Posts Tagged ‘collections strategy’

You’ve got to know when to hold ‘em, know when to fold ‘em

Know when to walk away and know when to run

I’ve always wanted to use the lines from Kenny Rogers’ famous song, The Gambler, in an article. But that is only part of the reason I decided to use the game of Texas Holdem poker as a metaphor for the credit risk strategy environment.

The basic profit model for a game of poker is very similar to that of a simple lending business. To participate in a game of Texas Holdem there is a fixed cost (buy in) in exchange for which there is the potential to make a profit but also the risk of making a loss. As each card is dealt, new information is revealed and the player should adjust their strategy accordingly. Not every hand will deliver a profit and some will even incur a fairly substantial loss, however over time and by following a good strategy the total profit accumulated from those hands that are winners can be sufficient to cover the losses of those hands that are losers and the fixed costs of participating and a profit can thus be made.

Similarly in a lending business there is a fixed cost to process each potential customer, only some of whom will be accepted as actual customers who have the potential to be profitable or to result in a loss.  The lender will make an overall profit only if the accumulated profit from each profitable customer is sufficient to cover the losses from those that weren’t and the fixed processing costs.

In both scenarios, the profit can be maximised by increasing exposure to risk when the odds of a profit are good and reducing exposure, on the other hand, when the odds of a loss are higher. A good card player therefore performs a similar role to a credit analyst: continuously calculating the odds of a win from each hand, designing strategies to maximise profit based on those odds and then adjusting those strategies as more information becomes available.


To join a game of Texas Holdem each player needs to buy into that game by placing a ‘blind’ bet before they have seen any of the cards.  As this cost is incurred before any of the cards are seen the odds of victory can not be estimated. The blind bet is, in fact, the price to see the odds.

Thereafter, each player is dealt two private cards; cards that only they can see. Once these cards have been dealt each player must decide whether to play the game or not.

To play on, each player must enter a further bet. This decision must be made based on the size of the bet and an estimate of the probability of victory based on the two known cards. If the player should instead choose to not play, the will forfeit their initial bet.

A conservative player, one who will play only when the odds are strongly in their favour, may lose fewer hands but they will instead incur a relatively higher cost of lost buy-ins. Depending on the cost of the buy-in and the average odds of winning, the most profitable strategy will change but it will unlikely be the most conservative strategy.

In a lending organisation the equivalent role is played by the originations team. Every loan application that is processed, incurs a cost and so when an application is declined that cost is lost. A conservative scorecard policy will decline a large number of marginal applications choosing, effectively, to lose a small but known processing cost rather than risk a larger but unknown credit loss.  In so doing though, it also gives up the profit potential on those accounts. As with poker betting strategies, the ideal cut-off will change based on the level of processing costs and the average probability of default but will seldom be overly conservative.

A card player calculates their odds of victory from the known combinations of cards possible from a standard 54 card deck.  The player has the possibility of creating any five card combination made up from their two known cards and a further five random ones yet to be dealt, while each other player can create a five card combination made-up of any seven cards except for the two the player himself has.  With this knowledge, the odds that the two private cards will result in a winning hand can be estimated and, based on that estimate, make the decision whether to enter a bet and if so of what size; or whether to fold and lose the buy-in.

The methods used to calculate odds may vary, as do the sources of potential profits, but at a conceptual level the theory on which originations is based is similar to the theory which under-pins poker betting.

As each account is processed through a scorecard the odds of it eventually rolling into default are estimated. These odds are then used to make the decision whether to offer credit and, if so, to what extent.  Where the odds of a default are very low the lender will likely offer more credit – the equivalent of placing a larger starting bet – and vice versa.

Customer Management

The reason that card games like Texas Holdem are games of skill rather than just games of chance, is that the odds of a victory change during the course of a game and so the player is required to adapt their betting strategy as new information is revealed.  Increasing their exposure to risk as the odds grow better or retreating as the odds worsen.  The same is true of a lending organisation where customer management strategies seek to maximise organisational profit but changing exposure as new information is received.

