AI in enhancing financial awareness. UNDP Report. 2023

11.   Chatterjee, S., and Roy, P. Enhancing financial literacy in rural areas through AI-enabled educational platforms. Journal of Rural Finance. 2023; 4(1): 12–27.

12.   Rao, S. User engagement with AI-based financial literacy programs: An empirical study. Journal of Financial Behavior. 2023; 5(1): 33–48.

13.   Chakrabarty, A. AI‐driven financial services: Reducing barriers in banking. International Journal of Financial Technology. 2022; 9(2): 101–120.

14.   Sharma, R., Kumar, V., and Singh, P. AI‐powered chatbots and voice-based banking: A case study of UPI123Pay. Journal of Digital Banking. 2022; 7(1): 55–70.

15.   Gupta, R., Sharma, A., and Verma, S. Integration of AI in digital payment platforms: Implications for financial inclusion. International Journal of Payment Systems. 2022; 8(2): 34–50.

16.   Sharma, R. The impact of AI‐driven KYC on banking efficiency. Journal of Digital Verification. 2022; 6(1): 20–35.

17.   Desai, P., and Mehta, R. Minimizing manual processing in public banking with AI automation. International Journal of Automation. 2022; 8(1): 88–102.

18.   Singh, R., and Roy, S. AI‐driven fraud detection in government financial schemes. Journal of Fraud Prevention. 2022;10(3): 55–72.

19.   KPMG. Enhancing security in DBTs through AI-powered anomaly detection. KPMG Research. 2022

20.   Verma, A., Kapoor, S., and Joshi, D. Detecting ghost beneficiaries using AI: Evidence from government subsidies. Journal of Financial Security. 2022; 11(2): 30–45.

21.   KPMG. AI‐driven predictive analytics for future fraud prevention in government programs. KPMG Research. 2022; 2(1): 15–29.

22.   Deshmukh, A. The role of AI in promoting savings behavior among low-income individuals. Journal of Consumer Finance. 2022; 7(2): 40–55.

23.   Singh, R. Digital financial education initiatives: The transformative power of AI. Journal of Financial Transformation. 2022; 3(2): 28–44.

24.   NITI Aayog. AI interventions and financial literacy improvements among underserved populations. NITI Aayog Research. 2022

25.   Mehta, K. AI adoption in microfinance institutions: Opportunities and challenges. Microfinance Journal. 2021; 5(3): 123–140.

26.   Kumar, A., and Gupta, S. AI‐enabled automation in Direct Benefit Transfers: Enhancing public banking efficiency. Journal of Public Banking. 2021; 11(2): 45–60.

27.   Patel, R. Reducing operational costs in public banking with AI. Journal of Banking Innovation. 2021; 7(2): 67–81.

28.   Singh, D., Kumar, R., and Mehta, A. AI and customer satisfaction in government banking services. Journal of Customer Service in Banking. 2021; 9(2): 33–47.

29.   Das, A., and Sharma, R. Predictive analytics and fraud prevention in government schemes. Journal of Predictive Analytics. 2021; 4(2): 44–60.

30.   Gupta, R. Integrating AI for tracking fraudulent activities in public financial schemes. International Journal of Fraud Management. 2021; 6(3): 78–94.

31.   Patel, R., and Sharma, A. Impact of AI-powered financial chatbots on financial literacy. Journal of Financial Education. 2021; 8(2): 50–65.

32.   Jain, P., and Kumar, S. Multilingual AI advisory platforms: Enhancing financial literacy in diverse populations. Journal of Global Banking. 2021; 9(1):  22–38.

33.   Jain, P., and Patel, R. Overcoming lending biases through AI: New paradigms in credit scoring. Journal of Banking and Finance. 2020; 14(4): 78–90.

34.   Rao, R., and Verma, A. Fund disbursement efficiency and AI: An empirical study in public finance. Journal of Public Finance. 2020; 12(3): 90–105.

35.   Chandra, S., and Singh, P. Reducing fraud opportunities with real-time AI monitoring. Journal of Fraud Analytics. 2020; 3(1): 12–25.

36.   Banerjee, S. Gamification and financial literacy: AI tools in banking education. Journal of Digital Learning. 2020; 5(3): 34–49.

37.   Kumar, V. Simplifying banking: The impact of AI advisors on financial literacy. International Journal of Financial Services. 2020; 6(1): 15–30.

38.   Banerjee, S., and Singh, R. Policy frameworks for AI in financial inclusion. Policy Insights. 2019; 4(2): 32–48.

39.   Banerjee, S. Enhancing process reliability through AI in public sector banks. Banking Technology Review. 2019; 3(4): 40–55.

40.   Sarma, M. Measuring financial inclusion: The development of a Financial Inclusion Index. Journal of Financial Services Research. 2008; 12(3): 45–62.

 

 

 

Received on 21.03.2025      Revised on 10.04.2025

Accepted on 25.04.2025      Published on 28.05.2025

Available online from May 31, 2025

Asian Journal of Management. 2025;16(2):134-138.

DOI: 10.52711/2321-5763.2025.00021

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