Abstract

The survey is the follow-up of the Diagnostic Review on Consumer Protection and Financial Literacy conducted by the World Bank in 2008-2009. The Diagnostic Review in Romania was the fourth in a World Bank-sponsored pilot program to assess consumer protection and financial literacy in developing and middle-income countries.1 The objectives of this Review were three-fold to: (1) refine a set of good practices for assessing consumer protection and financial literacy, including financial literacy; (2) conduct a review of the existing rules and practices in Romania compared to the good practices; and (3) provide recommendations on ways to improve consumer protection and financial literacy in Romania. The Diagnostic Review was prepared at the request of the National Authority for Consumers' Protection (ANPC), whose request was endorsed by the Ministry of Economy and Finance. Support was provided by the National Bank of Romania (BNR), which supervises banks and non-bank credit institutions. Further assistance was given by the supervisory commissions for securities (CNVM), insurance (CSA) and private pensions (CSSPP).

The Diagnostic Review found that the basic foundations needed for consumer protection and financial literacy are in place in Romania but they benefit from further strengthening support. The Review proposes improvements in six areas: consumer awareness, information and disclosure for consumers, professional competence, dispute resolution, financial education and financial literacy surveys.

Consequently, in 2010 the World Bank commissioned a nation-wide survey of the levels of financial literacy. A consultant (sociologist Manuela Sofia Stanculescu) developed the survey methodology (sampling methodology and questionnaire) in line with the Financial Literacy Survey in Russia (the World Bank, 2008) and the baseline survey Financial Capability in the UK (Financial Services Authority, 2005).2 The final form of the questionnaire was agreed with representatives of the National Bank of Romania (BNR), the Romanian Banking Institute (IBR), the National Authority for Consumers' Protection (ANPC), and the Financial Companies Association in Romania (ALB). The Institute for World Economy (Romanian Academy) collected the data in May 2010.

The main objective of this work is the establishment (and later the evaluation) of a well targeted national program of financial education.

Geographic coverage

National

Analysis unit

Household, individual

Universe

Non-institutionalized persons aged 18 or older

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the survey is probabilistic, two-stage, stratified, representative at national level with an error of +/- 3% at a 95% confidence level.

The sample is based on two stratification criteria: (i) historical region (8 regions) and (ii) type of locality (7 types depending on the city size, in urban areas, and on the synthetic index of community development,4 in the rural ones).

The sample volume is 2048,5 out of which 148 cases represent a boost of persons aged 16, 17 or those had their 18th birthday after November 2009.6 Respondents were randomly selected from electoral registers corresponding to 185 voting sections (randomly selected), located in 141 localities (77 communes, 63 towns/cities and the capital Bucharest).

The sample includes a slight over-representation of men, rural respondents, and elderly particularly due to the boost of young but also to the fact that people left abroad concentrate among the 25-44 age category. Nevertheless, the sample fairly reproduces the structure (by gender, age categories and area of residence) of the country population 16+ years according to the data for 2009 provided by the National Institute for Statistics. Socio-demographic structure of the sample is presented in table 3 of the survey report.

Demographic data and data regarding the use of financial services were collected for all members of respondents? households. In the respondents? households live 5406 persons overall. This extended sample has also a slight over-representation of rural respondents and an under-representation of children (0-14 years) and persons 25-24 years (most probably young people who left abroad with children).

MORE INFORMATION ON THE SAMPLING METHODOLOGY

Sample volume: 2,200 non-institutionalized persons aged 18 or older. In addition, the sample will be boosted with 180 persons aged 16-18 years old. Overall, at least 2,000 valid questionnaires should be completed during fieldwork.

Type of the sample: Probabilistic, two-stage, stratified, representative at national level, with an error of +/- 2.8% at a 95% confidence level.

