Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 10, No. 2, 2023 330 The Impact of Fintech on Economic Growth in Singapore Jiarong Huang1, Yinggang Jie2, Song Wu3 1 Faculty of Business and Economics, University of Malaya, Kuala Lumpur, 50603, Malaysia 2 Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia 3 Faculty of Business Management, City University Malaysia, Selangor, 46100, Malaysia Abstract: The study examines the impact of financial technology on economic growth in Singapore. Using time-series data on Singapore's financial technology from 2010 to 2022, The study constructs a financial technology index based on a comprehensive set of indicators. Through VAR modeling, the study assesses the effects of financial technology on the interest rate market, consumer market, and investment market, as well as its role in driving economic growth. The results indicate that financial technology has a positive impact on Singapore's economic growth. The calculation of the Fintech Index reveals a continuous improvement in Singapore's level of financial technology. The VAR analysis demonstrates that the development of financial technology has significant impacts on the interest rate market, consumer market, and investment market in Singapore. This suggests that the advancement of financial technology helps lower market interest rates, enhances the experiences of consumers and investors, and promotes consumption and investment growth. These findings highlight the significant role of financial technology in driving economic growth in Singapore. Keywords: Financial technology, Financial technology index, Economic growth, Entropy, VAR model. 1. Introduction Despite the thriving Fintech industry globally, its specific impact on economies, particularly Singapore's, remains under-studied. This study aims to elucidate this relationship. With Singapore being a global financial hub and a leader in both traditional finance and Fintech innovation, examining its Fintech advancement offers invaluable local and international implications. Coupled with its high digital transformation acceptance, demonstrated by widespread digital payment and e-commerce usage, and a developed economy with extensive international financial connections, Singapore provides an ideal research setting. This research investigates Fintech's impact on Singapore's economic growth from 2010 to 2022 by constructing a Fintech indicator framework and utilizing a VAR model. It explores the state of Fintech in Singapore and its impact on interest rates, consumption, and investment markets, probing the mechanisms through which Fintech influences economic growth. The goal is to analyze Fintech's direct and indirect influence on Singapore's economic sectors, thereby guiding policy recommendations to further enhance Fintech development and economic growth. By examining Fintech's actual influence, this research strives to reveal its potential applications, stimulate efficient financial resource allocation, and contribute to Singapore's emergence as a global Fintech hub. 2. Literature Review "Fintech," a portmanteau of "finance" and "technology," represents a transformative sector leveraging innovative software to deliver on-demand services, primarily in finance (Gomber, Koch, and Siering, 2017; Shin and Choi, 2019). With the sector's genesis in the United States, it has flourished notably in Britain and Singapore, with significant global financial investments made year-on-year. Singapore has proven itself a technological powerhouse, with advancements in artificial intelligence, Internet of Things, and Fintech, making it an attractive destination for Fintech-related companies and investors. Its surge in the Global Financial Centre Index (GFCI) 2022 is testimony to its strategic advantages and Fintech development, fostering its global standing and economic growth. The inherent value of Fintech lies in its ability to enrich financial functions, optimize resource allocation, and foster green economic growth (Leckow, Haksar,2017; Junhua Han,Quan Zhou,Hongchang Wang,2019). However, while the interplay of Fintech and economic growth is evident, literature gaps exist, with most analysis being qualitative or theoretical (Luo,Sun,Yang,Zhou,2022). Key questions remain unanswered: How does Fintech affect economic growth? Is its effect promoting or inhibiting? What pathways does Fintech utilize to influence economic growth? This study attempts to bridge this gap by quantitatively examining the relationship between Fintech and economic growth, particularly in Singapore, from 2010 to 2022. The research process includes conceptual definition, theoretical analysis, and empirical methodology. It assesses the direct and indirect impact of Fintech on interest rates, consumer markets, investment markets, and economic output. The findings will enhance understanding and inform policy recommendations, fostering Fintech innovation and driving Singapore's economic development. 