Related solutions:> Churn prevention.
> Customer segmentation.
> Sales forecasting.
Risk assessment is a term that refers to process by which a company identifies hazards and risk factors associated to the activity that they develop and that may cause any possible harm.
Depending on the activity sector of each company, several risk factors could arise such as credit and reputation risk for banks or the associated risk to the evaluation of the profile of a client that takes out an insurance for insurance companies.
Neural networks, due to their ability to generalize knowledge, are an effective tool to quantify the risks of different activity areas of a company. They also allow to assess the likelihood of risk occurrence based on objective reasons based on previous experiences.
Due to their exposure to clients and financial markets, banking and insurance are two of the main sectors to which risk assessment can be applied. This will help to minimize the credit or market risks among others for banking and the risk of a bad evaluation of the profile of a client that asks for an insurance.
Neural Designer is a machine learning software that can manage large amount of data using advanced the most suitable mathematical methods for the user's objective.
Neural Designer will analyze all the potential risk factors as well as their characteristics and will assess each of them according to the probability of occurrence and its severity so that your company can properly act to prevent any possible harm. This information will be arranged in a data set.
Depending on the case of study, the dataset could contain different information going from characteristics of a client to financial information of a company. The accuracy of the assessment will depend on the quality of the variables included in the dataset and Neural Designer will provide you with advanced methods to study in depth the variables so the most relevant ones are selected for the analysis.
The next image shows a representation of a neural network that could be used for this case. As inputs, it will receive information about personal characteristics, family history, academic variables and any other external variables that may be considered relevant. As output, the neural network will assess the risk.