Once the first round of betting has been completed and each player’s starting position has been determined, the dealer turns over three ‘community cards’.  These are cards that all players can see and can use, along with their two private cards, to create their best possible poker hand. A significant amount of new information is revealed when those three community cards are dealt. In time two further community cards will be revealed and it will be from any combination of those seven cards that a winning hand will be constructed. So, at this point, each player knows five of the seven cards they will have access to and three of the cards their opponents can use. The number of possible hands becomes smaller and so the odds that the players had will be a winner can be calculated more accurately. That is not to say the odds of a win will go up, just that the odds can be stated with more certainty.

At this stage of the game, therefore, the betting activity usually heats up as players with good hands increase their exposure through bigger bets. Players with weaker hands will try to limit their exposure by checking – that is not betting at all – or by placing the minimum bet possible. This strategy limits their potential loss but also limits their potential gain as the total size of the ‘pot’ is also kept down.

As each of the next two community cards is revealed this process repeats itself with players typically willing to place ever larger bets as the new information received allows them to calculate the odds with more certainty. Only once the final round of betting is complete are the cards revealed and a winner determined. Those players that bet until the final round but still lose will have lost significantly in this instance. However, if they continue to play the odds well they will expect to recuperate that loss – and more – over time.

The customer management team within a lending organisation works with similar principals. As an account begins to operate, new information is received which allows the lender to determine with ever more certainty the probability that an account will eventually default: with every payment that is received on time, the odds of an eventual default decrease; with every broken promise-to-pay, those odds increase; etc.

So the role of the customer management team is to design strategies that optimise the lender’s exposure to each customer based on the latest information received. Where risk appears to be dropping, exposure should be increased through limit increases, cross-selling of new products, reduced pricing, etc. while when the opposite occurs the exposure should be kept constant or even decreased through limit decreases, pre-delinquency strategies, foreclosure, etc.


As the betting activity heats up around them a player may decide that the odds no longer justify the cost required to stay in the game and, in these cases, the player will decide to fold – and accept a known small loss rather than continue betting and risk an even bigger eventual loss chasing an unlikely victory.

Collections has too many operational components to fit neatly into the poker metaphor but it can be most closely likened to this decision of whether or not to fold. Not every hand can be a winner and even hands that initially appeared to be strong can be shown to be weak when the latter community cards are revealed. A player who was dealt two hearts and who then saw two further hearts dealt in the first three community cards would have been in  a strong position with the odds that the fifth heart they need to create a strong ‘flush hand’ sitting at fifty percent. However, if when the next two cards are dealt neither is a heart, the probability of a winning hand will drop to close to zero.

In this situation the player needs to make a difficult decision: they have invested in a hand that has turned out to be a ‘bad’ one and they can either accept the loss or invest further in an attempt to salvage something. If there is little betting pressure from the other players, they might choose to stay in the game by matching any final bets; figuring that because the total pot was large and the extra cost of participating small it was worth investing further in an unlikely win. Money already bet, after all, is a sunk cost. If the bets in the latest round are high however, they might choose to fold instead and keep what money they have left available for investment in a future, hopefully better hand.

As I said, the scope of collections goes well beyond this but certain key decisions a collections strategy manager must make relate closely to the question of whether or not to fold. Once an account has missed a payment and entered the collections processes the lender has two options: to invest further time and money in an attempt to collect some or all of the outstanding balance or to cut their losses and sell or even to write-off the debt.

In cases where there is strong long-term evidence that the account is a good one, the lender might decide – as a card player might when a strong hand is not helped by the fourth community card – to maintain or even increase their exposure by granting the customer some leeway in the form of a payment holiday, a re-aging of debt or even a temporary limit increase. On the other hand, in cases where the new information has forced a negative re-appraisal of the customer’s risk but the value owed by that customer is significant, it might still be preferable for the lender to invest a bit more in an attempt to make a recovery, even though they know that the odds are against them. This sort of an investment would come in the form of an intensive collections campaign or the paid involvement of specialist third party debt collectors.

As with a game of cards, the lender will not always get it exactly right and will over invest in some risky customers and under-invest in others; the goal is to get the investment right often enough in the long-term to ensure a profit overall.

