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Archive for the ‘Credit Risk Strategy’ Category

We usually assume that in a given situation, the more conservative of two strategies will better protect the bank’s interest. So, in the sort of uncertain times that we are facing now, it is common to migrate towards more conservative approaches, but this isn’t always the best approach.
In fact, a more conservative approach can sometimes encourage the sort of behaviour that it aims to prevent. Provisions are a case in point.

Typically provisions are calculated based on a bank’s experience of risk over the last 6 months – as reflected in the net roll-rates. This period is long enough to smooth out any once-off anomalies and short enough to react quickly to changing conditions.
However, we were recently asked if it wouldn’t be more conservative to use the worst net roll-rates over the last 10 years. While this is technically more conservative (since the worst roll-rates in 120 months are almost certainly worse than the worse roll-rates in 6 months) it could actually help to create a higher risk portfolio. Yes, the bank would immediately be more secure, but over time two factors are likely to push risk in the wrong direction:

1)        The provision rate is an important source of feedback. It tells the originations team a lot about the risk that is coming into the portfolio from internal and external forces. The sooner the provisions react to new risks, the sooner the originations strategies can be adjusted. So, because a 10 year worst case scenario is an almost static measure and unaffected by changes in risk, new risk could be entering the portfolio without triggering any warnings. A slow and unintentional slide in credit quality will result.
2)        Admittedly, other metrics can alert a lender to increases in risk, but there is another incentive at work because provisions are the cost of carrying risk; by setting the cost of risk at a static and artificially high level you change the risk-reward dynamic in a portfolio.
A low risk customer segment should have a low cost of risk, allowing you to grow a portfolio by lending to low risk/ low margin customers. However, if all customers were to carry a high cost of risk regardless, only high margin customers would be profitable; and since high margin customers are usually also higher risk, there would be an incentive to grow the portfolio in the most risky segments.

In cases where the future is expected to be significantly worse than the recent past, it is better therefore to apply a flat provision overlay, a once-off increase in provisions that will increase coverage but still provide allow provisions to rise and fall with changing risk.

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The thing is: no one really cares about banking products. There’s no idolizing of the guys who started AmEx Cards or CapitalOne, no queue outside HSBC the night before a new card is launched. This is a problem because people only buy things they care about, or things they need and for which there is no alternative.

Banks used to keep outside competitors away with the huge capital and regulatory costs of setting-up a payments system but as more commerce moves online and as these other costs drop, those barriers will fall.
The problem is cards are essentially commodities. With a few exceptions, a credit card is a credit card is a debit card, even. This is especially true as the actual plastic starts to play a smaller role in the transaction. In freeing customers from location-specific branch and ATM networks, online banking has also removed the personal relationship that may once have made a bank something more than a logo on a card.
The credit card survives – and indeed still thrives – because it is the most convenient way for most people to make most payments, at the moment, but this is changing. With more and more online and mobile alternatives, banks will have to start competing with more retail-savvy competitors and to do that they need to reconsider the way they consider and market their products.
Traditionally banks spent large amounts on above the line advertising to attract customers and retain customers who they offered a suite of standard products; a one-size-fits-all model. Then, stand alone credit card issuers and other niche companies started to attack the banks’ market share with tailored products offered through direct marketing campaigns; an altered-by-the-in-store-tailor, still not 100% customized.
Direct marketing is no longer enough because it works on a some key principals which are being undermined: the contact must be made at a time and place where the customer is open to the idea of a new card, but in a flooded market the chances of your contact reaching a customer before a competitors in this window period is getting smaller and you’re almost always contacting them at home; the contact must come in a medium that is relevant to a customer, both mail and email are becoming less relevant to customers; and the offer should appeal to a particular niche, but a direct marketing campaign, even a niche one, must involve a degree of choice compromisation.
A new model is needed that can reach customers at a convenient time and place, through a relevant medium to offer products tailored to their needs, cheaply. The last word is especially important because banks have long used vague pricing structures to protect themselves from commodity prices but new laws and competition from more transparent – and even ‘no cost’ –competitors will drive prices down, making only the most efficient banks profitable.
This article is an attempt to run with that idea, sometimes beyond the limits of practicality; hopefully in doing so I will raise some interesting questions about what is and isn’t important in the modern, mass market credit card business.

