Improving Repossession Results Through Deeper Data Analysis

Data analytics has become a driving force in many industries in today’s economy. However, the application of “big data” analytics in the repossession industry has not kept pace. This is due to several challenges, including operational fragmentation, lack of centralization of relevant data, and low prioritization of the issue within many lending organizations. Overcoming these constraints can yield significant results for lending institutions managing delinquent loans.

Current Challenges in Repossession Management

  1. Operational Fragmentation: The services involved in the recovery and remarketing process are typically spread across many vendors, leading to inefficiencies.
  2. Lack of Centralization: Without a centralized system to aggregate all relevant data, it’s challenging to gain comprehensive insights.
  3. Low Prioritization: Many lending organizations do not prioritize the integration of advanced analytics in their repossession processes.

Despite these challenges, implementing advanced data analytics can transform repossession strategies and enhance overall performance. This article outlines several strategies for applying advanced analytics to your recovery business, illustrated with case study examples from Resolvion’s own experience.

Optimizing the Timing of Vendor Rotation

Most large lenders utilize multiple repossession management companies in a predetermined sequence and defined intervals. However, this strategy is rarely supported by robust analytics, often leading to suboptimal results.  By analyzing recovery curves by vendor during the assignment period, you can determine the optimal time to rotate cases to a new vendor. For example, in one portfolio managed by Resolvion for a top lender, the ideal rotation time was between 21-24 days. Rotating cases at the right time ensures fresh eyes on your cases, reduces depreciation, and maximizes recovery rates.

Monitoring these recovery curves consistently also provides early warnings of performance changes. For instance, an earlier flattening of the recovery curve in August on the graph below might indicate a significant shift, warranting closer scrutiny.

Knowing When to Apply Additional Resources and Strategies

Understanding the point of diminishing returns with a current strategy or vendor is crucial. For example, one lender added skip services 30 days earlier than usual, significantly increasing pre-charge off recovery rates. While the recovery cost increased due to additional skip tracing resources, charge-offs were avoided, and net recovery values rose due to faster liquidation.

Proper Geographic Analysis Informs Strategy

Even a basic analysis of state-level results can reveal significant performance differences among repossession management vendors. For instance, a state-level analysis might show that Vendor #4 performs best overall in Florida. However, a deeper, three-digit zip code analysis might reveal that their performance varies significantly across different areas.

Table of Repossession Management State-Level Results

Incorporating this granular knowledge into the assignment strategy can dramatically improve overall recovery rates. For example, while Vendor #4 might excel statewide, they could underperform in specific zip codes. Adjusting assignments based on this detailed analysis optimizes results.

Repossession Management Segmenting to 3 digit Zip Table

The Opportunity at the Forwarder Level

As a repossession management company, we at Resolvion have a unique opportunity to use advanced analytics to enhance performance for our clients. With a network of approximately 700 agents, many overlapping in coverage, we face the challenge of determining the best agent for each case.

Aggregate data analysis is a starting point but not sufficient on its own. Breaking down performance by pre and post charge-off cases and overlaying this with a three-digit zip code analysis helps us understand each agent’s strengths and weaknesses. This detailed understanding allows us to optimize case placements and improve recovery performance significantly.

Table of Repossession Agent Recovery Performance

Leveraging advanced data analytics in repossession management can yield significant improvements in recovery rates and overall financial performance. By centralizing data, investing in the right tools and skills, and making informed, data-driven decisions, lending institutions can optimize their repossession strategies and achieve better results.

By adopting these practices, lending institutions can not only manage delinquent loans more effectively but also position themselves for greater financial stability and success.


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