Financial Features. These features include insurance debits, investment frequency etc. They are returned in the uganda/features/financial endpoint.Definitions of these features generated by Pngme are:
| Feature | Definition | Use Case | Value Proposition | Response value | |
|---|---|---|---|---|---|
| count_insurance_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through insurance events. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Enhance customer engagement and retention by offering tailored product recommendations for insurance helping them manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may point to individuals owning high-valued properties that necessitate insurance. • Understanding a user’s activity related to insurance can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_investment_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through investment. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Enhance customer engagement and retention by offering tailored product recommendations for investment helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may be indicative of an individual’s interest in financial growth through investments • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_salary_events _{t0_days}_{t1_days} | The number of SMS received indicating that the user received income through salary. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Use the data to build predictive models that forecast the likelihood of a user defaulting on a loan based on estimated monthly income • Improve data quality and reliability by using SMS data as a source of truth for verifying salary income of customers. | • This feature may be indicative of the inflow an individual receives on a monthly basis, serving as useful data for estimating income. • Understanding a user’s activity related to Salary income can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_insurance_debits _{t0_days}_{t1_days} | The amount of money user spent on insurance events. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Enhance customer engagement and retention by offering tailored product recommendations for insurance helping them manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may point to individuals owning high-valued properties that necessitate insurance. • Understanding a user’s activity related to insurance can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_investment_debits _{t0_days}_{t1_days} | The amount of money user spent on investment. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Enhance customer engagement and retention by offering tailored product recommendations for investment helping them make cost-conscious decisions and manage their finances better. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • This feature may be indicative of an individual’s interest in financial growth through investments • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_salary_credits _{t0_days}_{t1_days} | The amount of money user received through salary. The credits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Use the data to build predictive models that forecast the likelihood of a user defaulting on a loan based on estimated monthly income • Improve data quality and reliability by using SMS data as a source of truth for verifying salary income of customers. | • This feature may be indicative of the inflow an individual receives on a monthly basis, serving as useful data for estimating income. • Understanding a user’s activity related to Salary income can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_service_debits _{t0_days}_{t1_days} | The amount of money spent on credit services. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Enhance customer engagement and retention by offering tailored product recommendations for microloans. • Improve data quality and reliability by using SMS data as a source of truth for verifying financial events of customers. | • Understanding a user’s activity related to financial events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |