Optimized Collection Efforts
Improve Collections
and Optimize Efforts
The Challenge
The NBFC sector has undergone a significant transformation over the past few years and
plays a significant role in the growth of any Financial system. NBFCs have outperformed
banks across several product lines by carving out niche products. They are more customer
focused and take the time to understand the customer behaviour and build customised
products and reach out to different segments of customers with customised loans and
customer friendly repayment plans and take higher risks. This however creates the
challenges of debt collection. Debt collection is important for the company to improve their
cash flow and in turn help businesses reduce the risks of incurring losses, and free up their
resources.
About the Company
- One of India’s largest NBFC Companies offering loans
- Offering flexible Loan products with innovative repayment plans
- 25,000+ customers
- USD 55+ cr loan disbursed
- ~ 20 lending partners
The Solution
CyborgIntell’s iTuring can be used to develop predictive models that identify customer
default early in their lending journey. It can accurately forecast delinquency movement for
the whole portfolio, across all customers and all buckets. The outputs of the default
prediction models and their explanations around customer behavior can help define
strategies to improve overall collection efforts and as a result improve portfolio.
The FinTech company we engaged with on Collection optimization was experiencing a default rate of ~12%. We used iTuring be build predictive models that predicted customer movements from one delinquency bucket to the next for pre-delinquency, early stage, late stage and recovery. iTuring developed accurate models which predicted default in the immediate next month with an accuracy of ~86%, enabling business to effectively manage portfolio monthly. This enabled the company to identify 9 customer segments based on probability of default and value at risk and develop collection strategies around the same. By focusing efforts on 72% of likely defaulters that were identified in the top 30% customers the company would improve collections by 116%.
The FinTech company we engaged with on Collection optimization was experiencing a default rate of ~12%. We used iTuring be build predictive models that predicted customer movements from one delinquency bucket to the next for pre-delinquency, early stage, late stage and recovery. iTuring developed accurate models which predicted default in the immediate next month with an accuracy of ~86%, enabling business to effectively manage portfolio monthly. This enabled the company to identify 9 customer segments based on probability of default and value at risk and develop collection strategies around the same. By focusing efforts on 72% of likely defaulters that were identified in the top 30% customers the company would improve collections by 116%.
Highlights
- 116% increase in collections by targeting the right customers with the same amount of collection effort
- 9 customer segments based on likelihood of default and value at risk identified
- 72% of likely defaulters identified in the top 30% customers
- 86% model 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
0
%↑
Time to Deployment
0
Weeks
Accuracy
0
%
Why CyborgIntell
Not only does iTuring provide highly accurate predictions of which customers are likely to default, it also enables you to identify key customer segments for optimized collection strategies. This allows collections team to focus on the customers based on their likelihood of default, value at risk and probability of repayment, thereby improving overall debt collection.
Contact us to find out more
Unlock the power of AI with CyborgIntell!
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