A company's earnings are its after-tax net income. This is the company's bottom line or its profits. Earnings are perhaps the single most important and most closely studied number in a company's financial statements.

EHUE is a Dataset developed by Banco de España containing information from a survey on the most significant cash-related issues, such as cash use and access to cash (with a special focus on municipalities without a stable branch presence), the use of cash alternatives, the level of knowledge of new forms of cash withdrawal (cashback or Correos Cash), of the digital euro project and more. Source of data: Data comes from 4,671 interviews with a representative sample of the Spanish population + 351 interviews in municipalities without a stable branch presence and 1,353 interviews with a representative sample of small retailers and restaurants and bars + 251 interviews in municipalities without a stable branch presence. Universe: Information on the habits of the Spanish population and smalls retailers and restaurants and bars with regard to different means of payment and, especially, cash. Analysis unit: People aged 18 or older and companies with less than 10 employees.

Content

Provides data from the IDA Statement of Cash Flows for the fiscal years ended June 30, 2013, June 30, 2012 and June 30, 2011. Sum of all cash flows represent the net changes in unrestricted cash.The values are expressed in millions of U.S. Dollars. Amount in millions of US Dollars, rounded.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore World Bank's Financial Data using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under a Creative Commons Attribution 3.0 IGO license.

Cover photo by Markus Spiske on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under Creative Commons Attribution 3.0 IGO

IntroductionTuberculosis incidence is disproportionately high among people in poverty. Cash transfer programs have become an important strategy in Brazil fight inequalities as part of comprehensive poverty alleviation policies. This study was aimed at assessing the effect of being a beneficiary of a governmental cash transfer program on tuberculosis (TB) treatment cure rates.MethodsWe conducted a longitudinal database study including people ≥18 years old with confirmed incident TB in Brazil in 2015. We treated missing data with multiple imputation. Poisson regression models with robust variance were carried out to assess the effect of TB determinants on cure rates. The average effect of being beneficiary of cash transfer was estimated by propensity-score matching.ResultsIn 2015, 25,084 women and men diagnosed as new tuberculosis case, of whom 1,714 (6.8%) were beneficiaries of a national cash transfer. Among the total population with pulmonary tuberculosis several determinants were associated with cure rates. However, among the cash transfer group, this association was vanished in males, blacks, region of residence, and people not deprived of their freedom and who smoke tobacco. The average treatment effect of cash transfers on TB cure rates, based on propensity score matching, found that being beneficiary of cash transfer improved TB cure rates by 8% [Coefficient 0.08 (95% confidence interval 0.06–0.11) in subjects with pulmonary TB].ConclusionOur study suggests that, in Brazil, the effect of cash transfer on the outcome of TB treatment may be achieved by the indirect effect of other determinants. Also, these results suggest the direct effect of being beneficiary of cash transfer on improving TB cure rates.

Here are a few use cases for this project:

1. Money Counter Application: The 'banknote' model could be adopted in banks or retail businesses that frequently deal with large amounts of cash. The model can be used in an automated machine or app to count different denominations of notes, increasing speed and reducing human errors associated with manual cash counting.

2. Fraud Detection: The model can assist in building systems to identify counterfeit notes. By recognizing the distinct patterns, colors, and markings of authentic RM banknotes, it could alert users to potential fake or modified banknotes.

3. Financial Educational Tools: The banknote model could be used in digital educational tools to teach children or adults about different types of RM banknotes.

4. Aid for Visually Impaired: The model could be incorporated into an application designed to assist visually impaired people identify the value of banknotes.

5. Virtual Reality Shopping: In a simulated environment such as VR shopping apps, the model could be used to identify and process transactions involving RM banknotes, providing a realistic shopping experience for users.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

See Methodology document for country-specific geographic coverage details.

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Analysis of ‘Youth statistics: Monthly amount of money available to young people aged 15-29, according to employment status’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-opendata-euskadi-eus-catalogo-estadisticas-sobre-juventud-cantidad-mensual-de-dinero-de-la-que-dispone-la-poblacion-joven-de-15-a-29-anos-segun-situacion-laboral- on 18 January 2022.

--- Dataset description provided by original source is as follows ---

The Basque Youth Observatory is an instrument of the Basque Government that provides a comprehensive and permanent overview of the situation and evolution of the youth world, allowing an assessment of the impact of the actions carried out in the CAPV by the different administrations in the field of youth.The Basque Youth Observatory regularly publishes more than 100 statistical indicators that can be consulted in euskadi.eus, together with other research and reports. Statistics are provided in various formats (csv, excel).

