Model for Predicting Risk of Non-Payment
Looking to reward loyal customers with generous offers while steering clear of those at risk for non-payment? Our model is specifically designed to do just that. It ensures you target the right customers, enhancing loyalty and minimizing financial risks.
This model is designed to be used in a range of industries, such as Telecom, Retail, Banking, Investment Services, and more.
Full model price: $45,000
- $4,500 initial deposit
- $18,000 first payment (invoiced at data access)
- $22,500 final payment (invoiced at model delivery)
PRODUCT INFO
Use Case
Minimize future revenue loss by filtering out customers unlikely to pay their bills with our advanced predictive model. This model provides crucial insights and customer rankings that help you make informed decisions about extending credit to specific customer groups, enhancing your financial stability and reducing risk.
AI/ML Modeling
A range of algorithms will be tested for the best AUC/outcome like XGBoost/GBM, Neural Network, SVM, ANOVA, KNN, K-Means, etc.
LLMs and NLP techniques may also be used to enhance model performance.
Model Delivery
One-time purchase:
- Propensity scores
- Customer ranking
- Leading model predictors
- Model performance
- Insight session
On-going monthly service additionally includes:
- Tracking leading predictors
- Model monitoring
- Seemless model refresh with market trend changes
- Strategy session every 3 months of service
- 10% discount on full model price
DELIVERY AND PAYMENT
We will contact you within 2 business days of your deposit to set the engagement date and provide instructions for the required data set.
The first model payment is invoiced at the data access date. Model delivery usually occurs 3-4 weeks from the data access date. The final model payment is invoiced at the model delivery date.
REFUND POLICY
Deposit fully refundable before engagement date.
If we cannot detect a pattern for a stable model, the final model invoice will be waived and we will provide you with data assessments, findings, insights and further recommendations.