Stratification criteria: The sampling scheme is based on two stratification criteria

(a) Historical region (8 regions) (b) Type of locality, with 7 theoretical strata

i. Urban areas - 4 strata 1. very small towns under 30 thou inhabitants 2. small towns 30,001-100 thou inhabitants 3. medium cities 100,001-199 thou inhabitants 4. large cities 200 thou inhabitants or more

ii. Rural areas - 3 strata determined based on the synthetic index of community development 37 1. poor communes (the 30% communes with the lowest level of development within the country) 2. medium developed communes 3. developed communes (the 30% communes with the highest level of development within the country).

Sampling stages: The sampling scheme includes two stages.

Sampling units: There are two sampling units corresponding to the two sampling stages. In the first sampling stage, voting sections are selected and in the second stage, non-institutionalized persons aged 18 years or more.

Selection: Random selection in all sampling stages.

Sampling scheme: In the first stage the sample is distributed proportionally with the volume of population for each of the 56(= 8 x 7) theoretical strata different from zero.

The corresponding number of voting sections for each strata is determined taking into account on the one hand, the volume of each strata sub-sample (= sample size x share of total population in that strata) and, on the other hand, a minimum level of 10 questionnaires for each sampling point. The voting sections which will represent sampling points are then randomly selected based on the exhaustive national list of voting sections (the latest available from the Permanent Electoral Authority).

The sample has 188 sampling points (voting sections) of which 104 are in urban areas, and 84 are in rural localities, including the capital city.

For each sampling point is computed the number of corresponding questionnaires by dividing the strata sub-sample by the number of sampling points of that strata. In the second sampling stage, the electoral registers corresponding to the voting sections (selected as sampling points) are used as sampling frame. Non-institutionalized persons aged 18 or more are randomly selected from the electoral registers based on the mechanical step method.

In those localities where the electoral registers are not available (or the municipality do not grant access), the random route method will be used. All these cases will be specified and explained in the fieldwork report, except for Bucharest, where the random route method will be used for all voting sections, as the rate of replacement from electoral registers is high in all national representative surveys.

The electoral registers include only persons 18 years or more. Accordingly, the sample will include a boost of persons aged 16, 17 or persons that had their 18th birthday after November 2009.39 For each voting section, one person aged 16-18 years will be added. They will be selected based on the random route method.

Mode of data collection

Face-to-face [f2f]

Response rate

The overall response rate of the survey is 95.2%. More detailed information is provided in "Table 2 Response rates and quality of the sampling frame by sampling method (%) " of the survey report.

This statistic presents the share of children who use apps for financial education as reported by their parents in Great Britain as of January 2016, depending on the child's age. Over all age groups, the largest share of children hasn't used any apps for financial education.

United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ data was reported at 90.223 % in 2017. This records a decrease from the previous number of 100.000 % for 2014. United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ data is updated yearly, averaging 90.223 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 100.000 % in 2014 and a record low of 89.893 % in 2011. United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).Secure Access versions of the Next Steps include:sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656. National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 held under SN 8681.Linked Student Loans Company Records for years 2007-2021 held under SN 8848.When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside. The Student Loans Company (SLC) is a non-profit making government-owned organisation that administers loans and grants to students in colleges and universities in the UK. The Next Steps: Linked Administrative Datasets (Student Loans Company Records), 2007 - 2021: Secure Access includes data on higher education loans for those Next Steps participant who provided consent to SLC linkage in the age 25 sweep. The matched SLC data contains information about participant's applications for student finance, payment transactions posted to participant's accounts, repayment details and overseas assessment details.

Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

The Student Loans Company (SLC) is a non-profit making government-owned organisation that administers loans and grants to students in colleges and universities in the UK. The Next Steps: Linked Administrative Datasets (Student Loans Company Records), 2007 - 2021: Secure Access includes data on higher education loans for those Next Steps participant who provided consent to SLC linkage in the age 25 sweep. The matched SLC data contains information about participant's applications for student finance, payment transactions posted to participant's accounts, repayment details and overseas assessment details.

This statistic shows spending on childcare and education in United Kingdom (UK) households as of 2014. Approximately ** percent of households pay for pre-school childcare. Only * percent of households pay for private education as of 2014.

This statistic presents the share of children with financial school education as reported by their parents in Great Britain as of January 2016, depending on the child's age. Over all age groups, the largest share of children hasn't received any financial eduaction at school. The share of children who received financial school education increased in the older age groups.

