Credit Risk Restructuring: a Six Sigma Approach in Banking

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1 Credit Risk Restructuring: a Six Sigma Approach in Banking In this article we show how the credit approval process for corporate customers of a large bank can be streamlined. The result of this optimization is an improved throughput time, so that the front office and the customer receive faster approval for the requested loan. The optimization is restricted by the requirement of the accurate execution of the risk function. In this respect the loan loss ratio (write-offs divided by total exposure) may not be affected by the streamlining efforts. After an explanation of the credit approval process, we show how the optimization has been carried out using simulation. Subsequently we show that the improvement of the throughput time is dramatic. Marco Folpmers holds a PhD in economics from the Free University Amsterdam and is GARP certified financial risk manager and ASQ certified Six Sigma Black Belt. He leads the financial risk management consulting segment within Capgemini Consulting NL. Jeroen Lemmens works for the Operational Excellence Group within Capgemini Consulting NL. He is ASQ certified Six Sigma Black Belt and he specializes in restructuring, LEAN and Six Sigma Introduction Consolidation in banking sweeps over Europe as Italian banks Unicredit and Capitalia merge (in 2007), Fortis NL and ABN Amro bank have been taken over by the Dutch government and German bank Commerzbank merges with Dresdner bank (both events in 2008). This is only the continuation of a trend that has started since the start of the century and its occurrence is not only witnessed by mega-deals such as the ones mentioned above, it is also apparent in EU banking statistics: the number of banks within the EU declines, whereas total assets of the EU banking sector increases, signaling the emergence of larger institutions. 1 The continuous shift in organizational boundary necessitates the rethinking of existing designs for both primary and support processes. In this article we will focus on the restructuring of the credit approval process. We will show how a generic credit approval process works and how it can be restructured and optimized. The credit approval process has not been the object of much recent research. A good description of best practices has been published by the national bank of Austria in 2004 (see reference list). However, this description focuses on internal control and risk management, whereas our focus is the optimization of the process in terms of efficiency (total throughput time, resource utilization) within the constraint of an adequate risk management performance. Overview of the credit approval process The objective of the credit approval process is to prevent two types of errors: substantive errors and procedural errors. A substantive error is the erroneous assessment of the credit exposure despite comprehensive and accurate presentation of the risk analysis. A procedural error is the incomplete and/or inaccurate presentation of the credit exposure, or the incorrect performance of the credit approval process. The latter case refers to fraud or intentional misconduct by the persons in charge of conducting the credit approval process. In short, the credit approval process is aimed at mitigating the risk of (1) a wrong presentation of the credit exposure, (2) an erroneous assessment of the credit exposure and (3) fraud. The effectiveness of the credit approval process is measured by the (net) loan loss ratio. The ratio captures the historical write-offs (loss after restructuring / liquidation of the collateral) against the total exposure. Often the ratio is converted to a sensitivity: an increase of one basis point of the loan loss ratio equals an increase in loan losses of 5 million, for example. The efficiency of the credit approval process is primarily measured by the total throughput time of the analysis and decision-making processes and the number of resources needed. 1 European central bank, EU Banking Structures, October 2007, 2007, p AENORM 63 April 2009

2 ORM Figure 1: breakdown of the credit approval process Efficiency optimization efforts should always be restricted by pre-set values of the net loan loss ratio: the business case for the reduction of a number of analysts that increases the loan loss ratio will often be doomed to fail since a small increase of the loan loss ratio has a considerable bottom-line impact. In order to increase efficiency, the credit approval process is implemented by standard or individual processes for risk analysis and decision-making. Standard processes allow for the automated processing of small-exposure, standardized loans, whereas individual processes assess each loan application separately. Often only retail loans 2 are suitable for standard processes, although a case can be made to include small corporate loans as well. Standard processes use pre-determined limits for the exposure of each customer. If a new loan application is within this limit, approval is granted with the help of mainly automatic procedures. On the other hand, individual processes are characterized by an adaptive design which makes it possible to deal with a variety of products, collateral, and conditions. 3 In the remainder of this article we will focus on individual credit approval processes for corporate loans. The credit approval process consists of four main processes that are illustrated in Figure 1. A transfer of the loan application and supporting documents takes place from the front office to the risk department. The risk analysis consists primarily in the review of the obligor s creditworthiness, the valuation of the collateral and the assessment of the exposure. The decision to grant the loan can be organized by either a credit committee or by a simple sign off. In both cases the principle of double voting needs to take place if the credit risk is considerable. Double voting means that two departments are needed for the approval of the credit: both the front office and the risk department. 4 The decision-making authority is obviously a (senior) management responsibility. The generic process illustrated in Figure 1 can be fine-tuned dependent on the customer s total exposure. An example is presented in Table 1. The authority structure illustrates that small exposures can be decided at front office level only (single vote). Medium size exposures need a risk analysis and two votes (whether implemented with the help of a credit committee or a sign off). For large exposures the risk department issues an advice after which it is decided at corporate level with two votes. The table illustrates as well the principle of bypassing of hierarchical layers, since the medium and large exposures are not decided at multiple levels. This is the recommended practice since multiple, subsequent decision makers have a tendency to rely on each other ( socialization of responsibility ). 5 In the example that is presented in the next section, the principles introduced above will be illustrated. An illustration: restructuring local credit risk centers for corporate clients Our example of local business centers for corporate clients is presented in Figure 2. The local business centers for corporate clients have their own risk analysts and local committees. In case of large exposures, the loan application is forwarded to the central risk department for further analysis and decision-making. The analysis can be placed on hold if the file is not complete. The analysis can only be completed once all relevant data has been delivered 2 For Basel II the retail asset class includes SME loans if the total exposure of the counterparty is below 1 million, see BCBS, International Convergence of Capital Measurement and Capital Standards, June 2006, art. 70. Apart from standardized credit approval processes, the risk analysis of the retail portfolio can also be made more efficient since Basel II allows the use of pooled risk parameters (PD, LGD), see BCBS, art Oesterreichische Nationalbank, Guidelines on credit risk management: credit approval process and credit risk management, 2004, p Doube voting should not be confused with the four eyes principle. Both are internal control measures, but double voting refers to the joint authority of two persons of unlinked departments, whereas the four eyes principle refers to the joint authority of two persons within the same department. 5 See Oesterreichische Nationalbank, o.c., p. 31. AENORM 63 April

