Financial technology and financial inclusion in remote areas of Algeria. Analytical study using data mining

. This study aimed to demonstrate how financial technology tools can be used to achieve financial inclusion by shedding light on the reality of financial technology and financial inclusion in Algeria, specifically in the remote areas of Algeria, as financial inclusion represents one of the main areas that economists and governments are trying to focus on to eliminate poverty. To reach the goal of the study, a statistical analysis method was adopted for the various questions asked to 200 participants in the survey during the period 2022–2023. A set of quantitative and qualitative data was used. The study population represents 200 individuals to whom the questionnaire was distributed. A data mining tool was used to analyze the study data, and the survey participants' K algorithm was later used to predict the behavior patterns of people from a similarly contextualized community regarding financial activities. The study concluded that financial technology, through its multiple tools, changes the structure of comprehensive financial services, in addition to the diversity and style of financial services provided to individuals, which has enhanced and increased their availability to a broader social group that did not have access to them. It was also shown that there is a significant impact of financial technology tools on enhancing financial inclusion indicators. It is recommended to adopt effective and modern financial and technological strategies that provide marginalized social groups with reasonable access to financial services and products that meet their needs, including transactions, payments, savings, credit, and insurance. Therefore, obtaining the added value of data and investing it will increase financial inclusion indicators.

Introduction.Technology has formed and will continue to constitute a revolution in the field of global and Arab financial systems, as it now meets many needs and provides services related to various operations.Technology-accredited institutions have succeeded in providing a variety of services, especially those financial institutions (Baptiste.V, 2019, p. 5), which include, among other things, payment services and digital currencies.Money transfer, as well as lending, crowd funding, and wealth management, in addition to insurance services, also succeeded in creating demand for those products.While in some countries and regions the formal financial sector (GPFI, 2016, p.5), which mainly consists of the banking system, serves most of the population, other countries, and regions, with which we highlight remote or rural areas and specifically Algeria, do not enjoy a large segment of their society, particularly the low-income group, with limited access to financial services, whether formal or semi-formal.As a result, many people necessarily must rely either on their own sources or informal sources of financing, which generally come at a high cost.Poverty is not just insufficient income; rather, it is the absence of a wide range of capabilities, including security and the ability to participate in economic and political systems.Hence, the importance of financial inclusion arises from the problem of the financial exclusion of nearly 3 billion people from formal financial services around the world.
Therefore, the following problem can be formulated: What is the reality of using financial technology in remote Algerian regions and its role in achieving financial inclusion?
To answer the above problem, the following main hypothesis is proposed: There is a role for financial technology in achieving financial inclusion in remote areas.The following sub-hypotheses were proposed: ➢ There is no relationship between parents' educational level and financial inclusion.
➢ There is no relationship between the type of bank and financial inclusion.
➢ There is no relationship between the distance to the bank and financial inclusion.
➢ There is no relationship between monthly income and banking inclusion.

Financial Inclusion
The definitions of financial inclusion (Chen XH, You XY, 2021, p.7) differed according to the source of the definition.We find: The World Bank defines it as having access to useful financial products and services at affordable prices that meet their needs.(Mirjana Peji ć, 2019, p. 2) The 1990s saw the introduction of the FI idea.Researchers who found that impoverished people in developed countries' periphery were denied formal credit due to racial or geographic prejudices, even though they had regular income streams and collateral, raised the issue of people being excluded from the formal financial system.(Thereza, 2021, p. 1) The procedure that guarantees accessibility and availability to the formal financial sector is known as financial inclusion.(Sarma, 2012, p.3) From the above, we can say that financial inclusion is the process of ensuring access to appropriate and formal financial products and services needed by all sectors of society in general, and vulnerable groups such as low-income people in particular, at a reasonable cost and in a fair and transparent manner by formal financial service providers.(Saliha Falaq, 2019, p.3).Therefore, the establishment of financial inclusion entails many development benefits, especially through the exploitation of information and communication technology and the use of digital financial services.There is also a close relationship between financial inclusion, financial stability, and economic growth, as financial inclusion aims to provide segments of society with official financial services at reasonable costs.
Through the legality of some unofficial channels, the diversity of their product offerings, attention to quality to draw in the greatest number of consumers and transactions, and other means, financial inclusion also increases competition amongst financial institutions.(Qasim, 2023, p. 5) Financial inclusion is a key factor for achieving the goals of sustainable development, as the generalization of financial services contributes to improving the standard of living.(Tamara Firas, 2020, p. 180) Financial inclusion is concerned with the social aspect, and this is in terms of caring for the poor and low-income people, through their fair access to financial products at low prices, thus developing their social and economic conditions.

Data mining
The term data mining ( Nihat, A., 2014, p.11) appeared in the mid-nineties in the United States of America, but data mining itself is a development for a sector with a long history.Interest in data mining began in 1989 during a workshop on discovering knowledge in databases, and since then it has been held.This workshop continued annually until 1995.The Knowledge Discovery and Data Mining International Conference.(Sanghamitra, 2005, p.04) became one of the most important events.Its development and modifications continued to the present day.Data mining (Dzeroski, S. 2002, p. 348) refers to extracting information and knowledge from a large amount of data using a set of statistical methods.And smart technologies and others, as a combination of multiple disciplines.(Margaret H., 2009, p. 2).
Researchers in the field of data mining proposed several approaches to increase the chances of success in implementing data mining projects, and among the most common processes is the six-step GRISP-DM process for data mining, which can be explained as follows: (Jiawei Han, 2012, p. 02) ➢ Understanding the business is necessary in order to identify the field of work and define the objectives of the application.

