Using risk connectivity and contagion to add value – how analytics are revolutionising the approach to risk

2018 ◽  
Vol 58 (2) ◽  
pp. 621
Author(s):  
Caron Sugars
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Risk Management ◽  
Competitive Advantage ◽  
Network Theory ◽  
Operational Risk ◽  
Risk Profile ◽  
Risk Profiles ◽  
Strategic Risk ◽  
Asking Questions

Companies are seeking more agility in how risk is managed. Businesses need a real-time view of risks underpinning decision making and, in an increasingly connected world, they want to plan for what is on the horizon to create competitive advantage. Management and boards are also asking questions about how strategic risks focus on, compare against and map to the well-grounded operational risk profile. Applying network theory, analytics and artificial intelligence creates an opportunity for risk management to drive strategy, reduce costs and add rigour to decision making. The significant competitive advantage gained through thinking about risk differently and incorporating greater focus on emerging risks can establish the resilience needed in a rapidly changing world. This paper considers how resilience, profitability and ultimately competitiveness can be enhanced through the application of technology, analytics and network theory to understand the interconnectivity, contagion as well as aggregation of risks and mapping of operational risk profiles to strategic risk profiles.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Organizational Data Mining (ODM)

2008 ◽  
pp. 2289-2295 ◽  
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

scholarly journals Effect of Risk Management Practices On Corporate Investment of Financial Institutions in Rwanda: A Case Study of Selected Commercial Banks

2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Emmanuel Byamungu ◽  
Irechukwu Eugenia Nkechi ◽  
Henry Jefferson Ogoi
Keyword(s):  
Risk Management ◽  
Financial Institutions ◽  
Commercial Banks ◽  
Management Practices ◽  
Operational Risk ◽  
Corporate Investment ◽  
Market Risk ◽  
Operational Risk Management ◽  
Strategic Risk ◽  
Strategic Risk Management

Risk management practices are currently a subject of interest and a novel impression beneath research and application by diverse organizations. Nevertheless, there seems much to be debated on this subject in terms of a general strategic risk management practices statement. There is uncertainty like, when there should be a declaration for each principal risk category the organization experiences or should exist a general risk management practices for the organization. A risk management practice is about achieving corporate goals. For many financial institutions (FIs), dual goals exist such as the social and economic perspectives. This study sought to analyze the effect of strategic risk management practices on corporate investment of selected financial institutions in Rwanda. The study aimed at establishing the effect of operational risk management practices, market risk management practices, compliance risk management practices and governance risk management practices on corporate investment in selected commercial banks in Rwanda. The study adopted descriptive research design. The study targeted 95 managers from finance, internal audit, risk compliance and operations departments. The sample size was 77 respondents. The research was conducted using primary and secondary data, which includes survey forms (questionnaires), interviews as well as reports of the targeted institutions. Information for the research were gathered utilizing organized surveys forms that were distributed to the targeted respondents. Narrative information obtained from interviews and open-ended questions in the questionnaire were analyzed using qualitative approaches. Validity and reliability of the instruments were tested using the Cronbach Alpha test retest methods. With the aid of Statistical Package for Social Science version 21.0, both descriptive statistics such as the means, modes, standard deviation, variances and inferential statistics were analyzed. The research revealed that management of operational risk has a constructive effect financial outcomes performance of financial institutions in Rwanda. The study found that there is a correlation between both operational risk management and market risk management and performance of the financial institutions. The research findings revealed that operational risk management (r=0.096, p<0.01), market risk management (r=0.506, p<0.01) and compliance risk (r=0.612, p<0.01) on corporate investments.

scholarly journals A novel group decision making method for airport operational risk management

2020 ◽  
Vol 17 (3) ◽  
pp. 2402-2417
Author(s):  
Bo Sun ◽  
◽  
Ming Wei ◽  
Wei Wu ◽  
Binbin Jing ◽  
...  
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Group Decision Making ◽  
Group Decision ◽  
Operational Risk ◽  
Operational Risk Management

