Dealer Default Management

Dealer Default Management

Reduce Customer Churn

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

The Solution

CyborgIntell’s iTuring was used to develop machine learning models that identify dealer default. It helped to accurately predict Dealer default month over month for the entire portfolio by product, region, and segment. Dealer risk transition from low to medium, and medium to high-risk segment helped build a strong debt collection strategy. Enable staff to collect money from risky Dealer and tighten up credit limit to prevent further revenue leakage. The models also provided insights around Dealer behavior to improve overall collection efforts.

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%.




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Time to Deployment

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Predictive Accuracy

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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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