Risk Management

Risk is defined in the English Oxford Dictionary as:

“a situation involving exposure to danger; including the possibility that something unpleasant or unwelcome will happen; a person or thing regarded as a treat or likely source of danger; A possibility of harm or damage against which something is insured; a person or thing regarded as likely to turn out well or badly in a particular context or respect; and the possibility of financial loss.”

In general, Risk Management is a process of identification, assessment, and prioritisation of risk followed by the coordination of actions and deployment of resources to minimise, monitor, and control the impact of undesired events. The objective of risk management is to ensure that uncertainty does not affect, or has a minimal impact on, the achievements of goals in general.

Most industries today recognise Six Sigma as a standard means to accomplish process and quality improvements. One of the principal Six Sigma structured methodologies is DMAIC (Define, Measure, Analyse, Improve, and Control).

Six Sigma DMAIC is applied across industries for process improvements, but it is not specifically utilised for Risk Management on an ongoing basis. Generally, DMAIC and Risk Management are generic, very compatible, and complementary processes.  Therefore, this Six Sigma DMAIC structured approach tactically merges the two processes resulting in a powerful synergetic Risk Management tool.

Bernstein stated, “The risk will always be there, so we must explore many interesting tools that can help us to control risks we cannot avoid taking” (Bernstein & Damodaran 1998), which motivated the author to explore possible improvements to Risk Management. The author’s research inspired his Six Sigma DMAIC method for Risk Management.

Financial Risk Assessment & Management was established as a scientific field some 50–70 years ago. Principles and methods were developed on how to conceptualise, assess and manage risk. These principles and methods still represent to a large extent the foundation of this field today, but many advances have been made, linked to both the theoretical platform and practical models and procedures.

Complementary to conventional methodologies, the Risk Assessment and Management approaches presented herein help to optimally achieve an objective, stochastically measure the risk management process and identify the root causes of variability and risks. It stochastically achieves an optimal objective (e.g., minimal costs, maximal profit, etc.), stochastically measures and evaluates associated risk factors, determines and applies improvements to achieve the objective and mitigate risks, and institutes continuous monitoring and corrections to eliminate a negative deviation from the predefined objective and to control and mitigate risks in future.

 Monte Carlo simulation is an Operations Research methodology that iteratively evaluates a deterministic model by applying a distribution of random numbers to account for uncertainty. Simulation enables the use of probability and statistics for analysis (Bratley, Bennett & Schrage 1983, Rubinstein & Kroese 2008). The terms “simulation” and “Monte Carlo simulation” are used interchangeably.

 Optimisation is an Operations Research method to find an optimal solution for a problem based on specified constraints. Decision Trees and Neural Networks are advanced Operation Research methods for Decision Analysis and Prediction respectively.

 In synergy with conventional methods, these approaches deliver important enhancements, which improve the risk management process, facilitate objective achievement and provide additional valuable information for decision support and predictions. The approaches can be applied across industries, businesses, and practices by considering their specific aspects, thus providing significant benefits. The generic characteristic of the approaches has been initially proven as being successfully applied in practice.

Note: The content herein focuses on the possibility of financial loss, i.e., Financial Risk.

Financial Risk Assessment & Management

What Is Risk Analysis?

The term risk analysis refers to the assessment process that identifies the potential for any adverse events that may negatively affect organisations and the environment. Risk analysis is commonly performed by corporations (banks, construction groups, health care, etc.), governments, and non-profits. Conducting a risk analysis can help organisations determine whether they should undertake a project or approve a financial application, and what actions they may need to take to protect their interests. This type of analysis facilitates a balance between risks and risk reduction. Risk analysts often work with forecasting professionals to minimise future negative unforeseen effects.

Understanding Risk Analysis

Risk assessment enables corporations, governments, and investors to assess the probability that an adverse event might negatively impact a business, economy, project, or investment.  Assessing risk is essential for determining how worthwhile a specific project or investment is and the best process(es) to mitigate those risks. Risk analysis provides different approaches that can be used to assess the risk and reward trade-off of a potential investment opportunity.

A risk analyst starts by identifying what could potentially go wrong. These events must be weighed against a probability metric that measures the likelihood of the event occurring.

Finally, risk analysis attempts to estimate the extent of the impact should the event occur. Many risks that are identified, such as market risk, credit risk, currency risk, and so on, can be reduced through hedging or by purchasing insurance, for example.

Almost all large businesses require a minimum level of risk analysis. For example, commercial banks need to properly hedge foreign exchange exposure of overseas loans, while large department stores must factor in the possibility of reduced revenues due to a global recession. It is important to note that although risk analysis allows professionals to identify and mitigate risks, they cannot be avoided completely.

The Purpose

The Financial Risk Assessment & Management examples in this website are purposed to help Business and Management roles across a multitude of industries to control Financial Risk.

Decision Analysis with Decision Trees

What Is Decision Analysis (DA)?

Decision analysis (DA) is a systematic, quantitative, and graphic approach to addressing and evaluating the important decisions that businesses sometimes face. Ronald A. Howard, a professor of Management Science and Engineering at Stanford University, is credited with originating the term in 1964. The idea is used by large and small corporations alike when making various types of decisions, including management, operations, marketing, capital investments, or strategic decisions.

Understanding Decision Analysis (DA)

Decision analysis uses a variety of tools to evaluate all relevant information to aid in the decision-making process and incorporates aspects of psychology, management techniques, training, as well as economics. It is often used to assess decisions that are made in the context of multiple variables that have many possible outcomes or objectives. The process can be used by individuals or groups attempting to make a decision related to risk management, capital investments, and strategic business decisions.

A graphical representation of alternatives and possible solutions, as well as challenges and uncertainties, can be created on a decision tree or influence diagram. More sophisticated computer models have also been developed to aid in the decision-analysis process.

The goal behind such tools is to provide decision-makers with alternatives when attempting to achieve objectives for the business, while also outlining uncertainties involved and providing measures of how well such objectives will be achieved. Uncertainties are typically expressed as probabilities, while frictions between conflicting objectives are viewed in terms of trade-offs and utility functions, i.e., objectives are viewed in terms of how much they are worth or, if achieved, their expected value to the organisation.

Despite the helpful nature of decision analysis, critics suggest that a major drawback to the approach is “analysis paralysis,” which is the overthinking of a situation to the point that no decision can be made. In addition, some researchers who study the methodologies used by decision-makers argue that this type of analysis is not often utilised.

The Purpose

The Decision Analysis examples in this website are purposed to help Business and Management roles across a multitude of industries to inform their decision-making.

Prediction with Neural Networks

What Is a Neural Network?

A neural network is a series of algorithms that endeavours to recognise underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

Neural networks can adapt to changing input; so, the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.

Understanding Neural Networks

Neural networks, in the world of finance, assist in the development of such processes as time-series forecasting, algorithmic trading, securities classification, credit risk modelling, and constructing proprietary indicators and price derivatives.

A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.

A neural network contains layers of interconnected nodes. Each node is known as a perceptron and is similar to multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.

The Purpose

The Prediction with Neural Network examples in this website is purposed to help Business and Management roles across a multitude of industries to predict the potential outcome of their initiatives and projects.