Competitive Intelligence in the Enterprise

2013 ◽  
Vol 5 (2) ◽  
pp. 50-64
Author(s):  
Relebohile Moloi ◽  
Tiko Iyamu
Keyword(s):  
Decision Making ◽  
Competitive Advantage ◽  
Network Theory ◽  
Case Study Research ◽  
Actor Network Theory ◽  
Competitive Intelligence ◽  
Study Research ◽  
Implementation Processes ◽  
Sociotechnical Theory

Due to increasing challenges, as well as competitiveness, many organisations have sought advantaging and beneficiary techniques and options. One of those options is through Competitive Intelligence (CI) products, which some organisations have come to rely upon for sustainability and competitive advantage. Unfortunately, and to some degree, fortunately, there are different CI products which organisations could choose from. The products are supposed to be selected and deployed based on organizational requirements from both technical and business perspectives. Some organisations deploy more than one competitive intelligence product. Others are not guided, and do not understand the essence of the deployment, regarding achieving the organisational objectives. The fortunate and unfortunate situations which occur in the deployment of CI products in organisations are drawn from relationships amongst stakeholders in the selection and implementation processes. The relationships are manifested from control of sources which use the power for decision making. The relationships emanate from the fact that there are no proper comparisons of the products, driven by requirements. As a result, the organisations are faced and challenged with duplication and waste of resources. They struggle to determine their competitive advantage. This situation further manifests the complexity of technical and business artefacts. Case study research was conducted to understand how CI products are deployed in the organisation. A sociotechnical theory, actor-network theory was employed in the analysis of the data, primarily to examine and understand how control of resources for power defined and shaped relationships.

Economic AI Literacy

2021 ◽  
pp. 135-152
Author(s):  
Dirk Nicolas Wagner
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Competitive Advantage ◽  
Business Models ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Information Goods ◽  
Economic Agents ◽  
Business Transformation ◽  
Economic Reasoning

This chapter introduces the concept of economic AI literacy as a source of competitive advantage in a world where artificial intelligence (AI) complements and transforms business models. The purpose of economic AI literacy is to allow for enhanced strategic decision making in firms that either offer and/or use AI. Data and information goods, economics of networks, and economic agents in artificially intelligent firms are introduced as basic elements of economic AI literacy. To illustrate application, the case of TensorFlow and related cases are presented. The discussion highlights the strategic relevance of economic reasoning in the light of the expected effects of AI on business transformation.

Risk in Strategic Management

2021 ◽  
Author(s):  
George M. Puia ◽  
Mark D. Potts
Keyword(s):  
Risk Management ◽  
Strategic Management ◽  
Dynamic Capabilities ◽  
Essential Element ◽  
Operational Risk ◽  
Two Dimensions ◽  
Multidimensional Model ◽  
Strategic Risk ◽  
Risk Environments ◽  
Risk Dimensions

Although risk is an essential element of the business landscape and one of the more widely researched topics in business, there is noticeably less scholarship on strategic risk. Business risk literature tends to only delineate characteristics of risk that are operational rather than strategic in nature. The current operational risk paradigm focuses primarily on only two dimensions of risk: the probability of its occurrence and the severity of its outcomes. In contrast, literature in the natural and social sciences exhibits greater dimensionality in the risk lexicon, including temporal risk dimensions absent from academic business discussions. Additionally, descriptions of operational risk included minimal linkage to strategic outcomes that could constrain or enable resources, markets, or competition. When working with a multidimensional model of risk, one can adjustment the process of environmental scanning and risk assessment in ways that were potentially more measurable. Given the temporal dimensions of risk, risk management cannot always function proactively. In risk environments with short risk horizons, rapid risk acceleration, or limited risk reaction time, firms must utilize dynamic capabilities. The literature proposes multiple approaches to managing risk that are often focused on single challenges or solutions. By combining a strategic management focus with a multidimensional model of strategic risk, one can match risk management protocols to specific strategic challenges. Lastly, one of more powerful dimensions of risky events is their ability to differentially affect competitors, changing the basis of competition. Risk need not solely be viewed as defending against potential losses; many risky occurrences may represent new strategic opportunities.

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