White Kite was launched as a platform for the development of a wide range of predictive models in financial and operational domains.

**What is a model?**

We take the word “model” to mean two distinct things:

• A mathematical and/or statistical tool for problem-solving or forecasting

• An application or piece of software, often developed in Excel

These concepts fit together. As steps in the overall model-building process, one usually precedes the other.

Solving problems and building predictive models is almost always an analytic task, while building a platform is the deployment of that model in the real world. We work seamlessly from one mode to the other.

**Risk Modelling**

Problems where uncertainty surrounds one or more key factors are the focus of risk modelling. Building a properly-formulated model to analyse these problems is the discipline of risk modelling.

In some instances, the model itself is the objective; in others, the model is an intermediate step, a platform to analyse the problem. White Kite calls upon a wide variety of techniques and methodologies to develop risk models and actively uses them to deploy solutions. Typically, the problem is one of forecasting the probable limits of a process or system when it is operating under a range of conditions from ‘normal’ to ‘stressed’.

At the heart of any risk analysis is a model—or multiple models—of some description. The core model platform could be of any size or shape and produce a variety of outputs.

Some typical examples of models and their purpose:

• Models for simulating the value-at-risk (VaR) of a portfolio of assets

• Frameworks for predicting default severity in the credit risk context

• Simulations of cash-flows within a contractual structure (such as in a leasing model)

• Regression-based models providing ranges for valuation of assets

• System Dynamics models of global networks or systems, such as aircraft or ship fleets

• Market risk models for forecasting the effects of volatility

• Decision-support platforms for timing investments

• Bayesian models for capturing expert opinion on risk

• Statistical models of aggrega te insurance claims

In terms of models for specific domains, we perform a range of credit risk modelling and validation services, from Basel II compliance to advice on methodologies and model management. The team calls upon a wide range of basic and advanced methods to solve problems; analyse complex data; and build predictive models. White Kite has experience in using ‘non-parametric’ techniques to solve problems where the client has issues with data (for example, small samples and missing or conflicting data) or where a degree of approximation or expert input is required. We are also skilled in the development of large, intricate Excel-based applications that

combine elements of simulation and optimisation. These applications can be built as stand-alone, distributable platforms with an interface that ensures usability across a broad audience.

Methods we employ typically fall into one of the following broad categories:

• Statistical model building

• Statistical data analysis

• Optimisation

• Simulation

• Credit risk modelling (for Basel II)

• Portfolio risk modelling

• Forecasting

These methods are elaborated on further under the section on Methodologies.