--- Original source retains full ownership of the source dataset ---

Abstract copyright UK Data Service and data collection copyright owner.The Financial Capability Survey is a nationally representative survey of UK residents, commissioned initially in 2005 by the Financial Services Authority and then from 2015 onwards by the Money and Pensions Service (formerly the Money Advice Service), to support the development and delivery of the Financial Capability Strategy for the UK. The Children and Young People's Financial Capability Survey, 2019 is a nationally representative study of the financial knowledge, attitudes, mindsets and behaviours of 7-17 year olds and their parents, living in the UK. A total of 3,745 children and young people and their parents were interviewed as part of this research.Children were asked about: how they get, save and spend money; their attitude to spending, saving and debt; their confidence and understanding about money; and how they recall receiving financial education. Their parents were asked about: their own attitudes and behaviours with money; their attitudes and approaches towards parenting relevant to money; and their view on their child's skills, abilities, attitudes and behaviours with money. The reports published so far from the 2019 survey can be found on the Money and Pensions Service Research webpage (Short Reports) and on the Money Advice Service Contributing Analysis Reports webpage. The 2019 survey updates and builds on the previous 2016 Children and Young People's Financial Capability Survey (not currently held at the UK Data Service) and provides robust measures of children and young people's financial capability across the UK, including separate analysis for each devolved nation. (Reports from the 2016 survey are also available at the web link above.) Main Topics: The survey includes questions around four topics:a. Financially capable behaviours: these are the behaviours that children and young people exhibit or the actions they take. Based on previous analysis, focus is on two key financially capable behaviours: Day to day money management and active saving.b. Financial enablers and inhibitors: these are the things that make financially capable behaviours either easier or more difficult for children and young people to achieve: Connection, e.g. having responsibility for money Mindset, e.g. having a saving mindset and shopping around Ability, e.g. skills and knowledge c. Some external factors, which are also important drivers of financially capable behaviours Financial means, i.e. receiving money, receiving it regularly, how much do they get. Parental influences , i.e. parent sets rules around money d. Demographics and other characteristics: both child and household characteristics including children's social-emotional, cognitive or behavioural skills.

Consumer Spending in the United States increased to 16350.20 USD Billion in the second quarter of 2025 from 16291.80 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Money Supply M2 in China increased to 330332.50 CNY Billion in June from 325783.81 CNY Billion in May of 2025. This dataset provides - China Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The online banking penetration rate in India was forecast to continuously increase between 2024 and 2029 by in total 19.3 percentage points. After the fifteenth consecutive increasing year, the online banking penetration is estimated to reach 64.34 percent and therefore a new peak in 2029. Notably, the online banking penetration rate of was continuously increasing over the past years.Shown is the estimated percentage of the total population in a given region or country, which makes use of online banking.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the online banking penetration rate in countries like Pakistan and Bangladesh.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

The 2014 Global Findex Database covers around 150,000 adults in more than 140 economies and representing about 97 percent of the world's population. See Methodology document for country-specific geographic coverage details.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Household Saving Rate in the United States remained unchanged at 4.50 percent in June from 4.50 percent in May of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1600.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

