TECHNICAL FIELD

The present disclosure relates to computer-implemented methods, software, and systems to predict a probability of success of change requests.

BACKGROUND

An organization, for example, a bank, usually has a change team, which is staffed by some of the organization's employees and which manages change requests received for various parts of the organization's information technology (IT) infrastructure or use environment. For example, the change team can manage upgrade requests for a customer online portal interface or a bug fix request for a backend application software tool.

SUMMARY

The present disclosure involves systems, software, and computer-implemented methods for automatically predicting, with a high degree of accuracy, a probability of success or failure of change requests for various parts of an organization's IT infrastructure or use environment. One example system includes at least one repository storing information corresponding to one or more prior change requests associated with an IT infrastructure of an organization, at least one memory storing instructions and at least one prediction tool, and at least one hardware processor interoperably coupled with the at least one memory and the at least one repository. Each change request corresponds to a request for a modification to one or more sections of the IT infrastructure. The instructions instruct the at least one hardware processor to receive one or more parameters corresponding to a new change request associated with the IT infrastructure. The one or more parameters are provided as input to the at least one prediction tool. In response to providing the one or more parameters as input to the at least one prediction tool, a probability of success of the new change request is received as an output of the at least one prediction tool.

Implementations can optionally include one or more of the following features. In some implementations, the instructions further instruct the at least one hardware processor to train the at least one prediction tool. The training comprises executing the at least one prediction tool; accessing the information corresponding to one or more prior change requests in the repository; providing, as input to the at least one prediction tool, the accessed information; and based on providing the accessed information as input to the prediction tool, configuring the at least one prediction tool to generate a target output.

In some implementations, providing the accessed information as input to the at least one prediction tool comprises determining, using the accessed information, a first subset of the one or more prior change requests that corresponded to successful changes; determining, using the accessed information, a second subset of the one or more prior change requests that corresponded to failed changes; comparing the first subset to the second subset; removing, based on the comparison, one or more change requests from one of the first subset or the second subset such that a difference between remaining numbers of change requests in the first subset and the second subset are within a threshold value; and providing information corresponding to the remaining numbers of change requests in the first subset and the second subset to the at least one prediction tool.

In some implementations, providing the accessed information as input to the at least one prediction tool comprises providing parameters corresponding to the one or more prior change requests as input to the at least one prediction tool, the parameters including a first parameter having a large number of possible values. The instructions instruct the at least one hardware processor to assign values of the first parameter corresponding to the one or more prior change requests to one of a number of specified groups, wherein the number of specified groups is less than the large number of possible values; and for each of the one or more prior change requests, provide as input to the at least one prediction tool, a respective specified group as a replacement of the first parameter.

In some implementations, providing the accessed information as input to the at least one prediction tool comprises providing parameters corresponding to the one or more prior change requests as input to the at least one prediction tool, each parameter having an associated weight representing an estimated impact of the parameter in effecting the requested modification; and configuring the at least one prediction tool comprises adjusting weights of one or more parameters to vary the output generated by the at least one prediction tool.

In some implementations, the output of the at least one prediction tool further comprises at least one of (i) an ordering of the parameters in accordance with estimated impact of each parameter in effecting the requested modification, or (ii) one or more recommendations for modifying the new change request. In some implementations, the one or more recommendations for modifying the new change request comprises recommendations for modifying at least one of the one or more parameters, or modifying one or more tasks to be performed to effect a requested modification associated with the new change request.

In some implementations, the instructions further instruct the at least one hardware processor to: store the output received from the at least one prediction tool in a database. In some implementations, storing the output in a database comprises storing the output along with information corresponding to the new change request in the at least one repository.

In some implementations, the system comprises a graphical user interface (GUI) tool that is interoperably coupled with the at least one hardware processor. The instructions instruct the at least one hardware processor to provide the output received from the at least one prediction tool to the GUI tool, wherein the GUI tool is configured to generate one or more user interfaces for display, the output being displayed using one of the one or more user interfaces. In some implementations, the GUI tool is further configured to: enable a user to enter, using an input mechanism coupled to the GUI tool, the one or more parameters corresponding to the new change request, including enabling the user to adjust values associated with the one or more parameters to vary the probability of success of the new change request output by the at least one prediction tool; and provide the one or more parameters entered by the user to the at least one hardware processor.

In some implementations, the instructions instruct the at least one hardware processor to: compare the probability of success of the new change request output by the at least one prediction tool to one or more threshold values; and in response to determining that the probability of success satisfies one or more criteria corresponding to the one or more threshold values, approve execution of the new change request, wherein a change request team of the organization is tasked with implementing a modification of the IT infrastructure corresponding to the new change request.

In some implementations, the instructions further instruct the at least one hardware processor to: analyze the one or more parameters corresponding to the new change request using the information corresponding to the one or more prior change requests stored in the repository; determine, based on the analysis, a risk of failure of the new change request; compare the risk of failure to one or more threshold values; and in response to a determination, based on the comparison, that the risk of failure is greater than the one or more threshold values, generate an alert about the risk of failure, wherein the alert triggers additional scrutiny of the new change request by a change request team of the organization.

In some implementations, least one of the information corresponding to the one or more prior change requests or one or more parameters corresponding to the new change request includes one or more of the following: number of members of a change request team of the organization assigned to implement the respective modification, experience level of the members assigned to implement the respective modification, number of tasks to be performed to implement the respective modification, whether a time period assigned to implement the respective modification is a normal time period or an expedited time period, a level of complexity of the change request, a requester who is requesting the change request, a level of difficulty in realizing the respective modification, a level of risk in implementing the respective modification, a weight assigned to a parameter, whether the prior change request was approved and the respective modification implemented, whether the respective modification for a prior change request was successful or failed, or, for a failed modification of a prior change request, level of severity of an impact of the failure on the IT infrastructure of the organization.

In some implementations, the at least one prediction tool includes a machine learning algorithm that is one of an artificial neural network, or a decision tree algorithm having one or more of feature importance or gradient boosting.

Similar operations and processes may be performed in a different system comprising at least one processor and a memory communicatively coupled to the at least one processor where the memory stores instructions that when executed cause the at least one processor to perform the operations. Further, a non-transitory computer-readable medium storing instructions which, when executed, cause at least one processor to perform the operations may also be contemplated. Additionally, similar operations can be associated with or provided as computer-implemented software embodied on tangible, non-transitory media that processes and transforms the respective data, some or all of the aspects may be computer-implemented methods or further included in respective systems or other devices for performing this described functionality. The details of these and other aspects and embodiments of the present disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims.

