Amsterdam, Integrated model landscape: the way forward

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1 Amsterdam, 2016 Integrated model landscape: the way forward

2 White paper: Integrated Models, the way forward 2016 Integrated Models, the way forward Integrated models that model the entire chain are the future of modelling In this White Paper, RiskQuest advocates an integrated modelling approach that increases consistencies, decreases costs and extends the application of risk models to the entire chain of the bank. Traditionally within the banking industry, models are developed for a specific purpose, such as client acceptance, pricing, fraud detection, risk monitoring, provisioning, regulatory capital calculations, etc. These types of models are developed by different departments, each concentrating on their own areas of expertise. The consequence is a collection of disparate models. While such approach may lead to quick results in some areas, this is not the most efficient approach in the longer term. We believe in an integrated approach where different centers of expertise work together on an integrated model. This way, the development costs are reduced, the bank can leverage on combined knowledge and the decision-making process can be improved. 2 Takeaways Integrated models can enhance cost efficiencies in the longer term. Integrated models may lead to better models and a competitive advantage. Risk models will no longer be used solely for risk management, but used for a wider range of applications. Regulatory compliance follows spontaneously from the integrated approach, but is no longer a goal in itself. Integrated models should be developed by mixed modelling teams, each bringing specific knowledge and areas of expertise to the table. An upfront design for an integrated model landscape is required with consistency in methods, data, IT and governance. The integrated use of (the output of) the models throughout the organization creates positive feedback loops. First, it increases the knowledge of your customer (KYC), helping to better serve customers, target the right clients in marketing, price risks in the right way and lower the capital requirements. Second, as these integrated models are used, the bank gains a better understanding of the data that is needed, improving the data collection and thus also improving the models.

3 White paper series RiskQuest How did we get here? Banks had been using models for various purposes for considerable time (e.g. valuation, client acceptance, etcetera), but modelling really took off when in 1996 the Basel framework allowed banks to calculate market risk using internal models. This trend continued when in 2004 internal models were also allowed for credit risk. As a consequence, banks started setting up modelling departments for each risk type. The models diverged as the requirements with respect to modelling standards, governance and data quality, became stricter and more model specific. In many instances, models now use similar but different definitions, data sources or methodologies. With the advent of the IFRS 9 accounting standards for provision calculation, even more internal models will need to be developed. The consequence of such a regulatory driven motivation for building internal models, is the significant increase in expenses for model development without a direct link to profits. Modelling is in danger of becoming a box-ticking exercise. Without full synergy between acceptance, pricing and capital, it will become increasingly difficult to assess the added-value of using models. The integrated model approach explained RiskQuest advocates an integrated model approach. What does this mean? In the integrated approach models are no longer developed for one specific purpose, but models are designed with the explicit goal of supporting the entire chain of the bank, from client acceptance, product approval, valuation, risk monitoring and intensive care. This does not necessarily mean that the solution lies in a single model. An integrated model may consist of several sub models or model components, but all model components are based on the same data and the same generalized methodology. This requires a modelling team that has the right skill set and has a thorough understanding of the processes and products of the bank. It requires a broader skill set that goes beyond quantitative knowledge. 3

4 White paper: Integrated Models, the way forward 2016 The way integrated models serve the different stages of the business chain can best be illustrated using an example. Let s consider a mortgage loan. First the bank has to go through an acceptance process and it can use a model for that. Such models typically use general information about the client (age, income, etcetera) and the collateral (loan to value, etcetera). The underlying data is stored in a data lake. Next, as the client is accepted more behavioral data are collected, e.g. monthly turnover on the current account, number and length of missed payments, etcetera). Using this data in addition to the data from the acceptance process, a risk monitoring model is developed that monitors the client s health and provides early warning signals if necessary. Alternately, this step can be seen as the acceptance model serving as input for the risk monitoring model. 4 Next, if the client ends up in default, data is collected regarding the circumstances of the default and its resolution. This can provide important information about the Loss Given Default (LGD). In practice, however, the modelling effort is not subsequent, but all models are developed and applied in one go as an integrated model. Using a single integrated approach, the bank now has the input for: 1. Client acceptance decisions 2. Fraud detection 3. Risk monitoring and reporting (limit setting, early warning, etcetera) 4. LGD estimates for intensive care 5. Calculation of IFRS 9 provisions 6. Calculation of capital requirements (ICAAP) 7. Stress testing. In contrast to the standalone approach, the bank will be able to trace the client through the stages in the internal process from front to back. This way banks will get to know their clients better and it enables the bank to anticipate on observed trends and developments. For instance, as the client goes into default, the bank can compare the information it had when the client was accepted, to what it knows now. This can lead to improvements in the recovery process, but also lead to improvements in the acceptance or pricing models. In short, integrated models allow the bank to better analyse trends and apply learning routines. Models are now key in improving strategic decision making for selecting growth markets and optimising the balance sheet.

