System and method for optimizing performance of a process

1 . A computer-implemented method for optimizing performance of a process, comprising:

a) constructing a KPI tree structure modelling workflow of the process using data received from a client device;

b) autonomously monitoring a plurality of interconnected metrics by analysing data from the KPI tree structure;

c) determining one or more root causes for an issue affecting the performance based on the autonomous monitoring of the plurality of interconnected metrics, wherein determining the one or more root causes further comprises using artificial intelligence models to identify behavioural drivers providing insights on movement and shifts in interrelated KPIs in near-real time;

d) recommending one or more actions correlating with one or more levels in the workflow to remediate the one or more root causes;

e) enabling performance of the one or more actions in the process workflow by integrating the one or more actions with execution of process workflow into a downstream application; and

f) tracking impact of the one or more actions in the downstream application using continually received feedback on implemented actions and analysis of data from the KPI tree structure.

2 . The computer-implemented method of claim 1 , wherein determining the one or more root causes comprises analysing data from the KPI tree structure and using AI models to identify one or more metrics exhibiting anomalous behavior.

3 . The computer-implemented method of claim 1 , wherein recommending one or more actions is based on insights obtained during the determination of the one or more root causes.

4 . The computer-implemented method of claim 1 , wherein recommending the one or more actions further comprises evaluating performance of KPI tree structure with one or more actions in controlled experiments, and determining alternative actions in addition to the recommended one or more actions based on said evaluation.

5 . The computer-implemented method of claim 1 , wherein values in the KPI tree structure is refreshed each time data is refreshed.

6 . The computer-implemented method of claim 1 , wherein the one or more actions identify the entity in the KPI structure on which the one or more actions are to be performed, a duration for performing the action, an expected outcome.

7 . The computer-implemented method of claim 1 , wherein the one or more levels in the workflow relate, but not limited, to an organization, an asset class, a unit, an individual user segment, or any combination thereof.

8 . The computer-implemented method of claim 1 , wherein the steps b) to f) are repeated until an optimized state of process is achieved.

9 . A system, comprising:

a memory device; and

at least one processing device in communication with the memory device and configured to execute instructions to cause the system to perform operations comprising:

a) constructing a KPI tree structure modelling workflow of the process using data received from a client device;

b) autonomously monitoring a plurality of interconnected metrics by analysing data from the KPI structure;

c) determining one or more root causes for an issue affecting process performance based on the autonomous monitoring of the plurality of interconnected metrics, wherein determining the one or more root causes further comprises using artificial intelligence models to identify behavioural drivers providing insights on movement and shifts detected by AI models in interrelated KPIs in near-real time;

d) recommending one or more actions correlating with one or more levels in the workflow to remediate the one or more root causes;

e) enabling performance of the one or more actions in the process workflow by integrating the one or more actions with execution of process workflow into a downstream application; and

f) tracking impact of the one or more actions in the downstream application using continually received feedback on implemented actions and analysis of data from the KPI tree structure.

10 . The system of claim 9 , wherein the determining the one or more root causes comprises analysing data from the KPI tree structure and using AI models to identify one or more metrics exhibiting anomalous behavior.

11 . The system of claim 9 , wherein recommending one or more actions is based on insights from analysis of the determined one or more root causes.

12 . The system of claim 9 , the recommending the one or more actions further comprises evaluating performance of KPI tree structure with one or more actions in controlled experiments, and determining alternative actions in addition to the one or more actions based on said evaluation.

13 . The system of claim 9 , wherein the values in the KPI tree structure is refreshed each time data is refreshed.

14 . The system of claim 9 , wherein one or more actions identify the entity in the KPI structure on which the one or more actions are to be performed, a duration for performing the action and an expected outcome.

15 . The system of claim 9 , wherein the one or more levels in the workflow relate, but not limited, to an organization, an asset class, a unit, an individual user segment, or any combination thereof.

16 . The system of claim 9 , wherein the at least one processing device causes the system to repeat operations b) to f) until an optimized state of process is achieved.

17 . One or more non-transitory computer-readable media storing computer-executable instructions that upon execution cause at least one processing device to perform operations comprising:

a) constructing a KPI tree structure modelling workflow of the process using data received from a client device;

b) autonomously monitoring a plurality of interconnected metrics by analysing data from the KPI tree structure;

c) determining one or more root causes for an issue affecting the performance based on the autonomous monitoring of the plurality of interconnected metrics, wherein determining the one or more root causes further comprises using artificial intelligence models to identify behavioural drivers providing insights on movement and shifts detected by AI models in interrelated KPIs in near-real time;

d) recommending one or more actions correlating with one or more levels in the workflow to remediate the one or more root causes;

e) enabling performance of the one or more actions in the process workflow by integrating the one or more actions with execution of process workflow into a downstream application; and

f) tracking impact of the one or more actions in the downstream application using continually received feedback on implemented actions and analysis of data from the KPI tree structure.