Emerging Trends in Model Management Introduction

Financial institutions rely more and Banks are also modeling many key As a result, the danger stemming from more upon advanced mathematical, elements of business planning and rare yet plausible occurrences might statistical and numerical models development, such as the appropriate be understated, as such occurrences to measure and manage risk bundling of product features, customer might not appear in reference data and manage their businesses.1 or on-customer income, or churn rates. sets or in historical data patterns. Models are used to calculate economic capital charges for various types of As the use of these models increases, In order to mitigate potential emerging exposure, subject to different risk so does the possibility that any , the discipline of types – such as credit, market, or particular model may not properly management (MRM) has evolved operational. Through their individual capture . Model error over the years to support emerging components the bank’s current liquidity refers to the simplification or regulatory and business agendas. position is projected under alternative approximation of reality within models, Until recently, industry MRM efforts scenarios, as well as the projection incorrect or missing assumptions, have focused upon the management of the balance sheet and income incorrect design processes, and of risk for individual models; now, statements for use in stress testing.2 errors in measurement or estimation. however, we are seeing more Model misuse is the application of institutions focus on quantifying and models outside the use for which aggregating enterprise-wide model risk. they were designed and/or intended.

2 Regulatory Guidance

As the use of complex models has become prevalent in the industry, regulators have continued to push financial institutions to invest in model risk management, with focus on establishing comprehensive frameworks for active model risk management including robust development, validation and monitoring capabilities.

In this regard, a high proportion of bank • As with other types of risk, model risk Within this framework, there are elements decisions are automated through decision can never be eradicated, only controlled of good governance that should be observed. models, which can be statistical in nature, by effective risk management, including In particular, in our view the three elements or a methodology that constitutes a rule-set.3 comprehensive model development and of model ownership (identification and There are several areas in which we see this rigorous model validation. measurement of the model risk to which the trend manifest itself. For example, an increased institution is exposed); model control (setting use of electronic trading platforms known • Institutions should establish an MRM limits, following up and independently as algorithmic trading that execute trade framework that is approved and overseen validating models); and model compliance commands which have been pre-programmed by senior management and the board (establishing processes to help perform (e.g., through timing, price or volume), and can of directors. ownership and control rules in accordance 4 be initiated with manual intervention. • A well-documented and structured with accepted policies) are essential. At this time, supervisory guidance on approach to model development and Overall, the supervisors expect institutions model risk management is limited, validation can be useful in managing to create MRM frameworks that feature and the regulations published to date model risk, but is not a substitute for the model development and validation criteria tend to be based on principles; that is, continuous process of improving models. which are formalized, promote careful they lack specificity in how to define • Prudent use of models is likely to involve model use, establish criteria to assess model and treat model risk. elements such as conservative adjustments, performance, and define policy governance stress testing or capital buffers, but an for consistent implementation of framework In the US, the Federal Reserve and the Office and applicable standards of documentation. of the Comptroller of the Currency (OCC) over-reliance on such elements may lead to the misuse of models. This holistic approach to model risk combined to publish the Supervisory Guidance management continues to be adopted by the on Model Risk Management, which lays out • There are many potential sources of model financial services industry, and we expect it 5 basic principles for model risk management: risk and therefore institutions should to become increasingly prevalent as more • Model risk is to be managed like other risks, consider the interaction of these factors institutions use such an approach. in that model risk managers should identify and try to quantify the aggregated model the sources of risk, assess the likelihood of risk that results from their combination. occurrence and the severity of any specific model failure.

3 Model Risk Governance Framework

As seen in Figure 1 below, the organizational structure for MRM should establish clear lines for development and monitoring and provide the right incentives while promoting cross-functional involvement in model risk discussions.

