Posts Tagged ‘bad debt recovery’

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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The Space Pen

We’ve surely all heard the old story of how, during the space race, America invested millions of dollars to develop a pen that could write in the zero gravity conditions of space while the Russian achieved the same goal using the humble pencil.  Over the years much of the story has been exaggerated for the sake of its telling but its key lesson has remained the same: where there are two ways of achieving a goal, the cheapest of these methods is best. 

In this story the goal was to allow astronauts to write without gravity driving the flow of ink flow through a traditional pen.  It could have been achieved using an expensive pen with pressurised ink or, so the story implies, just as easily using a cheap pencil.

Learnings for Debt Management

If we were to apply the learnings to our debt management function, doing so would surely back-up the case for implementing a broadly inclusive self-cure strategy: that is a strategy that allows debtors a period of time in which to pro-actively repay their outstanding debt before investing the organisation’s time and money to contact them re-actively to make a direct request for that payment.  Since the value of a collections recovery is the same regardless of how it is achieved, it makes sense that the method used to generate that recovery should be the cheapest effective method available.  And, likewise, it makes sense that the cheapest method would be the one in which no costs are incurred.

However, by delving deeper into the history of the space pen we find that some caution is required before making that logical leap.

You see, the real story behind the space pen does not end at the same point that the anecdote does.  In fact, there are two pertinent points that are seldom mentioned.  Firstly, NASA had been using pencils prior to the development of the space pen and had decided they needed to be replaced.  Secondly, after the introduction of space pens at NASA, the Russians also started to use them.

Why would both teams have replaced the cheaper solution with a more expensive one if both did the same job?  Well it turns out that they had identified several indirect costs of pencil use; broken pieces of pencil lead can pose a risk in a zero gravity environment and the wood is flammable.

So the key lesson of the story remains true: the cheapest affective method to solve a given problem is the best method.  However, the measurement of ‘cheapest’ must include all direct and indirect costs.  This is true as much for a debt management function as it is for the space programme.

When designing a comprehensive self-cure strategy therefore, a lender must understand both is expected benefits and its direct and indirect costs before deciding who to include and for how long.

Estimating the Expected Benefits of a Self-Cure Strategy

The expected benefit of a self-cure strategy is simply the expected number of payment agreements to be achieved as a percentage of all customers in the strategy – or the probability or payment. 

A standard risk based collections strategy will segment customers into a number of risk groups each of which can then be treated differently.  As a natural product of this, each of these groups will have a known probability of payment based on their observed behaviour over time.  But it is important to take care when using these numbers in relation to a proposed self-cure strategy.

The probabilities of payment associated with the existing risk groups inherently assume that each account will proceed through the current debt management operational strategies as before.  By making that assumption invalid, you make the numbers invalid.  The expected benefit of a self-cure strategy can therefore not be assumed to be equal to the currently observed probability of payment; they actual probabilities of payment will likely be significantly lower.

Therefore, early iterations of a self-cure strategy should include a number of test-and-learn experiments designed to determine the probability of payment under a self-cure strategy.  A good starting point is to allow a test group a very short self-cure period – perhaps just two or three days.  In many organisations this amounts to little more than de-prioritising these accounts so that the time taken to work through the rest of the accounts can serve as the self-cure period.  Once the basic risk assumptions have been tested, the self-cure period can be extended – though usually to not longer than fifteen days.

It is also important to note that the probability of payment must not be measured as a single, static figure.  The way it will be applied in the eventual self-cure model means that it is important to measure how the probability of payment changes over time.

Some customers in the early stages of debt management will be ‘lazy payers’, that is customers who have the will and means to meet their obligations but tend to pay late on a regular basis; their payments will likely come in the first few days after the due date.  Other customers may have been without access to their normal banking channels for whatever reason; their payments may be more widely spread across the days after due date.  Regardless of the exact reasons, in most portfolios the majority of self-cure payments will come in the first few days after due date and thereafter at an ever-slowing rate.

