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Boosting Loan Recovery for a Leading Financial Servicer
Navigating the complexities of default loan collections, Menhir AI empowered a top-tier servicer with a strategy that integrates advanced digital channels and traditional methods, optimizing capital efficiency and boosting recovery rates.
Overview
Menhir's intervention transformed the loan recovery process by integrating sophisticated AI analytics with a practical, hybrid collection approach.
- Inefficiency in Traditional NPL Workouts: Most non-performing loan (NPL) recovery strategies heavily rely on manual efforts rather than effectiveness, leading to suboptimal outcomes.
- Overly Complex Outsourcing Chains: The prevalent practice of outsourcing loan recovery leads to diluted responsibilities and unnecessary costs due to multiple layers of service providers.
- Need for a Strategic Overhaul: The servicer faced challenges with a €4+ billion non-performing asset (NPA) portfolio, necessitating a shift to more efficient and predictive recovery tactics.
The challenge
Menhir was tasked with enhancing loan recovery efficacy by preemptively identifying recoverable loans and optimizing asset management strategies.
- Low Data Quality: The client's loan data suffered from poor quality, which hindered effective analysis and strategy development.
- Lack of Robust Processes: Existing asset management lacked the methodology for consistent implementation of effective processes.
- High Stakes Portfolio: Managing a large volume of distressed assets required precision to avoid significant financial repercussions from small errors.
The Approach
Menhir developed a comprehensive AI-driven solution to predict payment likelihood and optimize asset management, increasing efficiency while cutting costs.
- Payment Anticipation Model: A machine learning model was developed to predict which loans were likely to incur payments before any management action was taken.
- Allocation Algorithm: An algorithm was created to distribute loans to the most appropriate management resources, enhancing focus on recoverable assets and reducing servicing expenses.
- Segmented Asset Management: Assets were divided into two groups for tailored management approaches:
- Assets with Payment Anticipation: €3,441.7 million
- Assets with Payment Anticipation & Allocation Algorithm: €721.2 million
The results
The AI-driven strategy led to significant improvements in recovery metrics and operational efficiency.Increase in Monthly Collection Rate: Collections rose by 81%, significantly enhancing the portfolio's profitability.Reduction in Collection Costs: Costs associated with collections were reduced by 41%, demonstrating the efficiency of the new approach.
The results
The AI-driven strategy led to significant improvements in recovery metrics and operational efficiency.Increase in Monthly Collection Rate: Collections rose by 81%, significantly enhancing the portfolio's profitability.Reduction in Collection Costs: Costs associated with collections were reduced by 41%, demonstrating the efficiency of the new approach.
Transforming Financial Recovery with Strategic AI Integration
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