That’s where the idea for the credit card vending machine took root: it is a symbol for efficient, convenient, and ‘productized’ transactional banking. Turning the credit card marketing model around to offer customized cards to customers in convenient locations, without paper work and at low cost.
I envisage a customer approaching a machine in a shopping mall, choosing a card design from the display, entering the relevant data, selecting product features, paying a fee based on the feature bundle, and then waiting while the machine embosses, encodes and produces their card.

The concept is simply an amalgamation of components that are all already available and automatable:
·        an online application form,
·        a means of automated customer verification (ID card scanning in HK and fingerprint reading in Hong Kong for example) ,
·        a secure communications channel,
·        a card embossing machine

Data Capture
I hate forms, especially hand written forms. Every time someone asks me to write out my name and address I immediately assume they value bureaucracy over customer service.
Instead, the data capture process should be designed to leverage stored data, focusing on verifying data rather than capturing it. In Hong Kong I can use my government-issued identity smartcard and a scan of my thumbprint to enter and leave the country, the same tools could provide my demographic which could then be supplemented by bureau and internal databases, requiring me to enter only minimal data. An ATM card and PIN code might do the same thing.
Where this is not possible, the interface would need to provide a vivid and easy means for manually capturing data.
Customer Acquisition
Credit acquisition strategies should already be automated. Very little about them will change, they’ll just be implemented closer to the customer. Hosting them in a vending machine – or doing it via secure link to the bank’s system – is also no different, just a lot of smaller machines processing the data rather than one big one. In fact if there is anything in your processes that can’t be automated in this way you should probably revaluate the cost:benefit trade-off of them anyway.
In terms of marketing, by being located closer to the point of use also makes it easier to do short-term, co-branded campaigns.
Product Selection
Once the data has been captured and the credit and profitability scores have been calculated, a list of product features can be made available, either explicitly or as shadow limits. The obvious way to do this would be to allow a customer to add features onto a low cost, low feature basic card: higher limits, a reward programme, limited edition designs, etc. all with an associated higher fee.
But I’m not threatening anyone’s job here. Any number of strategies can be implemented in the background. The product characteristics might be customer selected, but the options provided and the pricing of those options will be based on analytics-driven credit strategies.
Even target market analysis is still important. In fact, you’ll have one more important data point: the demographic data will allow you to model risk and behaviour based on home address, but you’ll now also now where they shop, allowing you to model behaviour in more detail.
Just because credit card designs don’t obviously affect the standard profit levers, it doesn’t mean they can’t be important influencers of application volumes, but most banks offer only two or three options in each product category.
In part this is because the major card companies want to protect their visual brand identities, but mainly it is because it is hard to advertise hundreds of different card designs to your customers without confusing them.
By filling each machine with a unique selection of generic and limited edition designs, though, you could offer a selection of designs to the market that is never overwhelming but which presents more opportunities for individualism across the market. You might even be able to offer an electronic display of all possible designs to be printed on white plastics.
Look, I started out managing fraud analytics on a card portfolio and I know my old boss will be fuming at this stage; there are risk involved in storing blank plastics and especially in storing the systems for encoding chips and magstripes. However, ATMs have many of the same risks and I believe that they are sufficiently controllable to support the rest of the idea at least in its intended purpose here.
Invoicing
Connecting the card to a funding account could be done offline afterwards, but I would prefer a model that had the customer link the card to their savings account by inserting their ATM card and entering the PIN; the bank could to the debit order/ standing order administration in the background.
Payment
Finally payments, I would propose a single cost model where the actual card is paid for by debiting the funding account when the invoice is created or by cash as with any other vending machine purchase; a single cost model makes the process more transparent and helps to reposition the card as a product purchased willingly.

The systems that make the credit card vending machine could also be leveraged for other, revenue generating purposes.
It provides a channel that could revitalize card upgrades. Instead of linking card upgrades to hidden product parameters, they can become customer initiated and feature driven: learning from the internet’s status-badge mindset, banks could allow customers to insert a card into the machine, pay a small upgrade fee and have it replicated on a new, limited-edition plastic made available based on longevity and spend scores for example, even linking it to retail brands so a Burberry Card might become available only if you spend $5,000 or more in a Burberry store on a vending machine card, etc. Multiple, smaller upgrades would create a new and different revenue stream.
The machines could also act as a channel for online application fulfillment. Customers who have applied online, who have a card, or who need to replace a lost card could have those printed at the most convenient vending machine rather than having to visit a branch.

