4.3 Finance
For the Finance LoB, we have pre-built scenarios for 3 use cases such as Company performance analysis, Late
payment management, Customer cash collection analysis. Depending on the use case and the functionality that
we are analyzing, we have picked up either the Automated Analytics or the Expert Analytics approach.
Basically for the Late Payment management use case, we have used the SAP HANA Live views for Finance as
the data source and sample data sets are available for the same. With regard to the Company performance
analysis and Customer cash collection analysis use cases, we have built a generic data set and seeded sample
data set for building the predictive models.
Note: Any SAP or non-SAP customer would be able to deploy or mimic the data structure, load the data and use
the pre-built models.
Company Performance Analysis
In this use case, we focus on leveraging the key trends such as fuel prices, unemployment and many more
variables to do more accurate predictions of revenue, margin and profit for a particular company.
Expert Analytics
Select the LUMS for Company_Performance_Correlation.lums
a. Launch Expert Analytics in SAP Predictive Analytics. Open menu File and choose Import to
folder and import or if the LUMS file is already imported browse through the Documents list for
Company_Performance_Correlation.lums file.
b. In the dialog box, enter the SAP HANA server details, your user name, and password.
Choose Prepare tab and ensure that the data has loaded properly
Switch to the
Predict
tab to view the predictive model.
Configure the filter components for filtering the data, when necessary
Adjust the properties of the Correlation of revenue with external factors component, for example, the
independent columns selected for the analysis.
Adjust the properties for the other algorithm components:
Correlation of profit with external factors
Correlation of margin with external factors
Choose
Run
to run the algorithm and execute the scenario
Choose
Yes
to switch to the
Results
view.
Late-Payment Management
In this use case, we focus on vendors who are likely to be the late payers ahead of time so that they can be
handled accordingly.
There are two LUMS files used for this scenario. In the configuration, you use the
F
inance_Customer_Late_Payments_train.lums
to train the prediction model. When predicting, you use the
Finance_Customer_Late_Payments_predict.lums.
.
Expert Analytics
Select the LUMS for Finance_Customer_Late_Payments_Train
