The migration from static risk assessment to driver-based management
Customer credit risk management is the rather static operational processing of credit limit decisions – at least this is how corporates have traditionally handled it. However, with the technical options available today for flexible KPI reporting in the area of credit risk it is possible to derive targeted procedures for balanced and dynamic risk management. Clean and comprehensive data as well as the right analysis instruments are of critical importance.
A credit manager's daily routine involves the assessment of existing customers exposed to risk or assessing the risk of new customers. This is often done on the basis of individual assessments by taking into account external ratings, payment behaviour and outstanding items. Real risk management occurs only when there is a trigger event, such as a message from the system that a customer is at risk of exceeding a pre-set credit limit.
Currently available standard software adequately reflects such required information, for example in the form of scorecards, which graphically present the trend in payment behaviour, industry and external ratings that can be used as a basis for fast and intuitive customer risk assessments.
This form of reporting provides answers to operational issues, such as the ratio of granted credit lines to major customer exposure. Such analyses are frequently static and only of immediate benefit at granular level in daily credit management operations. Reports on ad hoc queries by management frequently require extensive input from IT specialist staff and therefore present real challenges in the daily routine of credit managers.
The question therefore is: What is actually feasible? What information is hidden away in the aggregate master data, receivables portfolio and receivables accounting? And how can it be used?
For example, instead of using customer scorecards, global exposure could be examined by aggregating and visualising the data in a skilful way. By using data analytics methods, detailed management reports at various levels of aggregation are no longer only a remote prospect.
What could this look like in practice?
To optimise processes when creating reports, various (monitoring) bots (software robots) can be used in the background, which generate automated standard reports and also send an alert if required (e.g. deterioration in payment behaviour, sector development or customer rating) to responsible parties and trigger a defined risk management process. Specific recommendations for mitigating risks can be sent directly to the credit manager, who can use this detailed and targeted information for making well-founded decisions.
The hot topic of data analytics pervades almost all corporate segments. New technologies in the area of data processing and analysis open up new opportunities and possibilities in corporate management. But what do such opportunities look like in the area of customer credit risk management? We have determined three fundamental issues that can be answered by applying analytical methods.
How high is the group-wide exposure at risk? Is the current hedging strategy the correct one? Is the risk appetite appropriate in the current market conditions? Or could this potentially lead to opportunity costs due to a miscalculation of risks that result in 'missed' contracts and thus revenue? Such questions are often answered based on a gut instinct these days. By determining the exposure at risk at group level in combination with industry and country developments, a sound basis for decisions can be created in this regard.
How accurate are my impairment losses on receivables recognised in the accounts? Are retained earnings reduced by excessive impairment losses on outstanding receivables? Especially with the introduction of IFRS 9, impairment rates are currently a frequently discussed accounting issue. Here also, depreciation rates are often estimated based on many years of experience, but can only be empirically verified with difficulty. The main reason are amounts receivable that have already been depreciated, so that the figures in the receivables accounts deviate significantly in some cases from the figures still obtained after depreciation (i.e. non-recurring income). The resulting lack of transparency logically makes control more difficult.
How do (group-wide) receivables change over time? Is there are trend towards earlier (or later) payment? What are the reasons? The major increase in customers with good credit ratings? Or is the payment behaviour of existing customers improving? Questions like these are frequently asked when determining a global strategy for receivables management, which is used as a basis for making decisions on issues such as securitisation, standards for payment terms or the handling of agreed payment terms.
These three examples illustrate that the range and complexity of such issues has generally increased. But great flexibility when using data analytics technologies also facilitates implementing and managing the instruments required for answering exactly these questions and thereby creates a new basis for taking decisions at management level.
There is undoubtedly an increasing need for transparency and information on the part of management, also with respect to details, and that they ask questions which can only be answered now that the right tools are available. Therefore, the catchword is driver-based management when dealing with credit management decisions. This does not mean taking a global standard measure (such as "reducing all terms of payment by 10%"), but deriving from reporting the most effective and efficient measures possible (such as "reducing the terms of payment by 10% for customers who tend to settle outstanding items before the due date").
Differentiated actions are only possible based on precise, complete and accurate information for making decisions. This is the only way for targeted marketing. Deriving effective, efficient measures tailored to achieving targets then becomes the rule, rather than engaging in global 'one-fits-all' activities.
Apart from greater agility in making credit management decisions, the new state of information based on analytics also allows verifying and ultimately optimising the group-wide hedging strategy for outstanding items. Through insights into local exposures and securities, savings in asset-backed receivables (for example by optimising the cost of factoring etc.) are made possible and synergistic effects can be highlighted across national borders.
Multivariate transparency in reporting and closer, automated (bot-supported) monitoring provide a better basis for information and making decisions, which allows taking targeted actions more quickly in crisis situations and proactively preventing credit losses. This not only permanently reduces losses through non-recoverable receivables, but also potentially increases revenue, because (new) customer exposures can be better estimated when signing agreements.
Source: KPMG Corporate Treasury News, Ausgabe 84, September 2018
Authors: Stephan Plein, Senior Manager, Finance Advisory, email@example.com; Anna-Lena Remmel, Assistant Manager, Finance Advisory, firstname.lastname@example.org
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