The way I have spoken about the credit card vending machine is as a new and somewhat quirky sales channel for of generic cards in a generic market place – a Visa Classic Card with a choice of limits, reward programmes and designs, for example. In other words, I have positioned it as a better way to make traditional credit cards relevant in a retail environment.
But it could also offer opportunities in other ways too, for example in the unbanked sectors in places like South Africa where branch networks are prohibitively expensive to roll-out in low-income, rural areas. There customers incur significant costs to reach a bank for even simple services. Though mobile banking is making inroads, there is still room for card based transactional banking. A credit card vending machine would be more difficult to get right in this sort of environment, but if done right it would be a cheap way to expand market share for innovative lenders.

This article is not intended to stand as business proposal, but rather to highlight the parts of the traditional lending business that I feel are most at risk from competition and irrelevance. A review of your marketing efforts and team structures with this in mind might reveal functions that are no longer needed, product parameters that are too complex or attitudes to customer service that need to be improved.

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In developed markets ‘comprehensive’ credit bureaus are common place; that is credit bureaus which store information relating to all of an individual’s past repayments, not just information relating to their defaults. Although there are some exceptions most borrowers, lenders and regulators in these markets believe that a trusted third-party holding a database of good and bad financial history provides borrowers with better products and at a fairer price.

But most markets don’t start at this point. In markets where credit bureaus are new or non-existent it is often difficult for regulators and borrowers to know how to decide between a positive bureau and a negative bureau. In many cases the costs of a positive bureau are relatively well understood – both the physical costs of development and the societal costs in the form of privacy concerns – while its benefits remain underestimated: usually assumed to be only those benefits accruing to lenders through better risk control. As a result, the initial push is often for a ‘negative data only’ bureau.

But a positive bureau also carries significant benefits to borrowers and I will use an applicable, albeit in the inverse, example to explain how and why this can is the case.

In Formula 1 motor-racing points are awarded to the ten best drivers in each race and accumulated over the season to identify an overall winner. The goal of this approach is to identify the ‘best’ driver in a given year and it serves this purpose well – sorting out the very best from the just very good. Since most stakeholders in Formula 1 – team owners, sponsors, drivers and spectators – are concerned almost exclusively with knowing which individual is the ‘best’ this system seldom comes under serious criticism.

But that doesn’t mean that it suits all purposes equally well. Imagine you are placed in charge of a new Formula 1 team that has started with a very limited budget. The team owners realize that this small budget effectively precludes the possibility of winning the title in the short-term but they also understand that if they can survive in the sport for two years they can gain a bigger sponsor and fund a more serious title challenge thereafter. So their goal it to survive for two years and to do that they need to maximize the exposure they provide their advertisers by finishing as many races as possible as far from the back as possible.

What the budget means for you as the team manager is that you can only afford to hire cheap drivers which we’ll assume means drivers who finished in the bottom ten places in the previous season. From that group you’ll still want to get the two best drivers possible but how will you identify the ‘best’ drivers in that group? The table below shows the driver standings at the end of the 2010 season:

As the table above shows, the present system is so focused on segmenting drivers at the top of the table that it struggles to differentiate drivers towards the bottom; in fact the bottom six drivers all finished with zero points. 
Vitantonio Liuzzi might look like a clear choice in more than double the next driver tally but is he really the best option and who would you choose to join him? A new model is needed for your purpose, one that separates the ‘worst’ from the simply ‘bad’.

Negative bureaus have a similarly one-sided focus, a focus that might have fits their initial purpose but that limits their use in other situations. A negative bureau only stores information on customer defaults; helping to separate the highest risk customers from the less high risk customers but struggling to segment low risk customers. Information is only created when a payment is missed – and usually only when it is missed for several months – and so an individual with a long history of timeously repaying multiple debts will be seen as the same risk as a customer who has only paid back one small debt, for example.

Returning now to the earlier scenario: the current Formula 1 model awards points for finishing in one of the top 10 places using a sliding scale of 25; 18; 15; 12; 10; 8; 6; 4; 2; 1. A model better suited for your new purpose should still retain the information relating to good performances but should also seek to create and store information relating to bad performances. The simplest way to do this would be to penalize drivers for finishing in the last ten places using the same scale but in reverse.

Implementing these simple changes across the 2010 season immediately provides more insight into the relative performance of drivers towards the bottom of the standings and in so doing the gap between each driver becomes clearer and useful information is been created. 