Estimating the Costs of a Self-Cure Strategy

If there were direct costs involved in a self-cure model, there would be a break-even point where the dropping effectiveness and the ongoing costs of the strategy would make it inefficient to continue.  However, because a self-cure strategy has no such direct costs the problem needs to be looked at differently.

But, as I mentioned earlier, a valuable lesson can be learned by following the story of the space pen all the way to its real conclusion: the total cost of a solution is never its direct costs alone but also includes all of its indirect costs.  In the space race, the pencil’s low direct cost was nullified by its high indirect risk costs.  In debt management, a self-cure strategy’s low direct cost may also be nullified by its high indirect risk costs.

The indirect risk costs of a self-cure strategy stem from the fact that the probability of making a recovery decreases as the time to make a customer contact increases.  Customers who are in arrears with one lender are likely to also have other pressing financial obligations.  While the one lender may follow a self-cure strategy and hold off on a direct request for repayment, their debtor may re-prioritise their funds and pay another, more aggressive, lender instead. So, while waiting for a free self-cure payment to come in a lender is also reducing their chances of making a recovery from the next best method should it become clear at a point in the future that no such payment is likely to be forthcoming. 

The cost of a self-cure strategy is therefore based on the rate at which the probability of receiving a payment from next best strategy decreases.  For every day that a self-cure strategy is in force the next best strategy must start one day later and this is the key cost to bear in mind.  Is one week of potential cheap recoveries from the self-cure model worth one week of opportunities lost for more expensive but more certain recoveries in the phone base collection strategy?

Building a Self-Cure Strategy

A self-cure strategy should be applied to all accounts for as long as they remain sufficiently likely to make a payment to compensate for the indirect costs of the self-cure strategy incurred by foregoing the opportunity to drive payments using the next best strategy.

As stated, the benefits of the strategy are equal to the probability of payment over a period of time and the costs are equal to the decrease in the probability of payment from the next best strategy over that same period.

If a customer is as likely to make a payment when they are called on day one as they are when called on day five, then there is no cost in a self-cure strategy for those first five days.  Therefore, no call should be made until day six regardless of how small the probability of receiving a payment from the self-cure strategy actually is.  This is because, with no costs, any recovery made is value generating and any recovery not made is value neutral. 

However, if after the first five days a customer who has not been contacted begins to become less likely to make a payment when eventually called, costs start to accrue.  The customer should remain in the self-cure strategy up to the point where the probability of payment from the self-cure strategy is expected to drop to a level lower than the associated drop in the probability of payment from the next best strategy.

The ideal time to move an account out of the self-cure strategy and into the next best strategy would be at the end of the period preceding the one in which this cross over of cost and benefit occurs.

Please note that the next best strategy does actually have a direct cost.  Strictly speaking, this direct cost should be added to the benefit of the self-cure strategy at each point in time.  However, in the early collections stages the next best strategy is usually cheap (text messages, letters or phone calls, etc.) and so these costs are insignificant.  However, if the next best strategy is expensive – legal collections or outsourcing for example – these costs could become a material consideration.  For the sake of simplicity I will not include the direct cost of the next best strategy in this discussion but will in an upcoming article covering the question of when to sell a bad debt/ escalate it to legal.


The cheapest method should always be used to make a recovery in debt management but, before the cheapest method can be identified, all direct and indirect cost must be understood.

I haven’t set out to discuss all the direct and indirect costs of debt management strategies here – not even all the direct and indirect costs of self-cure strategies.  Rather, I have attempted to explain the most important indirect costs involved in self-cure strategies and how it can be used to identify the ideal point at which an account should be moved out of a self-cure strategy and into the first lender-driven debt management strategy.

This point will vary based on each customer’s risk profile and the effectiveness of existing debt management strategies.  The probability of payment for the next best strategy will decrease faster for higher risk customers than for lower risk customers; bringing forward the ideal point of escalation.  The probability of payment will fall slower for more intense collection techniques (such as legal collections) than for soft collections techniques (such as SMS) but costs also vary; the structure of an organisation’s debt management function will also move the ideal point of escalation.

