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Automated risk monitoring method and system

  • US 10,460,384 B2
  • Filed: 06/02/2014
  • Issued: 10/29/2019
  • Est. Priority Date: 02/24/2005
  • Status: Active Grant
First Claim
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1. An automated monitoring and signaling device for automated adaption and real-time adjustment of monitored portfolios of entities based on automatically determined recovery rate factors and entity-specific MonteCarlo parameters comprising:

  • processing circuitry configured to, for each entity of a plurality of entities of a portfolioextract one or more asset parameters of the entity, wherein entity-specific data, including control data of externally accessible entity data and of internally accessible entity data, is updated and transferred in real-time to the monitoring device via a communication network, the control data including one or more of a time since foundation of the entity, a number of employees of the entity, and a balance sheet of the entity, and wherein the one or more asset parameters of the entity are extracted from the control data for the entities concerned by the processing circuitry,determine stochastically and store, a corresponding entity-specific asset distribution by stochastically evaluating the one or more asset parameters,determine and standardize by a standardization factor of the processing circuitry a recovery rate factor in an event the entity defaults based on a particular entity-specific asset distribution, the recovery rate factor indicating an expected percentage share of a loan that will be recovered from the entity in an event of default on the loan by the entity,determine and store an associated threshold value based on the particular entity-specific asset distribution, wherein the associated threshold value corresponds, in each case, to an expected value of one or more asset parameters, wherein default by the entity occurs when an asset parameter of the entity falls below the threshold value time-dependently varying default probability and recovery rate stochastically whereby the occurring defaults follow a stochastic intensity process depending on timing, and probability comprises a probability for unforeseeable default occurrings, and wherein the recovery rate depends on a probability of default that is negatively correlated with the recovery rate, such that the recovery rate is decreased in response to a detected failure rate being above a predetermined threshold,perform a MonteCarlo simulation to identify one or more entities in the portfolio that have recovery rates above a predetermined probability threshold, wherein MonteCarlo parameters are generated for each entity to determine entities in which a corresponding recovery rate factor can be expected, and wherein based on the determined recovery rate factors and the threshold value chosen in each case a particular positive or particular negative risk influence of each entity on the portfolio is determined, andadjust directly, and dynamically in real-time, the portfolio based on the recovery rates and the threshold values for the one or more entities identified in the MonteCarlo simulation based on specified recovery rate factors and the MonteCarlo parameters by using receiver devices to transmit access-controlled portfolio management data via a physical interface to a connected transaction server for automated real-time adjustment of the monitored portfolios of entities, wherein the receiver devices comprise configurable filter facilities and simultaneously receive more than one DB channel, wherein the processing circuitry is further configured toidentify and store, in a database, expected recovery rates that are determined to be a minimum value of the recovery rate expected by the MonteCarlo simulation,output, via a network interface of the device, the minimum value of the recovery rate to remote user devices, whereinthe one or more asset parameters are extracted from control data used to assess a credit risk of the entity, and the processing circuitry is further configured to;

    update the control data at regular intervals, andrepeat performance of the MonteCarlo simulation and adjustment of the portfolio based on the updated control data.

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