Analytics & Machine Learning

Through data visualization and descriptive statistics, we generate insights, enabling analysts to comprehend even the most complex phenomena. This capacity to “explain” past events is known as Business Intelligence.

Predictive Models

Stopping at descriptive analysis, however, means limiting oneself to understanding the past and possibly using the understanding to imagine the future. Instead, we believe that companies need tools that enable them to look ahead and anticipate phenomena, with an important degree of accuracy, to move from imagining the future to predicting it.

We then use predictive and machine learning models to support decision making, moving from understanding events to identifying their emerging trends.

Which customers are most likely to abandon a service? Qual è il miglior prodotto da raccomandare al cliente in questo momento? How high is the risk in granting credit? When is the next failure in a production line expected to occur?

Risk assessment

One of the Areas in which predictive analysis is particularly effective is the Early risk assessment. Whether financial or operational risks, accurately predict the probability that a given “adverse” event occurs allows companies to take preventive measures and reduce the impact of any critical issues.Machine learning

Whether we are talking about risk control or marketing and sales support, the goal is still, always, to enable our clients to gain meaningful insights from the data and provide tangible value to the analyst, supporting them in more informed decisions and enabling them to translate the information gained into targeted and timely actions.

Added Value

With extensive experience in machine learning techniques, including supervised and unsupervised models, deep learning with neural networks, and decision trees, we tailor solutions to the unique needs of each use case, ensuring optimal results in predictive contexts as well as in anomaly detection and identifying atypical behavior. This allows us to tailor the most appropriate technology to the specific needs of use cases, ensuring optimal results both in predictive contexts and in more complex applications such as anomaly detection or atypical behavior.

We are fully aware that machine learning and deep learning techniques do not necessarily need to replace traditional statistics, but should integrate with it when they add value. Our experience allows us to pragmatically understand when it is more appropriate to use established statistical models rather than machine learning techniques, ensuring that the solution adopted is always the most suitable for the specific context, the project’s objectives, and the required investment, in the most “appropriate” way possible.