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Distribution Forecasting

z = r (cosθ + isinθ) x + by + cz + d = 0 (f(−x) = f(x), ∀ x ∈ D) −x + 2y + z + t = −1 2x + 2y + z + 4t = 0 C = {z = x + iy x, y ∈ IR} z = −1 + i (f(−x) = f(x), ∀ x ∈ D)

Distribution Forecasting

Data-Driven & What If Analysis

The ability to evaluate different viable strategies in a quantitative and predictive manner is increasingly important in the economic and business environment in which we live.

z = r (cosθ + isinθ) x + by + cz + d = 0 (f(−x) = f(x), ∀ x ∈ D) −x + 2y + z + t = −1 2x + 2y + z + 4t = 0 C = {z = x + iy x, y ∈ IR} z = −1 + i (f(−x) = f(x), ∀ x ∈ D)

The Data-Driven & What If Analysis solution provides companies with a tool to assign each entity of interest to a class (e.g. financial risk classes, execution times, quantities processed or produced, etc.). This way, it is possible to optimize processes and plan specific interventions on various classes.
Our solution also simulates how the expected scenario would vary as the parameters that the company governs change, and therefore to evaluate the effect on the classes composition.

Targeted interventions

Thanks to accurate predictions, actions aimed at resolving any issues (e.g. reducing exposure risk, procurement risk etc.) can be implemented

Predicting impacts

By studying different scenarios in advance, one can predict the occurrence of certain eventualities and prepare for them or make the "first move"

Optimal strategy

By comparing different scenarios, one can identify the best course of action

Examples

Sectors

Application examples of Data-Driven What If Analysis

Industrial

Forecasting raw material requirements to drive procurement

Utilities

Assessing the evolution of credit exposure by predicting the proportion of unpaid invoices

Services

Credit risk simulation according to the characteristics of its debt holders

Deepening

Operating logic

Our solution is based on a machine learning algorithm that provides, for each entity, the probability of belonging to certain definable classes such as time, risk, quantity and more.

Method