Posts Tagged ‘Debt Collection Agencies’

I wrote in my last article that a debt collection agency (DCA) working on a commission basis had the ability to ‘cherry pick’ the accounts that they worked, distributing their invested effort across multiple customer segments in multiple portfolios to generate significantly higher rewards.  In this article I will walk through a simple example of how a DCA could do this across three portfolios and then discuss how the same principles can be applied by primary lenders.


A DCA Example

A third-party DCA is collecting debts on behalf of three different clients, each of which pays the same commission rate and each of which has outsourced a portfolio of 60 000 debts. 

Half of the accounts in Portfolio A have a balance of €4 000 while the other half are split evenly between balances of €2 000 and balances of €5 500.  After running the accounts in question through a simple scorecard, the DCA was able to determine that 60% of the accounts are in the high risk group with only a 7% probability of payment, 20% are in the medium risk group with a 11% probability of payment while the remainder are in the low risk group and have a 20% probability of payment.

Portfolio B ia made-up primarily of account with higher balances, with half of the accounts carry a balance of €13 500 and the remainder are equally split between balances of €7 500 and €5 000.  Unfortunately, the risk of this portfolio is also higher and, after also putting this portfolio through the scorecard, the DCA was able to determine that 50% of the accounts were in the highest risk group with an associated probability of payment of just 2% while 30% of the accounts were in the medium risk group with a probability of payment of 5% and 20% of the accounts were in the lowest risk group with a probability of payment of 13%.

In portfolio C the accounts are evenly split across three balances: €4 500, €8 000 and €10 000.  After a similar scorecard exercise it was also shown that 70% of accounts are in the highest risk group with a 7.5% probability of payment, 30% of accounts are in the medium risk group with a probability of payment of 14% and the final 10% in the low risk group have an 18% probability of payment.

The DCA now has a few options when assigning work to its staff.  It could assign accounts randomly from across all three portfolios to the next available staff member, it could assign accounts from the highest balance to the lowest balance or it could assign specific portfolios to specific teams, prioritising work within each portfolio but not across them.  Some of these approaches are better than others but neither will deliver the optimal results.  To achieve optimal results, the DCA needs to break each portfolio into customer segments and then prioritise each of those segments; working the highest yielding segment first and the lowest yielding one last.

Using the balance and probability of payment information we have, it is possible to calculate a recovery yield for each of the nine segments in each portfolio; the recovery yield being simply the balance multiplied by the probability of recovering that balance.  Once the recovery yield has been determined for each of the nine segments in each of the three portfolios it is possible to prioritise them against each other as shown below.

With the order of priority determined, it is possible now to assign effort in the most lucrative way.  For example, if the DCA in question only had enough staff to work 50 000 accounts they would expect to collect balances of approximately €27.7 million if they worked the accounts randomly, approximately €40.4 million if they prioritised their effort based on balance but as much €53.3 million if they followed the recommended approach – an uplift of 92%.  As more staff become available so the less the apparent uplift decreases but there is still a 44% improvement in recoveries if 100 000 accounts can be worked.

If all accounts can be worked then, at least if we keep our assumptions simple, there is no uplift in recoveries to be gained by working the accounts in any particular order. 


Ideal Staff Numbers

However, that is not to say that the model becomes insignificant.  While the yield changes based on which segment an account is in, the cost of working each of those accounts remains the same.  Since profitability is the difference between yield and cost and since cost remains steady, a drop in yield is also a drop in profit.  So, continuing along that line of reasoning, there will be a level of yield below which a DCA is making a loss by collecting on an account.

So, it stands to reason then that a DCA working all accounts is unlikely to be making as much profit as they would be if they were to use the ‘cheery picking’ model to determine their staffing needs.  New staff should be added to the team for as long as they will add more value than they will cost.  As each new member of staff will be working on lower yield accounts there are diminishing marginal returns on staff until the point that a new member of staff will be actually value destroying.

Assume it costs €30 000 to employ and equip one collector and that that collector can work 1 500 accounts in a year.  To be value adding then, that collector must be assigned to work only accounts with a net yield of more than €20. 

Up to now, I simply referred to recovery yield as the total expected recovery from a segment.  That was possible at the time as we had made the simplifying assumption that each portfolio earned the same commission and were only looking to prioritise the accounts.  However, once we start to look at the DCA’s profit, we need to look at net yield – or the commission earned by the DCA from the recovery. 