3. Methodology 3.1. Introduction Exploring financial technology measurement, this research employs qualitative and quantitative analysis, constructing a financial technology index via the entropy method to measure regional fintech development. It provides a comprehensive assessment of fintech's influence on interest rates, consumer and investment markets, and overall economic output. Qualitative analysis allows for a detailed study of fintech trends, policy environment, and market dynamics, while 331 quantitative analysis improves the study's scientific validity and reliability through data and econometric analysis. 3.2. Intrinsic Principles 1) The Mechanism of Fintech's Effect on Economic Growth Fintech expands financial development, improving its efficiency and enhancing capital's marginal productivity. It bolsters economic growth quality through increased capital liquidity, investment enthusiasm, and through asset portfolio management that raises investment returns, mitigates liquidity risks, and promotes investment specialization. 2) The Mechanism of Fintech's Effect on the Interest Rate Market Fintech aids in achieving balanced interest rates within a fair, reasonable, and transparent market framework, impacting investment and savings, which are economic growth drivers. 3) The Mechanism of Fintech's Impact on the Consumer Market Fintech provides technical basis for banks to manage long tail customer groups. With its rapid development, financial services are no longer limited by time and space, extending their reach, reducing operational costs, and enhancing customer relationship management efficiency. 4) The Mechanism of Fintech's Impact on the Investment Market Fintech's advantages include swift information processing, simplified methods, and low costs. It introduces new financial products, enriching individual investment choices. Also, it provides diverse financing methods for small and medium- sized enterprises, such as Internet P2P loans, crowdfunding, and supply chain finance, improving financing efficiency and promoting physical project investments. 3.3. Conceptual framework Figure 1. Conceptual framework 3.4. Index Calculation 3.4.1. Introduction Various regulators, including FCA, PBOC, FSOC, FINMA, ASIC, and FSA use unique approaches to calculate the Fintech index. Absence of unified standards adds complexity to cross-border comparisons and comprehensive analyses. Machine learning algorithms like Random forest and gradient lifting tree suffer from randomness and inexplicability, leading us to prefer the entropy method for the Singapore Fintech Index. This method, offering comprehensive, objective results and high flexibility, adapts to different scenarios and ensures comparability across different regions or timeframes. Table 1. Variable Statement Number General Indicators Effect Specific Indicators Variable Code Unit 1 Fintech Micro Indicators Positive Number of newly listed Fintech institutions NNLFI Q1 Count 2 Positive Number Of Financial And Insurance Services Talents FIS Q2 Count 3 Positive Number of Fintech users NFU Q3 Count 4 Positive Number Of Patents Applied PAP Q4 Count 5 Positive Number Of Patents Awarded PAW Q5 Count 6 Positive Number Of Financial Institutions NFI Q6 Count 7 Fintech Macro Indicators Positive Singapore's Inward Direct Investment Flows By Finance Industry SIDIFFI Q7 Millions Of Singapore Dollars 8 Positive Turnover On The Singapore Exchange (Equities) - Finance (Value) TSE-F Q8 Million Dollars 9 Positive R&D Expenditure RDE Q9 Million Dollars 10 External Evaluation Indicators Negative Annual ranking of Singapore in GFCI ARS-GFCI Q10 Rank Data sources include Monetary Authority of Singapore, Ministry of Manpower, Agency for Science, Technology and Research, and Singapore Department of Statistics. The Fintech development measurement considers three 332 dimensions: Micro, Macro, and External Evaluations with several indicators. We use the Global Financial Centres Index (GFCI) for the external evaluation, offering a relative comparison and trend analysis of global financial centers. 3.4.2. Data Collection Indicator data, ranging from 2010-2022, are selected from the Singapore region, and are mainly sourced from reliable institutions like the Singapore Department of Statistics, Monetary Authority of Singapore, Ministry of Manpower of Singapore, and Science, Technology and Research Bureau of Singapore. 