Recommended Reading

• Enterprise Architecture, Enterprise Data Management, Enterprise Technology

M.I.T. Service Oriented Architecture as a strategy for business improvement in the enterprise

https://dspace.mit.edu/handle/1721.1/43122

BIAN Banking Industry Architecture Network

Case studies: https://bian.org/deliverables/case-studies/

David Luckham, Emeritus Professor, Electrical Engineering, Stanford University

Event-Based Execution Architectures for Dynamic Software Systems

https://link.springer.com/content/pdf/10.1007/978-0-387-35563-4_17.pdf

 An Event-based Architecture definition language

https://www.academia.edu/en/56678234/An_event_based_architecture_definition_language

Dr Michael Atkin -Enterprise Knowledge Graph  Maturity Model

https://vimeo.com/493504978

 Enterprise Knowledge Graph Foundation (EKGF)

Releases First Draft of Maturity Model

https://www.ekgf.org/ekgfmm-version1

 Introduction to System Dynamics Modelling

https://systemdynamics.org/introduction-to-system-dynamics-modeling/

• Risk Model, Risk Quantification

Dr John C Hull - A Neural Network Approach to Understanding Implied Volatility Movements

https://www-2.rotman.utoronto.ca/~hull/DownloadablePublications/VolSurfaces_NeuralNet.pdf

Dr Jon Danielsson - Model risk of risk models

https://www.riskresearch.org/papers/DanielssonJamesValenzuelaZer2015a/

MIT Library-Consumer credit risk measurement: challenges for the Paraguayan banking system

https://dspace.mit.edu/handle/1721.1/124582

• Climate Change

MIT Library -Preparing Cities for Climate Change

https://dspace.mit.edu/handle/1721.1/89521

https://dspace.mit.edu/handle/1721.1/89521

• Enterprise risk management: A capability-based perspective

Professor Yevgen Bogodistov, Professor Veit Wohlgemuth

https://www.researchgate.net/publication/313895551_Enterprise_risk_management_A_capability-based_perspective

• “Liquidity Risk and Correlation Risk:Implications for Risk Management”

Dr Viral V Acharya, Dr Stephen Schaefer

https://pages.stern.nyu.edu/~sternfin/vacharya/public_html/acharya_schaefer2.pdf

• Correlations among risks: Lessons from the Silicon Valley Bank collapse

Professor Giampaolo Gabbi, Risk Management Practice SDA Bocconi School of Management

https://cepr.org/voxeu/columns/correlations-among-risks-lessons-silicon-valley-bank-collapse

• Modeling Liquidity Risk, With Implications for Traditional Market Risk Measurement and Management

Bangia, A., Diebold, F.X., Schuermann, T, and Stroughair, J. (2001), "Modeling Liquidity Risk, With Implications for Traditional Market Risk Measurement and Management," in S. Figlewski and R. Levich (eds.), Risk Management: The State of the Art . Amsterdam: Kluwer Academic Publishers, 2002, 1-13. Published in abridged form as "Liquidity on the Outside," Risk, 12, 68-73, 1999.

https://www.sas.upenn.edu/~fdiebold/papers/paper25/bds.pdf

• Measuring Liquidity Mismatch in the Banking Sector

Professor Arvind Krishnamurthy, Professor Jennie Bai, Dr Charles-Henri Weymuller

This paper implements a liquidity measure, “Liquidity Mismatch Index (LMI),” to gauge the mismatch between the market liquidity of assets and the funding liquidity of liabilities. The LMI measure is informative regarding both individual bank liquidity risk as well as the liquidity risk of the entire banking system. We compare the LMI measure of liquidity to other measures such as Basel III's liquidity coverage ratio and net stable funding ratio, and show that LMI performs better in many dimensions. The outperformance of LMI partially results from the contract-specific time-varying liquidity sensitivity weights which are driven by market prices.