If we assume a 10% commission is earned on all recoveries then for the yield of €1 800 in highest yielding segment becomes a net yield of €180.  Using that assumption we are able to see that the ideal staffing contingent for the example DCA is 104: allowing the DCA to work the 156 000 accounts in segment 24 and better. 

At this level the DCA will collect approximately €96 million earning themselves €9.6 million in commission and paying out €3.1 million in staff costs in the process; this would leave them with a profit of €6.4 million.  If they lay off two members of staff and work one less segment their profit would decrease by €6 000.  If, instead, they hired 5 more members of staff and worked one more segment their profits would be reduced by nearly €40 000.

Commission Rate Changes

Having just introduced the role of commission, it makes sense to consider how changes in commission rates might impact on what we have already discussed. 

The simplest change to consider is an across-the-board change in commission rates.  This doesn’t change the order in which accounts are worked as it affects all yields equally.  It does, however, change the optimal staff levels.  In the above example an across-the-board decrease in commission from 10% to 5% would halve the yields of each block meaning to still achieve a net yield of €20 a segment would have to have a gross yield of €400.  In turn this would mean that staff numbers would need to be cut back to 59: now working 88 000 accounts and generating a total profit of €2 million.

A more common scenario is that commissions are fixed over the term of the contract but that these commissions vary from portfolio to portfolio. 

Most DCAs will charge baseline commission rates which vary with the age of the debt at the time it is taken on.  For example, a DCA may charge a client 5% of all recoveries made on accounts handed over at 60 days in arrears but 10% of all recoveries made on accounts handed over at 120 days in arrears.  This compensates the DCA for the lower recovery rates expected on older debt and encourages primary lenders to outsource more debts to the DCA.

When a DCA is operating across portfolios which each earn different commission rates it should use the net yield in the prioritisation exercise described above rather than the gross yield.  Assume that the DCA from our earlier example actually earns a commission of 5% for all recoveries made from Portfolio A, 7.5% on all recoveries made from Portfolio B and 10% on all recoveries made from Portfolio C. 

Now, the higher rewards offered in Portfolio C change the order in which accounts should be worked.  The DCA no longer concentrates on the largest recovery yield but rather the largest net yield. 

Primary Lenders

Of course, the concept and models described here are not unique to the world of DCAs, primary lenders should structure their debt management efforts around similar concepts.  The only major difference during the earlier stages of the debt management cycle is that there tends to be more strategic options, more scenarios and a wider diversity of accounts.

This leads to a more complex model but one that ultimately aims to achieve the same end result: the optimal mix between cost and reward.  Again a scorecard forms the basis for the model and creates the customer segments mentioned above.  Again the size of the balance can be used as a proxy for the expected benefit.   There is of course no longer a commission but there are new complexities, including the need to cost multiple strategy paths and the need to calculate the recovery rate as the recovery rate of the strategy only – i.e. net of any recoveries that would have happened regardless.  For more on this you can read my articles on risk based collections and on self-cure models.


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In the other articles I’ve written here I looked at risk-based collections strategies from a primary lender’s point-of-view and with a particular focus on the earlier stages of the process.  However, although the basic principles are universally applicable, there are a number of thought changes that need to be made when one is considering risk-based collections strategies from a third party’s point-of-view or when the debt in question has been delinquent for a longer period of time.  In this article I will try to highlight the most important of those changes.


Late Stage Collections

The most important difference between early stage collections and late stage collections is driven by changes to risk distribution over time.  A random group of accounts in early stage collections is likely to be made-up from a diverse distribution of actual account risks: a lot of low risk accounts, a lot of medium risk accounts, several high risk accounts and a few very high risk accounts.  As this group of accounts proceeds through the collections process the distribution becomes more homogeneous with a bias towards the more risky accounts.  This is not because there are more risky accounts present per se, but rather because most of the lower risk accounts have left collections.  As accounts proceed through collections it is the lower risk accounts that leave at a faster rate and the higher risk accounts therefore begin to dominate as illustrated below.


The practical implication of this is that it becomes harder and harder to segment accounts into risk groups.  Since risk-based strategies are built upon customer segmentation, it follows that these strategies also become more difficult to design.

For this reason, specialist scorecards and strategies are recommended for late stage collections.  In most cases, a traditional behavioural scorecard starts to lose its effectiveness as a debt portfolio reaches about 60 days of delinquency from where its performance drops steadily, seldom being of significant value after 120 days.

Early stage collections scorecards may add value for longer but once a debt has passed 210 days of delinquency a specialist scorecard is almost always needed.

