[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118731-en":3,"doc-seo-118731-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118731,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Improvement of Key Financial Performance Indicators in the Insurance Industry Using Machine Learning - A Quantitative Analysis","AI and machine learning support financial forecasting, risk identification, and the targeting of performance drivers in banking and insurance. The study reviews prior ML and AI approaches for KPI management, then analyzes and improves insurance financial key performance indicators using ML algorithms. It maps business-impact attributes and target attributes to model-ready datatypes, applies hashing to convert string features into numeric values, and uses a decision tree to generate rule sets for predictive, rule-based inference over financial datasets and ledger-transaction patterns.","International Journal of Smart Sensor and Adhoc Network  \nVolume 3  \nIssue 4 Role of Emerging & Intelligent Article 2  \nTechnologies for the Society.  \nJanuary 2023  \nImprovement of Key Financial Performance Indicators in the Insurance Industry Using Machine Learning – A Quantitative Analysis  \nVineeth Jeppu  \nNYIT, [vineethj007@hotmail.com](vineethj007@hotmail.com)  \nFollow this and additional works at: [https://www.interscience.in/ijssan](https://www.interscience.in/ijssan)  \n Part of the Computer and Systems Architecture Commons  \nRecommended Citation  \nJeppu, Vineeth (2023) \"Improvement of Key Financial Performance Indicators in the Insurance Industry Using Machine Learning – A Quantitative Analysis,\" International Journal of Smart Sensor and Adhoc Network: Vol. 3: Iss. 4, Article 2.  \nDOI: 10.47893/IJSSAN.2023.1225  \nAvailable at: [https://www.interscience.in/ijssan/vol3/iss4/2](https://www.interscience.in/ijssan/vol3/iss4/2)  \nThis Article is brought to you for free and open access by the Interscience Journals at Interscience Research Network. It has been accepted for inclusion in International Journal of Smart Sensor and Adhoc Network by an authorized editor of Interscience Research Network. For more information, please contact [sritampatnaik@gmail.com](sritampatnaik@gmail.com).  \nImprovement of Financial KPIsin the Insurance Industry Using Machine Learning–A  \nQuantitative Analysis  \nVineeth Jeppu 1, Ayan Singh2, Alex Gonzalez3 1New York Institute of Technology, NYC, New York  \n2San Jose State University, San Jose, California  \n3Rutgers University, New Brunswick, New Jersey  \n[1](1vineethj007@hotmail.com)[v](1vineethj007@hotmail.com)[ineethj007@hotmail.com](1vineethj007@hotmail.com)  \nAbstract—AI and Machine learning are playing a vital role in the financial domain in predicting future growth and risk and identifyingkey performance areas.We look at how machine learning and artificial intelligence (AI) directly or indirectly alter financial management in the banking and insurance industries. First, a non-technical review of the prior machine learning and AI methodologies beneficial to KPI management is provided. This paper will analyze and improve key financial performance indicators in insurance using machine learning (ML) algorithms. Before applying an ML algorithm, we must determine the attributes directly impacting the business and target attributes. The details must be manually mapped from string values to fit the model and its required datatypes for applying these specific features to an ML model. We propose hashing to convert string values to numeric values for data analysis within our model. After the string values are hashed, we can introduce our model. In our case , we have chosen to use a decision tree model. Decision Trees are beneficial for this use case as this algorithm generates rulesets that govern the target value output. These rulesets can then be applied to the financial dataset and infer the “best fit” value that might be wrong/missing. Finally, because of the model, we can use this most accuratedata version to detect general ledger transactional data patterns.  \nKeywords—Machine Learning,Decision Tree, Hashing, Key Performance Indicator (KPIs) , Insurance Metrics  \nI. INTRODUCTION  \nThe last two decades have seen a dramatic accelerating pace in the development and adoption of new technologies; however, rapid technological change can outpace the capacity of society to adapt [1] . On the other hand, we can see this overflow of new tech as an opportunity to improve upon your outdated legacy systems at your own pace. These obsolete systems include analysis via Excel, simple algorithms that need regular heavy supervision, SAS code, etc. This exploratory paper will discuss how to use a machine learning algorithm and how it can help a business improve its current functions and processes. We will also discuss the workflow for implementing a Machine Learning algorithm. The focus of our paper will be on usin","cbCaicnMIytA9nEV","https://ap.wps.com/l/cbCaicnMIytA9nEV","pdf",365020,1,7,"English","en",105,"# Introduction\n## Overview of technology change and ML workflow for insurance KPIs\n# Background\n## Definitions and importance of KPIs\n## Role of AI in KPI recognition, grouping, and decision support\n## KPI design using the SMART framework","[{\"question\":\"How does the paper use machine learning to improve insurance financial KPIs?\",\"answer\":\"It maps relevant business attributes and target attributes to model-compatible datatypes, converts string values to numeric features via hashing, and builds a decision-tree model to infer KPI-related outputs from financial datasets.\"},{\"question\":\"Why are decision trees used in the proposed approach?\",\"answer\":\"Decision trees generate rule sets that govern the target value output, enabling rule-based inference on financial data and support for detecting patterns in general-ledger transactions.\"},{\"question\":\"What prerequisites does the model require before applying an ML algorithm?\",\"answer\":\"The approach requires identifying the attributes directly impacting the business and the target attributes, then manually mapping details from string values to the datatypes needed by the ML model.\"}]","Improvement of Key Financial Performance Indicators in the Insurance Industry Using Machine Learning - A Quantitative Analysis | PDF",1785719963,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improvement-of-key-financial-performance-indicators-in-the-insurance-industry-using-machine-learning-a-quantitative-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improvement-of-key-financial-performance-indicators-in-the-insurance-industry-using-machine-learning-a-quantitative-analysis/118731/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the paper use machine learning to improve insurance financial KPIs?","Question",{"text":75,"@type":76},"It maps relevant business attributes and target attributes to model-compatible datatypes, converts string values to numeric features via hashing, and builds a decision-tree model to infer KPI-related outputs from financial datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are decision trees used in the proposed approach?",{"text":80,"@type":76},"Decision trees generate rule sets that govern the target value output, enabling rule-based inference on financial data and support for detecting patterns in general-ledger transactions.",{"name":82,"@type":73,"acceptedAnswer":83},"What prerequisites does the model require before applying an ML algorithm?",{"text":84,"@type":76},"The approach requires identifying the attributes directly impacting the business and the target attributes, then manually mapping details from string values to the datatypes needed by the ML model.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]