3 ORM Table 1: the authority structure for three exposure volumes by the front office (modeled as a delay / file on hold period ). A distinction is made between full analysis and short analysis. Short analysis is applicable for standardized products if the new loan amount is within a pre-defined limit. The principle of bypassing is not implemented since the loan application for large exposure customers is decided both locally and centrally. In our case, during the Measure phase process data has been gathered with the help of Work Sampling and File Tracking: Work Sampling refers to the measurement of time spent by the resources in the credit approval process (the analysts and risk managers). Work Sampling has been implemented with the help of forms on which the analyst records on a daily basis his or her activities for blocks of 10-minutes. The activities are categorized across analysis tasks (such as: collect data needed for a risk analysis), decision related tasks (such as: prepare credit committee) and other tasks (such as: lunch). The Work Sampling data allows the analysis of net resource availability for analysis and decision-related tasks and the net process durations (excluding waiting times) of the processes depicted in Figure 2; File Tracking follows the file through the processes. At each stage, the date/time stamp is added to the form. The File Tracking data allows the analysis of current throughput times, including waiting times and delays. The measurement phase comprises six weeks at three local business centers for corporate clients. All forms have been entered into a database. With the help of statistics from this database, the statistical parameters as shown in Figure 2 have been calculated. A few remarks apply: The arrival process has been modeled as the usual exponential distribution for the interarrival time. 6 The mean interarrival time is 1.5 hours. The model uses a 9 hour working day, so we expect six new loan applications to arrive at the local risk department per day. Since the use of an exponentially distributed interarrival time implies a Poisson process, the number of arrivals per day is Poisson distributed (with parameter lambda equal to six). There is no restriction on the maximum number of arrivals per day (infinite calling population); For the local approval process, a full analysis is needed in 66% of the cases. A short analysis is allowed for standardized products and low transaction volumes; If the file is not complete (in 33% of the cases), the file is placed on hold, waiting for additional information from the front office. This is modeled as a delay with the help of a continuous uniform distribution with minimum equal to 2 hours and maximum equal to 27 hours; The analysis processes (local full and short risk analysis; large exposure full risk analysis by the central risk department) have been modeled as triangular distributions. The triangular distribution is a good option for the distribution used for transaction processes in simulation models; 7 The local analysis processes are carried out by 2.33 risk analysts on average per day. If all analysts are busy and new files arrive for analysis, a queue starts to develop. The files waiting for analysis are served by the available analysts on a first-come first-serve basis. Again, this is the usual queue discipline assumed in simulation models; The central analysis for this particular business center for corporate clients is carried out 6 See Hillier, F.S., Lieberman, G.J., Introduction to operations research, 1995, 663, Law, A.M., W.D. Kelton, Simulation modeling and analysis, 2000, p In Gross, D., C.M. Harris, Fundamentals of queueing theory, 1998, p. 377, the distinction is made between a diagnostic process (we must find the trouble in order to fix it) and a repetitive service process (the longer a file is in process, the greater the probability of completion in a given interval of time). The diagnostic process is memoryless and a Poisson process could be applied, whereas for the repetitive service process, the Poisson process is not an option. For the use of the triangular distribution, see Gross, o.c., p AENORM 63 April 2009