Although Vitantonio Liuzzi had previously looked like an obvious pick his good performances – a 6th place in Korea and a 7th place in Australia – were overshadowed by his many more poor performances – last place in Abu Dhabi, Brazil and Singapore and second-last place in Japan and China. When the whole picture is seen together, he is no longer such an attractive prospect. A better bet would be to approach Jaime Alguersuari who, although he never placed better than 9th finished in 13th place or better in 80% of his races and only came last once. Sebastien Buemi also finished last on only one occasion though he spread his remaining results more unevenly with both more top ten and more bottom ten finishes than Jaime.
Both of these drivers would offer theoretically better returns having placed worse on the accepted scale but with more of the sort of results you’re team is looking for.
Of course this isn’t the perfect model and real Formula 1 fans might take exception but it does illustrate how creating a holistic view of the relative performance of all drivers, not just the very good ones, can be value-adding. 

Similarly, a negative-only bureau suits the simple purpose of identifying the very worst of your potential customers but it struggles to identify good customers or to segment the ‘middle’ customers by relative risk; users of the bureau that wish to merely avoid the worst borrowers are well served by this information but lenders who wish to target the best customer segments for low risk/ low margin products or who wish to match pricing to risk are unable to do so. 
The societal costs of a negative only bureau are therefore born by the best performing borrowers in that market who are given the same products at the same price as average risk borrowers. 
A comprehensive positive and negative bureau avoids this societal cost though it usually does so with added build and maintenance costs. 

When deciding which bureau is best for a given market then, borrowers and regulators should focus on the trade-off between the borrowers privacy concerns and the borrowers access to fair products at a fair price while lenders should focus on the trade-off between the cost of a comprehensive bureau – passed onto them in the form of higher bureau fees – and the expected benefits to be achieved through more profitable niche products.

* * * 

The fact that the 2011 season has just finished stands testament to how long this article has sat in draft form, awaiting publishing. However, the big delay does at least afford me add an addendum on the performance of the proposed model.
Of course far too many factors are at play to make a scientific comparison, not least the fact that Vitantonio Liuzzi, the man our model told us not to pick, changed teams but here goes anyway:
Vitantonio Liuzzi didn’t qualify for one race, retired from 5 and ended the season without a top 10 finish and only 7 finishes within the top 20. In all, he didn’t manage to collect a single point and joined seven other drivers in joint last place. 
Both models suggested Sebastien Buemi and he also finished the season placed 15th with 7 top ten finishes against five retirements and no finishes outside of the top 15. While Jaime Alguersuari, our model’s wildcard pick, finished one spot better on the overall standings with 5 top ten places, 3 retirements and only one finish outside of the top 20.
Never shy to identify a trend from two data points, I’d call that a 2-1 win to the comprehensive model

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In terms of credit risk strategy, the lending markets in America and Britain undoubtedly lead the way while several other markets around the world are applying many of the same principles with accuracy and good results. However, for a number of reasons and in a number of ways, many more lending markets are much less sophisticated. In this article I will focus on these developing markets; discussing how credit risk strategies can be applied in such markets and how doing so will add value to a lender.

The fundamentals that underpin credit risk strategies are constant but as lenders develop in terms of sophistication the way in which these fundamentals are applied may vary. At the very earliest stages of development the focus will be on automating the decisioning processes; once this has been done the focus should shift to the implementation of basic scorecards and segmented strategies which will, in time, evolve from focusing on risk mitigation to profit maximisation.

Automating the Decisioning Process

The most under-developed markets tend to grant loans using a branch-based decisioning model as a legacy of the days of fully manual lending. As such, it is an aspect more typical of the older and larger banks in developing regions and one that is allowing newer and smaller competitors to enter the market and be more agile.

A typical branch-lending model looks something like the diagram below:

In a model like this, the credit policy is usually designed and signed-off by a committee of very senior managers working in the head-office. This policy is then handed-over to the branches for implementation; usually by delivering training and documentation to each of the bank’s branch managers. This immediately presents an opportunity for misinterpretation to arise as branch managers try to internalise the intentions of the policy-makers.

Once the policy has been handed-over, it becomes the branch manager’s responsibility to ensure that it is implemented as consistently as possible. However, since each branch manager is different, as is each member of branch staff, this is seldom possible and so policy implementation tends to vary to a greater or lesser extent across the branch network.