3.4.3. Data cleaning To ensure the model's stability and reliability, the collected data undergo standardization, converting different indicator values into Z-scores or mapping them within the range of 0 to 1, facilitating cross-indicator comparison and analysis. The Min-Max normalization method is utilized in this study. Positive indicators: š‘„ ,..., ,... ,..., Negative indicator: xā€²ij max x1j,...,xnj xij max x1j,...,xnj min x1j,...,xnj 3.4.4. Establishing Model Step 1, calculating the proportion of the i-th sample value to the j-th indicator: pij xij āˆ‘ xij n i 1 , i 1, . . . , n, j 1, . . . m Step 2, calculating the entropy value for each standardized indicator. Entropy ej is used to judge the Statistical dispersion of an indicator. The greater the Statistical dispersion, the greater the impact of the indicator on the comprehensive evaluation. The formula is as follows: ej k āˆ‘ pijln pij n i 1 , j=1,..,m Where k=1/ln (n)>0 , satisfies ej ā‰„ 0. Step 3, calculating the information entropy redundancy (difference): dj 1 ej, j 1, . . . , m Step 4, considering the importance of each indicator, calculate the corresponding weights assigned to each indicator. The weight represents the relative importance of each indicator in the comprehensive evaluation. The formula is as follows: Ļ‰j dj āˆ‘ dj m j 1 , j=1,..,m Step 5, summing up all weighted entropy values to obtain the comprehensive score of the Fintech index. The formula is as follows: si Ļ‰jxij m j 1 , i 1, . . . , m Where xij is the standardized data. 3.4.5. Calculation results 3.4.5.1 Weight calculation results Table 2. Weight Distribution Entropy weight method Variable Information entropy value e Information utility value d Weight(%) Q1-NNELFI 0.799 0.201 14.232 Q2-FIS 0.909 0.091 6.419 Q3-NFU 0.839 0.161 11.405 Q4-PAP 0.827 0.173 12.218 Q5-PAW 0.874 0.126 8.936 Q6-NFI 0.856 0.144 10.179 Q7-RDE 0.885 0.115 8.150 Q8-SIDIFFI 0.848 0.152 10.739 Q9-TSE-F 0.836 0.164 11.633 Q10-ARS-GFCI 0.914 0.086 6.088 The weight calculation results of the entropy method show that the weights of Q1-NNELFI standardization are 14.232%, Q2-FIS standardization is 6.419%, Q3-NFU standardization is 11.405%, Q4-PAP standardization is 12.218%, Q5-PAW standardization is 8.936%, Q6-NFI standardization is 10.179%, Q10-RDE standardization is 8.15%, Q7-SIDIFFI standardization is 10.739%, and Q9-TSE-F standardization is 11.633% The weight of Q12-ARS-GFCI standardization is 6.088%, with the maximum value of indicator weight being Q1-NNELFI standardization (14.232%) and the minimum value being Q12-ARS-GFCI standardization (6.088%). 3.4.5.2 Variable Weight Histogram Q1-NNELFI has a relatively high weight in the Fintech index, while Q1-NNELFI has a relatively low weight in the Fintech index. In the calculation of the weight of the Fintech index, the proportion of Micro indicators in Fintech is the highest, accounting for 63%, as it better reflects the actual situation of Fintech in the industry and institutions. The proportion of Macro indicators in Fintech is 31%, but a single indicator of R&D funding from government departments has a relatively significant impact on the Fintech index. External evaluation selection is an authoritative international evaluation that serves as a reference and has a low weight. It can indirectly reflect the gathering center of financial technology. 333 Figure 2. Variable Weight Histogram 3.4.6. Result analysis From 2010 to 2014, the Singapore Financial Technology Index experienced rapid growth. During this period, the Fintech index gradually increased from 0.028 to 0.410, indicating a clear growth trend. Next, from 2014 to 2016, the Fintech index continued to grow, but the growth rate slowed down. From 2017 to 2020, the Fintech index once again showed rapid growth overall. During this period, the Fintech index increased from 0.519 to 0.619, indicating another significant growth trend. However, from 2020 to 2022, the growth trend of the Fintech index seems to have slowed down. Although the Fintech index has increased from 0.619 to 0.674, the growth rate has slightly slowed down compared to the previous period. Overall, it can be said that the Singapore Fintech index has shown a growth trend, but the growth rate may fluctuate over different time periods. From 2010 to 2022, the index gradually increased, but the rate of growth varied over different time periods. Figure 3. Trends in Fintech Index 3.5. Empirical Analysis 3.5.1. Research Assumptions H1: The improvement of financial technology has led to an increase in social consumption levels. H2: The improvement of financial technology has promoted the improvement of social investment level. H3: The enhancement of financial technology development level positively influences economic growth. H4: Financial technology affects the corresponding changes in the consumption and investment markets with the fluctuation of IR, thereby promoting the growth of consumption and investment. 