Dealer Default Management
Dealer Default Management
The Challenge
A prominent Indian conglomerate’s agriculture business has been providing 120-150 days credit line to their dealers for more than 100 SKU (Such as Water Soluble, Fertilizers, Pesticide, etc.). They were experiencing challenges in receiving payments which were delayed by more than 365 days. More than 20% of their dealers have been defaulters and they were looking to develop the right treatment strategy to collect debt efficiently. This was required to improve their cash flow and in turn help businesses reduce the risk of incurring losses.
About the Company
- One of India’s largest conglomerates in several businesses including agriculture
- Over 100 plus products offered through Dealers
- 410 branches and 4,000+ touch points
- USD 3.1 Million in credit line
The Solution
With a default rate of ~20%, we used iTuring to build AI models that predicted Dealer default in the immediate next month with an accuracy of 95%, enabling business to effectively manage portfolio monthly. This enabled the company identify 9 customer segments based on probability of default and value at risk to develop collection strategies around the same. By focusing efforts on top 30% of likely defaulters, model captured 98% defaulter and the company improved collections by 332%.
Highlights
- 332% increase in collections by targeting the right Dealer with same collection effort
- 9 customer segments based on likelihood of default and value at risk identified
- 98% of likely defaulters identified in the top 30% customers
- 95% predictive accuracy
- Models trained on 12 months cohort data across several data tables
- Model validated on next 6 months cohorts and maintained consistency and accuracy over time
Impact
Collections
Time to Deployment
Predictive Accuracy
Why CyborgIntell
Not only does iTuring provide highly accurate predictions of which Dealers are likely to default, it also enables you to identify key Dealer segments for optimized collection strategies. This allows collections team to prioritize dealers based on their likelihood of default, value at risk and optimal credit limit recommendation, thereby improving overall debt collection.
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