It is also true that a lender who consistently shies away from investing in the collection of marginal debt – one that chooses too easily to write-off debt rather than to risk an investment in its recovery – may start to create a reputation for themselves that is punitive in the long-run. A lender that is seen as a ‘soft touch’ by the market will attract higher risk customers and will see a shift in portfolio risk towards the high-end as more and more customers decide to let their debt fall delinquent in the hopes of a painless write-off. Similarly a card player that folds in all situations except those where the odds are completely optimal, will soon be found out by their fellow players. Whenever they receive the perfect hand and bet accordingly, the rest of the table will likely fold and in so doing reduce the size of the ensuing pot which, although won, will be much smaller than it might otherwise have been. In extreme cases, this limiting of the wins gained from good hands may be so sever that the player is unable to cover the losses they have had to take in the games in which they folded.


The goal of credit risk strategy, like that of a poker betting strategy, is to end with the most money possible. To do this, calculated bets must be taken at various stages and with varying levels of data; risk must be re-evaluated continuously and at times it may become necessary to take a known loss rather than to risk ending up with an even greater, albeit uncertain, loss in the future.

So, in both scenarios, risk should not be avoided but should rather be converted into a series of numerical odds which can be used to inform investment strategies that seek to leverage off good odds and hedge against bad odds. In time, if accurate models are used consistently to inform logical strategies it is entirely possible to make a long-term profit.

Of course in their unique nuances both fields also vary quite extensively from each other, not least in the way money is earned and, most importantly, in the fact that financial services is not a zero sum game. However, I hope that where similarities do exist these have been helpful in understanding how the profit levers in a lending business fit together. For a more technical look at the same issue, you can read my articles on profit modelling in general and for credit cards and banks in particular.


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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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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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Debt management tends to be treated as purely an operational function even by lenders that operate sophisticated risk analysis processes in other areas of their organisations; that is to say that its core objective is seen to be managing its internal costs as efficiently as possible.  The predominant philosophy seems to be that in order to deliver better results you can either try to get more effort out of each member of staff or, if you’re already at the point of maximum output, you can increase headcount.  This philosophy is outdated.  It focuses on working harder and fails to take into account the benefits of working smarter. 

Debt management departments no longer add value only by being efficient but also by being effective.  In fact in most cases improvements made to the strategies employed in a debt management function lead to significantly bigger rewards than improvements made in the efficiency of operations.

The best way to drive an increase in debt management effectiveness is to implement risk based collections.  A risk based collections strategy is simply one that starts with the core belief that not all customers will respond equally to any one action and so a one-size-fits-all approach to debt management must lead to areas where too much is invested in collecting a bad debt and areas where too little is invested; areas where good customers are lost and where bad customers are retained. 

A risk based collections strategy should start by seeking to measure the risk of each customer and to measure the cost and performance of each possible action.  With this knowledge, a series of specific strategies can be created to optimise the investment made in collecting funds against the expected returns of those investments.  Some customer groups will pass through a light strategy with few direct contacts involved, others may receive more contacts, some messages will be communicated in a light tone, others in a firm tone, etc.  The total investment made in the debt management function may remain unchanged but the distribution of that investment will certainly change.

One analogy that might help to clarify the role of risk based collections comes from the world of card games.  Let’s assume the simplest of card games: a deck of cards is shuffled and a player’s money is doubled whenever a red card is drawn.  Because the card order is random, the player is forced to treat every card as equal and therefore to play the same strategy at each draw.  After an extended period of play the player will end the night even having doubled their money (red card drawn) as often as the lost it (black card drawn).  However, if the player can count the number of red and black cards dealt and can retain that data, they can begin to see patterns emerge and can begin to make bigger bets whenever the odds are more in their favour and smaller bets when the odds are against them.  Or, in other words, they can apply differentiated strategies based on perceptions of risk.  Following such a strategy the player could end the night as a winner. 

Risk based collections seeks to do the same thing.  If an organisation does not know the risk of each customer they must treat every customer as if they were the same and so apply a one-size-fits-all strategy, perhaps only varying their approach based on the size of the debt at stake.  The results of such a strategy are muted.  However, once the organisation begins to segment its customer base into groups of similar risk it can begin to customise strategies for each of these groups – leading to significantly improved results even with the same staff using the same operational systems.

Three major components are required to implement a risk based collection strategy: a scorecard; strategy management software; trained staff.