https://www.nber.org/system/files/working_papers/w22729/w22729.pdf

• Liquidity Mismatch Measurement

Professor Markus Brunnermeier, Professor Gary Gorton, Professor Arvind Krishnamurthy,

https://www.gsb.stanford.edu/faculty-research/publications/liquidity-mismatch-measurement

• Endogenous and Systemic Risk

Professor Jon Danielsson, Professor Hyun Song Shin, Professor Jean–Pierre Zigrand

https://www.nber.org/system/files/chapters/c12054/revisions/c12054.rev0.pdf

• Measuring Event Risk

Professor Peter M. Nyberg, Professor Anders Vilhelmsson

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1253782

Portal Note: Jump or event risk refers to the probability of a sudden and significant price movement in either direction of a financial asset. It is often an unexpected event that could adversely impact a leveraged position taken with an assumption of a lower volatility.

• The (effort) elephant in the room: What is effort, anyway?

Professor Keela Thomson, Professor Daniel Oppenheimer 

The concept of effort is critical to the understanding of many subfields of psychology, human factors, and behavioral economics. How does cognitive effort differ from other kinds of effort, like physical effort, emotional effort, or social effort? Should those be part of the Effort Elephant too?

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4019816

(Portal: Bank's Human Capital Management capability is a critical success factor)

• Copulas for Finance - A Reading Guide and Some Applications

Professor Erick Bouyé, Professor Valdo Durrleman, Professor Ashkan Nikeghbali, Professor Gael Riboulet and Professor Thierry Roncalli

Copula is a very powerful tool for enterprise risk management at it provides the ability to model the dependence between the individual risks.

https://wrap.warwick.ac.uk/id/eprint/1826/1/WRAP_Bouye_fwp01-01.pdf

• Copulas for Finance - A Reading Guide and Some Applications

Professor Erick Bouyé, Professor Valdo Durrleman, Professor Ashkan Nikeghbali, Professor Gael Riboulet and Professor Thierry Roncalli

Internal models for credit, market and operational risks  face an important problem which is the modelling of joint distributions of different risks. A copula is a function that links univariate marginals to their multivariate distribution.

• Artificial Intelligence risk measurement

Professor Paolo Giudici, Professor Mattia Centurelli, Professor Stefano Turchetta 

First, we propose an integrated AI risk management framework that can assess, in compliance with the emerging AI regulations, the risks of artificial intelligence applications, using four main statistical principles of SAFEty: Sustainability, Accuracy, Fairness, Explainability.

Second, for each principle we contribute with the proposal of a set of integrated statistical metrics: the Key Artificial Intelligence Risk Indicators, that can be used to measure AI SAFEty and implement an effective AI risk management system for any AI applications.

https://www.sciencedirect.com/science/article/pii/S0957417423017220

• Asset Liability Management and Interest Rate Risk in the Banking Book: an overview.

Dr Eric Schanning Group Head of Market and Valuation Risk | Nordea

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5007032

• The impact of the Fundamental Review of the Trading Book: evaluation on a stylized portfolio

Dr Paulo Carvalho,Professor of Finance  ISCTE,  Dr Carlos Manuel da Silva  Pinheiro  Associate Professor at the European University in Lisbon, Ms. Marta Sofia Rodrigues, Banking Prudential Supervision Department at Banco de Portugal.

We investigate the impact of the Basel Fundamental Review of the Trading Book (FRTB) on banks' market risk capital requirements under the internal models approach

The impact of the Fundamental Review of the Trading Book: evaluation on a stylized portfolio

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4919843

• Granger causality in risk and detection of extreme risk spillover between financial markets

Professors Yongmiao Hong, Yanhui Liu and Shouyang Wang 

https://www.sciencedirect.com/science/article/abs/pii/S0304407608002248

The research is useful in investigating large comovements between financial markets, such as financial contagions.