5 White paper series RiskQuest There is also a cost argument to opt for this approach. While requiring significant upfront investment in a data lake and integrated application platform, the long run costs will be significantly lower through lower model maintenance costs, reuse of functions and libraries, less duplication, centralized data sourcing and cleansing, etc. Finally, there is an innovation argument. In a world were banking profits are quickly eroded by new entrants, such as FinTechs and pension funds directly underwriting mortgages, banks should focus on their competitive advantage: the knowledge and relationships banks have with their clients. Mathematical example Suppose one develops a model that describes the bank s key rate measure Y as a function of the client specific risk drivers X using the relation: Y=f(β,X)+ϵ 5 Suppose that the bank uses the functional form of Y to model the Probability of Default (PD). Under the standalone approach, β is an one dimensional set {β 1,β 2,..,β n } with estimates for the various coefficients and X are the risk drivers X = {X 1,X 2,..,X n }. One uses these specification to estimate one single model, for example the PD for mortgages. Under the integrated modelling approach Y is a column vector: Y=[Y 1 Y 2 Y m ] T. Where one defines for example Y 1 as the PD for Economic capital, Y 2 as the PD for IFRS 9, Y 3 as the PD for regulatory capital and Y 4 the probability of fraud, etc. The generalised functional does not change where β and X are now matrices:

6 White paper: Integrated Models, the way forward Challenges Of course, implementing an integrated model framework is not without challenges. Firstly, a model architect is needed to design a proper model landscape that is embraced by all departments. This model landscape should capture all the necessary models in the most general form with explicit characteristics for each purpose. Second, a comprehensive assessment of model requirements is needed. Third, the different modelling departments should be reorganized along the business chain and include people with different areas of expertise. Fourth, the data should be made available throughout the chain, preferably in a data lake. Fifth, the developed models should be flexible, such that they can be changed as a result of feedback or reused for other purposes. Sixth, ensuring a consistent model governance is an important prerequisite. In the integrated model approach, models are not specifically designed to satisfy regulatory requirements. This may pose a challenge in convincing the regulator of the approach taken. On the other hand, in the integrated model approach models are an integral part of the decision making process and as required by the use-test. Moreover, the refined feedback loop will lead to models that are more robust and more predictive. Regulatory compliance should follow spontaneously. Where necessary adjustments may be made to satisfy specific requirements, such as a floor or a segmentation. Conclusion Internal model development has been driven too much by regulatory considerations. Why narrow the application of models to risk management? We believe in the wider application of models. Banks should set-up a consistent and flexible modelling landscape that adds value throughout the chain, e.g. acceptation, valuation, pricing, monitoring, stress testing, intensive care and reporting. An integrated model landscape, where regulatory compliance is just a by-product, has many (cost) efficiency advantages and makes the bank ready for the future.

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8 White paper: Integrated Models, the way forward 2016 RiskQuest is an Amsterdam based consultancy firm specialised in risk models for the financial sector. The importance of these models in measuring risk has strongly increased, supported by external regulations such as Basel II/III and Solvency II. Advanced risk models form the basis of our service offer. These models may be employed in a frontoffice environment (acceptance, valuation & pricing) or in a mid-office context (risk management and measurement). The business areas that we cover are lending, financial markets and insurance. In relation to the models, we provide advice on: Strategic issues; Model development; Model validation; Model use. Herengracht 495, Amsterdam info@riskquest.com This report is prepared by RiskQuest for general guidance on matters of interest only, and is not intended to provide specific advice on any matter, nor is it intended to be comprehensive. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, RiskQuest does not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it. If specific advice is required, or if you wish to receive further information on any matters referred to in this paper, please speak directly with your contact at RiskQuest or those listed in this publication. Our general conditions apply to services rendered from us, to our quotations, offers, propositions and calculations RiskQuest. All rights reserved.