In our view, these are the key • Model Administrator. The model • Model Risk Committee. This is the roles involved in the model administrator is usually the supervisor senior management committee with governance process: of the model owner and may be responsible responsibility for overseeing the model for assisting the model owner with governance program. The committee • Model Owner. Ideally a person is assigned certain governance responsibilities. reports on risk issues and trends to responsibility for developing or maintaining the risk committee of the board, or a model, including documenting the model. The model administrator guides the takes other action as appropriate. The model owner will also lead the model model developers in his or her team to implementation process, along with the complete the model documentation, • Internal Audit. The internal audit function model user. The model owner is usually complying with the model validation undertakes the independent review of all responsible for ongoing model monitoring. department’s model document template. model risk and governance activities.

• Model User. The model user has • Model Validation and Review. Independent While some overlaps may occur among the responsibility for the use of a model from the model owner, a validation unit is roles of model owners, model users and for business purposes. Each model assigned the responsibility to implement model administrators, the model validation, can have multiple model users. and oversee the model validation and model risk committee and internal audit review functions and the model governance functions should remain independent. program on a daily basis.

Figure 1. Model Risk and Governance Framework

Board

Board risk committee

Treasury Model risk committee

Finance Enterprise analytics Risk Line of business Line of business Model administrator(s) Internal audit Model user Model owner(s)

Model validation and review Model Model Model Change Ongoing Methdology implementation development documentation management monitoring

Source: Accenture, August 2015

4 The Analytics Function

As seen in Figure 2 below, the preferred approach to setting up an efficient modeling function is to have a modular team structure with specialized groups and/or centers of excellence addressing different aspects of the model lifecycle.

A well-structured analytics • Documentation development of model function should follow an design and testing using standardized ordered process including: approved templates across lines of business (LoB) and risk types. • Methodology design, including model design research and development. • Implementation of a set of processes that leverages cross-functional teams • Execution of the development process under specializing in model implementation a well-defined and established process. and user acceptance testing (UAT).

• Reporting of model risk rating and inventory.

Figure 2. Proposed Analytics Function

Model risk committee

Enterprise analytics

Methodology Development Documentation Implementation Validation Responsible for model Executes the Documents models via Cross-functional (LoB, Independent validation design research, development steps standardized approved IT, analytics) team and review function. develops list of approved for the model(s) templates, works with specializing in model methodologies and steps. concerned, follows an all other functions. implementation and established process. UAT process.

Source: Accenture, August 2015

5 The Validation Function

Finally, the key part of a model risk management framework in our view is establishing a strong and independent validation function. This function should be able to address the quantitative and qualitative review of models across the five broad areas of data, methodology, documentation, processes and governance. Figure 3 highlights some of the key themes that should be considered by the validation team while going through the process.

As institutions continue to build out their with specialized skill sets to help drive An emerging trend in model validation is with validation function, they should consider the validation for all internal and vendor- regard to models for stressed capital (e.g., as the following building blocks: based models. used in applications such as Comprehensive Capital Analysis and Review (CCAR) or a) Well established policies and procedures c) Access to a wide variety of tools and the Dodd-Frank Act stress test (DFAST)), to support consistent execution across applications such as centralized model currently at an early phase of development as all validations. submission capabilities, standardized compared to other types of models, such as documentation templates, and reporting methodologies for measuring market Value- b) Access and appropriate alignment tools for better governance and control. at-Risk (VaR) or credit default prediction.6 of a strong pool of human capital

Figure 3. Model Validation

• Is the development sample • Does validation result support appropriately chosen? discriminatory power and calibration Quantitative validation • Is the data base su cient for the of the rating model, even in times of defined rating requirements? economic cycles? • Is the quality of the data su cient • Is the rating system stable over time? enough to develop a model? • Is the bank’s rating system accurate? • Is the data being transferred correctly • Is the rating approach state-of-the-art? between the systems? • Are there appropriate and relevant Methodology • How is data integrity assured? Data theories and assumptions regarding the • Is application security eective? Validation rating models in place? Has this been areas verified by appropriate validation? • Are the processes e cient enough to Processes Governance secure an eective execution of the • Are the organizational governance ratings calculation? policy frameworks for the development, • Are the rating models well documented ongoing monitoring and use of the with su cient developmental evidence? rating models robust enough? • Are the rating outputs eectively used • Is there a set mechanism for annual to run and manage the business? review of rating models? • Is there a feedback process in place to Quantitative validation • Is the rating model’s performance update the tool in case of identified being monitored, and included in inadequacies or issues? audit processes?

