Claims Process Research Articles

Analisis Pending Claim Badan Penyelenggara Jaminan Sosial (BPJS) Rawat Jalan pada Rumah Sakit Petrokimia Gresik

This research aims to analyze the causes and impacts of pending claims for outpatient services under the Social Security Administering Body (BPJS) at Petrochemical Hospital Gresik. The study uses a descriptive approach with data collection through interviews with various parties involved in the claim process, including administration, collecting, complication, and finance departments. The results show that the main causes of delayed BPJS claims are incomplete documentation, coding errors, and issues in administrative verification. The impact of pending claims includes disruptions to the hospital's cash flow, hindering operational smoothness and patient care services. This research also provides recommendations for improvement, such as enhancing staff training, evaluating the claims system, and improving coordination between related departments to expedite and simplify the BPJS claim process.

The Function of the Deposit Insurance Corporation (LPS) as a Legal Protection Strategy for Customer Deposits Due to Bank Liquidation

This research aims to explore the role of the Deposit Insurance Corporation (LPS) in protecting customers when banks fail. The research methods used include policy analysis, case studies of failed banks, and reviews of related laws. It is hoped that the research results will provide a deeper understanding of the effectiveness of the legal protection provided by LPS to customers in the context of bank failures and the related compensation claims process. The Deposit Insurance Corporation (LPS) has a crucial role in protecting customer deposits when banks experience liquidation. In this context, LPS functions as a legal protection strategy for customer deposits held in banks. This action is important to maintain public confidence in banking amidst economic uncertainty. In response to the 1998 monetary crisis, the Indonesian government issued a policy of guaranteeing bank payments, including public deposits, through LPS. The importance of LPS is revealed when banks experience failures that have the potential to threaten the security of customer funds. In this situation, LPS guarantees the customer's refund up to a certain limit, providing the necessary financial protection. This creates a sense of security for customers and promotes banking system stability. With LPS, customers can be assured that their funds are safe even if unexpected events occur in the banking world. The role of LPS as a deposit insurance institution is inherent in the government's efforts to regulate and supervise the financial sector. LPS plays a role as the front guard in protecting customer interests, maintaining financial system stability, and preventing potential losses that could arise due to the possibility of bank failure.

From Traditional to Transformational: Leveraging Digital Twins for Advanced Testing in Life Insurance

The Life Insurance industry is undergoing a profound digital transformation, transitioning from traditional, static methodologies to agile, data-intelligent ecosystems capable of adapting to dynamic customer demands and stringent regulatory complexities. At the forefront of this shift is the adoption of digital twins—high- fidelity virtual replicas of physical systems, processes, and assets—specifically for testing and optimizing Life Insurance platforms. This paper provides a deep dive into the role of Digital twins in the Life Insurance domain, exploring their powerful applications in underwriting, policy lifecycle management, and claims processing. By accurately simulating complex, real-world scenarios, Digital twins enable predictive testing environmentsthat refine system resilience, preemptively identify risk exposures, and ensure operational continuity. Through this sophisticated digital infrastructure, insurers gain a transformative tool for enhancing precision, boosting reliability, and driving efficiency in core insurance processes. This research investigates the architecture, applications, and strategic trajectory of Digital twins as next generation testing mechanisms reshaping Life Insurance. Keywords: Digital Twin, Life Insurance, Underwriting, Policy Lifecycle Management, Claims Processing, Virtual Simulation, System Resilience, Operational Efficiency, Data-Driven Testing, Digital Transformation, Predictive Modeling, Testing Framework, Real-World Scenarios

Utilizing Artificial Intelligence (AI) in Healthcare Insurance to Transform Risk Assessment, Claims Processing, and Fraud Detection

Artificial Intelligence (AI) is changing the face of healthcare insurance by innovating key operations such as risk assessment, claim processing, and fraud detection. By applying advanced algorithms in machine learning, insurers are able to reach a more accurate risk profiling and thus offer personalized insurance pricing based on individual health conditions and lifestyles. AI- powered automated claims processing enhances operational efficiency, shortens processing time, and minimizes errors. Additionally, fraud detection systems powered by AI proactively identify suspicious activities and save insurers and policyholders from financial loss. The paper discusses the various dimensions in which AI is making an impact on the health insurance ecosystem, highlighting the potential benefit to insurers through cost savings and improved decision-making while offering customers speedier services and personalization. The paper further points to some key regulatory challenges that will need consideration in terms of data privacy, algorithmic transparency, and ethical considerations to ensure equity and compliance in the adoption of AI technologies in health insurance. Keywords: Artificial Intelligence, Health Insurance, Risk Profiling, Claims Processing, Fraud Detection, Pricing of Insurance, Machine Learning, Regulatory Challenges, Data Privacy, Operational Efficiency

