BankSystem

Returns to be submitted in XBRL formats as per taxonomy published by RBI. The CDR should be populated with clean and valid data and should synchronize financial and non-financial data from various source systems of the bank.



Proof Of Concept

Purpose for the solution:

  • CDR:- Central data repository

  • ADF:- Automated Data Flow

  • MIS :- Management information system requirements of the Bank

  • XBRL:- Returns to be submitted in XBRL formats

Returns to be submitted in XBRL formats as per taxonomy published by RBI
The CDR should be populated with clean and valid data and should synchronize financial and non-financial data from various source systems of the bank.
Eliminate multiple data version by creating single version of data at any point of time
Provide integrated system with existing Source systems (CBS, Treasury, CIS, HRM, LAPS, etc) and other applications.

Scope Of Work

  • Study of Existing Data Sources.

  • Project Planning & preparation of Software Requirement Specifications (SRS)

  • Data Extraction from existing applications for meeting all ADF & MIS requirements.

  • Data Validation mechanism.

  • Setting up of Central Data Repository (CDR).

  • Automated Extract-Transform-Load process for a near real-time/T+1 data availability for reporting.

  • Automated Data Flow as per RBI requirements

  • Data Security and Data Governance features.


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Proof Of Concept

  • Bank has provided the customer data in the Excel File

  • ETL Package prepared for the data extraction from the customer data excel file.

  • Data extracted and imported into staging table using SSIS.

  • Performed transformations for applying the business rule and validations on the staging server table (Raw Data).

  • Load data from staging database to CDR database using SSIS.

  • Generate the Commercial and Consumer text file including error file from ADF Application in desired format.

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Features Of The Solution

App

Software is web based, menu driven and user friendly.
Access is through user login.
User defined access control available. The access can be configured on need to know basis.
Audit trails provided for each login, logout, and Addition/deletion/modification by individual users.
Uploading of data in electronic form as well as data entry mode available.
Provision of Data Governance by using feature such as Maker / Checker.
Software compiles statements of sundry deposits, sundry credits, and suspense debits.
Is able to configure the amount in Thousands, Lakhs, and Crores.
Supports multiple currencies.
Compiles statements relating to Asset Classification, Allocation of Advances & Unrealized Interest.
Has provisions to accommodate increase in no. of branches ,zones etc and also various other fields which may come in future.
Uploading of data any number of times permitted at various stages like pre-audit, post audit and post MOC.


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Data Management



    CBS & other source systems to Stage Database

  • All the Required data from Finacle CBS and other systems (Source tables) Extracted to the Stage Database. Configurable frequency to ensure returns periodicity.

  • In POC bank had provided customer data in the excel file

    Stage Database to CDR

  • Other set of ETL packages use this Staged Database for transformation, mapping and cleaning various data elements and loading into CDR from where the reports & returns are generated.

  • Generate the Commercial and Consumer text file including error file from ADF Application in desired format.

Legacy system data

In case any data required in the CDR from existing legacy system which does not have RDBMS then the data that is collected in Flat files/Excel File and transform into CDR database.


Gap data

Data not available in any system is fetched through data entry programs. Which provides for maker checker facility.



Data Integration



App
CBS to Stage Database

All the Required data from CBS and other systems (Source tables) Extracted to the Stage Database which acts as a single repository of data for all MIS, BI and reporting purposes. Transformation for mapping and cleaning various data elements is done at on this

  • Other ETL packages use this Staged Database for loading into Reporting server and CDR from where the reports & returns are generated.
  • Legacy system data is collected in Flat files and transfer to database through ETL packages.
  • Data not available in any system is fetched through data entry programs.
  • Data Integration

    App
    • SSIS is used as underlying platform Integration

    • It is a Enterprise ETL platform
      -High performance
      -High scale
      -More trustworthy and reliable

    • Best in class usability
      -Rich development environment
      -Source control


    Data Integration

    • Data Extraction from Disparate Operational Source Data

      .Data Adaptors .Custom Data Maps and Transformation Rules .Sources – Finacal Data bases, Excel, Flat Files, etc.

    • Consolidation of Data

      .Data Validation .Data Standardization .Generation of Data Sets for .Predictive Modeling

    • Historical Data Population through ETL
    • Auto-scheduled ETL
    • Audit Trails

    App


    Data Cleansing



    Data Profiling – to detect defect levels in variable values
    Deployment of Data Profiling functions for incremental data
    Development of Business Rules for transformation / standardization
    Customer ID creation
    Standardization of categorical variable values
    Conversion (Pin Code to City Name)
    Missing value population with Business Rules
    Creation of Bins for functions like frequency distribution (e.g. Age)


    Data Quality



    Profiling

    • Develop a complete assessment of the scope and nature of data quality issues in CBS and other source databases along with existing MIS database of the Bank.

    • Create an inventory of data assets.

    • Inspect bank’s CBS and other Source data bases for errors, inconsistencies, redundancies and incomplete information in the data.

    • Build the foundation for future data management initiatives to provide clean, consistent, effective and efficient data.

    Quality

    • Plan and prioritize data correction initiatives.

    • Identify and resolve problematic data.

    • Standardize, normalize and transform data before loading into bank’s CDR.

    • Validate and improve the overall accuracy of data.