Source: Accenture, August 2015

6 Various techniques exist to help validate Compared to just over a decade ago, we are This could be of concern if the a stressed capital model, each capable of seeing a heightened supervisory focus on assessment of overall capital adequacy informing on the robustness of a few of the validation of stressed capital models.7 is an important application of the model. the desired model properties. Note that Based on our work in this area, stressed Improvements in these areas could include some validation techniques are effective in capital model parameter harmonization and further benchmarking and industry- the examination of only certain properties, cross-bank benchmarking are now viewed wide exercises, backtesting, profit and where by effective we mean the ability of by regulators as weak points in model loss analysis and stress testing. a test to detect departures from a desired development. On the positive side, supervisors state of affairs. For example, many validation may have the impression that banks are Additionally, institutions should recognize procedures exist that effectively assess doing well with respect to quantifying risk that when validation is partially or improperly risk sensitivity (e.g., does the stressed on a relative basis (i.e., between LoB or completed, users of those models and senior capital model reflect the state of the risk types) and producing models that exhibit management should be informed of the economy?). Yet, in measuring the accuracy sensitivity to risk factors (e.g., to the state situation. Such communication is in our of economic capital (i.e., is the estimator of the economy). Although there is view necessary so that model users and of the loss distribution quantile estimate scope for practices to improve further, senior management understand that there predictively accurate?), few techniques are the signs of progress in these areas is greater uncertainty around the models’ available to do this reliably. When used are moderately encouraging. output and that the model output should be in combination and in an environment of treated with greater conservatism. In that controls and model governance, a broad In other respects, we believe industry vein, model users and senior management set of validation techniques could offer validation practices are weak, particularly should understand and explore the more substantial evidence regarding when the total capital adequacy of the potential costs of using models that have performance of a stressed capital model. bank and the overall calibration of the not been validated (i.e., if key assumptions model is an important consideration. It is in the models prove to be inaccurate). There still remains in our view much scope recognized within the industry that this for the industry to enhance the robustness validation task is intrinsically difficult since of its validation practices. These can inform it typically requires the evaluation of high on how well stressed capital models are quantiles of loss distributions over long calibrated, in particular for cases where periods combined with data scarcity, and the assessment of overall capital is an coupled with technical difficulties such as tail important application of the model (e.g., estimation. We also feel validation practices to set a financial institution’s total risk will depend on what the model is being used “budget”), as opposed to situations where for. Nevertheless, weaknesses in validation it is not (e.g., where the model is used to practices targeted at the evaluation of overall establish the relative risks of different LoB). performance might result in banks operating with inappropriately calibrated models.

7 The Audit Function

A key development in the industry is the active involvement of the internal audit function in managing model risk. As the focus on capital and liquidity management has gained prominence, it has put incremental pressure on financial institutions to revisit their audit function’s operating models. As the third line of defense to manage risk, the audit function is increasingly expected to perform self-testing that should challenge the model’s conceptual design, data reliability, and risk management controls. In this capacity, the role of auditing and self-testing is not to duplicate model risk management activities, but rather to assess how effectively a model risk management framework meets business and regulatory requirements. Figure 4, shows a suggested framework for conducting a MRM audit review.

The incremental expectations have created Traditionally, internal audit would become entire model development and validation in our view a staffing challenge for internal involved toward the end of the process, after lifecycle as opposed to limited involvement at audit functions looking for talent with the review of the model development process the end of the process. In response to the specialized skills in model development, by independent validation. In this new model, increased expectations placed on audit validation and in-depth knowledge of existing internal audit have check-point reviews as functions, institutions should consider and emerging regulations. model developers remediate issues raised developing MRM relevant audit processes by MRM or by regulators, independently of and policies and build a dedicated team with Another emerging trend in model the model validation process. This allows the specialized quantitative skills across credit, audit is the paradigm of real-time audit. audit to be more closely involved across the market and operational risks.

