Radiodifusión en México

OPERATIONAL RISK ANALYSIS

Operational risk can be analysed by employing methods that can use a combination of qualitative and quantitative data. Qualitative operational risk analysis involves estimating operational risk in words. Quantitative operational risk analysis involves the numerical estimation of operational risk.

2.3.3.1 CAUSAL MODELLING

During causal modelling, scenarios and simulations are used to predict the potential behaviour of processes and to estimate potential losses [7]. Causal modelling involves the development of graphical representations of events, their causes and a simulation that derives their cumulative probability distributions. Historical loss data and scenarios can be used to create these distributions. Causal modelling is beneficial in operational risk management for the following reasons:

• the determination of the causes of operational loss event provides management with clues regarding how best to develop effective strategies that can address the identified causes;

• the operational risk capital can be determined by calculating the forecasted operational losses; and

• considering that causal models are represented graphically, the determination of operational losses and probabilities are less abstract than other only mathematical risk analysis methods.

Causal modelling can be performed by using linear and non-linear methods. Linear methods (e.g. multifactor modelling and multivariate factor analysis) require more data than non-linear methods (e.g. neural networks and Bayesian networks). Thus, the latter methods are more favourable than the former.

2.3.3.1.1 NEURAL NETWORKS

Neural networks are simple computational tools for examining data and developing models that help to identify interesting patterns or structures in the data [24]. They consist of interconnected elements that are referred to as neurons. Neural networks operate similarly to the human and animal brains, as they are able to respond to external input by learning and to encode the information using the strengths of the connections between the neurons. Neural networks are distinguished according to their topology and algorithm. A neural network can be made to perform a particular task by the adaptation of its topology. The main topology types are the feed-forward, limited recurrent and fully recurrent topologies. The main types of neural network algorithms are the supervised, unsupervised and the re-enforced learning algorithms. The forecasting of data can be achieved using the following steps:

1. preparing and training the network using historical data; and 2. forecasting the data using the trained network.

Data preparation entails activities such as data cleansing, selection, pre-processing, scaling, normalisation and symbolism to numeric translation. Neural network training entails data analysis and the adaptation of the inter neuron connection weights in a manner in which the dependencies in the data are reflected. The trained network is used to forecast the data that is required to be forecasted.

Neural networks have the following advantages: • they can detect hidden patterns from data;

• they can develop a representation of the data; and

• they require minimal apriori assumptions about the data patterns thereby allowing the historical data to become the main factors that determine the forecasted values.

OP ERATI ONAL RIS K ANA LYS IS FOR THE M ANAGE MENT OF R AI LWAY INFR AS TRUC TURE MAINTE NAN CE

P HUMZILE DHLAM INI MAY 2010 22

• the amount of data is proportional to the accuracy of the forecast, thus a lot of data is required for the optimum accuracy of a neural network’s forecast; and

• neural networks are treated like “black boxes” thus the user is oblivious to how the forecasts are made.

2.3.3.1.2 BAYESIAN NETWORKS

Bayesian network causal models consist of random variables that are, based on its dependence, linked together. A variable “A” is dependent on another variable “B” if a change in the state of “B” causes a change in the probability distribution for the states in “A” [15]. Bayesian networks are the most commonly used compared to other non-linear methods for the following reasons:

• the arrows in Bayesian networks represent the link between events and their causes whereas in fuzzy logic and neural networks, arrows represent the flow of information during reasoning;

• Bayesian networks allow more modelling flexibility than system dynamic simulation models;

• objective and subjective data can be combined to obtain an estimated operation loss; and

• operational losses that have little or no operational losses such as those with low frequency and high severity are effectively modelled with causal modelling. The main disadvantage of using causal modelling is that it often requires a lot of apriori assumptions about the conditional probabilities.

2.4 CONCLUSION

Operational risk can be identified using one or more structures, techniques and information sources. The proposed operational risk identification methodology is structured as a discussion using the organisational chart technique with historical data as the main source of information.

Operational risk analysis allows managers to predict possible future operational losses in order to evaluate a risk management strategy that can minimize these losses. An

organisation’s operational risk analysis can be done with either a top-down or a bottom- up approach. The proposed operational risk analysis methodology for the management of railway infrastructure corrective maintenance adopts a bottom-up approach. This approach was selected as it assists managers to gain sufficient knowledge to effectively manage the operational risk of their departments.

There are three main types of operational risk analysis, namely qualitative, quantitative and a combination of both. Qualitative operational risk analysis often involves estimating operational risk that difficult or impossible to calculate numerically. Qualitative operational risk analysis results are often expressed using risk maps. Examples of qualitative operational risk analysis methods are risk self-assessment, risk process flow analysis and scenario formulation and analysis. Quantitative operational risk analysis involves the numerical estimation of operational risk. Examples of quantitative risk analysis methods are the actuarial approach and stress testing. Combinations of qualitative and quantitative risk analysis allow the advantages of both methods to be used. Examples of combinations of qualitative and quantitative operational risk analysis

are causal models using neural networks or Bayesian networks. The credibility of Bayesian network causal models that use historical data is higher than

that of qualitative operational risk assessment because this form of assessment is highly subjective. Assuming that past events are good predictors of the future, Bayesian network causal models that use historical data are provide more accurate predictions than parametric loss distribution actuarial models. The use of Bayesian network causal models that use historical data is more effective than actuarial models for the management of operational risk as the causes of losses are specified in causal modelling. The frequency and severity of operational losses due to worst case scenarios can be predicted by stress testing using Bayesian network models that use historical data. Thus the proposed operational risk assessment methodology for the management of railway infrastructure corrective maintenance uses Bayesian network causal models.

OP ERATI ONAL RIS K ANA LYS IS FOR THE M ANAGE MENT OF R AI LWAY INFR AS TRUC TURE MAINTE NAN CE

P HUMZILE DHLAM INI MAY 2010 24

CHAPTER 3 : THE PROPOSED