• Credit Risk Analysis using Machine and Deep learning models

This work was achieved through the Laboratory of Excellence on Financial Regulation (LabEx ReFi) under the reference ANR-10-LABX-0095. It benefited from French government support managed by the National Research Agency (ANR) within the project Investissements d’Avenir Paris Nouveaux Mondes (Investments for the Future Paris-New Worlds) under the reference ANR-11-IDEX-0006-02. 

https://www.mdpi.com/2227-9091/6/2/38

• Modelling Sovereign Debt induced Banking Crises: Theory, Applications and Policy Conundrums

Dr Jide Lewis, Deputy Supervisor, Financial Institutions Supervisory Division;

University of the West Indies

https://proceedings.systemdynamics.org/2013/proceed/papers/P1314.pdf

The paper examines the relationship between sovereign debt dynamics and the stability of financial institutions using a system dynamics framework. The model incorporates three main agents: banks, a central government and a rating agency.

• Bank distress in the news: Describing events through deep learning

Dr Samuel Rönnqvist, Turku Centre for Computer Science, Finland, Dr Peter Sarlin,CEO and co-founder of Silo AI.

https://www.sciencedirect.com/science/article/abs/pii/S0925231217311062

We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data. We demonstrate applicability to the study of financial risk based on news (6.6M articles), particularly bank distress and government interventions (243 events), where indices can signal the level of bank-stress-related reporting at the entity level, or aggregated at national or European level, while being coupled with explanations.

• Bank Capital and Liquidity Creation: Granger-Causality Evidence

Professor Roman Horvath, Charles University in Prague; Dr Jakub Seidler Czech National Bank (CNB); Professor Laurent Weill, University of Strasbourg - LaRGE Research Center.

We examine the relation between capital and liquidity creation.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2178383

• Explainable Machine Learning Models of Consumer Credit Risk

Amir E. Khandani , Adlar J. Kim Andrew W. Lo

MIT Open Access Articles-Consumer Credit-Risk Models Via Machine-Learning Algorithms

https://dspace.mit.edu/bitstream/handle/1721.1/66301/Consumer Credit Risk.pdf?sequence=1

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data for a sample of a major commercial bank’s customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults.

• Risk Appetite in Practice: Vulgaris Mathematica

Dr Bertrand K. Hassani:  By measuring its exposure against its appetite, a financial institution is assessing its coupled risk-return. But this task may be difficult as banks face various types of risks, for instance, operational, market, credit, and liquidity, and these cannot be evaluated on a standalone basis; interaction and contagion effects should be taken into account. In this paper, methodologies to evaluate banks’ exposures are presented along with their management implications, as the purpose of the risk appetite evaluation process is the transformation of risk metrics into effective management decisions.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2672757

• Artificial intelligence and knowledge management: A partnership between human and AI

Professor Mohammad Hossein Jarrahi, Professor  David Askay, Professor Ali Eshraghi Professor Preston Smith 

https://www.sciencedirect.com/science/article/pii/S0007681322000222

We propose practical ways to build the partnership between humans and AI in supporting organizational Knowledge Management activities and provide several implications for the development and management of AI systems based on the components of people, infrastructures, and processes.

• SOA, EDA, BPM, and CEP are all Complementary – Practical Examples in Open Source Software

Travis Carlson has about 3 decades of experience in the software industry. His passion is creating systems which are robust, easily maintainable, and solve the customer's real needs in the most agile and efficient manner possible.

https://www.slideshare.net/slideshow/ed-bpm10-travis-carlson/48366395

As Professor David Luckham writes in his article "SOA, EDA, BPM and CEP are all Complementary", there is great potential for synergy when these technologies work together. This paper provides examples of combining these approaches using open source software as well as real-world customer use case.

• Extensible Architectures: The Strategic Value of Service Oriented Architecture in Banking

Richard Baskerville, Georgia State University,  Marco Cavallari, Teamlab,   Kristian Hjort-Madsen, The IT University of Copenhagen, Jan Pries-Heje, The IT University of Copenhagen,  Maddalena Sorrentino, Universita degli Studi di Milano, Francesco Virili, Universita degli Studi di Milano

https://aisel.aisnet.org/ecis2005/61/