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The growth of rapid financial technology (fintech) is expected to contribute more than 12% of compound annual growth rate (CAGR) to the economy. India is gradually becoming the hub for many prominent fintech startups such as Paytm, Pine Labs, PayU, Razorpay, and others. However, as per the survey by
“Literacy a bridge from misery to hope.” –Kofi Annan
Financial technology, or fintech, is a digital framework that includes big data, cloud computing, artificial intelligence (AI), biometric technology, and augmented reality. In this paper, we examine financial applications including payment, budgeting, borrowing and financing, trading and investing money, health insurance, and data security, as well as their knowledge among working women. This paper investigates fintech applications and their impact on people’s daily lives in order to make independent financial decisions.
A person is said to be “financially literate” (FinLit) when he or she can effectively manage financial decisions such as personal finance management, budgeting, and investment. India’s FinLit rate has been steadily growing due to various factors, including the development of rural areas, ease in borrowing funds, ease in doing business transactions, and the growth of micro, small, and medium enterprises (MSMEs). The number of digital payment transactions in India grew 5X, from 10.04 billion in 2016–2017 to 55.54 billion in 2020–2021. Furthermore, a 20% compound annual growth rate (CAGR) is expected from $66 billion in 2019 to $138 billion in 2023.
In the recent few years, India has become the world’s largest millennial market. The millennial generation accounts for more than 30% of the country’s population. This research was written to investigate the financial habits of millennials. Millennials are people who were born between 1980 and 1996 and are between the ages of 25 and 40. People begin earning at the age of 23 and settle down in their careers and families by the age of 25–26. This group of people is being studied to determine their level of understanding of fintech awareness and the adequacy of their literacy level. The papers look into the relationship between millennials’ financial goals, their attitudes and behaviors, and their understanding of fintech’s impact on their financial wellbeing.
Huston (
Despite the fact that the survey’s target population ranges from students in high school to professionals and the technique used varies from objective to subjective assessments, this research shows that a sizable segment of the population lacks financial literacy. Vera et al. (
Fallan (
The planned behavior theory was used to evaluate the relationship between financial education and desires for increased retirement payments. According to the study, “people’s insights into preparing, developing, thinking, and self-assessed planning have significant unintended effects on behavior.” Widiastuti (
According to Marzieh et al. (
Setyorini et al.’s (
The aim of the paper is to understand the relationship between the level of financial literacy and fintech awareness. Descriptive research is conducted to understand millennials’ perceptions of financial literacy and their awareness and usage of fintech applications. Financial literacy is measured using components such as planning and managing finances, setting financial goals, and financial attitude and behavior.
The responses gathered from 172 people focused on their financial planning and management, on attaining their financial goals, their behavior toward finance management, and their awareness of fintech usage. K-mean cluster analysis is used to understand the important dimensions of the paper. Cluster analysis the technique of grouping the variables based on their individuals, objects, or cases into relatively homogeneous (similar) groups. The variables grouped within the cluster are similar to each other, but there is dissimilarity between the clusters.
When the variable is used to create a distinct cluster, the cluster centers are selected by default. Three clusters are formed from the 29 variables, and each variable joins the nearest cluster center until the stopping criteria is reached. The factor scores from the cluster analysis are applied, and the respondents are classified on this basis.
The criteria for the randomness of the observations in the various groups are not satisfied in our situation since the groups are purposefully generated according to their distance from one another in the multidimensional space; therefore, the findings of the dispersion analysis are merely descriptive. However, the variations in the F-ratios (the F-column in the ANOVA table) allow for generalizations about the contribution of the various mean variables to the formation of the clusters.
In
The research design.
| Types of research | Descriptions |
| Sample size | 175 (Millennials of 25–40 years of age) |
| Sampling area | Selected localities in Chennai |
| Sampling method | Probability sampling—random sampling |
| Data collected | Primary (survey method) and secondary data (books, journals, and internet) |
| Research instrument | Structured questionnaire |
| Validity and reliability of questionnaire | Overall Cronbach’s alpha value = 0.674 |
| Software used for analysis | SPSS (version 25) |
* Authors’ preparation.
The final cluster center.
| 1 | 2 | 3 | |
| Fintech awareness | 1.14 | 1.52 | 1.61 |
| Gender | 1.51 | 1.55 | 1.57 |
| Spending money is more rewarding to me than long-term investing. | 1.56 | 1.34 | 2.48 |
| I’m willing to take a small financial risk in order to save or invest. | 1.59 | 1.38 | 2.35 |
| Accountability for making daily financial decisions for your home | 1.7 | 1.65 | 2.26 |
| I’m content with how things are right now financially. | 1.89 | 1.82 | 2.96 |
| Action plan taken to attain the financial goal | 2.11 | 2.38 | 4 |
| Education | 2.16 | 2.82 | 1.52 |
| I keep a careful eye on my own finances. | 2.21 | 2.15 | 2.91 |
| Even if a bank collapses, I think the money there will be safe. | 2.23 | 2.49 | 3.43 |
| I make long-term financial plans and work to fulfill them. | 2.33 | 1.49 | 2.78 |
| Profession | 2.63 | 2.68 | 2.96 |
| I currently owe too much money. | 2.66 | 2.3 | 3.26 |
| I think banks should investigate a company’s ethics before offering financial services to it. | 3.3 | 2.49 | 2.17 |
| Age | 3.8 | 3.65 | 2.96 |
| I promptly pay my household dues. | 3.8 | 2.56 | 2.26 |
| Income | 4 | 2.65 | 2.83 |
| I carefully assess if I can afford anything before making a purchase. | 4.07 | 3.28 | 3.96 |
| Medium used for managing household | 4.37 | 1.46 | 1.3 |
*Authors’ preparation.