Scorecards sit at the heart of risk based collections.  A scorecard is a statistical tool used for forecasting behaviour.  It works by first examining a database of known data and identifying similar characteristics within groups of accounts and then matching the on-going behaviour of each of those homogeous groups with regard to a particular metric of interest – percentage in default after 12 months, percentage to make a payment within 1 month, etc.  So then, whenever a new account enters debt management its characteristics can be analysed and compared to the characteristics of each of those groups, allowing the organisation to assign it to a particular segment and to infer its most likely future behaviour.

The output of a scorecard is a score that relates to a given probability of an event happening.  Unless lots of detailed information is needed, the actual score is usually summarised into a score band, each of which represents a group of accounts whose performance falls in a similar position somewhere along a risk continuum.  For example, it may produce a score between 0 and 100 which is summarised into three risk bands representing groups with a high, medium and low probability of default.  This data is usually augmented with other data relating to the size of the debt outstanding or some other proxy for the impact on profit should an account be written-off.  Combining the two measures provides a simple risk matrix which can be the starting point for a fairly complex risk based collections strategy.

Simply understanding the risk inherent in each customer segment does not add any value to an organisation in itself.  That organisation also needs to change the physical manner in which it handles each of these segments in order to do that.  Thus, individualised collections strategies need to be developed for each identified customer segment taking into account the differences in expected behaviour.  This is the second component of risk based collections.

Building a individualised debt management strategy involves selecting the series actions to be taken, determining the timing of those actions and, in certain cases, the specific script or letter format to use.  In order for these to lead to continuous improvement the use of test-and-learn experiments should also be ingrained in the processes and the results of those experiments, as well as the business as usual strategies, should be reported on and monitored by management.  Small portfolios might get by using manual procedures to design and manage these strategies but larger business will need suitable systems.  A good strategy management system must be able to assist in the design and the management of strategies. 

During the design phase it is important to have access to accurate and flexible data analytics.  The strategy management system should be able to access historical databases and to display that data in a way that makes trend spotting and advanced analysis easy.  The most advanced systems will even allow simulation of possible strategies – were data can be fed through competing strategies in the same way they would be processed live and the results compared for any important metric so that, for example, the most profitable or the cheapest strategy of several possible alternatives can be identified even where complicated interactions exist.  Such advanced features can save a lot of time in the early stages of the strategy design process and so can add significant value to debt management strategies.  However, the lack of such advanced tools need not stop the implementation of risk based collections.

During the strategy management phase it is important to monitor the performance of each strategy: comparing the performance of the strategies in real life to expectations as well as the results of test-and-learn experiments to business as usual.   Whenever a strategy involves people – as debt management strategies do – it becomes very dynamic and so procedures must be in place to facilitate fast and regular strategy changes. 

These strategies become real in the operational environment where the lender’s staff deal with customers.  The most common means of implementing these strategies in the operational environment is by making adjustments to the three Ts as discussed in another article in this section.  I will not go into great detail again here but I have included a diagram below which summarises the sort of strategy differences one might consider for each segment.

Suffice to say, each of the three Ts will be adjusted according to the risk of the account and the size of the expected recovery/ loss.  So, on the one extreme an outstanding debt that is high risk and has a large outstanding balance is one in whose recovery the lender should be prepared to invest by using reliable treatments (even if those are more expensive); contacting customers early and often; by using a sever tone of voice at a time that is convenient to themselves.  On the other extreme, an outstanding debt that is of low risk and that has a small balance should be passed through a strategy with a different set of priorities.  In this case the treatments should be, first and foremost, cheap (even if this means they are less reliable); contact can be initiated late in the process and need only be infrequent; while the tone should be kept conciliatory and messages should be delivered at a time that is convenient to the debtor.

In order for staff to be able to successfully deliver on these dynamic strategies a new skill set is often needed.  Gone are the days of debt managers as bullies.  It is no longer sufficient for staff to simply understand the technical intricacies of their front-end systems.  In today’s environment debt managers must also be sales managers and so staff need to understand customer behaviour and how to adapt to it.  Some good training courses exist that can impart such knowledge on teams and this should always be considered alongside any risk based collections implementation.  The technical changes made above will come to nought if the staff expected to implement the changes do not know how to do so effectively or if they simply do not want to change from their old ways of doing things. 

* * *

Customer education is always important and it should be a priority for all lenders to point higher risk customers towards websites like these and other services that can aid them with their personal finances and budgeting.

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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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