Applied study
To reveal the extent of financial inclusion and exclusion in the region and to identify the factors that support financial inclusion, respondents were carefully selected to ensure that all groups of society in the region are subject to the survey.In the first step of the analysis, we come up with the statistical results of the survey, which present the facts and figures.In the second step, we apply data mining techniques to the data set collected through the survey process.

Statistical results from the survey
A statistical analysis of various questions asked to 200 survey participants over the period 2022-2023 has been found, and the main areas of analysis are presented below.

A. Awareness of the banking service and its use
People's awareness about the various facilities available with modern banking services such as ATMs, money transfers, etc. was also found to be 12% of the percentage of people who actually use this type of facility.

B. Bank account
This analysis shows the current state of banking inclusion.This information is very important for understanding the state of financial inclusion in the region.The percentage of people with a bank account is 65.23%, and the percentage of people without a bank account is 34.77%.

C. Bank account details
This is the division of the respondents according to their choice of banking institution.Mostly, we have banks that are subject to government banking groups and private banks, but their availability in remote areas is very limited, and therefore 25% resort to government banks only.

D.
Having a bank account 35% of the respondents do not have accounts with any of the financial institutions.Lack of awareness and accessibility are the main reasons for not having accounts at banks.Until now, many public-sector banks did not exist in remote villages.

E. The type and source of credit available
Among the respondents, 40% did not benefit from any loans from formal financial institutions, and 60% had loans from formal banking institutions.These loans are mainly for agricultural purposes.

Data mining approach to financial inclusion
The data mining approach to financial inclusion is to discover hidden patterns and valuable knowledge from data collection.These patterns are highlighted by factors affecting financial inclusion and exclusion.The specific patterns from data mining analysis in this area will be a set of rules that reveal socioeconomic and educational conditions that lead to either financial inclusion or exclusion.The survey respondents' Kalgorithm was subsequently used to predict the behavior patterns of people in a similar context with regard to Fuzzy association rules use fuzzy logic to convert numerical attributes into linguistic attributes, such as "income = high", thus preserving the integrity of the information conveyed by these numerical attributes.On the other hand, explicit association rules use sharp division to convert numeric attributes into binary attributes, such as "income = [-500020000]".
To find associations between novel linguistic features derived from the survey database, we used weighted fuzzy association grammar mining techniques using Kneam software.The association rules generated from the survey database can be later converted into classification rules so that we can extrapolate facts about financial inclusion and its relationship to fintech.To find the associations of technology and financial inclusion, we take the maximum value from the groups in a record for each attribute and assign this group that has the maximum membership for an attribute with that attribute (for example, in the first, if we consider the Have a bank account field, the associated groups are low, medium, and high with values, respectively, so we set the Money Transfer field with a high value for that record).This is done for all records and attributes.If we take the first five records from the derived database, we get the following correlations in Table 2 and the results of the study in Table 3.

Conclusion
In this work, we started with a database that was collected for statistical analysis on the situation of financial inclusion in a remote area of Algeria.The results of the analysis gave a clear picture of the statistics of the current situation in this area in the field of financial inclusion and exclusion, upon careful analysis of the rules and patterns that were created.We can easily reach the conclusion that modern information technology and modern banking systems in remote areas are the main factors affecting financial inclusion, and through data mining techniques, we have concluded that there is a relative use of technology in these remote areas, and we have found fields It clearly indicates that awareness of modern technology plays a vital role in financial inclusion in these regions, and this is within the framework of the efforts made by Algeria in the field of technology by providing multiple digital services, including ATMs, smart cards, etc., as we have come to and based on the results in Table 2: ➢ there is a positive relationship between the educational level of parents and banking inclusion, which means that there is a relationship between the educational level of parents and banking inclusion, and this denies the validity of the first sub-hypothesis; ➢ there is a positive relationship between the type of bank and financial inclusion, which means that there is a relationship between the type of bank and financial inclusion, which negates the validity of the second sub-hypothesis; ➢ there is a positive relationship between the distance to the bank and financial inclusion, which means that there is a relationship between the distance to the bank and financial inclusion, which negates the validity of the third sub-hypothesis; ➢ there is a negative relationship between monthly income and financial inclusion, which means that there is no relationship between monthly income and banking inclusion, which confirms the validity of the fourth sub-hypothesis; ➢ therefore, despite the efforts made by Algeria in the field of technology, it has provided various digital services, including ATMs, smart cards, and so on.
Data understanding: selecting and creating a target data set.(Nemiche Mohamed, 2015, p. 13) ➢ Data processing: It includes selecting expected variables and sample size (Rasha Odeh Lafta, 2019, p. 10), formulating new variables to build effective models, and rearranging data fields as required in the data mining model.(Jiawei Han, 2012, p. 357) ➢ Building the model: This process needs the help of specialists in data mining (Cheng Che, 2019, p. 13) in order to investigate the options and choose which model is best for handling the study's challenge.(Dimitrios, 2022, p. 10-13)

Table 2 .
Rules for the first level association between the variables of financial inclusion and financial technology

Table 3 .
The results of the study of the relationship between financial technology and financial inclusion