Finally, you might find it strange that I didn’t talk about which clients should be included in a self-cure strategy.  The reason is that, in theory, every customer should first be considered for a self-cure strategy.  The important part of this statement is that I used the words ‘considered for’ not ‘included in’.  Because of the mechanics of the model proposed, higher risk customers may well have an ideal point of escalation that is equal to the day they enter debt management and so, while ‘considered’ for inclusion in the self-cure strategy they won’t actually be ‘included’.  At the same time, medium risk customers may be included and escalated after five days while the lowest risk customers may be included and escalated only on the fifteenth day.  This will all vary with your portfolio’s make-up and so it is equally possible that no customer group will be worth leaving in a self-cure strategy for more than a day or two.

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Many lenders feel that once an account has entered the debt management process it is time to start terminating the relationship.  This is an attitude that may be valid in low risk environments where debt management tends to see only the worst accounts.  However in today’s environment lenders should not view debt management as purely an exit channel for bad customers but also as an alternative sales channel.    

In other words, in the diagram below you can no longer view the procession of an account as always being from left to right but need to consider the reverse movement; turning ‘bad’ customers ‘good’ again.   

Debt Management fulfils the same role as sales and account management

In times of strong economic growth it might be possible to drive portfolio growth solely on the back of new customer acquisition.  In these times the market is full of ‘good’ potential customers looking for new credit and the customers that end up in debt management are few and of very high inherent risk.  However as the economy slows down, two significant things happen: ‘good’ customers stop borrowing and so new sales slow (and the quality of new customers tends to drop) and more customers find themselves in the debt management process (and conversely the quality of those customers tends to rise).   

In such times then, the lender should invest more effort in identifying the best prospects from within their debt management portfolio.  Customers in debt management provide an attractive prospect pool for a few reasons: the expected ‘response rate’ to an ‘offer’ is likely to be higher than in marketing campaigns; the lender has extensive data on each customer and their habits; there is a cost of not recovering; and, in the case of revolving products, the ‘new’ customer comes with an already established balance – mimicking a traditional balance transfer.   

But not all customers are worth retaining and so it is important to understand the relative risk and value of each customer in debt management before assigning a retention strategy.  Risk segmentation is ideally done using a dedicated debt management scorecard but, at least in the earliest stages, it can also be done using a behavioural scorecard.   

Customers who are high risk are by definition likely to re-offend.  Customers like this, who are regularly in debt management, are expensive to retain and consume both operational resources and capital provisions.  Unless the balance outstanding is large or the price premium charged is very high, it may be best to expedite these customers through the process by outsourcing this debt to a third-party debt collector.    

The fact that the balance outstanding is small though should not, on its own, be used to label a customer as ‘not worth retaining’.  The most important value is not the current value but the potential future value of a customer.  The lender should consider the potential for future loans and cross-sells too.  When the relationship is sacrificed with one product, as it surely will be with an expedited outsourcing/ write-off process, it is sacrificed for all other current and future products too.    

So segmentation should only be done based on a full customer view which includes a measure of risk and reward.  As always, the way to do this is through data analysis, scorecards and test-and-learn strategies.    

Where good customers have been identified it is worth investing in their retention.  This investment must be made in long-term and short-term retention strategies.    

The debt management process provides lenders with a rare opportunity to spend a significant amount of time speaking to their customers on a one-to-one basis.  Viewed in this light, debt management provides a wonderful opportunity for long-term relationship building.  Make sure your organisation can benefit from this opportunity by having staff that are skilled in customer handling and sales techniques – not just in demanding repayment.  In the long-term, investing in staff training should be a priority for every organisation.  Good training in this area will include references to reading a customer, over-coming objectives and structuring budgets/ payment plans (I’d recommend speaking to Mark Smith for all of your collections staff training needs).   

