Early Non-Payment Warning Model
Do you want to proactively engage with customers showing early signs of moving towards a high risk of non-payment, even before they enter into collections? Our model identifies these early indicators, allowing you to intervene in a timely manner and prevent future financial issues.
This model is designed to be used in a range of industries, like 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
Utilize our model to identify the key early warning drivers of non-payment and develop customized offers aimed at preventing these issues. This proactive approach allows you to understand and mitigate potential risks before they escalate, ensuring better financial stability and customer retention.
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.