How can we quantify risks ? A deceptively simple question indeed.

The state of the art in risk management would lead one to the following approaches to

quantify risks.

1.The probabilistic approach

2.The statistical approach

3.The operations research approach

4.The aritifical intelligence approach

The probabilistic approach seeks to find the probability of each occurence and the payoff

associated with it. This leads us to a decision tree with the payoffs associated with each

outcome. The expected monetary value (EMV) is then found out for each outcome.

My experience in modelling real life problems is that it is very hard to find out the probability of each outcome. Nevertheless a probabilistic distribution can be inferred if there is

enough data. A more powerful technique is Bayesian modelling. A common scenario that

I have modelled is the following.

Given that a supplier has delivered the material 10 days late last month, what is the

probability that he will deliver on time this month ?

Bayesian statistics is a rapidly evolving branch and is the basis of forecasting engines such

as Demantra. At the heart of it though is the Bayes theorem. My current work

involves improving the accuracy and stability of forecasts by tuning the forecasting engine.

Quantification of credit risks for fixed income securities is another area where I have used

probabilistic techniques.

Statistical approaches rely on inferring based on data. Regression analysis is a common

technique. I have quantified marketing risks using JMP in the past.

MINITAB is another friendly tool for statistical analysis. It is not as heavy duty as SAS

but is powerful enough for many practical applications. Some of the scenarios where I have

used are finding the factors that lead to mutual fund redemptions . I know my cousin

used it to quantify the factors that lead to the risk of malnutrition - specifically fluorosis.

The OR based approaches typically formulate a linear/non linear model and try to solve it.

SOLVER is a powerful tool here. I have used it for the quantification of strategic supply chain

risks and what if scenarios. I have also used it to find out the amount of insurance required

for factories. The other powerful technique for risk quantification is Monte Carlo simulation.

Crystall Ball is a powerful tool here. I have used it to quantify project schedule risks,

budget risks, the probability of background processes getting delayed , compensation risks

and in post acquisition integrations.

Some of the AI approaches to risk quantification include neural networks, data mining and

fuzzy logic. Fuzzy logic , developed by Prof.Zadeh is used in consumer devices such as

washing machines. Fuzzy sets differ from regular sets in that they each member of a

fuzzy set has a degree of association with that set from 0 to 1 while regular sets either have

members (degree =1 ) or do not have them (degree = 0). I have read about fuzzy logic having been used to reduce the risk of overdoze of anesthesia administered to patients.

I tried fuzzy logic almost a decade ago but I think I should revisit that approach in the light of the business knowledge that I have gained over the years.

Neural networks are powerful but have been less prevalent in the industry.

The amount of data needed to train a neural network has been an issue .

Oracle's DARWIN is a powerful suite for data mining algorithms. There are tons of them. I have used them in the past to improve forecast accuracy where data was sparse.

Finally we should not disregard the quasi quantiative techniques such as FMEA-

failure mode and effects analysis. FMEA does not require one to be a mathematician.

On the other hand it takes into account the inputs from all stakeholders to quantify

risks. The outcome , therefore, is generally acceptable to all , although FMEA might

involve conducting several sessions (sometimes heated) with the stakeholders to ensure

convergence of opinions.

I have used FMEA in scenarios such as assessing project risks, evaluating design options,

enforcing KYC regulations in the banking sector etc. FMEA is a key component of the six sigma

methodology and is a technique developed during the second world war.