Credit by Scheduled Commercial Banks
The "Credit by Scheduled Commercial Banks" dataset provided by the Reserve Bank of India (RBI) captures information on the credit extended by scheduled commercial banks in India to various population groups, bank groups, occupation groups, and occupation subgroups. The dataset covers various types of credit, such as agricultural credit, personal loans, housing loans, and loans to the industrial sector, among others.
The dataset is organized across four dimensions, including population group, bank group, occupation group, and occupation subgroup. The population group dimension includes categories such as rural, semi-urban, urban, and metropolitan, while the bank group dimension includes categories such as nationalized banks, private sector banks, foreign banks, and regional rural banks, among others. The occupation group dimension includes categories such as agriculture and allied activities, industry, and services, while the occupation subgroup dimension includes categories such as self-employed individuals, salaried individuals, and others.
This dataset is useful for researchers, policymakers, and financial analysts who are interested in understanding the credit flow in the Indian economy, analyzing the distribution of credit across different population groups and sectors, and identifying trends in credit disbursement by scheduled commercial banks.
Data Dictionary
- ID
- id
- Type
- int4
- Label
- index
- ID
- year
- Type
- text
- Label
- Year
- ID
- region
- Type
- text
- Label
- Region
- ID
- state_name
- Type
- text
- Label
- State Name
- ID
- state_code
- Type
- text
- Label
- State Code
- ID
- district_name
- Type
- text
- Label
- District Name
- ID
- district_code
- Type
- text
- Label
- District Code
- ID
- population_group
- Type
- text
- Label
- Population Group
- ID
- bank_group
- Type
- text
- Label
- Bank Group
- ID
- occupation_group
- Type
- text
- Label
- Occupation Group
- ID
- occupation_sub_group
- Type
- text
- Label
- Occupation Sub Group
- ID
- no_of_accounts
- Type
- numeric
- Label
- Number of Accounts
- ID
- credit_limit
- Type
- numeric
- Label
- Credit Limit
- ID
- amount_outstanding
- Type
- numeric
- Label
- Amount Outstanding
Additional Information
| Field | Value |
|---|---|
| Data last updated | March 14, 2024 |
| Metadata last updated | August 22, 2024 |
| Created | October 8, 2023 |
| Format | CSV |
| License | Open Data Commons Attribution License |
| Additional info | To get to the exact table from the source link, Under the Time Series Tab -> "Time Series Detailed Data" -> "Bank Credit of SCBs - Bank Group, Population Group, Occupation (Sector), District Wise - Annual" |
| Data extraction page | https://dbie.rbi.org.in/DBIE/dbie.rbi?site=publications#!19 |
| Data insights | The dataset provides information on the credit extended by different bank groups, with nationalized banks having the highest share of credit followed by SBI and its associates, private sector banks, and foreign banks. The majority of the credit is extended to the industry sector, followed by the services sector and agriculture sector. Personal loans and other priority sectors account for a smaller share of credit. The dataset shows that credit growth is higher in urban and metropolitan areas compared to rural and semi-urban areas. The district-wise data shows that the credit patterns vary across different regions, with some districts having higher credit growth compared to others. |
| Data last updated | 2024-02-01 00:00:00 |
| Data retreival date | 2023-09-15 00:00:00 |
| Datastore active | True |
| District no | 764 |
| Frequency | Yearly |
| Granularity | District |
| Has views | True |
| Id | 733d27c5-e6a7-4cbf-a03e-b7cda032b608 |
| Idp ready | True |
| Lgd mapping | yes |
| Methodology | Reserve Bank of India releases information about the number of accounts, credit limit and amount outstanding categorised as per population group, bank group and occupation under each district yearly. |
| Mimetype | text/csv |
| No indicators | 3 |
| Package id | eb565ca6-aa06-47fd-9c81-05c7ed6c538b |
| Position | 5 |
| Size | 250.5 MiB |
| Sku | rbi-groupwise_credit_by_scbs-dt-yr-vvi |
| State | active |
| States uts no | 36 |
| Tags | ['Banking', 'Credit', 'Scheduled Commercial Banks', 'Bank Group\n Population Group', 'Occupation', 'Sector', 'District-Wise', 'India\n Finance.'] |
| Url type | upload |
| Years covered | 2010-2023 |
| Methodology | Reserve Bank of India releases information about the number of accounts, credit limit and amount outstanding categorised as per population group, bank group and occupation under each district yearly. |
| Indicators | |
| Similar Resources | |
| Granularity Level | District |
| Data Extraction Page | https://dbie.rbi.org.in/DBIE/dbie.rbi?site=publications#!19 |
| Data Retreival Date | 2023-09-15 00:00:00 |
| Data Last Updated | 2024-02-01 00:00:00 |
| Sku | rbi-groupwise_credit_by_scbs-dt-yr-vvi |
| Dataset Frequency | Yearly |
| Years Covered | 2010-2023 |
| No of States/UT(s) | 36 |
| No of Districts | 764 |
| No of Tehsils/blocks | |
| No of Gram Panchayats | |
| Additional Information | To get to the exact table from the source link, Under the Time Series Tab -> "Time Series Detailed Data" -> "Bank Credit of SCBs - Bank Group, Population Group, Occupation (Sector), District Wise - Annual" |
| Number of Indicators | 3 |
| Insights from the dataset | The dataset provides information on the credit extended by different bank groups, with nationalized banks having the highest share of credit followed by SBI and its associates, private sector banks, and foreign banks. The majority of the credit is extended to the industry sector, followed by the services sector and agriculture sector. Personal loans and other priority sectors account for a smaller share of credit. The dataset shows that credit growth is higher in urban and metropolitan areas compared to rural and semi-urban areas. The district-wise data shows that the credit patterns vary across different regions, with some districts having higher credit growth compared to others. |
| IDP Ready | Yes |
| LGD Mapping | Yes |