Data Dictionary

ID
id
Type
int4
Label
index

ID
month
Type
date
Label
Month

ID
bank_name
Type
text
Label
Bank Name

ID
no_of_debit_transactions
Type
numeric
Label
Debit Transaction Volume

ID
amt_of_debit_transactions
Type
numeric
Label
Debit Transactions Value

ID
no_of_credit_transactions
Type
numeric
Label
Credit Transaction Volume

ID
amt_of_credit_transactions
Type
numeric
Label
Credit Transactions Value

Additional Information

Field Value
Data last updated May 2, 2025
Metadata last updated May 2, 2025
Created April 17, 2024
Format CSV
License Open Data Commons Attribution License
Additional infonan
Data extraction pagehttps://rbi.org.in/Scripts/NEFTView.aspx
Data insightsThe "NEFT Transaction Metrics" dataset, with its granularity, offers invaluable insights into the digital transactional behaviors and trends in India. Through a monthly trend analysis, stakeholders can ascertain the rhythm and pace of NEFT transactions, pinpointing any anomalies, surge periods, or slow phases. These trends can be essential indicators of the broader economic landscape, signaling growth phases or potential economic slowdowns. By examining bank-specific data, a clear picture of each institution's stance in the NEFT landscape emerges. Which banks are the most active in facilitating NEFT transfers? Do certain banks have a higher debit transaction rate compared to credits or vice versa? This type of insight can provide a lens into the financial health and customer engagement strategies of individual banks. An intriguing facet of the dataset is the distinct data on 'Debit Transactions' and 'Credit Transactions'. By scrutinizing these columns, one can discern the balance of funds outflow and inflow for each bank. If a bank consistently has higher debit values compared to credit values, it might indicate more outward transfers than incoming ones. On the other hand, banks with a dominant credit value might be the recipients of a more significant number of inward funds, indicating trust or preference by customers or businesses.
Data last updated2025-03-01 00:00:00
Data retreival date2025-04-15
Datastore activeTrue
FrequencyMonthly
GranularityBank
Has viewsTrue
Id10a39a51-20d1-441a-9e92-64ca0c5c6f36
Idp readyTrue
Lgd mappingna
MethodologyThis data is collated by the Reserve Bank of India, leveraging the periodic submissions and transactional reports from the different banks functioning within India's borders.
Mimetypetext/csv
No indicators4
Package ideb565ca6-aa06-47fd-9c81-05c7ed6c538b
Position10
Size2.6 MiB
Skurbi-bankwise_neft_transactions-in-mn-aaa
Stateactive
Tags['NEFT Transations', 'Funds Transfer Analysis', 'Bankwise Data']
Url typeupload
Years covered2008-2025
Methodology This data is collated by the Reserve Bank of India, leveraging the periodic submissions and transactional reports from the different banks functioning within India's borders.
Indicators
Similar Resources
Granularity Level Bank
Data Extraction Page https://rbi.org.in/Scripts/NEFTView.aspx
Data Retreival Date 2025-04-15
Data Last Updated 2025-03-01 00:00:00
Sku rbi-bankwise_neft_transactions-in-mn-aaa
Dataset Frequency Monthly
Years Covered 2008-2025
No of States/UT(s)
No of Districts
No of Tehsils/blocks
No of Gram Panchayats
Additional Information nan
Number of Indicators 4
Insights from the dataset The "NEFT Transaction Metrics" dataset, with its granularity, offers invaluable insights into the digital transactional behaviors and trends in India. Through a monthly trend analysis, stakeholders can ascertain the rhythm and pace of NEFT transactions, pinpointing any anomalies, surge periods, or slow phases. These trends can be essential indicators of the broader economic landscape, signaling growth phases or potential economic slowdowns. By examining bank-specific data, a clear picture of each institution's stance in the NEFT landscape emerges. Which banks are the most active in facilitating NEFT transfers? Do certain banks have a higher debit transaction rate compared to credits or vice versa? This type of insight can provide a lens into the financial health and customer engagement strategies of individual banks. An intriguing facet of the dataset is the distinct data on 'Debit Transactions' and 'Credit Transactions'. By scrutinizing these columns, one can discern the balance of funds outflow and inflow for each bank. If a bank consistently has higher debit values compared to credit values, it might indicate more outward transfers than incoming ones. On the other hand, banks with a dominant credit value might be the recipients of a more significant number of inward funds, indicating trust or preference by customers or businesses.
IDP Ready Yes
LGD Mapping Not Applicable