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy. Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC). There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).Secure Access versions of the Next Steps include:sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656. National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 held under SN 8681.Linked Student Loans Company Records for years 2007-2021 held under SN 8848.When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside. SN 5545 - Next Steps: Sweeps 1-9, 2004-2023 includes the main Next Steps survey data from Sweep 1 (age 14) to Sweep 9 (age 32).Latest edition informationFor the seventeenth edition (September 2024), data and documentation for Sweep 9 (Age 32) have been added to the study. Main Topics: The content of the Next Steps Sweep 9 (Age 32 Survey) covers the following topics: Household relationship - Module includes information on: current relationship, previous cohabiting relationships, children (previously reported and new to household), childcare, non-resident children, non-resident parents, other household members (previously reported and new to the household).Housing - Module includes information on: current and previous housing, homelessness.Activities and employment - Module includes information on: activity history, current activity, current employment, employment at age 25, employment details for first job after full-time education, second job, prospective employment, employment support, work attitudes, and partner employment.Finance - Module includes information on: current pay/salary main job, pay/salary from second job, debt, income from other sources, partner's salary, benefits, other income, household income, pensions, savings and investments, subjective disposable income and attitudes to debt/saving/pension and future planning.Educational qualifications - Module includes information on: current education, educational qualifications, fees paid while in education, partner educationHealth - Module includes information on: general health, height and weight, illness/disability, exercise, sleep, diet, Covid-19.Identity, attitudes, and social and political participation - Module includes information on: attitudes, ethnic group, leisure, national identity, partner ethnicity, politics, social networks and social support, trust.Self Completion - Module includes information on: age at menarche, cognitive assessment, crime, difficult events, domestic violence, drinking, drugs, financial circumstances, financial literacy, gender identity and sexual orientation, health, loneliness, mental health, migration, personality, pregnancy history, relationship quality, school, sexual behaviour, smoking and well-being. The content of the Next Steps Sweep 8 (Age 25 Survey) covers the following topics: Household relationships: This module included information on current relationship, previous cohabiting relationships (dating back to September 2006), children, childcare, non-resident children, non-resident parents, and other household members.Housing: This module covered current and previous housing (summary data is collected about the different addresses the study members have lived in since they were 16, if other than the parents' home).Employment: Included information about current activity, current employment, second job, prospective employment (for unemployed), activity history, employment details for first job after September 2006 (aged 16), employment support, work attitudes, and partner employment. Data on current economic activities and activity history was obtained back to the time of the last interview and no earlier than September 2006.Finance: This module captured current pay/salary main job, pay from second job, income from other jobs, partner's income, benefits, income from other sources, household income, pensions, and debt. Education and job training: The module included job training, education since previous interview/September 2006, current education, fees, and partner's education.Health and wellbeing: Included information on general health, height and weight, exercise, sleep, diet, accidents and injuries. Identity and participation: This module provided information on young people's ethnicity and religion, measures of trust, risk, patience, meritocratic beliefs, adult identity, leisure, politics, social networks and social media participation.Self-completion module: The self-completion module included data on gender identity, locus of control, overall life satisfaction, mental health, self-harm, crime and harassment, drinking and smoking behaviour, drugs, bullying, sexual behaviour, and pregnancy history. A key component of the Age 25 Survey sweep is data linkage to administrative records held about individuals by government departments. At Sweeps 1-4 information was gathered on: the young person's family background;parental socio-economic status;personal characteristics;attitudes, experiences and behaviours;attainment in education;parental employment;income and family environment as well as local deprivation;the school(s) the young person attends/has attended;the young person's post-16 plans. The questionnaires at Sweeps 5-7 consisted of two modules: Household Information Module: included questions on the young person's household situation details of any persons living with themYoung Person Module: topics included demographics, attitudes to local area, activity history and current activity, jobs and training, qualifications being studied, higher education, attitudes to work and debt, childcare and caring responsibilities, young people Not in Education Employment or Training (NEET), Apprenticeships, information, advice and guidance, risk behaviours, relationships and sexuality, and own children. The additional 'Monthly Main Activity' dataset takes responses to the Activity History section of the questionnaire at Sweeps 4-7 and synthesises this information into variables that represent a monthly time series running from September 2006 (two months after the respondents completed compulsory education) until May 2010 (the first month of interviews for Sweep 7). For each of the 45 months in this period, this file contains the respondent's derived 'main' activity which is classified as one of Education, Employment, Apprenticeship/Training or Unemployed/Inactive (NEET). Multi-stage stratified random sample Telephone interview Self-administered questionnaire: Computer-assisted (CASI) Face-to-face interview

