Strategic Risk Analysis
Financial risk analysis is a core competence of Chairmont. Our work across most assignments involves some level of financial risk analysis, whether its market (price), operational, credit and counterparty, alternative risk transfer market and general risk assessment.
An operations department typically attends to those administrative, logistical, and other duties necessary for an organization's day-to-day functioning. Depending on the nature of the organization, an operations department, sometimes called a back office, may be responsible for a diverse range of responsibilities. The operations department is responsible for ensuring a company operates as efficiently and economically as possible.
Catastrophe Risk Management Diagnostic
Part of our Balanced Risk Management methodology and advice to clients includes addressing seemingly unlikely "catastrophic" events that may impact an organisation's likelihood of survival.
Read more on our Catastrophe Risk Management Diagnostic
We refer to the identification and management of such events as "Catastrophe Risk Management" and relevant events range from natural disasters to changes to Government legislation.
Structural Path Equation Modelling
Chairmont has an enhanced diagnostic that overcomes the rudimentary weaknesses of traditional statistical methods. This will provide greater understanding of relationships and transmission mechanisms between the independent variables.
The widely used applications of Structural Equation Modelling (SEM) analysis typically focus on observing the simple 1 on 1 relationships between “independent” and “dependent” variables in order to understand cause and effect transmission dynamics. An example in the financial services industry might be the relationship between interest rate increases and home loan arrears; Chairmont has an enhanced diagnostic that overcomes the rudimentary weaknesses of such traditional methods. This modelling approach is referred to as Structural Path Equation Modelling (SPEM) which introduces a third variable type of a "mediating" variable that sits between dependent and independent variables.
Mediating variables may increase or decrease the impact of an independent variable on a dependent variable, as measured through multiple elasticity metrics. Under a SPEM framework the diagnostic applies behavioural measurement techniques in identifying these mediating variables through the use of psychological based statistical collection methods. In 2010 Chairmont conceptually applied its diagnostic to the Government’s 2008/2009 Stimulus Package which included cash transfers of $12.2bn.The diagnostic was aimed a better understanding the effectiveness of the cash transfers through the relationships and transmission mechanisms between the independent variables (who got what - amount by recipient type) and the dependent variables (impact on the economy - GDP, Retail Sales, Unemployment). Chairmont addressed this through the identification of the mediating variables, i.e. the behavioural patterns of the stimulus recipients, and the associated economic flow-on effects from those behaviours to the dependent variables.
Click on the links below to view how this has been applied to the Federal Government's stimulus package.
Part 1: Macroeconomics and the Household Stimulus.
Part 2: Behavioural Modelling of the Household Stimulus.
Part 3: Statistical Analysis Considerations.
Touchpoint Pattern Analysis Diagnostics
Chairmont has developed a Touch Point Pattern Analysis diagnostic within a Big Data world aimed at improving the effectiveness and efficiency of businesses and governments.
“Big Data” refers to the increasing volume and detail of information captured by enterprises and through the rise of multimedia and social media that will drive the accelerating growth in data going forward. Chairmont has developed a Touch Point Pattern Analysis diagnostic within a Big Data world aimed at improving the effectiveness and efficiency of businesses and governments. This diagnostic has been conceptually applied to the effectiveness of social security and welfare expenditure.
Governments often allude to measurement deficiencies in respect of their “value for money” principle in respect of inputs and outputs versus desired outcomes. These deficiencies are in respect of the lack of evidence of a clear relationship between expenditures and outcomes. Chairmont’s Pattern Analysis tool leverages our SPEM diagnostic (refer separate case study) as applied to the welfare system.