Machine learning architecture for risk modelling and analytics

A computer-implemented method for forecasting the issuance of at least one financial instrument, the method comprising a processor and a memory, the method comprising the operations of:

• generating, by the processor, a user interface on a display, said user interface comprising a user-selectable issuance tab;
• wherein selecting the issuance tab causes the processor to at least:
• receive raw data in a plurality of disparate formats;
• scrub the raw data for anomalies and null values using a set of rules and generate a structured data set;
• and
• wherein selecting said issuance tab causes the processor to perform measurement of at least one financial instrument issuer's propensity to issue the at least one financial instrument, and to assign a propensity score which estimates the relative likelihood the issuer will issue the at least one financial instrument within a time frame;
• and
• output an issuance recommendation for the issuer.

A computer readable medium storing instructions executable by a processor to carry out the operations comprising:

• aggregating raw data from a plurality of data sources comprising contemporaneous trading data and fundamental data covering a series of time periods and one or more aspects of quantitative investing and market monitoring, said raw data in a plurality of disparate formats;
• transforming said raw data in the plurality of disparate formats into a single standard format to generate structured data;
• extracting at least one data element of value associated with at least one financial instrument from the structured data in accordance with one or more pre-programmed functions;
• establishing a plurality of input nodes and an output node for a recurrent neural network model for each aspect of quantitative investing and market monitoring;
• using the recurrent neural network model to build at least one model;
• inputting the structured data into the recurrent neural network model using the plurality of input nodes;
• training each of the recurrent neural network using said inputs until an error function associated with an output value that corresponds to an aspect of quantitative investing and market monitoring is minimized;
• and
• using one or more weights from the trained recurrent neural network models to identify a set structured data by element of value and output that will be used as an element of value summary for use as an input to each of one or more predictive models;
• normalizing each of the one or more sets of structured data by data element of value, refining the sets of structured data by the data element of value,
• creating a summary of a refined transaction data set for each data element of value, and
• using the data element of value summaries as inputs to a predictive model for each of the one or more aspects of quantitative investing and market monitoring where the aspects of quantitative investing and market monitoring comprising managing trading activities, managing risk, making portfolio funding allocations, predicting a time horizon for issuance of the at least one financial instrument;
• predicting an issuer of the at least one financial instrument within the predicted time horizon;
• predicting a price of the at least one financial instrument, matching a buyer with the at least one financial instrument, and combinations thereof, and
• wherein the predictive models are useful for completing tasks comprising managing trading activities, managing risk, making portfolio funding allocations, predicting a time horizon for issuance of the at least one financial instrument;
• predicting an issuer of the at least one financial instrument within the predicted time horizon;
• predicting a price of the at least one financial instrument, matching a buyer with the at least one financial instrument, and combinations thereof.