3.5.2. Model Settings Given the assumptions, Fintech enhances market efficiency and service levels, reflected in lower IR and higher PCE/GCE/RSI/GFCF/MPPI. This leads to boosted market consumption and investment, indirectly influencing economic growth. Our model reflects this, where Y represents economic growth, C reflects social consumption, and I represents social investment. 3.5.3. Data Collection and Variable Statement Quarterly economic data from Singapore (2010-2022) were selected for analysis, obtained from trusted authorities. The model calculations were performed using SPSS software.The data sources are as follows: Singapore Department of Statistics ļ¬ https://www.singstat.gov.sg/publications/reference/sing apore-in-figures/trade-and -investment ļ¬ Singapore-Ministry-of-Manpower ļ¬ https://stats.mom.gov.sg/Statistics/Pages/labourforce.as px ļ¬ Monetary Authority of Singapore ļ¬ https://eservices.mas.gov.sg/Statistics/msb/ExchangeRa tes.aspx ļ¬ The Association of Banks in Singapore 6.088% 6.419% 8.150% 8.936% 10.179% 10.739% 11.405% 11.633% 12.218% 14.232% 0.000% 2.000% 4.000% 6.000% 8.000% 10.000% 12.000% 14.000% 16.000% Q10ā€ARSā€GFCI Q2ā€FIS Q7ā€RDE Q5ā€PAW Q6ā€NFI Q8ā€SIDIFFI Q3ā€NFU Q9ā€TSEā€F Q4ā€PAP Q1ā€NNELFI 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 334 ļ¬ https://www.abs.org.sg/benchmark-rates/rates-sibor Table 3. Variable Description Number Economics Indicator Code variable Unit 1 Measures of the money supply I1 M2 Million Singapore dollars 2 Singapore Inter Bank Offered Rate I2 IR % 3 Private Consumption Expenditure C1 PCE Million Dollars 4 Government Consumption Expenditure C2 GCE Million Dollars 5 CPI C3 CPI Index 6 Retail Sales Index C4 RSI Index 7 Gross Fixed Capital Formation IV1 GFCF Million Dollars 8 Manufactured Products Price Index IV2 MPPI Index 9 GDP Year On Year Growth Rate Y1 GDP_growth % 10 Fintech Index Score - Fintechindex Score To ensure the objectivity and scientificity of the empirical results, the collected data is processed as follows (See Table 3): the GDP deflator of some data is used to eliminate the impact of price fluctuations. Since the logarithmic processing of data is conducive to better discovering the relationship between data, this study will conduct logarithmic processing of index data to eliminate the interference of homoscedasticity and heteroscedasticity. 3.5.4. Descriptive Statistics This study will conduct statistical analysis on the data of all indicators, and measure the mean, median, maximum, minimum, skewness, and kurtosis. The results obtained are shown as Table 4. Table 4. Descriptive Statistics Variable Sample size Mean Median Maximum Minimum Kurtosis Skewness LnFintechindex 52 0.305 0.338 0.515 0.028 -0.934 -0.406 LnM2 52 13.233 13.199 13.541 12.983 -1.081 0.349 LnIR 52 0.521 0.345 1.545 0.223 0.814 1.172 LnPCE 52 10.565 10.586 10.834 10.275 -0.579 -0.366 LnGCE 52 9.306 9.328 9.901 8.626 -0.279 -0.296 LnCPI 52 4.598 4.607 4.716 4.465 1.747 -0.625 LnRSI 52 4.555 4.566 4.698 4.049 19.933 -3.594 LnGFCF 52 10.209 10.241 10.449 9.908 -0.32 -0.704 LnMPPI 52 4.64 4.617 4.79 4.49 -1.364 0.018 LnGDP_growth 52 0.064 0.06 0.259 -0.159 2.694 -0.028 3.5.5. Empirical Testing Based on VAR Model 3.5.5.1 Stationarity test The stationarity test, ADF test and Root of unity test of time series data are the preconditions for establishing VAR model (Li et al.,2022). The test results are shown as Table 5. The results indicate that LnRSI and LnGDP_ The original sequence of growth is stationary, while the first-order differential sequence of Fintechindex, LnM2, LnIR, LnPCE, LnGCE, LnGFCF, LnMPPI is stationary, and the second- order differential sequence of LnCPI is stationary. Table 5. Stationary Test Original sequence First Order Difference Second Order Difference ADF t P AIC Result t P AIC Result t P AIC Result LnRSI -4.767 0.000*** -73.348 Stationary LnGDP_growth -3.348 0.013** - 149.446 Stationary LnFintechindex -1.339 0.611 - 140.883 -7.390 0.000*** - 133.323 Stationary LnM2 1.060 0.995 - 228.055 -3.596 0.006*** - 224.035 Stationary LnIR -2.046 0.267 -58.93 -3.651 0.005*** -56.097 Stationary LnPCE -1.764 0.399 - 101.566 9.013 0.000*** -96.405 Stationary LnGCE -0.413 0.908 - 133.194 -5.027 0.000*** - 125.748 Stationary LnGFCF -2.349 0.157 -90.33 -5.747 0.000*** -81.232 Stationary LnMPPI -1.734 0.414 - 177.425 -3.680 0.004*** - 172.047 Stationary LnCPI -1.413 0.576 - 300.093 -1.958 0.305 - 289.914 - 7.637 0.000*** - 299.898 Stationary 3.5.5.2 Testing the Impact of Fintech on the Interest Rate Market 1) Determination of Lag Order The optimal lag order is determined based on the optimal lag criterion (AIC, SC, LR), and is generally determined based on the principle of minimizing AIC and SC values. According to Table 6, the optimal lag order is 11 orders, but due to a sample size of 52, an excessive lag order can lead to 335 a sharp decrease in sample size, which is not