A major study conducted by the Young Foundation and funded by the Economic and Social Research Council investigating the use of high cost credit in Wales. The research was carried out throughout 2015 with the final report - Credit where credit’s due? Understanding experiences of high cost credit in Wales – published in May 2016. An estimated 12 million people across the UK lack access to affordable credit, something the majority take for granted in everyday life. The alternatives for many are high cost lenders – payday loans, doorstep lenders, or expensive rent-to-own stores on the high street. This financial exclusion is harmful to individuals and families, our communities and the wider economy. Objectives: We set out to understand the scale of the issue, pathways into and journeys through high cost credit, and the impact this has. We aimed to identify opportunities and offer suggestions for new and improved products, services and ways of engaging consumers – and to outline what might work as alternatives to high cost credit. Key Findings: • Six per cent of the Welsh population have used one or more of rent-to-own stores, home credit and payday loans in the last year. • Customers come from all walks of life but are most likely to be young families. • Reasons for using high cost credit range from paying for Christmas or buying new items for the home, to simply paying the bills and making ends meet. For most, these represent essential purchases. • The majority of people turn straight to high cost credit without considering different types of credit or comparing offers between lenders. • High cost credit customers are typically extremely aware of their income and outgoings, often using ‘jam jar’ and other informal money management solutions. • Many see high cost credit options as being ‘for people like me’ and one of a very limited set of financial options. • The majority of high cost credit customers live in communities where these types of borrowing are normal. • Home credit providers especially are in a strong position to encourage repeat borrowing. • Customer perceptions of payday loans still firmly reflect the pre-cap market. • By contrast, rent-to-own and home credit have largely slipped through the net of negative publicity. • Regulating all forms of high cost credit out of existence is not the answer. • There is a clear need for market growth in the affordable credit market. The Young Foundation took a mixed-methods approach combining robust survey data with deep qualitative insights: (1) A nationally representative survey of 1,000 members of the Welsh population (conducted in person, June 2015). (2) A survey of 134 customers of high cost credit and/ or credit unions across Wales (conducted in-person, October 2015). (3) In-home depth interviews with 24 high cost credit customers. (4) Nine focus groups with 77 high cost credit and affordable credit customers. (5)Telephone interviews with 26 expert stakeholders. The research focused on three kinds of credit – home credit, rent-to-own and payday loans. (1) Home credit – Often known as doorstep loans, repayments on cash loans are collected by an agent from the customer’s home. Leading home credit lenders include Provident and Morses Club. (2) Rent-to-own – Sometimes referred to as hire-purchase, the customer typically pays a weekly amount for a fixed term. At the end of the term the customer owns the product but until that point it is only leased, allowing the customer to return it if they wish, or the lender can repossess the goods if payments are not made. Leading providers include Brighthouse, Perfect Home, Family Vision and Buy-As-You-View. (3) Payday loans – Payday loans are a form of short-term credit, typically for small amounts of money. They are available online and in high street shops. Interestingly our research found that payday loans were not perceived well due to the pre-cap market, reinforced by media portrayals and past experiences. Payday loan lenders include Wonga, Quick Quid and Sunny.

Egypt Banknote detection using yolov5

this is a trained model to detect egyptian money papers to help visually impaired people identifying the money they use in their daily life

• i trained a yolov5s model on a part of kaggle dataset containing 2000 img and 6 classes for egypt banknote, also i used pretrained weights from yolo5v repo on github

The smartphone penetration ranking is led by Canada with 97 percent, while the United Arab Emirates is following with 97 percent. In contrast, Mozambique is at the bottom of the ranking with 9.48 percent, showing a difference of 87.52 percentage points to Canada. The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This report provides information at the state and town level of people served by the Connecticut Department of Social Services for the Calendar Years 2012-2024 by demographics (gender, age-groups, race, and ethnicity) at the state and town level by Medical Benefit Plan (Husky A-D, Husky limited benefit, MSP and Other Medical); Assistance Type (Cash, Food, Medical, Other); and Program (CADAP, CHCPE, CHIP, ConnTRANS, Medicaid, Medical, MSP, Refugee Cash, Repatriation, SAGA, SAGA Funeral, SNAP, Social Work Services, State Funded Medical, State Supplement, TFA). NOTE: On March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients has changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This change in methodology causes a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged.

Change-In-Cash Time Series for Insulet Corporation. Insulet Corporation develops, manufactures, and sells insulin delivery systems for people with insulin-dependent diabetes in the United States and internationally. The company offers Omnipod platform products comprising Omnipod 5 automated insulin delivery system, which includes a proprietary AID algorithm embedded in the pod that integrates with a third-party continuous glucose monitor to obtain glucose values through wireless Bluetooth communication; and Omnipod DASH insulin management system that features a Bluetooth enabled pod that is controlled by a smartphone-like personal diabetes manager with a color touch screen user interface. It also provides pods for Amgen for use in the Neulasta Onpro kit, which is a delivery system to help reduce the risk of infection after intense chemotherapy. The company sells its products to end-users through the pharmacy channel; and independent distributors. Insulet Corporation was incorporated in 2000 and is headquartered in Acton, Massachusetts.

Here are a few use cases for this project:

1. Cash Handling Automation: The "Malaysian Banknote" model could be integrated into Automated Teller Machines (ATMs) or self-service checkout systems to automatically identify and validate denominations of cash being deposited or given as change, reducing human errors.

2. Financial Apps: This model can be integrated into financial apps to enable users to track their cash spending efficiently. Users can take a picture of the banknote they spend, and the app will automatically log the amount based on the identified denomination.

3. Anti-Counterfeit Measures: Authorities could use the model as a tool for spotting counterfeit banknotes. Although it does not directly detect counterfeit features, the model could flag discrepancies in recognition that could suggest a note being fake or tampered with.

4. Aid for the Visually Impaired: This computer vision model could be used to develop applications that help visually impaired or blind people identify the denomination of Malaysian banknotes they are handling, promoting greater financial independence.

5. Educational Tools: The model can be used within educational settings, teaching students about Malaysian currency, its different denominations, or could even be used in projects exploring computer vision and AI technology.