Initial Line Assignment

ABAP Field Symbols are very powerful. Usage of Field-Symbols would provide performance gain, but for that you must use it correctly otherwise you may run into issues.

Field-symbols can provide great performance over using workarea as I have mentioned in my earlier article – Use of Field-symbols vs Work area. I have mentioned many articles using Field-Symbols but wanted to note down the basics as part of this code snippet.

Declare a Field Symbols

  TYPES: tt_mara TYPESTANDARD TABLE OF mara. DATA: t_mara TYPE tt_mara. *FIELD-SYMBOLS: <lfs_mara> LIKELINE OF t_mara. " <<*" Field symbol without typeFIELD-SYMBOLS: <lfs_any_tab> TYPEANY TABLE, <lfs_any> TYPEANY.  

APPEND and INSERT using Field Symbols

  * Append lineAPPENDINITIAL LINETO t_mara ASSIGNING <lfs_mara>. <lfs_mara>-matnr = '123456'. ** insert tableINSERTINITIAL LINEINTO t_mara ASSIGNING <lfs_mara> INDEX2. <lfs_mara>-matnr = 'ABCDEF'.  

Access ITAB rows using Field Symbols

  * Read tableREAD TABLE t_mara ASSIGNING <lfs_mara> WITH KEY matnr = '123456'. IF sy-subrc EQ0. WRITE: <lfs_mara>-matnr. ENDIF. ** Access via LoopLOOP AT t_mara ASSIGNING <lfs_mara>. WRITE: <lfs_mara>-matnr. ENDLOOP.    

Modify an entry using Field-Symbols:

  * READ and MODIFYREAD TABLE t_mara ASSIGNING <lfs_mara> WITH KEY matnr = '123456'. IF sy-subrc EQ0. <lfs_mara>-ersda = sy-datum. ENDIF. ** LOOP and MODIFYLOOP AT t_mara ASSIGNING <lfs_mara>. <lfs_mara>-ersda = sy-datum + 1ENDLOOP.  

CHECK field Symbols using IS ASSIGNED

Check if Field Symbol is assigned to a valid reference

  * Check if Field-Symbol is assignedIF <lfs_mara> ISASSIGNED. WRITE: 'Assigned'. ELSE. WRITE: 'Unassigned'. ENDIF.  

Remove the Reference of the Field Symbols

  "remvoe the reference UNASSIGN <lfs_mara>.  

Field-Symbols on SAP Help
 

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Situation

Client was experiencing high delinquency rates on its New to Bank Credit Card bookings. 


Approach

Axtria built a predictive model to modify the acquisition strategy and create an initial line assignment strategy.

  • Data Collection
    • Collected approved accounts data for last 24 Months
    • Collected Bureau data
    • Collected Application data
    • Created data dictionary to identify missing rates and reason for missing values
    • Outlier identification
  • Data Collection
    • Created definition of default as 6M 60 DPD rate
    • Independent variable creation: bureau score, bureau variables, application variables
    • Missing value imputation
    • Outlier treatment
    • Variable reduction using various techniques:
      − CART
      − One step logistic
      − Bivariate plots
  • Segmentation
    • Created segmentation to identify various segments of accounts
    • Identification of segments:
       − Red: Default  rate>20%
       − Amber: Default rate 10-20%
       − Green: Default rate<5%
    • Computed 12M EBIT for each segment thus created
    • Swap out sets identified with 12M EBIT as negative
    • Validation of segmentation on OOT samples
  • Line Assignment Strategy
    • Average 12 M Utilization look up tables created by credit limit
    • For each segment sensitivity of EBIT computed for various combinations of credit limit – utilization




Result

  • Created as segmentation for identifying swap out sets
  • Positive EBIT impact on Swap Out
  • Created a line assignment strategy
  • New Strategy resulted in $15 Mn savings in NCL

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