Data Dictionary

ID
id
Type
int4
Label
index

ID
month
Type
date
Label
Month

ID
bank_name
Type
text
Label
Bank Name

ID
inward_interbank_volume
Type
numeric
Label
INWARD Volume Interbank

ID
inward_costumer_volume
Type
numeric
Label
INWARD Volume Customer

ID
inward_total_volume
Type
numeric
Label
INWARD Total Volume

ID
inward_percentage_volume
Type
numeric
Label
Percentage of Total INWARD Volume

ID
inward_interbank_amt
Type
numeric
Label
INWARD Value Interbank

ID
inward_costumer_amt
Type
numeric
Label
INWARD Value Customer

ID
inward_total_amt
Type
numeric
Label
INWARD Total Value

ID
inward_percentage_amt
Type
numeric
Label
Percentage of Total INWARD Value

ID
outward_interbank_volume
Type
numeric
Label
OUTWARD Volume Interbank

ID
outward_costumer_volume
Type
numeric
Label
OUTWARD Volume Customer

ID
outward_total_volume
Type
numeric
Label
OUTWARD Total Volume

ID
outward_percentage_volume
Type
numeric
Label
Percentage of Total OUTWARD Volume

ID
outward_interbank_amt
Type
numeric
Label
OUTWARD Value Interbank

ID
outward_costumer_amt
Type
numeric
Label
OUTWARD Value Customer

ID
outward_total_amt
Type
numeric
Label
OUTWARD Total Value

ID
outward_percentage_amt
Type
numeric
Label
Percentage of Total OUTWARD Value

Additional Information

Field Value
Data last updated May 2, 2025
Metadata last updated May 2, 2025
Created October 9, 2023
Format CSV
License Open Data Commons Attribution License
Additional infonan
Data extraction pagehttps://rbi.org.in/Scripts/NEFTView.aspx
Data insightsFrom the dataset, several insightful narratives can be pieced together about the RTGS transaction landscape in India. One of the most pivotal points to consider is the transactional trend portrayed by individual banks. Observing the 'INWARD Total Volume' and 'OUTWARD Total Volume' provides a snapshot of which banks might be at the forefront of RTGS transactions. Those with consistently high volumes might be perceived as the more dominant or trusted entities in this sphere. Diving deeper, the dataset does a commendable job differentiating between interbank and customer transactions. Banks showing a substantial 'INWARD Volume Interbank' could be inferred as playing a more influential role in interbank settlements. This demarcation between interbank and customer-driven transactions offers a nuanced understanding of a bank's RTGS transactional ecosystem. However, the true depth of this dataset is realized when delving into the monetary values associated with these transactions. By juxtaposing 'INWARD Total Value' with 'INWARD Total Volume', one can decipher the average transaction size for each bank. This might unravel whether certain banks are more favored for large-scale corporate transactions or for smaller, perhaps retail-based transfers.
Data last updated2025-03-01 00:00:00
Data retreival date2025-04-30 00:00:00
Datastore activeTrue
FrequencyMonthly
GranularityBank
Has viewsTrue
Ide9cef902-3a0f-4bc2-96e7-f9735e1ebffb
Idp readyTrue
Lgd mappingna
MethodologyThe data is compiled by the Reserve Bank of India from the transaction reports submitted by individual banks on a regular basis
Mimetypetext/csv
No indicators16
Package ideb565ca6-aa06-47fd-9c81-05c7ed6c538b
Position8
Size5.7 MiB
Skurbi-bankwise_rtgs_transactions-in-mn-aaa
Stateactive
Tags['RTGS', 'Interbank Transactions', 'Customer Transaction', 'Banking Data Analysis']
Url typeupload
Years covered2008-2025
Methodology The data is compiled by the Reserve Bank of India from the transaction reports submitted by individual banks on a regular basis
Indicators
Similar Resources
Granularity Level Bank
Data Extraction Page https://rbi.org.in/Scripts/NEFTView.aspx
Data Retreival Date 2025-04-30 00:00:00
Data Last Updated 2025-03-01 00:00:00
Sku rbi-bankwise_rtgs_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 16
Insights from the dataset From the dataset, several insightful narratives can be pieced together about the RTGS transaction landscape in India. One of the most pivotal points to consider is the transactional trend portrayed by individual banks. Observing the 'INWARD Total Volume' and 'OUTWARD Total Volume' provides a snapshot of which banks might be at the forefront of RTGS transactions. Those with consistently high volumes might be perceived as the more dominant or trusted entities in this sphere. Diving deeper, the dataset does a commendable job differentiating between interbank and customer transactions. Banks showing a substantial 'INWARD Volume Interbank' could be inferred as playing a more influential role in interbank settlements. This demarcation between interbank and customer-driven transactions offers a nuanced understanding of a bank's RTGS transactional ecosystem. However, the true depth of this dataset is realized when delving into the monetary values associated with these transactions. By juxtaposing 'INWARD Total Value' with 'INWARD Total Volume', one can decipher the average transaction size for each bank. This might unravel whether certain banks are more favored for large-scale corporate transactions or for smaller, perhaps retail-based transfers.
IDP Ready Yes
LGD Mapping Not Applicable