conducive to model calculation. Therefore, this article chooses the second best lag order, which is 1 order. With the results of stationarity test and lag order, perform differential treatment on the corresponding order of variables LnFintechindex, LnM2, and LnIR. Table 6. Determination of Lag Order Lag Order logL AIC SC HQ FPE 0 245.357 -18.208 -18.093 -18.164 0 1 257.286 -18.525 -18.062 -18.35 0 2 257.332 -18.361 -17.542 -18.051 0 3 256.302 -18.144 -16.963 -17.699 0 4 253.81 -17.853 -16.303 -17.272 0 5 284.191 -19.011 -17.084 -18.293 0 6 283.1 -18.791 -16.48 -17.934 0 7 286.308 -18.761 -16.057 -17.764 0 8 298.86 -19.174 -16.071 -18.036 0 9 311.146 -19.594 -16.083 -18.316 0 10 331.734 -20.45 -16.524 -19.031 0 11 384.75 -23.014* -18.663* -21.453* 0.0* 2) VAR model estimation LnIR=-0.934*LnFintechindex(-1)+0.545*LnM2(- 1)+0.698*LnIR(-1)+0.016 3) Root of unity inspection It can be seen from Figure 4 that the non characteristic root is outside the Unit circle, and the regression residual sequence obtained is characterized by Normal distribution, no heteroscedasticity and correlation, so the model established is stable, and then the VAR model can be continued to be established. Figure 4. Root of unity Test Results 4) Granger Test Since the Root of unity test variables are all stationary variables of the same order, Granger Test is conducted. Table 7 shows the results of Granger Test, including paired samples, sample size, F statistics, and significant P values. By analyzing the significance of F statistics, if it is significant (P<0.05), it indicates that the original hypothesis is rejected (one group of time series is not the reason for the other group of time series), that is, the left variable can cause the change of the right variable, with Granger causality, otherwise there is no Granger causality. The inspection results are as follows: Based on the variables LnM2 and Fintechindex, the significance P-value is 0.940, which does not show significance and cannot reject the original hypothesis. LnM2 cannot cause changes in LnFintechindex. Based on the variables LnFintechindex and LnM2, the significance P-value is 0.677, which does not show significance and cannot reject the original hypothesis. LnFintechindex cannot cause a change in LnM2. Based on the variables LnIR and LnFintechindex, the significance P-value is 0.776, which does not show significance and cannot reject the original hypothesis. LnIR cannot cause changes in LnFintechindex. Based on the variables LnFintechindex and LnIR, the significance P-value is 0.023**, showing significance. Rejecting the original hypothesis, LnFintechindex can cause changes in LnIR. Based on the variables LnIR and LnM2, the significance P- value is 0.047**, showing significance. Rejecting the original hypothesis, LnIR can cause changes in LnM2. Based on the variables LnM2 and LnIR, the significance P-value is 0.750, which does not show significance and cannot reject the original hypothesis. LnM2 cannot cause changes in LnIR. From the results, it can be seen that LnFintechindex can cause changes in LnIR and LnM2, indicating that the development of financial technology affects changes in the interest rate market. Table 7. Granger Test Paired sample F P LnM2 LnFintechindex 0.006 0.940 LnFintechindex LnM2 0.175 0.677 LnIR LnFintechindex 0.082 0.776 LnFintechindex LnIR 5.56 0.023** LnIR LnM2 4.159 0.047** LnM2 LnIR 0.102 0.750 Note: * * *, * *, * represent significance levels of 1%, 5%, and 10%, respectively. 5) Impulse response analysis The impulse response function measures the impact of 336 financial technology on the financial interest rate market. From Figure 5, it can be seen that when a positive impact of 1 unit is given to the financial technology index, the financial market interest rate will rapidly decrease in the first two periods, reach the minimum value, and then begin to rise and gradually stabilize after a pullback. When there is a positive impact of 1 unit on market interest rates, the Fintech index will also rapidly decline in the first two periods, then begin to decline and gradually stabilize. Figure 5. Analysis of LnFintechindex and LnPCE Pulse Response Figure 6. Analysis of LnFintechindex and LnRSI pulse response 3.5.5.3 Impact Test of Financial Technology on the Investment Market 1) Determination of Lag Order According to the same principle for determining the lag order, it can be seen from Table 8 that the optimal lag order is 11 orders. However, due to the sample size of 52 and the large lag order, it is not conducive to model calculation. Therefore, this article chooses the second best lag order, which is 1 order. Table 8. Determination of Lag Order Lag Order logL AIC SC HQ FPE 0 258.309 -18.726 -18.611 -18.682 0 1 262.01 -18.718 -18.255* -18.542 0.0* 2 261.549 -18.537 -17.718 -18.227 0 3 269.274 -18.696 -17.515 -18.251 0 4 264.618 -18.323 -16.773 -17.742 0 5 264.596 -18.14 -16.213 -17.422 0 6 261.358 -17.803 -15.491 -16.945 0 7 260.627 -17.566 -14.863 -16.569 0 8 279.271 -18.241 -15.138 -17.103 0 9 300.343 -19.067 -15.556 -17.789 0 10 302.317 -18.979 -15.053 -17.56 0 11 332.236 -20.321* -15.97 -18.76* 0 337 2) VAR model estimation LnGFCF=-0.615*LnFintechindex(-1)-0.283*LnGFCF(- 1)+0.327*LnMPPI(-1)+0.018 LnMPPI=-0.198*LnFintechindex(-1)-0.019*LnGFCF(- 1)+0.392*LnMPPI(-1)+0.003 3) Root of unity inspection It can be seen from Figure 7 that the non characteristic root is outside the Unit circle, and the regression residual sequence obtained is characterized by Normal distribution, no heteroscedasticity and correlation. Therefore, the established model is stable, and then the VAR model can be further established. Figure 7. Root of unity Test Results 4) Granger Test Since the Root of unity test variables are all stationary variables of the same order, Granger Test is conducted (See Table 9). Table 9. Granger Test Paired sample F P LnGFCF LnFintechindex 0.091 0.764 LnFintechindex LnGFCF 4.652 0.036** LnMPPI LnFintechindex 0.173 0.679 LnFintechindex LnMPPI 4.235 0.045** LnMPPI LnGFCF 0.357 0.553 LnGFCF LnMPPI 0.08 0.779 Note: * * *, * *, * represent significance levels of 1%, 5%, and 10%, respectively. This chapter only selects significant results related to the LnFintechindex variable for analysis. The results are as follows: Based on the variables LnGFCF and LnFintechindex, the significance P-value is 0.764, which does not show significance and cannot reject the original hypothesis. LnGFCF cannot cause changes in LnFintechindex. Based on the variables LnFintechindex and LnGFCF, the significance P-value is 0.036**, showing significance. Rejecting the original hypothesis, LnFintechindex can cause changes in LnGFCF. Based on the variables LnMPPI and LnFintechindex, the significance P-value is 0.679, which does not show significance and cannot reject the original hypothesis. LnMPPI cannot cause changes in LnFintechindex. Based on the variables LnFintechindex and LnMPPI, the significance P-value is 0.045**, showing significance. Rejecting the original hypothesis, LnFintechindex can cause changes in LnMPPI. It can be seen that LnFintechindex can cause changes in LnGFCF and LnMPPI, indicating that the development level of Fintech affects the total fixed capital formation and changes in the price index of manufactured goods, thereby affecting the level of social investment in Singapore. 5) Impulse response analysis The impulse response function measures the impact of Fintech on the investment market. From Figures 8 and 9, it can be seen that when a positive impact is given to the Fintech index of 1 unit, the total fixed capital formation is affected by this. The finished product price index will rapidly decline in the first two periods, and then begin to rise and have a positive impact. After reaching its peak in the third period, it will begin to decline and stabilize in the sixth period. When the total fixed capital formation is given a positive impact of 1 unit on the manufactured goods price index, the fluctuation of the Fintech index is significant in the early stage, but tends to stabilize after the fifth period. 338 Figure 8. Analysis of pulse response of LnFintechindex and LnGFCF Figure 9. Analysis of LnFintechindex and LnMPPI Pulse Response From the pulse response results, it can be seen that Fintech has a significant impact on the entire Singapore investment market. This is related to the large scale and active investment market of Singapore, which, as a global financial center, is enthusiastic about investing in the Fintech industry. 4. Results 4.1. Variance estimation Based on the interest rate transmission mechanism, conduct a simple regression equation analysis on the impact of the consumer market and investment market on economic growth to verify whether it meets the research hypothesis. Table 10. Variance estimation Linear regression analysis results n=50 Non standardized coefficient Standardized Coefficient t P VIF RĀ² Adjust RĀ² F B Standard error Beta Constant -1.102 0.531 - -2.076 0.044** - 0.4 01 0.348 F=7.546 P=0.000*** LnPCE -0.154 0.207 -0.151 -0.742 0.462 3.111 LnRSI 0.255 0.117 0.357 2.184 0.034** 2.004 LnGFCF 0.036 0.134 0.044 0.269 0.789 1.993 LnMPPI 1.196 0.273 0.529 4.385 0.000*** 1.095 Dependent Variableļ¼šLnGDP_growth Note: * * *, * *, * represent significance levels of 1%, 5%, and 10%, respectively. The results in Table 10 show that the analysis of the results of the F-test can get that the significance P value is 0.000***, showing significance at the level, and rejecting the original assumption that the regression coefficient is 0, so the model basically meets the requirements. For