The ANOVA between the Cluster
ANOVA between the clusters.
| Cluster |
Sum of squares | Sig. | |||
| Sum of squares | Df | ||||
| Accountability for making daily financial decisions for your home | 72.937 | 174 | 0.383 | 9.214 | 0.000 |
| Managing household | 593.794 | 174 | 1.335 | 136.412 | 0.000 |
| Keeping money in your wallet or at home | 31.394 | 174 | 0.174 | 4.402 | 0.014 |
| Saving money into a < savings/deposit > account | 42.549 | 174 | 0.234 | 4.923 | 0.008 |
| Buying bonds or time deposits | 28 | 174 | 0.135 | 17.782 | 0.000 |
| Investing in stocks and shares | 30.309 | 174 | 0.159 | 9.541 | 0.000 |
| Investing in mutual funds | 42.194 | 174 | 0.232 | 5.109 | 0.007 |
| Action plan taken to attain the financial goal | 125.714 | 174 | 0.363 | 87.354 | 0.000 |
| Survival without the main source of income | 243.714 | 174 | 0.908 | 48.177 | 0.000 |
| Att_Beh_1 | 258.949 | 174 | 1.08 | 33.839 | 0.000 |
| Att_Beh_2 | 140.857 | 174 | 0.719 | 12.02 | 0.000 |
| Att_Beh_3 | 153.737 | 174 | 0.747 | 16.904 | 0.000 |
| Att_Beh_4 | 124.994 | 174 | 0.583 | 21.175 | 0.000 |
| Att_Beh_5 | 184.857 | 174 | 0.875 | 19.638 | 0.000 |
| Att_Beh_6 | 96.834 | 174 | 0.499 | 10.997 | 0.000 |
| Att_Beh_7 | 155.714 | 174 | 0.757 | 16.894 | 0.000 |
| Att_Beh_8 | 146.994 | 174 | 0.604 | 35.736 | 0.000 |
| Att_Beh_9 | 84.709 | 174 | 0.357 | 32.551 | 0.000 |
| Att_Beh_10 | 90.377 | 174 | 0.427 | 19.771 | 0.000 |
| Fintech awareness | 41.349 | 174 | 0.201 | 17.069 | 0.000 |
*Authors’ preparation.
The F-ratio is the proportion of cluster variance to error variance. The large F-value variables, which show that they are essential for segregating the clusters, include household management, action plans to reach financial goals, and survival without a primary source of income. A small F-value (nearly 1) shows variables that are not very useful in determining cluster membership.
The F-statistic should not be understood traditionally, and hence the significance value associated with the F-value cannot be interpreted traditionally, in terms of acceptance or rejection of the null hypothesis.
From
Percentage of cluster.
| No. of cases in each cluster | Percent | ||
| Clusters | 1 | 70 | 40 |
| 2 | 82 | 47 | |
| 3 | 23 | 13 | |
| Valid | 175 | 100 | |
*Authors’ preparation.
The questions that load highly on factor 2 seem to relate to the risk of losing the main source of income. Therefore, this factor is labeled “pessimistic of wealth.” The questions that load highly on factor 3 seem to relate to attaining their financial goals.
Therefore, this factor is labeled “overlookers.” Making informed choices about the factor structure requires careful consideration of the rotation criterion and factor loading assessment.
H1: There is a significant difference between the “architect of money” and Factor 1 among the respondents (millennials) with respect to fintech awareness.
For the cluster group “architect of money,” the mean and standard deviation (SD) of the cluster are 2.17 and 0.810, and it is skewed to −0.312/0.184; the Z-score skewness is −3.49383. The probability value (
H2: There is a significant difference between “Pessimistic of Wealth” Factor 2 and other factors among the respondents (millennials) with respect to fintech awareness.
For the cluster group “Pessimistic of Wealth,” the mean and SD of the cluster, respectively, are 2.89 and 1.557, and it is skewed to 0.165/0.184; the Z-score skewness is −1.35517. The probability value is 0.088508, which is less than 0.05. Respondents in Cluster 2 who are risk averse try to save money in more conservative ways, such as fixed deposits, and are concerned about losing their primary source of income during an emergency. This cluster does not have a significant relationship with the awareness of fintech apps and their usage.
H3: There is a significant difference between “overlookers,” or Factor 3 of the respondents (millennials), with respect to fintech awareness.
For the cluster group “overlookers,” the mean and SD of the cluster, respectively, are 1.73 and 0.680, and it is skewed to 0.392/0.184; the Z-score skewness is −4.80882. The probability value is 0.00001, which is more like 0.05. The respondents in Cluster 3 have significant relationships with fintech awareness.
The ability to manage finances, financial attitudes, and behaviors has a greater impact on awareness and usage of fintech applications. A great percentage of the respondents manage their funds by using fintech applications for planning, budgeting, tax planning, and investment. Very few respondents use neo-investments such as cryptos and non-fungible tokens (NFTs).
While a small percent of respondents are able to cover their living expenses during the loss of their main source of income, respondents are aware of the fintech application, but close to one-third of the respondents are reluctant to use it. This paves the way for identifying the reasons why they are not using the fintech app for financial planning.
All the work in this research manuscript is original and contributed only by the RJ.