In the short-term, monetary investments – waived fees, discounted settlements, etc. – should be considered on a case by case basis.  These are the easiest incentives to provide though they should not be the first solution to which a lender turns.  When a ‘good’ customer falls into arrears it is, almost by definition, because they lack the ability to pay rather than the willingness to do so.  This means that the customer is usually willing to work with the lender to find a payment plan that will lead to full repayment while still accommodating their temporary financial difficulties.   

If the customer’s income source has temporarily disappeared – through a loss of job, etc. – then a payment holiday should be considered with a term extension to cater for increased interest repayments.  While term extensions can be used on their own, as can debt consolidation, where the problem stems simply from monthly costs exceeding monthly incomes – as perhaps in the case of rising interest rates or falling commission earnings.  In all case a payment plan should of course be accompanied by education and budgeting assistance.

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Lake Malawi is a huge feature of life in the country of the same name; covering almost 25% of its land mass.  It provides irrigation for crops, trade routes for goods, tourism opportunities and, of course, fish.  Lake Malawi is home to more species of fish than any other lake in the world and, although the fish caught in the lake provide a valuable source of protein for the locals, they are sought after for other reasons too.  In particular, the brightly coloured cichlid that is a feature of so many aquariums around the world is in fact endemic to the region and thus present fisherman with a rare opportunity to earn foreign currency.

The fishing methods employed on the lake are simple: each morning fishermen paddle their dugout canoes to a favourite spot before casting their home-made nets into the water; these nets are then dragged behind the boat; their catch hauled-in and, in the evening, taken ashore and to the market.  Each fisherman has the same goal – to maximise his profit – and each fisherman follows the same approach.  Our story follows one fisherman who thinks that there is a better way to maximise his profits than the fairly random process described here.

Three types of cichlid live in Lake Malawi and each of these sub-species fetches a different price at the market.  The most easily caught of these is the larger, green variety.  Because they are so easy to catch, they make-up a major portion of all fish sold at the market and thus sell for less than a dollar each.  There is also an orange variety which isn’t too difficult to catch.  They are smaller than their green cousins but still large enough and slow enough to be caught with reasonable ease.  Although they are by no means as widely available as the green fish, they are sufficiently common to also limit their sales price.  Finally, there is a very elusive red variety.  These fish are small, agile and quick and are seldom caught using the traditional method.  On the rare occasions that they are found at the market, they can fetch as much as fifty dollars each – a princely sum in such a poor country.

The village elders tell the young fisherman that to make more money they must catch more fish; and that to catch more fish, they must spend more time on the lake fishing.  The hero of our story, however, believes that to maximise his profit he should concentrate not on catching a few more fish each day but rather on catching the right type of fish – in other words by catching more red and orange fish.

Since each variety is physically different, he reasons that they might each be best caught using a different approach.  A tightly woven net with very small holes would be perfect for catching the red fish.  However, since it would also catch all the other fish he fears it would quickly fill with the less valuable fish leaving little space for the valuable red ones.  The large green fish would surely be easiest to catch in a very large, loosely woven net that required little maneuvering and which could be easily dropped into the lake and dragged ashore.  However, such a net would let the other, more valuable fish, escape easily.  Meanwhile, the medium-sized orange fish best suit a net with proportions somewhere between those two extremes. 

So there appears to be no way to maximise profit using a ‘one net’ approach.  He therefore needs to implement a three net strategy: using each specialist net as needed.  However, the amount of twine he has available to make nets presents a constraint.  In fact, the only twine he has is the twine that makes up his current net.  If he deconstructed his existing net, he could use that twine to create one small tightly woven net, one large loosely woven net and one net of a medium size and a medium weave.

However, a three net approach is not without its own problems.  Firstly, there is not be enough time to paddle across the lake three times, once with each net.  Secondly, it was shown that each net would be no more affective than the next one if used across the whole lake.