variable col-linearity, the VIF is all less than 10, so the model has no Multicollinearity problem, and the model is well constructed. The formula for the final model is: 339 LnGDP_growth =-1.102 - 0.154*C1-LnPCE + 0.255*C4- LnRSI + 0.036*IV1-LnGFCF + 1.196*IV2-LnMPPI The results further demonstrate that the year-on-year growth rate of GDP is LnGDP_Growth is positively correlated with the retail sales index LnRSI and negatively correlated with the production price index LnMPPI. The P- values of both variables pass the test. 4.2. Empirical Results Through empirical research, it has been found that Fintech has a local impact on the interest rate market and investment market. Among them, for the financial market, when a positive impact is given to Fintech, the efficiency of the financial market will decline in the early stage and then decline. It also means that financial technology has led to a decrease in market interest rates, thereby having an impact on the financial market. For the consumer market, when a positive impact of 1 unit is given to the Fintech index, the private consumption expenditure and retail sales index will reach their peak after the initial decline, then gradually decrease and stabilize. Financial technology can help improve consumption levels, and it has led to significant changes in the consumer market. For the investment market, when there is a positive impact of 1 unit on the Fintech index, the total fixed capital formation will be affected, and the manufactured goods price index will rapidly decline in the early stage and then decline after reaching its peak, gradually stabilizing. It indicates that financial technology can help improve investment levels, and financial technology leads to significant changes in the investment market. From the variance estimation results, it can be seen that financial technology has led to a decrease in market interest rates, and changes in the interest rate market have had a certain impact on the consumer and investment markets. The positive changes in the consumer market and investment market will drive a positive change in the GDP growth rate. According to the equation estimation of each market and economic output growth, it can be seen that the consumer market and investment market have a positive impact on the total economic output. In short, financial technology has led to a decrease in market interest rates, improved financing efficiency, and, through the direct or indirect effects of interest rate mechanisms, increased consumer and investment markets, ultimately promoting economic growth (Lin, 2019) 5. Conclusion The manuscript should include a conclusion. In this section, summarize what was described in your paper. Future directions may also be included in this section. Authors are strongly encouraged not to reference multiple figures or tables in the conclusion; these should be referenced in the body of the paper. 6. Discussion Through research on Fintech in Singapore, this study has confirmed its positive impact on the nation's economic growth, thereby addressing the initial hypothesis and achieving set objectives. Fintech's influence is evident across interest rate markets, consumer markets, and investment arenas, driving growth by enhancing efficiency in financial institutions, altering consumer behaviors, fostering investments, and developing innovative financial products. These findings provide crucial policy insights for the Singaporean government and associated entities, emphasizing the need for reinforced regulatory frameworks and increased research and innovation in Fintech for its sustainable growth and market stability. However, the study is confined to data from 2010-2022, limiting the assessment of Fintech's impact outside this period. Furthermore, the VAR model used imposes certain restrictions on the results. Hence, while generalizing the findings, cautious evaluation and adjustments are required. Future studies should expand data scope and employ more economic indicators and models to deepen the understanding of the relationship between Fintech and economic growth. 