US 2020/0265393 A1Published application

Predictive analytics for abnormal event resolutions

AssigneeHighradius Corporation

InventorsVishal Shah, Sonali Nanda, Srinivasa Jami

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Abstract

Systems are provided to utilize machine learning to identify abnormal event resolutions and provide guidance for resolution. For example, in a two-way event system, a normal response will typically close the loop on an initially generated event. However, there are cases where processing of the event uncovers contingent response strategies. In an accounting implementation, machine learning techniques are used to identify the potential of a deduction to be invalid. Machine learning algorithms are trained based on historical deductions and their resolution attributes. Models may further be used to predict whether a deduction is valid or invalid. Contingencies addressed include shortages, pricing adjustments, promotional activity, and other types of deductions that may occur in a provider, supplier, and consumer account resolution system. Automation allows focus on invalid deductions and may automatically close non-cost effective events as having been resolved without further inquiry or research.

Claims15

Dependent claims

A computer system comprising:

one or more processors;
a memory communicatively coupled to the one or more processors and storing instructions executable by the one or more processors to cause the computer system to:
receive a first abnormal event resolution request in response to an initiating event, the first abnormal event resolution request containing a set of attributes;
obtain historical information regarding previous processing of a set of abnormal event resolution requests related to the first abnormal event resolution request based on the set of attributes;
evaluate the first abnormal event resolution request using machine learning and the historical information to determine a confidence level score, the confidence level score providing an indication of validity with respect to the first abnormal event resolution request;
categorize the first abnormal event resolution request based on a value attributed to the first abnormal event resolution request and the confidence level score;
determine if the first abnormal event resolution request qualifies for automatic resolution;
based on a determination that automatic resolution is appropriate, automatically resolve the first abnormal event resolution request;
and
based on a determination that automatic resolution is not appropriate, queue the first abnormal event resolution request for further processing.

7 more elements

A non-transitory computer readable medium comprising computer executable instructions that, when executed by one or more processing units, cause the one or more processing units to:

receive a first abnormal event resolution request in response to an initiating event, the first abnormal event resolution request containing a set of attributes;
obtain historical information regarding previous processing of a set of abnormal event resolution requests related to the first abnormal event resolution request based on the set of attributes;
evaluate the first abnormal event resolution request using machine learning and the historical information to determine a confidence level score, the confidence level score providing an indication of validity with respect to the first abnormal event resolution request;
categorize the first abnormal event resolution request based on a value attributed to the first abnormal event resolution request and the confidence level score;
determine if the first abnormal event resolution request qualifies for automatic resolution;
based on a determination that automatic resolution is appropriate, automatically resolve the first abnormal event resolution request;
and
based on a determination that automatic resolution is not appropriate, queue the first abnormal event resolution request for further processing.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/411,532Filed 14 May 2019
Priority: IN 20194100615615 Feb 2019
Published: 20 Aug 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06F 16/906
Details of database functions independent of the retrieved data types: clustering; Classification
G06Q 20/102
Specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems: bill distribution or payments
G06F 9/542
Interprogram communication: event management; Broadcasting; Multicasting; Notifications
G06F 16/9035
Querying: filtering based on additional data, e.g. user or group profiles