Since the distribution of risk has been reversed, so too should be the focus of the scorecard.  Rather than trying to predict which few of the many good accounts will eventually go bad, the scorecard now needs to predict which few of the many bad accounts will eventually cure.  The traditional ‘bad definition’ is replaced with a ‘good definition’. 

The exact ‘good definition’ will vary with business requirements but is usually related to whether an account will make a payment, a certain number of consecutive payments or payments equal to a certain percentage of balance outstanding.

Specialised late stage collections scorecards of this kind tend to focus on events that happened post delinquency rather than pre-delinquency: number of times in collections, number of previous collection payments, promise-to-pays kept, number of negative bureau remarks, number of legal claims outstanding, etc. 

Despite their technical limitations, late stage scorecards can still offer significant value.  In a recent implementation of late stage scorecard built off very limited data I have seen a portfolio segmented and summarised into four risk groups with the 25% of accounts in the lowest risk group four times as likely to make a payment as the 25% of accounts in the highest risk group.


External Collections

Managing late stage collections is typically a drawn-out and operationally intensive processes.  For this reason, many lenders choose to outsource the function to third parties.

Once a debt has left the primary lender, its nature changes; most obviously because the ‘balance’ side of the lending profit model is no longer a consideration.  The third-party can no longer profit from commission or interest charges levied on new balance growth, can no longer charge annual fees and can no longer generate cross-sell opportunities.  So only the cost side of the traditional lending profit model remains. 

That is not to say that debt collection agencies don’t earn revenue of course, it’s just that they earn their revenue from the cost side of the traditional lending profit model; the side dealing with risk costs and bad debt losses.

There are two dominant business models for debt collection agencies based on whether the original debt was sold outright or merely outsourced by the primary lender.  When a debt collection agency buys a portfolio of debt outright, it tends to see that portfolio in a similar way to the primary lender.  When, on the other hand, it only collects the debts on the original lender’s behalf, the portfolio is usually viewed quite differently.


Purchased Debt

When a portfolio of debts is purchased by a debt collection agency they usually pay a price equal to a given percentage of the balances outstanding.  They then need to recover a high enough percentage of those balances to cover this initial price as well as all the operational costs that need to be incurred in making those recoveries.

This business model means that the buyer takes on all the risk inherent in a portfolio at the time of purchase and has little ability to adjust their level of investment thereafter.  The price paid at the start is the critical factor in overall profitability; managing the costs incurred in the collection process and using better techniques to gain an up-lift in recoveries usually make a lesser impact.

A debt collection agency interested in purchasing a new portfolio should therefore invest considerable time and resources to accurately estimate the expected recoveries from – and the expected cost of working – any new portfolio.

Unfortunately, these efforts are usually complicated by a lack of quality data.  It is uncommon for the purchaser to have access to extensive data relating to the portfolio for sale, often because this simply doesn’t exist but also sometimes due to a reluctance to share data on the part of the seller.  Therefore, it is often necessary to make some compromises.

The best way to deal with a lack of specific data is to deploy a generic model.  Generic models can either be built in-house (if the purchaser has experience collecting debts on other, similar portfolios) or they can be purchased from a specialist firm with access to pooled industry data. 

Rather than running accounts through the generic scorecard on a case-by-case basis as one would if it were deployed in its typical form, the scorecard is used to segment a random sample of accounts from the new portfolio in order to create an estimate of that portfolio’s total risk make-up.  The expected recovery rates of the model create a baseline estimate for the expected recovery rate of the portfolio.  The generic strategy paths of the model can be used to create a baseline estimate for the cost of the recovery.

This baseline can then be adjusted upwards or downwards to take into account any variance from the norm the purchaser expects to stem from their own environment: for example the expected recover rate would be adjusted downwards if the purchaser had never collected a debt in the market in question or upwards if they had a track record of consistently achieving higher than average recoveries.

A generic scorecard might be less accurate than a bespoke scorecard would be in each specific case but it is more broadly applicable than the bespoke scorecard would have been.  A generic approach is also quicker and cheaper to implement.  This will be the preferable solution, therefore, whenever there is either simply no data with which to build a bespoke model or where the total value of the portfolio does not justify a larger investment.


Third-Party Debt

If the debt collection agency has not bought the portfolio outright but is collecting on behalf the primary lender or debt owner, the profit model changes somewhat.  Unlike the traditional lending model, the profit model of the third-party debt collector is not heavily influenced by bad debt write-offs.  Instead of incurring small risk costs for each account in arrears and large write-off costs, the third-party debt collector earns a fee or commission for every recovery made.  This means that if a large debt is written-off there is no direct cost as such, just a lost opportunity for a commission-earning recovery. 