In fact, the only conceivable way to gain larger profits would be to use three separate nets in three different situations/ environments.  In order to succeed, he must know where to use each net and that is only be possible if he knows where each type of fish is most likely to live.  To find out this information, he designed a series of tests.  The next morning when he took his dugout canoe into the lake he stopped at regular intervals along his route and dived into the water; looked around; took note of the fish he saw; took note of his underwater surrounding and captured all of this information in his notebook.  This process of testing might have cost him some time in the short-term but he believed it would pay off handsomely in the long-term.  After a week of this unusual behaviour, he had enough information to build himself a model. 

The red fish, it turns out, tend to live in shallow, rocky areas while the green fish prefer the deeper, open areas of the lake and the orange ones are most common in the natural channels that run between Lake Malawi’s many small islands.

Armed with this information and a weighted rope, he was able to put his new specialist strategy into action.  Each time he stopped his canoe in the lake he would first drop the weighted rope into the water to measure its depth: if the water was less than 5 metres deep he would deploy his small tightly wound net and haul in the valuable red fish; if it was deeper he would deploy the much larger but more loosely wound net and haul in as many green fish as he could, as quickly as he could; if the water was fast moving he would deploy his medium sized net and haul in the orange fish.

After his first day of fishing like this, he headed to the market with a lighter – but significantly richer – load.  Since his new nets were smaller than his old one, he had caught fewer green fish but the economics involved meant that he had made significantly more profit.  In fact, every red one caught was as valuable to him as fifty green ones that might have got away!  The other villagers could only increase their incomes by fishing harder and that has limited returns.  A 10% increase in working hours will produce roughly 10% more income and there is a limited amount of good sunlight each day.  However, he could increase his income by working smarter and the returns on this were exponential.  If he caught half as many fish as the others, but they were all red ones, he could earn as much as 25 times more money than each of them – a feat not possible when volume is the only lever.

Much of what was true in this scenario is also true of bad debt collections.  It is not possible to optimise a collections process when all accounts are subjected to the same strategy.  In such a scenario, a 10% increase in staff volumes will lead to a 10% increase in collections, a 10% increase in associated costs and thus a limited benefit.  On the other hand, specialist strategies for each customer segment – based on risk, propensity to pay, etc. – can produce exponential improvements in collection performance.

Where our fisherman was trying to catch different fish with different associated values, a collections process aims to cure different customers with different associated risks; where our fisherman distributed a fixed amount of twine across different nets to catch each type of fish, a collections process will distribute a fixed amount of resources across different strategies to cure different customer segments; where our fisherman used an understanding of the underwater geography to decide which strategy to apply in each case, a collections process should use customer behaviour data – or some other understanding of risk data – to decide which strategy to apply in each case.  This is risk based collections. 

Within each collections process there are different customer risk segments – ranging from low risk customers who are likely to correct their accounts on their own accord to high risk customers unlikely to be cured without litigation.  That these groups are known to be present does not mean that a risk based collections strategy is in place; for this to be true two other factors must also exist: each group must be identifiable by the similar behavioural characteristics of its members and each group must be treated differently once identified.

Firstly, the groups must be identifiable based on shared characteristics.  For example, a high risk group might be identifiable based on their shared history of previous delinquencies and high balance utilisation while a low risk group might be identifiable by their shared history of conservative borrowing habits.  Here it is important to have an analytical tool, such as Experian’s Strategy Manager, that can analyse large data sets and run the statistical tests need to identify these characteristics.  Without such a tool, risk based collections can still be implemented but the effectiveness of such an implementation would be restricted.  A full collections scorecard that ranks each customer entering collections and prioritises them based on their unique risk profile is the ideal.  However, there are many less sophisticated solutions that can also add tremendous value to an organisation. 