OECD Education statistics database includes the UNESCO/OECD/EUROSTAT (UOE) database on education covering the outputs of educational institutions, the policy levers that shape educational outputs, the human and financial resources invested in education, structural characteristics of education systems, and the economic and social outcomes of education, learning and training throughout life, including on employment and unemployment. Also included in the database are the PISA 2015 dataset, Teaching and Learning International Survey (TALIS) data, the annual Education at a Glance data and data relating to Gender equality in education.

Pan London financial capability data to support Local Authorities Child Poverty Needs Assessments, updated in April 2011 with 2010 data.

This data is designed to help local authorities improve their understanding of the areas within their borough where low financial capability is most likely to exist. This could be useful to child poverty needs assessments, and subsequent work to develop and target support services for residents within their borough.

Supporting Documents

Technical information about the datasets is available in the readme.txt file.

A support note prepared by MAS and CPU is available to advise local authorities on using the data in Child Poverty Needs Assessments.

Profiles of the data categories are available in the Pen Portraits report and details of the underlying model used by Experian are available in Technical Model report.

Further Information

https://s3-eu-west-1.amazonaws.com/londondatastore-upload/mas_web_graphic.jpg" alt="money advice service logo" />

For more information on the Money Advice Service (formerly the Consumer Financial Education Body): http://www.moneyadviceservice.org.uk

For more information on Child Poverty Unit: http://www.education.gov.uk/childrenandyoungpeople/
families/childpoverty

For details of the Experian model:
http://webarchive.nationalarchives.gov.uk/
http://www.hm-treasury.gov.uk/thoresen_review_index.htm

Snapshot img

Personal Finance Software Market Size 2024-2028

The personal finance software market size is forecast to increase by USD 296.46 million, at a CAGR of 4.76% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing use of smartphones and mobile devices for financial management. This trend is fueled by the convenience and accessibility that mobile applications offer, enabling users to manage their finances on-the-go. Another key driver is the rise in adoption of artificial intelligence (AI) and machine learning (ML) technologies in personal finance software. These advanced technologies enable more accurate budgeting, investment recommendations, and fraud detection, enhancing the user experience and value proposition. However, the market also faces challenges, including the high cost of personal finance software, which may limit adoption for some consumers.
Additionally, ensuring data security and privacy remains a significant challenge, as users increasingly rely on digital platforms to manage sensitive financial information. Companies seeking to capitalize on market opportunities must focus on offering affordable, secure, and user-friendly solutions, while effectively addressing the integration of AI and ML technologies to provide differentiated value.

What will be the Size of the Personal Finance Software Market during the forecast period?

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The market continues to evolve, with cloud-based storage and financial modeling tools becoming increasingly prevalent. These advancements enable users to access their financial data from anywhere, facilitating real-time financial analysis and planning. Financial literacy resources integrated into these platforms help users improve their understanding of various financial concepts. Moreover, financial planning software offers goal setting features, allowing users to create and monitor progress towards their financial objectives. Tax preparation software and mobile accessibility further enhance the convenience of managing finances on the go. Cloud-based storage ensures data security through secure data encryption, while financial data aggregation enables users to view all their financial accounts in one place.

