Feeds:
Posts
Comments

Posts Tagged ‘Exposure Management’

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.

Originations

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.

Collections

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.

Summary

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.

Read Full Post »