StatC Engine

The need to minimize the risk of making business decisions (e.g. giving loans to insolvent customers) or improving the overall decision-making process creates the demand for an adequate decision engine.

StatC Engine is a solution designed to support the decision processes in businesses by processing and evaluating data (in the form of variables) sent from outside systems.

Due to its high flexibility, the offered solution can be applied everywhere, where one requires a system for efficient data processing and computation of various parameters concerning risk evaluation, monitoring of states, predicting statuses, determining the action to undertake, or calculating commissions .

Depending on the subject of the application, our offer may include:

  • deployment of the Applicational StatC Engine – a universal decision engine,
  • deployment of the Transactional StatC Engine – a solution dedicated to a given project aimed at transactional data scoring,
  • monitoring of operations inside IT systems – signaling the occurrences of operations departing from established patterns.
  • defining various processes of data verification and evaluation,
  • implementation of expert rules, scoring and rating models for risk evaluation,
  • using cross-checking mechanisms for verifying and comparing data,
  • defining expert rules on different stages of the decision process,
  • remote activation of evaluation services using various methods of communication (for example as Web services),
  • saving to the engine’s database of various information concerning the results and processed data,
  • report parameterization,
  • using the WWW interface for input and evaluation of applications,
  • integration with any system servicing decision processes and credit applications.
  • evaluation of credit applications
    • automation of the proces of scoring and rating credit applications (i.a. determining the indicatators, evaluation by points based on information from the application and additional sources, determining risk class membership,
    • detecting fraudulent credit applications (applicational fraud).
  • debt recovery
    • predicting the probability of repayment,
    • optimization of debt recovery paths.
  • evaluation of transactional data (payment transactions, operations inside IT systems)
    • detecting payment card frauds,
    • detecting atypical transactions (for instance connected with money laundering practices).
  • analytical CRM
    • targeting for BTL campaigns,
    • estimating the probability that a customer will stop using the services (churn analysis),
    • detecting frauds connected with loyalty programs,
    • personalization of marketing and sales actions.
  • other tasks which require efficient data processing in on-line as well as off-line modes.