Figure 4. Audit Review Framework

Gap assessment Review scoping Document review Regulatory and industry assessment and documentation

Review submitted documents Finalize relevant Finalize review scope against regulatory expectations documents, policies, Develop methodology gaps (MRA, MRIA, internal reviews) and internal policies and procedures and standards industry leading practices

Compile relevant internal Assess against regulatory Identify gaps in model Gather relevant information policies, procedures, guidance such as Basel, development process standards OCC, NPR, etc.

Identify gaps in model Review issue and Review of submitted validation process materiality assessment Collect relevant model documents against documents, validation internal submission reports, testing files Test planning standards and procedure Develop discussion (steps, timing, resources) guidelines documents and conduct reviews with MRM and LoB Assessment of MRA: Matters requiring attention Assess submitted completeness of MRIA: Matters requiring documents with industry submission Develop audit immediate attention best practices and assessment report NPR: Notice of proposed rule-making emerging market trends Source: Accenture, August 2015 8 Identification, Quantification and Mitigation of Model Risk

Defining a Model 2. Processing apparatus. This is a The definition of a model is still subject method, technique, system or algorithm to interpretation, however. Institutions One of the major conceptual issues for transforming model inputs into have to decide for themselves what is in model risk management is defining model outputs; it may be statistical, within the scope of a model and what is what constitutes a model. According to mathematical or judgmental. subject to model risk and affected by supervisory guidance, any quantitative model risk policies and supervisory guidance. method, methodology or set of rules could 3. Reporting component. This is A precise definition of this scope will not be a model; but a more rigorous definition the system for converting model usually be available, so specialist, subjective might state that a model applies statistical, outputs into a form that is useful judgment will remain an important part economic, financial, or mathematical theories, for making business decisions. of the process. Supervisory guidance techniques or assumptions to transform dictates that this includes a structured input data into quantitative estimates. Supervisory guidance also indicates that a model also encompasses quantitative set of policies and processes to determine Generally, the model has approaches in which the inputs are partially what is defined as a model that is subject three components: or completely qualitative or specialist- to full validation, as opposed to tools and based, although the output is quantitative calculators which may be cataloged but not 1. Inputs. These inputs may take the in nature. The concept of a model, therefore, subject to full validation requirements. form of data (either “hard” or based on is far broader than the concept of an opinions of subject matter specialists), algorithm; the model includes elements of hypotheses or assumptions. judgment and specialist-based knowledge.

9 Model Monitoring Framework: Risk Rating and Inventory

A risk rating systems for models is The Federal Reserve notes in its CCAR should incorporate model risk management essential, along with a comprehensive guidelines that banks with lagging practices and the complexity of the change as model inventory. The level of review were unable to identify all models in their inputs. Change management should applied to each model should be capital computation process (see Figure 5).8 have standardized processes for change commensurate with the risk posed by the recommendation (deciding when a change model. More frequent and more extensive To be effective, model monitoring should is required) as well as for execution. This review steps should be applied to the most be regular and comprehensive, covering should also include a periodic review process, complex and most high-impact models. all aspects of model performance (not just to be carried out at least annually or when discrimination performance). A “traffic light” there is a significant change to a model or Similarly, a model inventory should system for model diagnostics has been to the applicable , to assess if a full be an organized, enterprise-level repository shown to work well in this area as shown model validation should be performed. of all models within the organization, or, at in Figure 6. When a change in models is a minimum, of all Tier 1 and Tier 2 models. required, the change management process

Figure 5. Model Monitoring: Risk Rating and Inventory Level of concern Model risk rating Low Med High Model The level of review applied to each model risk should be commensurate with the level of High materiality, risk posed by the model; more frequent Tier 1 external disclosure/ Qualification Finding Finding and more extensive review steps will be regulatory impact applied to the most complex and highest impact models.