Since the commission is usually a small percentage of the total balance outstanding the impact of balance size is diluted; but the cost to make a recovery is relatively unchanged so the focus shifts to ‘cherry picking’.  For a given operational cost it can be more profitable to contact the accounts that are most likely to pay than it is to try to prevent large balance accounts that are at risk of defaulting from doing so.

Using a combination of the expected recovery rate and the expected recovery value on a recent project, we were able to identify segments of a portfolio that generated returns over 8 times higher than the average returns for that portfolio.  Identifying and focusing on similar segments from each of its portfolios, rather than treating each portfolio as internally and externally homogenous, will allow a debt collection agency to generate significantly higher profits by ensuring all high yield opportunities are followed while all low yield opportunities are not pursued.

This ability to shift effort to where it is most profitable is the key difference between the two business models.  While the eventual profitability of a single deal is still dominated by the contracted commission rates, the debt collection agency can more easily shift their investment to other more profitable deals as soon as it becomes apparent that the actual recovery rates on a given portfolio are lower than initially expected. 

Assume a debt collection agency has signed a new contract to collect debt on a lender’s behalf in return for a commission of 10% of all recoveries.  Assume too that they signed the deal at this price because their internal calculations suggested that they would receive payments from every second debtor which, in turn, would be sufficient to cover their costs and profit requirements.  If, after a month of working the new portfolio, that they were to discover that they were only receiving payments from every fourth debtor they could reduce the number of staff they had working on the new portfolio and re-assign them to more profitable work on another portfolio; restricting their efforts on the new portfolio to only those customer segments identified as still profitable.

In reality, contracts signed between lenders and agencies try to overcome this problem so the application of the theory is seldom this ‘clean’.  In most cases a large primary lender will assign their debt to more than one collections agency to allow a comparison of the performance of each against the other.  They usually then distribute their debt in a ratio based on that performance so that, for example, the best performing agency may receive 60% of all outsourced debt, the second best agency 30% and the worst agency 10%. 

Clauses like this complicate the calculation of optimal investments but don’t change the fact that it is still possible for a third-party debt collector to adjust their level of investment in any one portfolio as more information is learned.

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I have been asked before how a debt collection agency can optimise the timing of when they hand accounts over to lawyers.  The model I suggest for these cases is an adaptation of the self-cure model I introduced in a previous article.  Rather than dedicate a whole article to this version, I have included a discussion here.  However, since the subject matter is a bit more technical from here onwards it may be of less interest to some readers.


Escalating to Legal

Once an account has reached an external debt collection agency there are not many options left in terms of further escalation; if an account leaves a debt collection agency it can either move sideways to another debt collection agency, be written-off or escalated to legal recoveries.

Knowing which of these routes is best for each account is an important factor in determining the overall profitability of a third-party debt collector.  The approach I would recommend is the same one I discussed here when talking about self-cure models.

It is the nature of the debt collection business that much of the work will go unrewarded, with only a small portion of accounts generating meaningful payments.  It is important to not over-invest in accounts where the likelihood of recoveries is too low.  However, since all accounts have already demonstrated either a lack of willingness to pay or a lack of ability to pay, determining when the likelihood to recover is too low is easier said than done.

The factors involved are very similar to those at play in a self-cure model where one is also seeking to not over-invest in accounts while simultaneously not compromising the long-term recovery rate of the portfolio by not working an account that may consequently go bad where they might otherwise not have.  In the case of a self-cure model the goal is to avoid needlessly paying to contact debtors that will pay regardless of a contact and in the case of an escalation model the table has been turned so the goal is to avoid needlessly paying to contact a debtor that will not pay regardless of a contact.

In order to calculate the optimal time to change the treatment of an account it is important to know the direct costs and expected benefits of each strategy and, most importantly, how those change over time. 

The expected recovery from a telephone and letter based debt collection strategy decreases over time.  Once a delinquent customer has been contacted telephonically on multiple occasions the probability of the next contact making a difference is small.  The expected recovery from a legal recovery strategy decreases more slowly.  The cost of each method is also different, with the standard approach having a small but regular cost where the legal method usually has a high but largely fixed cost associated.  It is these differences that create an opportunity to profit from a change in strategy.

In summary, the cheaper method should always be employed unless the lost opportunity for recoveries from not using the more expensive method outweighs those savings.  So, in the case of a debt collection agency an account should be retained for the next period unless the decrease in expected recoveries from a legal recoveries strategy over the same period is greater than the cost difference between the two methods as shown in the table below.


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