Dataset Description This dataset contains details of various bank transactions. The data includes both debit and credit transactions made using different modes such as card, ATM, and UPI. Each transaction record provides comprehensive information, including the type of transaction, the mode of payment, the amount transacted, the current balance after the transaction, timestamps, and additional details such as narration and transaction ID.

Columns type: The type of transaction (DEBIT or CREDIT). mode: The mode of the transaction (e.g., CARD, ATM, UPI, OTHERS). amount: The amount involved in the transaction. currentBalance: The account balance after the transaction. transactionTimestamp: The timestamp of when the transaction occurred. valueDate: The date the transaction is valued. txnId: A unique identifier for the transaction. narration: A brief description of the transaction. reference: Additional reference information, if any.

Usage This dataset can be used for various analytical purposes, including but not limited to:

• Analyzing spending patterns.
• Studying the frequency and types of transactions.
• Developing predictive models for transaction behaviors.
• Financial and economic research.

Source The dataset is a synthetic creation for educational and analytical purposes. It provides a realistic representation of transaction data typically found in bank statements. It is generated using real data

National Electronic Funds Transfer (NEFT) is an electronic funds transfer system maintained by the Reserve Bank of India (RBI). NEFT enables bank customers in India to transfer funds between any two NEFT-enabled bank accounts on a one-to-one basis. It started in November 2005 and done via electronic messages.

As of November 30, 2019, NEFT facilities were available at 1,48,477 branches/offices of 216 banks across the country and online through the website of NEFT-enabled banks. NEFT has gained popularity due to the ease and efficiency with which the transactions can be concluded.

This data set includes the volume of transactions and the value of the amount from June 2008 to June 2020 ( May, Nov, Dec 2009 Missing). The debit and the credit amount is in Million Indian Rupees.

This is my first effort to start with kaggle tasks. An upvote 👍 would be much appreciated.

Thanks to RBI for the availability of valuable data.

NEFT FAQ Data Source

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

The sample excludes the Northeast states and remote islands. The excluded area represents approximately 10% of the total adult population.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in India was 3,518 individuals.

Mode of data collection

Face-to-face [f2f]

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 over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Excluded populations living in Northeast states and remote islands and Jammu and Kashmir. The excluded areas represent less than 10 percent of the total population.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

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 hand-held 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 traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for India is 3000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

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. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

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. Sample excludes Northeast states and remote islands. In addition, some districts in Assam, Bihar, Jammu and Kashmir, Jharkhand, and Uttar Pradesh were replaced because of security concerns. The excluded areas represent less than 10% of the population.

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.

The sample size in India was 3,000 individuals.

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 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.

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

Sample excludes Northeast states and remote islands, representing less than 10% of the population.

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 3000.

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

India Mobile Banking Transactions: Volume data was reported at 17,117.198 Unit mn in Mar 2025. This records an increase from the previous number of 15,003.146 Unit mn for Feb 2025. India Mobile Banking Transactions: Volume data is updated monthly, averaging 245.260 Unit mn from Apr 2011 (Median) to Mar 2025, with 168 observations. The data reached an all-time high of 17,117.198 Unit mn in Mar 2025 and a record low of 1.080 Unit mn in Apr 2011. India Mobile Banking Transactions: Volume data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI017: Mobile Payments. [COVID-19-IMPACT]

The dataset contains year-, month- and state-wise data on number of Wilful defaulter accounts, together with the total amount involved, as per the data maintained by Experian Credit Information Company (CIC)

Note:

As per Credit Information Companies (CICs) Act, the CICs are independent institutions licensed by Reserve Bank of India (RBI) that collect and maintain financial information about individuals and businesses, which help banks and non-banking financial institutions to determine a borrower's creditworthiness.

Deposit Growth in India increased to 10.20 percent in the week ending November 14 from 9.70 percent two weeks before. This dataset provides - India Deposit Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Number-of-Consecutive-Periods-With-Dividend-Growth Time Series for UCO Bank. UCO Bank provides various banking and financial services in India and internationally. The company's deposit products include current accounts, saving accounts, salary accounts, PPF accounts, fixed deposits, recurring deposits, accounts in foreign currency, and fee collection accounts. It also offers agri, home, education, gold, personal, vehicle, and mortgage loans; finance to micro and small enterprises; working capital financing; term loans; infrastructure finance; and agriculture credit. In addition, the company provides life, health, and general insurance products; credit and debit cards; internet banking services; and international banking services, such as NRI banking, foreign currency loans, finance to exporters and importers, remittances, forex and treasury services, resident foreign currency deposits, and correspondent banking services to Indian customers, corporates, NRIs, overseas corporate bodies, foreign companies/individuals, and foreign banks. Further, it provides government deposit schemes/bonds, pension payments/schemes, and tax collection services; merchant banking services; sovereign gold bonds; and mutual funds. The company was formerly known as The United Commercial Bank Ltd. and changed its name to UCO Bank in 1985. UCO Bank was incorporated in 1943 and is headquartered in Kolkata, India.

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction

This Indian English Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Indian English speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world BFSI voice coverage.