Tier 2 High materiality Comment Qualification Finding

Tier 3 Other Comment Comment Qualification

Contents of a good model inventory Model inventory Model name and description Version number (if applicable) A model inventory should be an organized enterprise level repository of all models Model owner within the organization or at a bare Business unit minimum of all Tier 1 and 2 models. The Fed notes in its CCAR guidelines that Model status banks with lagging practices were unable Risk type to identify all models in their capital Model documentation available or not and if available where is it located computation process. Model tier Status (in development, being validated, in production, etc.) Production system/platform(s) and production system/platform owner(s) Date of the most recent independent validation or the expected completion date Validation status

Source: Accenture, August 2015 10 Within the three major areas of model risk – In quantification, firms should be aware In mitigation, a data quality assurance identification, quantification and mitigation of the sensitivity of model output to the process is an essential element. Other keys – there are a number of key factors which exclusion of variables, or to the incorrect use to mitigation include a quantitative capital firms should be able to isolate and address. of data points and/or time periods. There may buffer for model risk, a conservative approach In identification, for example, these elements be errors in statistical estimation or in the to measuring inputs, estimates and outputs, can include data errors, missing variables, use of market benchmarks. Teams involved and model backtesting and stress testing. insufficient time periods and insufficient in quantification should be able to measure Mitigation efforts also require a strict model sample size. There may be unresolved decay in predictive power between re- control environment, proper governance and issues related to computational complexity estimations, and to measure the impact of not ongoing monitoring via limits, alerts and or uncertainty about the parameters of using the model at all. other techniques. a sample. The model may be used for the wrong purpose or the model itself might be incorrectly designed.

Figure 6. Model Change Management

Monitoring Escalation process Change approval Level of concern Discrimination Model Change approver Low Med High rating Scoring Model High Model risk risk materiality, Tier 1 committee (MRC) System stability external Tier 1 Q F F approves changes disclosure/ Character stability regulatory Tier 2 MRC impact Calibration Analytics head High Tier 2 C Q F Tier 3 approves changes Overall materiality with cc to MRC Tier 3 Other C C Q

Q=Qualification F=Finding C=Comment

Change implementation Change validation Change execution

Implementation of Independent validation of changes Re-development recommended changes by executed by the model validation the implementation team. team, with report to the MRC. Re-calibration Dropping/addition of variables

Source: Accenture, August 2015

11 Measurement and Aggregation of Model Risk

Measuring and aggregating model risk is a new and rapidly evolving field; multiple methodologies are under consideration but none has yet been established as a standard (see Figure 7).

Approaches include: With this approach, each inventoried model • Model risk mitigation factors include is assessed based on factors such as inherent ongoing model risk management by model 1. Model Risk Scorecard model risk, model risk mitigation and overall owners (the first line of defense) in areas The firm creates a qualitative scorecard model risk. such as model performance monitoring, ongoing model benchmarking and ongoing that considers inherent model risk factors • Inherent risk factors include model model backtesting, as well as MRM control and model risk mitigation activities to present complexity, higher uncertainty about inputs functions (the second line of defense) an aggregate view of model risk across and assumptions, broader use and larger such as model validation and other model the institution. potential impact. control activities.

Figure 7. Measurement and Aggregation of Model Risk

Approach 1 Approach 2 Model risk scorecard Advanced

A qualitative scorecard approach that A quantitative approach to model risk considers inherent model risk factors and aggregation based in part on the event and model risk mitigation activities to present scenario modeling techniques of the an aggregate view of model risk across Advanced Measurement Approach (AMA) to an institution. operational risk.

A 1 p h p c ro a a o c r h p

p Measurement 2 A and aggregation of model risk

A pproach 3

Approach 3 Model uncertainty

A bottom-up, quantitative approach to measuring model risk based on model risk sensitivity and model risk control activities such as benchmarking, backtesting, consideration of alternative modeling techniques, and corresponding impact.

