A risk management device includes: a memory for storing a plurality of verification units each composed of one or more scenario data each including a predicted value of loss occurrence frequency, a verification range that is a collection of the plurality of verification units, and actual loss occurrence numbers corresponding to the scenario data; and a processor connected to the memory. The processor is programmed to determine by using a goodness-of-fit test on a Poisson distribution whether the total value of the loss occurrence numbers corresponding to the scenario data included in the verification range follows a Poisson distribution that the total value of predicted values of loss occurrence frequency in the scenario data included in the verification range is defined as a mean.

Risk-management device

Techniques for estimated compound probability distribution are described. An apparatus comprising a configuration component, perturbation component, sample generation controller, an aggregation component, a distribution fitting component, and statistics generation component. The configuration component operative to receive a compound model specification and candidate distribution definition. The perturbation component operative to generate a plurality of models from the compound model specification. The sample generation controller operative to initiate the generation of a plurality of compound model samples from each of the plurality of models. The distribution fitting component to generate parameter values for the candidate distribution definition based on the compound model samples. The statistics generation component to generate approximated aggregate statistics.

Techniques for estimating compound probability distribution by simulating large empirical samples with scalable parallel and distributed processing

The system and method of the present disclosure allow the user to enumerate a set of risk factors for each financial metric time-profile projection, and respond to a set of questions that is linear in the number of risk factors. In one embodiment, the risk elicitation on each risk factor uses user input of likelihood of risk and impact and/or severity. On each of the risk factors for any given nominal, financial projection estimate, the inputs are systematically converted into a net impact distribution for that nominal, financial estimate.

Method and system for estimating risk in the financial metrics of a business case

In accordance with an embodiment, described herein is a system and method for aggregating values through risk dimension hierarchies to create risk models in a multidimensional database. The system can include a cube that stores a plurality of standard risk measures organized into different risk dimension hierarchies, and a pluggable calculation engine executing a plurality of scripts to dynamically operate on any value in the cube. The system can aggregate values through the plurality of risk dimensional hierarchies, and either store an aggregated value against a dimension member, or dynamically calculate the aggregated value on demand. By aggregating values through the risk dimension hierarchies, a plurality of risk models can be built to measure a variety of risks.

System and method for aggregating values through risk dimension hierarchies in a multidimensional database environment

An adaptive data storage platform includes a plurality of nodes and a plurality of data stores, each associated with a different one of the nodes. An immutable journal distributed is between the plurality of nodes. Access to the data stores is based upon a consensus of trust determined by the plurality of nodes. The data is cyphered as it is received to form ciphered data that is sharded into equally sized shards that are distributed across the nodes for storing on a corresponding ones of the data stores and tracked using the immutable journal. The shard may be periodically sent to a different one of the nodes for storing on the corresponding data store. The data is thereby ciphered and distributed across the plurality of data stores and is not stationary.

Secure adaptive data storage platform

A method is provided for analyzing operational risk associated with one or more organizations, comprising receiving operational loss data from a plurality of organizations at a third-party risk management entity that is a separate entity from the plurality of organizations. The operational loss data includes confidential information regarding one or more of the plurality of organizations. The data received from the plurality of organizations, including the confidential information, is pooled. The pooled data is used to generate an operational risk model for one of the plurality of organizations, and this operational risk model is transmitted to the organization. A system for modeling operational risk for a plurality of organizations is provided, comprising a first data store configured to collect data regarding operational losses from the plurality of organizations, whose data includes confidential information regarding one or more of the organizations. The system includes software instructions configured to pool the operational loss data collected from the plurality of organizations and software instructions configured to generate operational risk models for the organizations using the pooled data, wherein the confidential information is not removed from the pooled data. The system also includes a second data store configured to store the generated models.

System and method for modeling consortium data

Systems and methods are provided for determining a loss mitigation reserve requirement based on a risk measure estimation and a confidence interval associated with the risk measure estimation. Distribution parameters of a frequency model and distribution parameters of a severity model are determined, and a covariance matrix representing the determined parameters of the distribution of the frequency model and the determined parameters of distribution of the severity model is generated. One or more analytical derivatives of a cumulative distribution function of the frequency model, one or more analytical derivatives of a cumulative distribution function of the severity model, and a parameter covariance matrix are calculated. A confidence interval is computed for the risk measure estimation based on a vector of derivatives of a cumulative distribution function.

Computer-Implemented Systems And Methods For Parameter Risk Estimation For Operational Risk

An apparatus includes a processor to: train a first neural network of a chain to generate first configuration data including first trained parameters, wherein the chain performs an analytical function generating a set of output values from a set of input values, each neural network has inputs to receive the set of input values and outputs to output a portion of the set of output values, and the neural networks are ordered from the first at the head to a last neural network at the tail, and are interconnected so that each neural network additionally receives the outputs of a preceding neural network; train, using the first configuration data, a next neural network in the chain ordering to generate next configuration data including next trained parameters; and use at least the first and next configuration data and data indicating the interconnections to instantiate the chain to perform the analytical function.

Staged training of neural networks for improved time series prediction performance

Evaluation of parameter risk via first order approximation of distortion risk measures

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System and method for modeling consortium data

Computer-Implemented Systems And Methods For Parameter Risk Estimation For Operational Risk

Staged training of neural networks for improved time series prediction performance

Evaluation of parameter risk via first order approximation of distortion risk measures