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We empower our clients to leverage the results of the qualitative risk assessment by utilising the quantitative approach to measuring and monitoring their risk appetite and tolerance.

These we achieve by introducing the Monte Carlo Simulation, Optimization and Sensitivity Analysis techniques. Most organisations are required by regulatory frameworks to institute risk management activities for compliance and auditability purpose while for others managing risk becomes second nature to their business, such as financial services. An argument remains that business decisions are made under uncertainty given the volatility of markets within which they operate. Decisions under uncertainty cannot be made without evaluating alternatives and chances of failures or success therefore use of probabilistic models facilitate such processes. We justify a need for risk analytics in the following areas of our focus:

Enterprise Risk Management
Traditional frameworks provide qualitative ERM approaches, including conceptual tools that facilitate enterprise wide risk identification and assessment.
The key risk indicators are used for monitoring emergence of potential risk events. In most cases the qualitative approach applies the rule of thumb and guesses, leaving the gap for more precise measurement which could be filled by the quantitative analytics.
IT Risk and Governance
IT Risk scorecard should record events with occurrence frequency and loss severity ideally valued in terms of probability and value of impact on assets or infrastructure.
This practise should equip CIO’s with evidence to justify the investment in infrastructure required to minimise such risk events.
Managing complex IT asset portfolios, project management and solution implementation should be subject to adequate valuation methods using a risk-adjusted cost of capital. Cost of infrastructure, hardware and software, capacity and threshold breaches are some of the relevant costs requiring the use of forecasting and appropriate market volatility estimates.
Financial Modelling and Decision Support
Financial modelling covers many aspects of business risks such as market, credit, operational and investment.
It is also used by non-financial services and public sector for a variety of applications ranging from budget preparation, cost optimisation, capital investment decisions, foresting commodity prices and many others.
From financial services to mining, engineering, healthcare and government a variety of statistical, econometric and operational research techniques are applied to solve problems and support the decision making process.
Few examples are the hedging of forex, estimating volatilities using GARCH, SIX Sigma for Quality Control, Optimization of resources and several others. The financial models are designed to quantify these many cost and revenue factors including the estimation of the net present values and other performance measures that improve the financial decisions.