Source: Accenture, August 2015

12 Overall model risk is a function of inherent This approach is empirically grounded and It can be used to capture model model risk and model risk mitigation subject to model validation, and it aggregates interdependence and also as input for activities; used to measure and monitor and quantifies model risk in a coherent estimating economic capital for model risk. model risk for individual models and in the manner. Institutions already using AMA can Its disadvantages include the subjectivity of aggregate, and for periodic reporting to leverage existing infrastructure and processes. measurement and aggregation assumptions; senior management and the board. Historical model risk event data tends to the time and resources needed to develop be sparse, so scenario-based frequency and individual, model-specific approaches; and The model risk scorecard can effectively severity estimates are often subjective and the need for ongoing performance of model aggregate model risk across the institution judgment-based. Distributional assumptions control activities such as benchmarking, and can leverage existing model inventory are also subjective and could affect backtesting, and external data comparisons and risk classifications. It provides clear outcomes. Finally, in our view this approach to perform effective maintenance. and easy-to-understand reporting tools is more complex to implement, especially for senior management, and can be rolled without an existing AMA infrastructure. out relatively quickly. However, while it aggregates model risk, it does not attempt to 3. Model Uncertainty quantify it. Scores, weights and overall results The firm uses a bottom-up, quantitative are subjective, and this approach has little approach based on model risk sensitivity ability to capture model interdependence. and model risk control activities such as 2. Advanced Operational Risk benchmarking, backtesting, consideration of alternative modeling techniques, and This is a quantitative approach to model corresponding impact. This approach uses risk aggregation based in part on the event information from multiple sources and and scenario modeling techniques of the translates model risk into metrics such as AMA to model risk. This approach views profit and loss (P&L) or capital impact. model risk as a function of the frequency Model uncertainty can provide bottom-up and severity of potential model risk events, quantification of risk for individual models, based on a combination of historical and it establishes a more rigorous and less experience and scenario analysis. subjective measurement of model risk.

13 Conclusion

Models can be tremendously helpful to financial services firms. As the use of models increases, however, so does model risk. This is an area in which the potential impact of flawed modeling can be great, and it is also an area receiving more regulatory attention and scrutiny. Firms can benefit from a structured approach to model risk that incorporates both a framework for developing and testing of models as well as effective governance of matters such as model validation, inventory and aggregation. When properly designed and implemented, models should be a valuable resource for financial institutions, but firms need well-conceived programs to improve models’ utility while identifying, quantifying and mitigating their potential risk.

