Apara

Continuous Auditing and Continuous Monitoring Solution

The business users will be able to extract in a simple way all the needed information from different sources to analyze irregularities and improper behaviors and thus make more accurate decisions. cuadro_auditoria_dossier_en

Apara’s CACM solution, based on its dVelox Enterprise product, is an evolution of the traditional surveillance systems based on rules and parameters. Using advanced statistical techniques, it is capable of detecting all the activities and movements in real time and of generating alerts and reports automatically.

If we compare predictive analytics tools with rule based systems, we see how the first one are able to identify more subtle differences and, in addition, they are able to learn continuously from the data they analyze, so that as changes occur, they will continue improving their accuracy and prioritizing those situations that are most worthwhile to investigate.

Comparative between dVelox and rules based system

RULES PREDICTIVE ANALYTICS with dVelox Enterprise
It requires a team of experts to define the rules based on their experience. Business knowledge is extracted directly from the data using advanced learning algorithms, and it is validated automatically.
Rules generate a list of cases to be investigated without assigning priorities based on their importance. Predictive models sort the cases to be investigated according to their relevance, thereby optimizing available resources.
Inability to detect all the relationships between a large number of variables. Predictive models consider all the variables, explaining the relationships between them, quantifying them and indicating the variables that most influence the proposed prediction.
Rules must be maintained and adjusted manually. Automated maintenance during which models learn and improve their accuracy as new investigations arise.
Rules predict what they know, but are not able to detect new, previously unknown irregularities. Models are able to identify new, previously unknown types of irregularities.
Rule-based systems does not transform the data in scorings or indicators to facilitate decision-making. The results are converted into business language in a simple chart of commands so that actual users may carry out their simulations.
They are systems that are very difficult to manage and maintain when the number of rules is high. Ease of maintenance whatever the number of variables involved.
Accuracy diminishes when the analyzed data is incomplete. High level of accuracy even when working with incomplete data, thanks to the fact that predictive models are based on probabilities.

 



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Apara develops pioneer technology in the area of automation learning process for decision making in order to convert data in accurate predictions.

Apara’s software products are applied in complex decision situations as fraud prevention, risk management, customer retention and marketing campaign optimization.

C/ Santa Leonor 65 · Edif. C · 4ª / 2ª
28037 Madrid (Spain)
Phone: +34 91 144 06 00
Fax: +34 91 304 49 15
apara@aparasw.com


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