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GEMAct

GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.

The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.

Please visit our website to see our documentation and tutorial.

The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.

APA citation:

Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022

BibteX citation:

@article{Pittarello_Luini_Marchione_2024, title={GEMAct: a Python package for non-life (re)insurance modeling}, DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science}, author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin}, year={2024}, pages={1–37}}

The manuscript pre-print is instead available at ArXiV:2303.01129.

Future work

Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.

Acknowledgments

Previous versions were presented at the Mathematical and Statistical Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.

We want to especially thank the students in Statistica per le assicurazioni, M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.

About

Repository of GEMAct source code. Enjoy!

gem-analytics.github.io/gemact/

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actuarial-scienceclaims-reservinginsurance-pricingpythonpython-packagereinsurance

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var __m = "github.com";
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Python applicationDownloadsDOI
GEMAct
GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.
The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.
Please visit our website to see our documentation and tutorial.
The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.
APA citation:
Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022

BibteX citation:
@article{Pittarello_Luini_Marchione_2024, title={GEMAct: a Python package for non-life (re)insurance modeling}, DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science}, author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin}, year={2024}, pages={1–37}} 
The manuscript pre-print is instead available at ArXiV:2303.01129.
Future work
Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.
Acknowledgments
Previous versions were presented at the Mathematical and Statistical
Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.
We want to especially thank the students in Statistica per le assicurazioni,
M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.
About
Repository of GEMAct source code. Enjoy!
gem-analytics.github.io/gemact/
Topics
actuarial-scienceclaims-reservinginsurance-pricingpythonpython-packagereinsurance
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Readme
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, 'i');
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Python applicationDownloadsDOI
GEMAct
GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.
The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.
Please visit our website to see our documentation and tutorial.
The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.
APA citation:
Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022

BibteX citation:
@article{Pittarello_Luini_Marchione_2024, title={GEMAct: a Python package for non-life (re)insurance modeling}, DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science}, author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin}, year={2024}, pages={1–37}} 
The manuscript pre-print is instead available at ArXiV:2303.01129.
Future work
Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.
Acknowledgments
Previous versions were presented at the Mathematical and Statistical
Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.
We want to especially thank the students in Statistica per le assicurazioni,
M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.
About
Repository of GEMAct source code. Enjoy!
gem-analytics.github.io/gemact/
Topics
actuarial-scienceclaims-reservinginsurance-pricingpythonpython-packagereinsurance
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BSD-3-Clause license
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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Python applicationDownloadsDOI
GEMAct
GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.
The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.
Please visit our website to see our documentation and tutorial.
The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.
APA citation:
Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022

BibteX citation:
@article{Pittarello_Luini_Marchione_2024, title={GEMAct: a Python package for non-life (re)insurance modeling}, DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science}, author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin}, year={2024}, pages={1–37}} 
The manuscript pre-print is instead available at ArXiV:2303.01129.
Future work
Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.
Acknowledgments
Previous versions were presented at the Mathematical and Statistical
Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.
We want to especially thank the students in Statistica per le assicurazioni,
M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.
About
Repository of GEMAct source code. Enjoy!
gem-analytics.github.io/gemact/
Topics
actuarial-scienceclaims-reservinginsurance-pricingpythonpython-packagereinsurance
Resources
Readme
BSD-3-Clause license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Python applicationDownloadsDOI
GEMAct
GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.
The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.
Please visit our website to see our documentation and tutorial.
The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.
APA citation:
Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022

BibteX citation:
@article{Pittarello_Luini_Marchione_2024, title={GEMAct: a Python package for non-life (re)insurance modeling}, DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science}, author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin}, year={2024}, pages={1–37}} 
The manuscript pre-print is instead available at ArXiV:2303.01129.
Future work
Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.
Acknowledgments
Previous versions were presented at the Mathematical and Statistical
Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.
We want to especially thank the students in Statistica per le assicurazioni,
M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.
About
Repository of GEMAct source code. Enjoy!
gem-analytics.github.io/gemact/
Topics
actuarial-scienceclaims-reservinginsurance-pricingpythonpython-packagereinsurance
Resources
Readme
BSD-3-Clause license
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Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
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try {
var __m = "*";
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Python applicationDownloadsDOI
GEMAct
GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.
The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.
Please visit our website to see our documentation and tutorial.
The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.
APA citation:
Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022

BibteX citation:
@article{Pittarello_Luini_Marchione_2024, title={GEMAct: a Python package for non-life (re)insurance modeling}, DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science}, author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin}, year={2024}, pages={1–37}} 
The manuscript pre-print is instead available at ArXiV:2303.01129.
Future work
Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.
Acknowledgments
Previous versions were presented at the Mathematical and Statistical
Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.
We want to especially thank the students in Statistica per le assicurazioni,
M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.
About
Repository of GEMAct source code. Enjoy!
gem-analytics.github.io/gemact/
Topics
actuarial-scienceclaims-reservinginsurance-pricingpythonpython-packagereinsurance
Resources
Readme
BSD-3-Clause license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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Python applicationDownloadsDOI
GEMAct
GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.
The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.
Please visit our website to see our documentation and tutorial.
The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.
APA citation:
Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022

BibteX citation:
@article{Pittarello_Luini_Marchione_2024, title={GEMAct: a Python package for non-life (re)insurance modeling}, DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science}, author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin}, year={2024}, pages={1–37}} 
The manuscript pre-print is instead available at ArXiV:2303.01129.
Future work
Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.
Acknowledgments
Previous versions were presented at the Mathematical and Statistical
Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.
We want to especially thank the students in Statistica per le assicurazioni,
M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.
About
Repository of GEMAct source code. Enjoy!
gem-analytics.github.io/gemact/
Topics
actuarial-scienceclaims-reservinginsurance-pricingpythonpython-packagereinsurance
Resources
Readme
BSD-3-Clause license
Activity
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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GEMAct
GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.
The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.
Please visit our website to see our documentation and tutorial.
The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.
APA citation:
Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022

BibteX citation:
@article{Pittarello_Luini_Marchione_2024, title={GEMAct: a Python package for non-life (re)insurance modeling}, DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science}, author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin}, year={2024}, pages={1–37}} 
The manuscript pre-print is instead available at ArXiV:2303.01129.
Future work
Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.
Acknowledgments
Previous versions were presented at the Mathematical and Statistical
Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.
We want to especially thank the students in Statistica per le assicurazioni,
M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.
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Repository of GEMAct source code. Enjoy!
gem-analytics.github.io/gemact/
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actuarial-scienceclaims-reservinginsurance-pricingpythonpython-packagereinsurance
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