Once the accounts have been grouped together, each segment must be subjected to a different strategy and the differences in these strategies should reflect the differences in perceived risk.  For example, low risk customers might receive less direct communication from the bank while high risk customers might receive more.  This should not simply be a case of distributing volume however.  The cost of taking any action in collections should also be weighed against the potential extra benefit that such an action is likely to provide.  A cheaper but less reliable communication method – such as sms – could be used when communicating with low risk customers while the more expensive and more effective channels – such as phone calls – might be reserved for use when communicating with high risk customers.  For more on this, read my article on  ‘Treatment, Timing and Tone’

In summary, a risk based collections strategy recognises the fact that each account in the collections process has a different risk and different value attached to it and, pursuant to this, that each account requires a different level of resources.  The net effect of this strategy is a better distribution of collections resources by avoiding over-investment in low risk accounts and under-investment in high risk accounts.  The difficult part of the process lies not in the theory of risk based collections but in the putting of that theory into practice. 

In order to effectively identify customer segments a good analytical product is needed at either the customer management or collections stage.  Such a system should be used by competent analysts and the results it generates should be applied through a collections work-flow system such as Tallyman.  As with all credit risk strategies, risk based collections strategies should be built on a platform of test-and-learn analytics so as to enable a series of continuous improvements.  Once differentiated strategies have been assigned to each identified customer group, they should be measured and adjusted as circumstances change and as new challenger strategies are shown to provide improved results.

***  I don’t really know how they fish in Lake Malawi or how many species of cichlid exist there but, it is nonetheless a beautiful country and one well worth visitng for both fishermen and non-fishermen alike 🙂

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 At the highest level, there are two types of customers in collections – customers who can’t meet their debt obligations and customers who choose not to.  The simplest labels to describe these respective customer groups are “unable” and “unwilling”.  Identifying the label that best describes a particular customer is the first step in selecting the optimal treatment, timing and tone of the collections strategy.

When a well-intentioned customer is unable to meet their debt obligations, the ability to correct that situation resides largely with the customer.  The bank can help, but it is ultimately the customer who must find ways to increase their income and/ or decrease their costs.  In these cases, where the customer is willing to work with the bank to rehabilitate their account, collections strategies should focus on designing a repayment schedule that works for both parties while adjusting the customer’s spending patterns to avoid relapses in the future.

The bank has more leverage to correct the situation when, on the other hand, a customer of sufficient means chooses to not fulfil their debt obligations.  In these cases, the bank must seek to adjust the customer’s attitude towards repaying the debt rather than adjusting the repayment terms.  To do this, the bank must first identify the source of the customer’s reluctance to pay.  It is possible that the source of their reluctance is internal to the bank’s processes – perhaps a dispute relating to the terms and conditions of the loan.  These cases are usually easy to correct and are better described as operational errors than as risk-indicative defaults.  It is more common, however, for the reluctance to have an external source.  Collections strategies in these cases can follow one of two broad approaches, either emphasising the benefits of paying or emphasising the negative implications of not paying.  If a customer in the early stages of delinquency displays the characteristics of someone unwilling to meet their debt obligations, education might be sufficient to correct the situation.  The benefits of correcting a delinquent account could include the waiving of penalty fees and interest, retaining access to further advances, etc.  However, where softer strategies have been unable to halt a customer’s descent into further delinquency, stronger strategies may be needed.  These would include strategies that that emphasise the negative aspects of defaulting and include the acceleration of the involvement of internal and external legal representatives.

Despite the importance of differentiating between these two broad customer groupings, it is not always easy to do so.  One useful tool, albeit a slightly reactive one, is the “can’t pay/ won’t pay” matrix.  Without such a tool, the process of assigning customers between categories is a complex and laborious one that must be repeated on a case-by-case basis.