7. Conclusion Financial technology (Fintech) plays an integral role in Singapore's contemporary financial system, contributing to operational efficiency, risk management, inclusive financial services, and market accessibility (Ankenbrand,T.,Bieri,D, 2018). By fostering developments in consumer behavior, investment markets, and interest rates, Fintech advances Singapore's economic growth. Our study, conducted using time series data (2010-2022) and the VAR model, indicates a positive correlation between Fintech growth and overall economic growth. It highlights how Fintech, by enhancing transparency in financial product pricing, enables accurate risk and interest rate predictions, thus influencing market interest rates. Given these findings, this paper suggests leveraging Fintech for economic growth by increasing capital investment, expanding Fintech infrastructure, and promoting Fintech innovation. Utilizing Fintech to bolster consumer markets is recommended by supporting businesses in building a digital consumption ecosystem, facilitating diverse, secure, and convenient payment methods, and innovating consumer finance products and services. Finally, enhancing Fintech's role in the real economy is crucial. This could be achieved through encouraging technological innovation and entrepreneurship, fostering collaboration between financial institutions and tech enterprises, and developing financial products that cater to the needs of the real economy. This comprehensive approach ensures Fintech's potential is harnessed to foster sustainable and inclusive economic growth in Singapore. References [1] Appiah-Otoo, I., & Song, N. (2021). The Impact of Fintech on Poverty Reduction: Evidence from China. Sustainability, 13(9), 5225. doi:10.3390/su13095225. [2] Ankenbrand, T., & Bieri, D. (2018). Where are the best Fintech centers? Retrieved fromhttps://innovation.thomsonreuters.com/content/dam/ope nweb/documents/pdf/innovation/case-study/ankenbrand- thomas-bieri-denis-ifz-Fintech-ranking-18053 0.pdf [3] Bettinger, A. (1972). Fintech-A series of 40 time shared models used at manufacturers hanover trust company. Interfaces, 2(4), 62-63. [4] Ciocoiu, C. N. (2011). Integrating digital economy and green economy: Opportunities for sustainable development. Theoretical and Empirical Researches in Urban Management, 6(1), 33-43. https://www.jstor.org/stable/24873273?seq=1 [5] Cumming, D., & Johan, S. (2007). The internet and regional economic development. Academy of Management Proceedings, 340 2007(1), 1-6. https://doi.org/10.5465/ambpp.2007.26508390 [6] Emery, R. F. (1967). The relation of exports and economic growth. Kyklos, 20(4), 470-486. doi.org/10.1111/j.1467- 6435.1967.tb00859.x [7] Gomber, P., Koch, J.-A., & Siering, M. (2017). Digital Finance and Fintech: Current research and future research directions. Journal of Business Economics, 87(5), 537ā€“580. doi:10.1007/s11573-017-0852-x [8] Gan, L., Zhang, H., Lu, T., et al. (2021). Water traffic safety factors based on entropy weight method [J]. China Navigation, 44(2), 53-58. [9] He, D., Leckow, R., Haksar, V., et al. (2017). Fintech and Finance Services: Initial Considerations [R]. IMF Staff Discussion Note 17/05. IMF. [10] Huong, A. Y.-Z., Puah, C.-H., & Chong, M.-T. (2021). Embrace Fintech in ASEAN: A Perception Through Fintech Adoption Index. Research in World Economy, 12(1), 1. https://doi.org/10.5430/rwe.v12n1p1. [11] Hieminga, G., & Lande, F. (2016). The Fintech index: Assessing digital and financial inclusion in developing and emerging countries. ING Economics Department. https://www.ing.nl/media/ING_EBZ_Fintech-index- report_tcm162-116078.pdf [12] Hu, S. (2018). Research on the Development Level of China's Science and Technology Financial Centerā€”Based on the Construction and Measurement of the Index of Science and Technology Financial Center. Journal of Finance and Economic, 2018(09). [13] Han, J., Zhou, Q., & Wang, H. (2019). Technology and financial integration risk and blockchain technology supervision in the era of big data. Scientific Management Research, 37(01), 90-93. [14] Luo, S., Sun, Y., Yang, F., & Zhou, G. (2022). Does Fintech innovation promote enterprise transformation? Evidence from Singapore. Technology in Society, 68, 101821. https://doi.org/10.1016/j.techsoc.2021.101821. [15] Larabi, A., & Nassour, A. (2021). Financial development in emerging countries: Fintech, the new wave of development and the financial future (Singapore as a model). Journal of Research in Finance and Accounting. [16] Liu, C., Fan, Y., & Zhu, X. (2021). Fintech Index Prediction Based on RF-GA-DNN Algorithm. 2021, 1-9. https://doi.org/10.1155/2021/3950981. [17] Li, Z., Yang, C., & Huang, Z. (2022). How does the Fintech sector react to signals from central bank digital currencies? Finance Research Letters, 50, 103308. https://doi.org/10.1016/j.frl.2022.103308. [18] Lin, L. (2019). Regulating Fintech: The Case of Singapore. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3481563. [19] Gnirck, M., & Visser, G. (2016). Singapore, the Fintech Hub for Southeast Asia. In The Financial Technology Handbook for Investors, Entrepreneurs and Visionaries (pp. XX-XX). Edited by S. Chishti & J. Barberis [20] Merton, R. C. (1995). A functional perspective of financial intermediation. Financial Management, 24(2), 23-41. doi:10.2307/3665532 [21] Narayan, S. W. (2019). Does Fintech matter for Indonesia's economic growth? Buletin Ekonomi Moneter dan Perbankan,22(4),437-456. doi:10.21098/bemp.v22i4.1237.