Systems and methods for repurposing paid time off
The present disclosure relates generally to utilizing paid time off. In one example, the systems and methods described herein may provide an infrastructure to repurpose paid time off into other uses, such as cash, travel, bill payments, and the like.
1. A computer-implemented method comprising:
obtaining paid time off (PTO) information in real time, wherein the PTO information corresponds to an employer, and wherein the PTO information indicates a set of group PTO conversions performed by an employee group associated with the employer;
training a machine learning algorithm, wherein the machine learning algorithm is trained using one or more datasets including workforce PTO conversions performed by a workforce associated with the employer over a period of time and insights corresponding to the workforce PTO conversions;
identifying one or more issues associated with the set of group PTO conversions;
generating a set of recommendations for benefits extendable to the employee group, wherein the set of recommendations is generated by processing the PTO information and the one or more issues through the machine learning algorithm; and
updating an interface to indicate the one or more issues and the set of recommendations.
2. The computer-implemented method of claim 1 , further comprising:
receiving a request to implement a PTO campaign for the employee group, wherein the request is generated based on the set of recommendations; and
transmitting a set of notifications to the employee group, wherein the set of notifications indicates incentives associated with the employer and corresponding to PTO usage amongst employees associated with the employee group.
3. The computer-implemented method of claim 1 , wherein the one or more issues are identified based on a PTO liability corresponding to the employee group over a period of time.
4. The computer-implemented method of claim 1 , further comprising:
generating one or more insights corresponding to PTO usage within the employee group, wherein the one or more insights are generated by processing the PTO information and performance information associated with the employee group through the machine learning algorithm, and wherein the one or more insights provide correlations between the PTO usage and employee performance within the employee group.
5. The computer-implemented method of claim 1 , wherein a subset of the set of PTO conversions is used for student loan payments, and wherein the set of recommendations includes a recommendation for implementing an education benefit plan to reduce PTO conversions for the student loan payments.
6. The computer-implemented method of claim 1 , wherein a subset of the set of PTO conversions is used to obtain a cash value, and wherein the set of recommendations includes a recommendation for implementing a benefits package that allows the employee group to save money between pay cycles in order to reduce PTO conversions for the cash value.
7. The computer-implemented method of claim 1 , wherein the set of recommendations is generated by the machine learning algorithm by identifying a cluster that corresponds to a particular root cause for the one or more issues and to the set of recommendations.
8. A system, comprising:
one or more processors; and
memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
obtain in real time paid time off (PTO) information corresponding to an employer, wherein the PTO information indicates a set of PTO conversions performed by an employee group associated with the employer;
train a machine learning algorithm, wherein the machine learning algorithm is trained using one or more datasets including PTO conversions performed by a workforce associated with the employer over a period of time and insights corresponding to the PTO conversions performed by the workforce;
identify one or more issues associated with the set of PTO conversions;
generate a set of recommendations for benefits extendable to the employee group, wherein the set of recommendations is generated by processing the PTO information and the one or more issues through the machine learning algorithm; and
update an interface to indicate the one or more issues and the set of recommendations.
9. The system of claim 8 , wherein the instructions further cause the system to:
receive a request to implement a PTO campaign for the employee group, wherein the request is generated based on the set of recommendations; and
transmit a set of notifications to the employee group, wherein the set of notifications indicates incentives associated with the employer and corresponding to PTO usage amongst employees associated with the employee group.
10. The system of claim 8 , wherein the one or more issues are identified based on a PTO liability corresponding to the employee group over a period of time.
11. The system of claim 8 , wherein the instructions further cause the system to:
generate one or more insights corresponding to PTO usage within the employee group, wherein the one or more insights are generated by processing the PTO information and performance information associated with the employee group through the machine learning algorithm, and wherein the one or more insights provide correlations between the PTO usage and employee performance within the employee group.
12. The system of claim 8 , wherein a subset of the set of PTO conversions is used for student loan payments, and wherein the set of recommendations includes a recommendation for implementing an education benefit plan to reduce PTO conversions for the student loan payments.
13. The system of claim 8 , wherein a subset of the set of PTO conversions is used to obtain a cash value, and wherein the set of recommendations includes a recommendation for implementing a benefits package that allows the employee group to save money between pay cycles in order to reduce PTO conversions for the cash value.
14. The system of claim 8 , wherein the set of recommendations is generated by the machine learning algorithm by identifying a cluster that corresponds to a particular root cause for the one or more issues and to the set of recommendations.
15. A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
obtain in real time paid time off (PTO) information corresponding to an employer, wherein the PTO information indicates a set of PTO conversions performed by an employee group associated with the employer;
train a machine learning algorithm, wherein the machine learning algorithm is trained using one or more datasets including PTO conversions performed by a workforce associated with the employer over a period of time and insights corresponding to the PTO conversions performed by the workforce;
identify one or more issues associated with the set of PTO conversions;
generate a set of recommendations for benefits extendable to the employee group, wherein the set of recommendations is generated by processing the PTO information and the one or more issues through the machine learning algorithm; and
update an interface to indicate the one or more issues and the set of recommendations.
16. The non-transitory, computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to:
receive a request to implement a PTO campaign for the employee group, wherein the request is generated based on the set of recommendations; and
transmit a set of notifications to the employee group, wherein the set of notifications indicates incentives associated with the employer and corresponding to PTO usage amongst employees associated with the employee group.
17. The non-transitory, computer-readable storage medium of claim 15 , wherein the one or more issues are identified based on a PTO liability corresponding to the employee group over a period of time.
18. The non-transitory, computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to:
generate one or more insights corresponding to PTO usage within the employee group, wherein the one or more insights are generated by processing the PTO information and performance information associated with the employee group through the machine learning algorithm, and wherein the one or more insights provide correlations between the PTO usage and employee performance within the employee group.
19. The non-transitory, computer-readable storage medium of claim 15 , wherein a subset of the set of PTO conversions is used for student loan payments, and wherein the set of recommendations includes a recommendation for implementing an education benefit plan to reduce PTO conversions for the student loan payments.
20. The non-transitory, computer-readable storage medium of claim 15 , wherein a subset of the set of PTO conversions is used to obtain a cash value, and wherein the set of recommendations includes a recommendation for implementing a benefits package that allows the employee group to save money between pay cycles in order to reduce PTO conversions for the cash value.
21. The non-transitory, computer-readable storage medium of claim 15 , wherein the set of recommendations is generated by the machine learning algorithm by identifying a cluster that corresponds to a particular root cause for the one or more issues and to the set of recommendations.