The “can’t pay/ won’t pay” matrix avoids much of that complexity by defining the two categories using simple and observable criteria: did the customer agree to a promise-to-pay arrangement; did the customer meet the terms of that agreement.  A customer who agrees to a promise-to-pay is considered “willing to pay” while the refusal to commit to such an arrangement would characterise a customer as “unwilling to pay”.  If a customer who had agreed to a promise-to-pay arrangement – and was therefore defined as “willing to pay” – later fails to meet the terms of that promise-to-pay, they will be characterised as “unable to pay”.  Although this simplification leads to a lower level of accuracy, the simplicity it adds to the collections process is ample compensation.




The "Unable to Pay/ Unwilling to Pay" Matrix

The "Unable to Pay/ Unwilling to Pay" Matrix


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The profit model for a portfolio of retail loans can be illustrated simply, as shown below: 

Profit = Revenue – Bad Debt – Costs

A bank can increase its profitability by adjusting either the revenue or the loss side of that model – for the purposes of this article, we’ll assume costs are fixed in the short-term. 

Revenue can be increased by increasing the interest rate or fees charged to customers.  The burden of these increases is borne directly by customers and so a 10% increase in revenue is likely to lead to a smaller eventual increase in profit as disgruntled customers leave for cheaper competitors.

On the other side of the equation, losses can be reduced by reducing the inherent risk of the portfolio.  Any change in risk has a multiplicative impact on profit.  For example, when the portion of accounts in default increases, bad debt losses increase.  At the same time, because accounts in default are not paying fees and interest, as they increase as a portion of the portfolio, revenue decreases too.

Fortunately though, this multiplicative impact works in both directions.  Strategies that aim to reduce risk are often a better way to increase profit, therefore, than strategies that aim to increase revenue.  Collections strategies are the best way to adjust risk in an existing portfolio.  A good collections strategy turns potential defaulters in revenue generating customers.  Collections strategies also have no direct customer cost implications to dissolve that multiplicative effect.  Even the harshest collections strategy, if correctly communicated, is unlikely to lead to significant attrition.

However, no one collections strategy can be equally effective in all cases.  It is therefore important to be able to adjust collections strategies to suit the situation and, to do that, one must understand the mechanics of collections strategies.  Almost all collections strategies are build around the “Three T’s” – Treatment, Timing and Tone.


The first step in designing a collections strategy is to decide on the treatment to which a delinquent customer will be subjected.  The most commonly employed treatments are letters, SMS text messages, phone calls and outsourcing – although other options do exist.  When choosing between treatments, it is important to weigh-up relative costs and benefits of each by constructing a profit model for the customer contact process.

Cost of the Treatment ÷ Average Success Rate <= Average Value of Collection

Although this may be a conceptually simple task, deriving these values can be difficult in practice. 

The Cost of the Treatment is the simplest figure to determine and is nothing more than the cost incurred whenever that treatment is initiated.  This includes the direct external cost of the communication medium – the cost of the call – as well as the indirect, internal costs – the cost of the staff and systems that enable that call.

The Average Success Rate of the treatment should ideally be measured using a test-and-learn approach although a simple estimate will often suffice.  The simplest way to measure the Average Success Rate is to monitor the accounts that were subjected to that treatment on a particular date and to calculate the percentage of those who made a payment within a given subsequent period.  This approach has several weaknesses but, primarily, it is difficult to isolate the impact of the treatment from the impact of other extraneous factors.  More advanced organisations should rather construct an experiment which will accurately measure the true Average Success Rate for each relevant customer group under various circumstances. 

The Average Value of Collection can, similarly, be estimated using a simple estimation or calculated more accurately using a test-and-learn experiment.  A common approach would be to use the outstanding payment – or some portion of the outstanding balance – as a proxy.  However, this approach does not take the change in risk into account.  A better approach would be to calculate the degree to which risk increases when an account doesn’t make a payment and the degree to which it decreases when it does.  Applying this info to the previous approach provides a more robust answer.

