Many companies "talk" about risk management, create lists, define mitigation strategies and make use of consultants to support them. But only fewer companies use the available data to quantify risks (financial and non-financial) and to support decision-making processes in a proactive and modern way.
Small and Big Data Analytics tools can be used to:
The primary objective of an analytical Fraud Detection project is to summarise, through a series of specific scores and alerts, the key variables that can anticipate fraudulent events. The analysis goal is to provide the Management with a series of tools for the preventive (and continuous) fraud management.
There are many possible fraud patterns and they change rapidly and continuously. In particular, there are two points that need to be examined with the help of statistical models:
The implementation of fraud forecasting models, assigning to each customer/current account the probability of being connected to fraudulent activities, is carried out through statistical processing of particular behaviors and characteristics (anomalous movements and/or presence of characteristics detected as risk factors, etc.). These behaviors of individual customers become manifest in a predetermined period of time, before the prediction moment.
The development of the statistical procedure for the credit risk quantification, according to the Internal Rating Based approach, requires the construction of statistical models suitable to estimate three indicators for each exposure in the portfolio:
For the estimation of each of the three components, one or more scoring models are designed according to the homogeneity of the portfolio exposures default rates.
In the construction of an internal rating system, the main aspects to be considered are: