stanford

pensionlabel

Object Detection

1

pensionlabel Computer Vision Project

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1688 images
Explore Dataset

Here are a few use cases for this project:

Use Case 1: Pension Fund Analysis The "pensionlabel" model can be used by financial analysts and consultants to quickly identify and extract key pension-related data from documents when conducting pension fund analysis, investment strategies or actuarial assessments.

Use Case 2: Auditing and Compliance The model can be leveraged by auditors when reviewing the financial statements and documents of pension funds to ensure accuracy, identify discrepancies, and deliver comprehensive audit reports.

Use Case 3: Data Entry and Management Companies working with pension documents can utilize the "pensionlabel" model to automate the process of data extraction and entry, making it faster and more accurate to update their databases with relevant pension information.

Use Case 4: Reporting and Visualization The extracted data can be utilized for creating responsive visualizations and generating insights about pension funds, including trends in employee and employer contributions, pension liabilities, and fund performance.

Use Case 5: Business Intelligence and Decision Making Executives and pension fund managers can harness the power of "pensionlabel" to provide valuable insights that help inform strategic decision-making, such as optimizing pension fund allocation or adjusting pension fund assumptions based on current trends.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            pensionlabel_dataset,
                            title = { pensionlabel Dataset },
                            type = { Open Source Dataset },
                            author = { stanford },
                            howpublished = { \url{ https://universe.roboflow.com/stanford/pensionlabel } },
                            url = { https://universe.roboflow.com/stanford/pensionlabel },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { aug },
                            note = { visited on 2024-03-29 },
                            }
                        

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Source

stanford

Last Updated

2 years ago

Project Type

Object Detection

Subject

boxes

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Downloads: 1

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License

CC BY 4.0

Classes

administrative_expenses assumption_changes benefit_pay_incref c_discount changes_in_benefit_terms covered_employee_payroll discount_rate dummy_label employer_contribs expected_rate_of_return expected_vs_actual_experience f n fnp_boy fnp_eoy interest_cost m_discount member_contributions net_change_in_fnp net_change_in_tpl net_changes_to_npl net_inv_inc nonemp_contributions npl_+_1% npl_-_1% npl_boy npl_cur npl_eoy other_impr p_discount plan_name refunds service_cost title tpl_boy tpl_eoy transfers_among_employers unit varitem year