Net worth calculators and budgeting tools help users track their spending and savings, while investment analysis tools provide insights into asset allocation strategies. Credit score monitoring and debt management features are essential components, helping users maintain a healthy financial profile. Multi-currency support caters to the needs of globally dispersed users. Data privacy regulations ensure that users' financial information remains secure. The ongoing integration of user-friendly interfaces, goal setting features, and tax preparation software ensures that personal finance software remains an indispensable tool for individuals and businesses alike. Continuous advancements in technology and evolving market dynamics underscore the importance of staying informed and adaptive in this dynamic market.

How is this Personal Finance Software Industry segmented?

The personal finance software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Home business users
  Individual users


Product

  Web-based software
  Mobile-based software


Geography

  North America

    US
    Canada


  Europe

    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By End-user Insights

The home business users segment is estimated to witness significant growth during the forecast period.

Personal finance software caters to the unique needs of home business owners, offering features that help manage both personal and business finances effectively. These solutions enable users to link accounts, ensuring seamless data aggregation. Offline data access and secure data encryption provide peace of mind, while personalized financial advice and investment analysis tools offer valuable insights. Retirement planning tools and goal setting features facilitate long-term financial planning, and budgeting tools help monitor expenses. Credit score monitoring and debt management features keep users informed of their financial health. Multi-currency support and asset allocation strategies cater to businesses with international transactions.

Expense tracking apps, portfolio management, and cash flow management tools provide real-time visibility into financial activities. Cloud-based storage and financial modeling software enable data access from anywhere, while financial literacy resources promote financial education.

In 2023/24, the government of the United Kingdom spent approximately 4.1 percent of its gross domestic product on education, compared with 4.2 percent in the previous financial year. During this time period, education spending as a share of GDP was highest in 2009/10 when it was 5.7 percent.

United Kingdom LR: MFI: excl OD: NFC: LB: Education data was reported at 126.000 GBP mn in Mar 2020. This records a decrease from the previous number of 187.000 GBP mn for Feb 2020. United Kingdom LR: MFI: excl OD: NFC: LB: Education data is updated monthly, averaging 148.000 GBP mn from Jan 2016 (Median) to Mar 2020, with 51 observations. The data reached an all-time high of 349.000 GBP mn in Jan 2019 and a record low of 72.000 GBP mn in Jan 2017. United Kingdom LR: MFI: excl OD: NFC: LB: Education data remains active status in CEIC and is reported by Bank of England. The data is categorized under Global Database’s United Kingdom – Table UK.KB018: MFIs Lending to Non Financial Businesses: Large Businesses.

United Kingdom Changes in OD: MFI: NFC: Education data was reported at 47.000 GBP mn in Mar 2020. This records an increase from the previous number of 17.000 GBP mn for Feb 2020. United Kingdom Changes in OD: MFI: NFC: Education data is updated monthly, averaging 15.000 GBP mn from May 2011 (Median) to Mar 2020, with 107 observations. The data reached an all-time high of 89.000 GBP mn in May 2015 and a record low of -133.000 GBP mn in Jan 2013. United Kingdom Changes in OD: MFI: NFC: Education data remains active status in CEIC and is reported by Bank of England. The data is categorized under Global Database’s United Kingdom – Table UK.KB016: MFIs Lending to Non Financial Businesses.

Snapshot img

Payday Loans Market Size 2025-2029

The payday loans market size is forecast to increase by USD 9.9 billion, at a CAGR of 4.5% between 2024 and 2029.

The market is characterized by growing awareness among the youth demographic and an increasing number of lenders offering these services. Simultaneously, payday loans face criticism for being perceived as predatory due to their high interest rates and potential for debt trap situations. These trends present both opportunities and challenges for market participants. On one hand, the expanding awareness and acceptance of payday loans among younger generations signify a potential customer base ripe for growth. Moreover, the increasing competition among payday lenders fosters innovation and improved customer service, potentially enhancing the overall market appeal. On the other hand, the negative perception surrounding payday loans poses a significant challenge.
The predatory nature of these loans can lead to long-term financial hardships for borrowers, prompting regulatory scrutiny and potential restrictions. As such, market players must navigate this delicate balance between meeting consumer demand and addressing concerns regarding ethical lending practices. To capitalize on market opportunities and effectively manage challenges, companies must focus on transparency, responsible lending practices, and effective communication with their customer base.