Let’s assume accounts that are up-to-date have a 0.5% chance of being written-off, accounts that are one month delinquent have a 5% chance of being written-off and accounts that are two months delinquent have a 15% chance of being written-off.  The simple approach might estimate the Value of Collection for an account that is one month in arrears and has a $10 000 balance as $500 (5% x $10 000).  The dynamic approach would estimate it to the difference between the value-at-risk should a payment not be received and the value-at-risk if the collections is made which, in this case, is equal to $1 450 (15% x $10 000 – 0.5% x $10 000).

Whether the components are calculated or estimated, combining them in the profit model will inform decisions regarding the optimal treatment for each collections strategy.


Having chosen how to contact a customer, the next step is to decide when to contact them.  There are three timings to consider – the optimal time in the cycle to contact the customer, the optimal time in the month to contact the customer and the optimal amount of time should you allow the customer to act before re-contacting them.  One might think that the time of day should also be a consideration here but I would argue that it is more of an issue of tone: contacting someone after hours or on week-ends communicates a serious tone more than it does anything else though of course in some cases it may be a decision based purely on availability.

Most important when building a collections strategy is the decision of when in the cycle to contact a customer.  An aggressive strategy might contact a customer the day after a missed payment, while a more lenient one may allow a few days respite.  Two competing forces act in the period between the missed payment and the first contact.  A longer period increases the likelihood of a customer making an unprompted payment and thus saving the bank the cost of an unnecessary contact.  It also increases the likelihood, however, that a customer of limited means will settle other debt and thus be unable to meet their obligations to the bank.  Therefore, the timing of contacts will tend to shift outwards as the perceived risk of a customer group decreases.

The optimal time of the month to contact a customer may initially seem to be closely related to the optimal time in the cycle to contact a customer.  However, although they may overlap at times, the underlying reasoning behind each is different.  The optimal time in the month to contact a customer is based on customer specific events – especially salary dates.  A customer’s payment may fall due on the 5th of a particular month or the 10th or the 15th.  In each case the optimal time to contact them in the cycle will shift accordingly.  However, if they are paid on the 25th then the day in which they are most able to meet their obligations will remain fixed and independent of the cycle date.

The final timing consideration relates to the amount of time a customer is given from when they are first contacted to when they are expected to act – usually the duration of the promise-to-pay agreement.   When choosing the optimal duration of a promise-to-pay it is important to consider the due-date of the payment not just as an opportunity to receive a payment but also as an opportunity to gather risk-indicative information.  Each time a customer misses an agreed payment the bank learns more about that customer and is presented with an opportunity to adjust or change strategies.  So, where a promise-to-pay falls due at the same date as the next instalment falls due, an opportunity for extra information is lost.  Wherever possible, therefore, it is preferable to set promise-to-pay arrangements – and any other customer tasks – so as to fall due between pre-existing agreements.


Once the treatment and timing of the contact have been decided, the content of the message itself must be considered.  The tone of a message will usually progress through a continuum, starting as a polite reminder, becoming a request and then a demand for payment and finally a threat of legal action.  Although a change in tone is usually communicated through a change in language or style it can, in some cases, also be communicated through a change in the packaging of the message. 

The purpose of a change in tone is to emphasise the escalating severity of a growing default position.  The tone should become more aggressive whenever a more aggressive treatment or timing is employed.  However, it is also possible to escalate the tone without escalating either the treatment or the timing. 

Changes to the tone of communications are especially effective when dealing with customers who are able to meet their debt obligations but are unwilling to do so.  This unwillingness could stem from a number of sources but can usually be overcome by altering a customer’s priorities.  Depending on the customer, the language can progressively underline the benefits of repaying one of the bank’s loans – by highlighting potential savings, by offering rewards for timeous repayment – or the negative consequences of failing to do so – by threatening legal action, by threatening a black-listing.

Also, as mentioned earlier, the time of day at which a contact is made can also be used to communicate tone.  In this regard, one would usually strive to contact a customer at a time convenient to them while the relationship was still good and the tone still light but once the relationship had devolved sufficiently, no more consideration would be made to the customer’s convenience and contacts would be made at a time most likely to result in a right party contact.


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