4 Figure 2: credit approval process current state by 1.22 central risk analysts on average per day; The local and central decision processes have been modeled as local and central committees that convene twice a week. With the help of the parameters mentioned above we can calculate that the average duration excluding waiting time for a small exposure transaction equals 19 hours, i.e. 2.1 working days (remember that a working days contains 9 hours). The average duration for a large exposure transaction equals 4.5 working days. The average duration for both small and large transactions equals 2.7 working days. However, the total throughput time includes waiting time and the only way to calculate the duration of the waiting time is to use simulation. With the help of simulation software (ARENA ), we have simulated the process for 2 replications of 200 consecutive days each. We allow the process to reach a steady-state after 20 days, so the statistics are based on two runs of 180 consecutive days each. 8 The average total throughput time for both small and large exposures including waiting time equals 2.9 working days. Resource utilization is approximately 75% for both the local risk staff and the central risk staff. Now the restructuring process starts. One of the first questions of the Six Sigma change managers will be: what requirements does the (internal) customer have? The front office, when asked this question, responds that it needs approval within a week (5 working days, i.e. 45 working hours). Current process performance is assessed using this benchmark as Upper Specification Limit. In Figure 3 the histogram of the total throughput time is shown (generated by Minitab ). From the figure we conclude that the credit approval process is not compliant with the five working days in 13.1% of the working days (approximately 131,000 defective PPM, parts per million). This level of process failures ( defects in Six Sigma) is far too high. So far we can conclude that, although the average throughput time seems to be fine, the process variance is very large so that a considerable number of files (13%) is not approved within the required five working days. The following restructuring measures are implemented: By improving the interface between the front and the risk departments, the percentage first time right (complete and accurate files) is improved from 67% to 95%. That means that after restructuring only 5% of the files needs to be placed on hold ; 9 The credit presentations in the committee 8 See Hillier & Lieberman, o.c., 925 for the warm-up period, an initial stabilization period for approaching a steady-state condition. 9 See Oesterreichische Nationalbank, o.c., p. 10, for the interface between sales and risk. Improvement measures includes: aligning the product segmentation used by front and risk and the use of internal templates of data to be delivered by front to the risk department for each type of analysis. AENORM 63 April

5 Figure 3: process capability current state meetings have been shortened so that the committees are able to convene every other day instead of twice a week; for large exposures, the first committee delivers an advice and not a first decision; The analysis durations have not been shortened, since the fear is that this would impact the loan loss ratio; In order to reduce waiting time variance, the risk departments of four local business centers for corporate clients have been merged. The improved level of load balancing reduces both the average waiting time and its variance without adding one single resource. The average total throughput time for both small and large exposures including waiting time now equals 1.8 working days (before restructuring: 2.9), a reduction of 35%. A larger improvement is seen in the variance reduction. The defects percentage has been decreased to 1.3%, a ten -fold reduction (see Figure 4, less than 13,000 defective PPM, parts per million). The restructuring project succeeded in drastically improving the process capability with the help of improving the first time right percentage and the load balancing. The centralization of four small local risk department into a larger one that covers a larger geographical area means that the proximity of risk analysts to the account manager is removed. The adequate digital transfer of relevant customer and transaction data is an important condition for centralization. However, often both the financial statements of corporate clients and their transaction parameters are already entered into banking systems and supporting documents such as asset valuation reports can easily be scanned and saved in an electronic repository. Although the physical separation of account manager and risk analyst will necessitate some adaptation, the centralization of risk analysts into a larger pool has some organizational benefits of its own: temporary dips in availability due to sickness and job vacancies are more easily accommodated and knowledge transfer from senior analysts to junior analysts is facilitated. A larger pool also encourages specialization (e.g. real estate loans or loans to the public sector) and offers more career opportunities. Up to now we have only described the Define, Measure and Analyze phases of a Six Sigma project. The new process design is implemented in the Improve phase and monitored in the Control phase. On behalf of this last phase a system of continuous performance measurement and management needs to be developed in order to maintain process capability at a high level (and the loan loss ratio at a low level). Conclusion: throughput time improvement by variance reduction In this article we have shown how the credit approval process can be improved with the help of Six Sigma principles. Although we are aware that other objectives may apply, our current scope has been limited to optimizing the throughput time. 36 AENORM 63 April 2009

6 Figure 4: process capability after restructuring We have shown that the centralization of the risk analysis staff from small departments of 2 or 3 analysts each to a larger one with 9 or 10 analysts reduces the variation in total throughput time dramatically due to a more effective load balancing. Small local risk departments servicing local business centers for corporate clients are often implemented as a result of the perceived necessity to have local couples of account manager and risk analyst. However, if there are no special reasons for their proximity and the credit approval process is assessed according to the number of files with a duration within a specific Upper Specification Limit, the case for the centralization of the risk analysis staff is very convincing. (2004). Guidelines on credit risk management: credit approval process and credit risk management, Oesterreichische Nationalbank. Pyzdek, T. (2003). The Six Sigma Handbook, revised and expanded: the complete guide for greenbelts, blackbelts and managers at all levels. References (2006). International Convergence of Capital Measurement and Capital Standards, BSBS, June. (2007). EU Banking Structures, European central bank, October. Gross, D. and Harris, C.M. (1998). Fundamentals of queueing theory. Hillier, F.S. and Lieberman, G.J. (1995). Introduction to operations research. Law, A.M. and Kelton, W.D. (2000). Simulation modeling and analysis. AENORM 63 April