Even when the policy is well implemented though, the nature of a single written policy is such that it can identify the applicants that are considered too risky to qualify for a loan but it cannot go beyond that to segment accepted customers into risk groups. This means that the only way that senior management can ensure the policy is being implemented correctly in the highest risk situations is by using the size of the loan as an indication of risk. So, to do this a series triggers are set to escalate loan applications to management committees.

In this model, which is not an untypical one, there are three committees: one within the branch itself where senior branch staff review the work of the loan officer for small value loan applications; if the loan size exceeds the branch committee’s mandate though it must then be escalated to a regional committee or, if sufficiently large, all the way to a head-office committee.

Although it is easy to see how such a series of committees came into being, their on-going existence adds significant costs and delays to the application process.

In developing markets where skills are short there a significant premium must usually be paid to high quality management staff. So, to use the time of these managers to essentially remake the same decision over-and-over (having already decided on the policy, they now need to repeatedly decide whether an application meets the agreed upon criteria) is an inefficient way to invest a valuable resource. More importantly though are the delays that must necessarily accompany such a series of committees. As an application is passed on from one team – and more importantly from one location – to another a delay is incurred. Added to this is the fact that committees need to convene before they can make a decision and usually do so on fixed dates meaning that a loan application may have to wait a number of days until the next time the relevant committee meets.

But the costs and delays of such a model are not only incurred by the lender, the borrower too is burdened with a number of indirect costs. In order to qualify for a loan in a market where impartial third-party credit data is not widely available – i.e. where there are no strong and accurate credit bureaus – an applicant typically needs to ‘over prove’ their risk worthiness. Where address and identification data is equally unreliable this requirement is even more burdensome. In a typical example an applicant might need to first show an established relationship with the bank (6 months of salary payments, for example); provide a written undertaking from their employer that they will notify the bank of any change in employment status; the address of a reference who can be contacted when the original borrower can not; and often some degree of security, even for small value loans.

These added costs serve to discourage lending and add to what is usually the biggest problem faced by banks with a branch-based lending model: an inability to grow quickly and profitably.

Many people might think that the biggest issue faced by lenders in developing markets is the risk of bad debt but this is seldom the case. Lenders know that they don’t have access to all the information they need when they need it and so they have put in place the processes I’ve just discussed to mitigate the risk of losses. However, as I pointed out, those processes are ungainly and expensive. Too ungainly and too expensive as it turns out to facilitate growth and this is what most lenders want to change as they see more agile competitors starting to enter their markets.

A fundamental problem with growing with a branch-based lending model is that the costs of growing the system rise in line with the increase capacity. So, to serve twice as many customers will cost almost twice as much. This is the case for a few reasons. Firstly, each branch serves only a given geographical catchment area and so to serve customers in a new region, a new branch is likely to be needed. Unfortunately, it is almost impossible to add branches perfectly and each new branch is likely to lead to either an inefficient overlapping of catchment areas or ineffective gaps. Secondly, within the branch itself there is a fixed capacity both in terms of the number of staff it can accommodate and in terms of the number of customers each member of staff can serve. Both of these can be adjusted, but only slightly.

Added to this, such a model does not easily accommodate new lending channels. If, for example, the bank wished to use the internet as a channel it would need to replicate much of the infrastructure from the physical branches in the virtual branch because, although no physical buildings would be required and the coverage would be universal, the decisioning process would still require multiple loan officers and all the standard committees.

To overcome this many lenders have turned to agency agreements, most typically with large private and government employers. These employers will usually handle the administration of loan applications and loan payments for their staff and in return will either expect that their staff are offered loans at a discounted rate or that they themselves are compensated with a commission.

By simply taking the current policy rules from the branch based process and converting them into a series of automated rules in a centralised system many of these basic problems can be overcome; even before improving those rules with advanced statistical scorecards. Firstly the gap between policy design and policy implementation is removed, removing any risk of misinterpretation. Then the need for committees to ensure proper policy implementation is greatly reduced, greatly reducing the associated costs and delays. Thirdly the risk of inconsistent application is removed as every application, regardless of the branch originating it or the staff member capturing the data, is treated in the same way. Finally, since the decisioning is automated there is almost no cost to add a new channel onto the existing infrastructure meaning that new technologies like internet and mobile banking can be leveraged as profitable channels for growth.