14 Reference

1. A classic example in the field of credit 4. A recent example of this is an automated 6. “Validation of economic capital models: risk is the structural modeling framework, trade command that occurred on May State of the practice, supervisory which underlies any of the leading models 6, 2010 and that resulted in a $4.1 expectations and results from a bank used in the industry (e.g., JP Morgan Chase billion “flash crash” of the New York study,” Michael Jacobs Jr., Journal of Risk & Co’s CreditMetrics), the classic reference Stock Exchange (NYSE). The NYSE fell Management in Financial Institutions, being Merton, R., 1974, “On the pricing more than 1,000 points, subsequently Volume 3, Number 4, Autumn 2010. of corporate debt: The risk structure of recovering to its previous value in only interest rates,” Journal of Finance 29(2): 15 minutes time. “Findings regarding the 7. “Range of practices and issues in 449–470. Access at: http://dspace.mit. market events of May 6, 2010, Report economic capital frameworks – final edu/bitstream/handle/1721.1/1874/ of the staffs of the CFTC and SEC to the paper,” Basel Committee on Banking SWP-0684-14514372.pdf?sequence=1. Joint Advisory Committee on Emerging Supervision, March 2009. Access at: Regulatory Issues.” Securities & Exchange http://www.bis.org/publ/bcbs152.htm. 2. Refer to the following regulatory guidance; Commission and Commodity Futures 8. Refer to the following regulatory guidance; “Principles for Sound Stress Testing Trading Commission, September 30, 2010. Practices and Supervision - Consultative “Principles for Sound Stress Testing Access at: https://www.sec.gov/news/ Practices and Supervision - Consultative Paper,” The Basel Committee on Banking studies/2010/marketevents-report.pdf. Supervision, May 2009. Access at: http:// Paper,” The Basel Committee on Banking www.bis.org/publ/bcbs155.htm. Academic, 5. A classic example in the field of credit Supervision, May 2009. Access at: http:// “A coherent framework for stress testing,” risk is the structural modeling framework, www.bis.org/publ/bcbs155.htm. Academic, Jeremy Berkowitz, Journal of Risk, Winter which underlies any of the leading models “A coherent framework for stress testing,” 1999/2000. Access at: https://www.risk. used in the industry (e.g., JP Morgan Chase Jeremy Berkowitz, Journal of Risk, Winter net/data/basel/pdf/basel_jor_v2n2a1.pdf. & Co’s CreditMetrics), the classic reference 1999/2000. Access at: https://www.risk. being Merton, R., 1974, “On the pricing net/data/basel/pdf/basel_jor_v2n2a1.pdf. 3. “Automated Decision Making Comes of of corporate debt: The risk structure of Age,” Thomas H. Davenport and Jeanne interest rates,” Journal of Finance 29(2): G. Harris, MIT Sloan Management 449–470. Access at: http://dspace.mit.edu/ Review, July 2005. Access at: http:// bitstream/handle/1721.1/1874/SWP-0684- sloanreview.mit.edu/article/automated- 14514372.pdf?sequence=1. “Supervisory decision-making-comes-of-age/. Guidance on Model Risk Management,” The Board of Governors of the Federal Reserve System, Office of the Comptroller of the Currency, SR 11-7, April 4, 2011. Access at: http://www.federalreserve. gov/bankinforeg/srletters/sr1107a1.pdf.

15 About the Authors Stay Connected About Accenture

Luther Klein Accenture Finance & Risk Services Accenture is a leading global professional Luther is a Managing Director and leads www.accenture.com/us-en/financial- services company, providing a broad range of the North America Finance and Risk services-finance-risk-business-service.aspx services and solutions in strategy, consulting, Analytics organization. He brings sizable digital, technology and operations. Combining experience from industry and consulting Connect With Us unmatched experience and specialized partnering with over 30 financial services www.linkedin.com/ skills across more than 40 industries and companies to deliver projects across analytic groups?gid=3753715 all business functions—underpinned by the modeling (CCAR, credit/market/operational world’s largest delivery network—Accenture risk, Basel, RWA), governance, validation, Join Us works at the intersection of business and operating model design, investment www.facebook.com/accenturestrategy technology to help clients improve their governance, M&A and risk management. www.facebook.com/accenture performance and create sustainable value for their stakeholders. With approximately Michael Jacobs Jr, PhD, CFA Follow Us 373,000 people serving clients in more than Mike is Principal Director, Accenture www.twitter.com/accenture 120 countries, Accenture drives innovation Risk Analytics. He has over 25 years of to improve the way the world works and financial risk modeling and analytics Watch Us lives. Visit us at www.accenture.com. experience in industry and consultancy. www.youtube.com/accenture Specialized in model development and validation for CCAR, PPNR, credit/market/ operational risk; Basel and ICAAP; model risk management; financial regulation; advanced statistical and optimization methodologies. Mike has presented at high profile industry venues and has been widely published in both refereed practitioner and academic journals.

Akber Merchant Akber Merchant is a Senior Manager, Accenture Finance & Risk Services. Based in New York, Akber has solid consulting and industry experience in financial services and risk management. He has worked with global and regional financial institutions across North America, Asia and Europe. His work in enterprise risk management, regulatory compliance, management, capital management and analytics helps clients become high-performance businesses while meeting regulatory mandates.

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