This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making financial domain model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

Number-of-Consecutive-Periods-With-Dividend-Growth Time Series for INDUSIND BANK LTD.. IndusInd Bank Limited engages in the provision of various banking products and services to individuals, NRIs, business owners, corporates, and government and financial institutions. It operates through four segments: Treasury, Corporate/Wholesale Banking, Retail Banking, and Other Banking Operations. The company offers current, savings, defense, and corporate salary; fixed and FCNR, sweep in/out, time and term deposits; and senior citizen schemes, young saver deposits, and recurring and RFC deposits, as well as Rupee multiplier products. It also provides home, personal, gold, car, two-wheeler, commercial vehicle, professional, agricultural, and medical equipment loans, as well as loans against properties and securities; credit cards; micro-finance, working capital, and MSME and business loans, as well as loans to merchants and retailers, and loan on credit cards. In addition, the company offers transaction banking services, including letters of credit/guarantees, structured trade and export finance, and import finance solutions, as well as cash management and remittance services; safe deposit lockers; investment products, such as demat account, mutual fund, gold bond, national pension system, and equity trading; project finance, supply chain financing; investment advisory, strategic mergers and acquisitions, and other advisory services; and health, general, life, and card protection insurance. Further, it provides debit, prepaid, forex, and commercial cards; individual outward remittances, foreign currency bank notes and demand drafts, and travelers cheques; financial inclusion products; trade and foreign exchange accounts; real estate developer financing and bullion services; correspondent banking services; and forex and derivative desk, information and advisory, and remittances through forex channel services. The company was incorporated in 1994 and is based in Mumbai, India.

India People Using At Least Basic Drinking Water Services: Urban: % of Urban Population data was reported at 95.761 % in 2022. This records an increase from the previous number of 95.582 % for 2021. India People Using At Least Basic Drinking Water Services: Urban: % of Urban Population data is updated yearly, averaging 93.794 % from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 95.761 % in 2022 and a record low of 91.829 % in 2000. India People Using At Least Basic Drinking Water Services: Urban: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Access to Services. The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.;WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).;Weighted average;

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Various official sources are linked below, with the ones currently used marked with a †

• List of NEFT IFSCs from [RBI website][combined]†
• List of RTGS IFSCs from [RBI website][rtgs]†
• RBI maintains several lists:
  • List of banks in India
  • Websites of banks in India.
  • Bankwise Volumes in ECS/NEFT/RTGS/Mobile
  • List of Banks permitted to provide Mobile Banking
• NPCI website has several lists:
  • [NACH Live Members][ach]†
  • RuPay Live Members
  • IMPS Live members
  • Banks and Apps live on AutoPay
  • Sub member banks IFSC and MICR Codes
  • Merged banks IFSC and MICR Codes
  • Routing codes used in API E-Mandates
  • Merged Banks on NACH
  • Blocked Banks on NACH

Gold Reserves in India increased to 880.18 Tonnes in the third quarter of 2025 from 880 Tonnes in the second quarter of 2025. This dataset provides - India Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India's 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, researchers from the World Bank, in collaboration with IDinsight, the Development Data Lab, and John Hopkins University sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

Households located in Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh

Kind of data

Sample survey data [ssd]

Sampling procedure

This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

A detailed note covering key features of each sample frame is available for download.

Sampling deviation

Details will be made available after all rounds of data collection and analysis is complete.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires covered the following subjects:

1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

Cleaning operations

The India COVID-19 surveys were conducted using Computer Assisted Telephone Interview (CATI) techniques. The household questionnaire was implemented using the CATI software, SurveyCTO. The software was deployed through surveyors’ smartphones, who called respondents via mobile, and recorded their responses over the phone. If unreached, surveyors would attempt to call back respondents up to 7 times, often seeking explicit appointments for suitable times to avoid non-responses.

Validation and consistency checks were incorporated into the SurveyCTO software to avoid human error. Extreme values and outliers were scrutinised through a real time dashboard set up by IDinsight. Surveys were also audio audited by monitors to check for consistency and accuracy of question phrasing and answer recording. Finally, supervisors also randomly back-checked a subset of interviews to further ensure data accuracy.

IDinsight cleaned and labelled the data for further processing and analysis. The Development Data Lab examined the data for discrepancies and errors and merged the dataset with their proprietary spatial data.

All personally identifiable information has been removed from the datasets.

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%

One of the metrics used by banks to check credit worthiness of an individual is credit score, but for people who have never taken a loan in the past don't have a credit score in first place, and it becomes difficult for banks to lend them.

The "Credit/Loan Dataset - Rural India" is a collection of data points of individuals without a prior credit score and the loan amount sanctioned to them.

The dataset can be used to train models to check the eligible loan amount.

Challenges: 1. The dataset is pretty raw and needs a lot of EDA/FE/FS 2. Some features have > 100 unique values

📝 Overview

This dataset contains monthly statistics of Unified Payments Interface (UPI) transactions in India from April 2016 to August 2025. It is compiled from official NPCI reports and enriched with derived metrics such as average transaction value, month-on-month growth, and year-on-year growth.

UPI has transformed India’s digital economy, and this dataset provides a comprehensive time series view of its adoption, growth, and transaction trends over nearly a decade.

📊 Columns

Month → Reporting month (2016–2025)

No. of Banks live on UPI → Number of banks integrated with UPI

Volume (in Mn) → Total transactions in millions

Value (in Cr.) → Total transaction value in INR crores

Avg_Txn_Value_INR → Average transaction size (derived)

MoM_Growth_Volume_% → Month-on-month growth rate (transactions)

MoM_Growth_Value_% → Month-on-month growth rate (value)

🔍 Use Cases

Time-series forecasting of UPI growth

Financial trend analysis in Indian digital economy

Academic research on fintech adoption

Comparative analysis with global digital payment systems

Building machine learning models for transaction prediction

📌 Why this dataset?

Covers 9+ years of UPI history (2016–2025)

Clean, structured, and Kaggle-ready

Includes derived features for faster analysis

Useful for researchers, data scientists, fintech analysts, and students