The Introduction of Scoring

With the basic infrastructure in place it is time to start leveraging it to its full advantage by introducing scorecards and segmented strategies. One of the more subtle weaknesses of a manual decision is that it is very hard to use a policy to do anything other than decline an account. As soon as you try to make a more nuanced decision and categorise accepted accounts into risk groups the number of variables increases too fast to deal with comfortably.

It is easy enough to say that an application can be accepted only if the applicant is over 21 years of age, earns more than €10 000 a year and has been working for their current employer for at least a year but how do you segment all the qualifying applications into low, medium and high risk groups? A low risk customer might be one that is over 35 years old, earns more than €15 000 and has been working at their current employer for at least a year; or one that is over 21 years old but who earns more than €25 000 and has been working at their current employer for at least two years; or one that is over 40 years old, earns more than €15 000 and has been working at their current employer for at least a year, etc.

It is too difficult to manage such a policy using anything other than an automated system that uses a scorecard to identify and segment risk across all accounts. Being able to do this allows a bank to begin customising its strategies and its products to each customer segment/ niche. Low risk customers can be attracted with lower prices or larger limits, high spending customers can be offered a premium card with more features but also with higher fees, etc.

The first step in the process would be to implement a generic scorecard; that is a scorecard built using pooled third-party data that relates to a portfolio that is similar to the one in which it is to be implemented. These scorecards are cheap and quick to implement and, as when used to inform only simple strategies, offer almost as much value as a fully bespoke scorecard would. Over time the data needed to build a more specific scorecard can be captured so that the generic scorecard can be replaced after eighteen to twenty-four months.

But the making of a decision is not the end goal; all decisions must be monitored on an on-going basis so that strategy changes can be implemented as soon as circumstances dictate. Again this is not something that is possible to do using a manual system where each review of an account’s current performance tends to involve as much work as the original decision to lend to that customer did. Fully fledged behavioural scorecards can be complex to build for developing banks but at this stage of the credit risk evolution a series of simple triggers can be sufficient. Reviewing an account in an automated environment is virtually instantaneous and free and so strategy changes can be implemented as soon as they are needed: limits can be increased monthly to all low risk accounts that pass a certain utilisation trigger, top-up loans can be offered to all low and medium risk customers as soon as their current balances fall below a certain percentage of the original balance, etc.

In so doing, a lender can optimise the distribution of their exposure; moving exposure from high risk segments to low risk segments or vice versa to achieve their business objectives. To ensure that this distribution remains optimised the individual scores and strategies should be consistently tested using champion/ challenger experiments. Champion/ challenger is always a simple concept and can be applied to any strategy provided the systems exist to ensure that it is implemented randomly and that its results are measurable. The more sophisticated the strategies, the more sophisticated the champion/ challenger experiments will look but the underlying theory remains unchanged.

Elevating the Profile of Credit Risk

Once scorecards and risk segmented strategies have been implemented by the credit risk team, the team can focus on elevating their profile within the larger organisation. As credit risk strategies are first implemented they are unlikely to interest the senior managers of a lender who would likely have come through a different career path: perhaps they have more of a financial accounting view of risk or perhaps they have a background in something completely different like marketing. This may make it difficult for the credit risk team to garner enough support to fund key projects in the future and so may restrict their ability to improve.

To overcome this, the credit team needs to shift its focus from risk to profit. The best result a credit risk team can achieve is not to minimise losses but to maximise profits while keeping risk within an acceptable band. I have written several articles on profit models which you can read here, here, here and here but the basic principle is that once the credit risk department is comfortable with the way in which their models can predict risk they need to understand how this risk contributes to the organisation’s overall profit.

This shift will typically happen in two ways: as a change in the messages the credit team communicates to the rest of the organisation and as a change in the underlying models themselves.

To change the messages being communicated by the credit team they may need to change their recruitment strategies and bring in managers who understand both the technical aspects of credit risk and the business imperatives of a lending organisation. More importantly though, they need to always seek to translate the benefit of their work from technical credit terms – PD, LGD, etc. – into terms that can be more widely understood and appreciated by senior management – return on investment, reduced write-offs, etc. A shift in message can happen before new models are developed but will almost always lead to the development of more business-focussed models going forward.

So the final step then is to actually make changes to the models and it is by the degree to which such specialised and profit-segmented models have been developed and deployed that a lenders level of sophistication will be measured in more sophisticated markets.

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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.

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