What will be the Size of the Payday Loans Market during the forecast period?

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The market continues to evolve, shaped by a complex interplay of factors including responsible lending practices, financial hardship, and the growing prevalence of online lending. Cash advances and payday loans serve as crucial financial solutions for individuals facing economic hardship, yet concerns around predatory lending, fraud prevention, and ethical considerations persist. Credit counseling and debt relief options have emerged as essential components of the market, offering debt management and financial planning resources to borrowers. Artificial intelligence and machine learning are increasingly utilized for loan origination and risk assessment, enhancing the application process and improving risk management. Prepayment penalties, interest rates, and financial literacy remain key areas of focus, with consumers demanding greater transparency and affordability.

Compliance management and government regulation are critical in ensuring fair lending practices and protecting consumers from identity theft and data security breaches. Third-party lenders and direct lenders have expanded their offerings, providing alternatives to traditional banking services such as overdraft protection and loan consolidation. Debt consolidation and income inequality have fueled the growth of alternative lending solutions, while the use of big data and credit scores streamlines the loan origination process. Financial education and consumer finance play a vital role in fostering financial inclusion and breaking the debt cycle. Repayment schedules, loan terms, and late fees are subjects of ongoing debate, with legal frameworks and public policy shaping the market's future trajectory.

The market's continuous dynamism underscores the importance of staying informed and adaptive to the evolving landscape. From credit checks and loan terms to risk management and ethical considerations, the industry's ongoing transformation offers opportunities and challenges for all stakeholders.

How is this Payday Loans Industry segmented?

The payday loans industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Storefront payday loans
  Online payday loans


Consumer

  Single
  Married


Age Group

  31-40
  21-30
  51 and above
  41-50
  Less than 21


Loan Type

  Small (U$500)
  Medium (U$500-U$1500)
  Large (U$1500)


Consumer Segment

  Individual
  Small Businesses


Geography

  North America

    US
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    UAE


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The storefront payday loans segment is estimated to witness significant growth during the forecast period.

The market encompasses various entities, including online lending, responsible lending, financial hardship, cash advance, payday advance, credit counseling, debt relief, economic hardship, debt management, artificial intelligence, legal frameworks, social welfare, data security, predatory

The dataset gathers historical series on the funding and enrolment in the UK public education system from 1833 to 2019. Funding and enrolment are distributed by level of education, funders and economic categories. It is based on the method of quantitative history which follows the principles of national accounting and provides a stable frame to integrate financial and other data, and allow comparisons across time and space

Local Authorities are required under section 251 of the Apprenticeships, Skills, Children and Learning Act 2009 to prepare and submit an education and children’s social care outturn statement. These statistics provide a detailed picture of local authority actual expenditure and provide details of the size and amount of school balances. They incorporate the statistics on school revenue balances that were previously published seperately. Source agency: Education Designation: Official Statistics not designated as National Statistics Language: English Alternative title: 2009-10 (section 251 formerly s52)

Pan London financial capability data to support Local Authorities Child Poverty Needs Assessments, updated in April 2011 with 2010 data. This data is designed to help local authorities improve their understanding of the areas within their borough where low financial capability is most likely to exist. This could be useful to child poverty needs assessments, and subsequent work to develop and target support services for residents within their borough. Supporting Documents Technical information about the datasets is available in the readme.txt file. A support note prepared by MAS and CPU is available to advise local authorities on using the data in Child Poverty Needs Assessments. Profiles of the data categories are available in the Pen Portraits report and details of the underlying model used by Experian are available in Technical Model report. Further Information For more information on the Money Advice Service (formerly the Consumer Financial Education Body): http://www.moneyadviceservice.org.uk For more information on Child Poverty Unit: http://www.education.gov.uk/childrenandyoungpeople/ families/childpoverty For details of the Experian model: http://webarchive.nationalarchives.gov.uk/ http://www.hm-treasury.gov.uk/thoresen_review_index.htm