Dec
10
2013

Collections scorecards and risk segmentation

The collections dynamics

The collections dynamics based on different organisations and product portfolios (unsecured/ secured lending; revolving / non-revolving) can have a different timing split and use different terms for each stage of the collections lifecyle ranging from pre-delinquency; early collections; mid-collections: late Collections and then legal and recovery. The simplest of these models will show accounts that are current and non-delinquent in the portfolio, after they miss their obligations on respective due dates. These represent the initial signs of their flow towards becoming delinquent. Products like credit cards have the advantage to identify such accounts getting delinquent earlier immediately after the payment due date is missed. These accounts are still in the pre-delinquent stage till the next cycle date. The organizations aware of this can proactively start contacting such accounts showing high probability of getting delinquent on the next cycle date. Pre-delinquency efforts focus on preventing the missed payments.

After an account becomes delinquent, the ”typical” collections efforts focus on customers that are still considered worth or rehabilitation and usually lasts between 90 and 210 days depending on the product and local regulation (some of these stages may be outsourced as per company policy). Legal/ recoveries is a more lawyer/ court driven process involving customers that are no longer considered redeemable, this is almost always outsourced or at least worked internally by a specialist team of lawyers.

Need of scorecards for collections

Scorecards are needed in collections to proactively segment the portfolio by identifying the risky customers. Appropriate treatments can be initiated at the earliest on the customers based on their risk levels to protect the business assets with the most cost effective mechanism applicable. Collections plays a significant role in the profitability of the business by minimizing the credit losses. This enables the business to minimize the provisions taken against the credit. The provisions directly impact the capital allocations that could otherwise be invested in the growth of the business. Customer retention plays a major role in securing future revenues for the business. The number of cases and volumes (outstanding balance) in collections are high and therefore demand an effective management, prioritization and follow up of the risky cases. Moreover, it’s extremely important from customer service point of view to differentiate between the good customers and intentional defaulters. Collections is widely considered as cost centre with high budget constraints, therefore minimizing the cost of collections by efficient and effective allocation and management of resources is one of the key objectives of scorecards in collections.

Complexity of collections scorecards

Customer level or account level: The collections portfolio consists of solo customers i.e. one product and combo customers with bundled products. Should the scorecards be developed at account or customer level?

Highly dynamic environment: The collections environment is very dynamic, whereby the status of the accounts change every day with partial payments, bounced payments, No Contacts, return posts etc. These actions pose challenges in risk segmentation and deciding on optimal treatments. There are different phases in Collections starting from Pre-Delinquency Early collections, Mid Term, Late stage collections to Litigation and Recovery. No single scorecard can cover all these stages.

Various collections stages: Due to various stages in collections, it is very complex to decide on what kind of scorecards (static or dynamic) would be optimal. In Static scorecards, the accounts are scored once at the time of entry to collections. In dynamic scorecards, the accounts are scored periodically when the accounts pass to the next collections stage.

Are scorecards adequate to design collections treatment strategies?

Scorecards lay the first foundation stone for risk segmentation as essential for designing collections strategies and treatment paths. The scorecards take into consideration the past behaviour, predict the riskiness of the account/ customer. However, analysis shows that just a bad score does not mean high risk and a good score does not mean low risk. The overall risk assessment can be further improved, if the output from the collections scorecards is combined with the key segmentation drivers. The collections strategies and actions should be based on the risk matrix combining collections scores and key segmentation drivers.

Need of segmentation drivers in addition to the collections score

The segmentation analysis based on the scorecard alone revealed that all good score accounts are not low risk and similarly all bad score accounts are not high risk.

The correlation analysis done between the score and the additional segmentation drivers shows a very strong correlation between the collections score and the following segmentation drivers.

Current status of delinquency: higher delinquency status will automatically lead to higher risk. An account classified as good in bucket 0 or 1 will no longer be good as it moves to higher buckets say 3 and 4. That account despite a good score (highly unlikely) has to be classified as high risk.

Number of months in collections: the number of months in collections can be high if the account is fluctuating or stabilising its delinquency status month on month. This results in higher risk and costs of collections. These accounts have to be segmented based on risk and managed with appropriate risk mitigation tools.

BAR – Balance at Risk: The outstanding balance at risk combined with the tenor is a significant segmentation driver.

Loan tenor covered: Loan tenor covered at the time of account entering to collections is an important indicator of the risk level. Eg. an account having bad score is classified as medium or high risk but if the account has paid over 80 per cent of the loan value, it is optimal to segment that account as low-risk and design a more lenient strategy to rehabilitate them and retain them for potential business in future..

Restructured: If any one of the bundled products is restructured and in collections that has to be taken as a risk indicator for the overall relationship. Such accounts should be segmented as medium or high risk.

Real-time case studies have proven that the enhanced risk model (collections score + segmentation drivers) can improve the risk segmentation up to 20 per cent in the medium and high-risk groups. Most of this improvement comes from the low-risk group i.e. without the key segmentation drivers, most of the high and medium-risk accounts would have been disguised as low-risk and missed the most optimal collections treatment. Therefore the enhanced risk model is the most robust available and the most optimal to manage the collections portfolio during the crisis situation.

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