Unpaid insurance claims: Language and communication gaps

Language and communication play pivotal roles in the insurance industry, impacting claims processing, customer satisfaction, and operational efficiency. This study investigates the dynamics of errors within insurance claims across six distinct languages—African, Afrikaans, English, French, German, and Asian languages. The purpose is to identify language-specific error patterns and their implications for customer-centric methods. This study utilises a dataset of insurance claims spanning multiple languages. The analysis encompasses the probability theories approach to unravel error distributions and their associations. The study reveals that linguistic diversity significantly impacts the insurance industry, emphasising the need for language-centric approaches to error reduction This highlights the need for customised communication strategies and enlightening initiatives to address the unique challenges faced by this linguistic demographic especially the complex legal jargon that is not easily understood by all. In conclusion, this paper advises insurance companies to navigate the complex terrain of multilingual interactions. It enriches the existing body of knowledge surrounding insurance claim management, offering a roadmap to navigate the intricacies of language diversity within the industry. By addressing the linguistic-specific challenges identified in this study, insurance providers can enhance their services, reduce errors, and ultimately foster improved customer experiences.

Real-Time Risk Assessment in Insurance: A Deep Learning Approach to Predictive Modeling

The insurance industry is experiencing a paradigm shift driven by the integration of big data analytics and machine learning technologies. This article presents a comprehensive framework for implementing advanced analytics in insurance operations, focusing on risk assessment optimization and predictive modeling applications. Building upon established actuarial methods, this article demonstrates how modern machine learning algorithms and real-time data processing enhance underwriting accuracy and claim processing efficiency. This article examines the integration of multiple data sources, including IoT sensors, telematics, and external databases, highlighting their collective impact on risk assessment precision. This analysis indicates significant improvements in underwriting accuracy and claims processing speed through the implementation of automated analytics workflows. This article also addresses critical challenges in data quality, regulatory compliance, and integration complexity, providing strategic recommendations for insurers pursuing digital transformation. This article contributes to the growing body of knowledge in insurance analytics by establishing a scalable framework for big data integration while maintaining regulatory compliance and operational efficiency

Insurtech\'s Role in Enhancing Healthcare Insurance Accessibility and Efficiency in India

Abstract: Insurance Technology (Insurtech) is transforming healthcare this paper explores how innovative technologies such as digital on boarding, automated claims processing, wearable health trackers, and predictive analytics are reshaping the industry. These advancements have not only streamlined processes but also brought insurance services to underserved areas, offering personalized solutions that significantly enhance customer satisfaction. Technologies like telemedicine, block chain for secure transactions, and AI-driven risk analysis are playing a transformative role in improving transparency, speeding up claims processing, and delivering tailored insurance products. These developments are creating a more inclusive and efficient insurance system. However, challenges such as data privacy concerns, regulatory hurdles, and low levels of digital literacy remain obstacles to fully realizing the sector's potential. This study also highlights trends in InsurTech funding in India, the increasing demand for health-focused insurance products, and the role of technology in improving policyholder engagement. InsurTech is poised to redefine healthcare insurance in India, driving operational excellence, expanding coverage, and enhancing service quality. The study offers insights into Insurtech and how to reshape healthcare insurance in India.

Jason Scott Palmer, Reparations in Domestic and International Mass Claims Processes: Justice and Money, Edward Elgar Publishing, Cheltenham: 2023, pp. 200

Jason Scott Palmer, Reparations in Domestic and International Mass Claims Processes: Justice and Money, Edward Elgar Publishing, Cheltenham: 2023, pp. 200

A Study of Underwriting Decision Making Based on ARIMA Modeling with Composite Poisson Processes

Facing the problem of frequent occurrence of natural disasters, how to accurately predict the scale of loss and the probability of insolvency is the key for insurance companies to reduce risk and optimize underwriting strategies. This paper explores the multiple influencing factors of catastrophe risk and works to establish a practical risk prediction model. The model is divided into two parts: the first part uses ARIMA model to predict the risk loss, considering the influence of disaster factors, vulnerability, exposure, and disaster prevention ability on the risk size; the second part establishes a positive risk model based on the bankruptcy theory, and assumes that the insurance enrollment process is a composite Poisson process, and constructs a model of the claims process. The probability of insolvency for each year is estimated by substituting the predicted loss values into the claims model and calculating the initial and annual premiums. The study conducted a five-year risk prediction for the Philippines and Australia, and the results show that there is a significant difference in disaster risk management between the two countries, which provides a theoretical basis and practical guidance for subsequent underwriting recommendations.

The Basics of Robotic Process Automation in Insurance Claims

Through integrating Robotic Process Automation (RPA) and artificial intelligence, insurance claims processing has achieved unprecedented efficiency and accuracy. Key results include a 90% reduction in processing time (from 72 hours to under 5 minutes), 40-70% cost reductions in claims operations, and 99% accuracy rates for standard forms. Insurance carriers implementing RPA report transformative benefits: a 65% decrease in customer service costs, 92% automation of routine customer interactions, and improvement in Net Promoter Scores by 30 points. The technology has particularly excelled in fraud detection, with AI-augmented systems identifying 53% more potential fraud indicators and saving mid-sized insurers an average of $4.2 million annually. Leading organizations are now achieving straight-through processing rates of up to 75% for standard claims, demonstrating how RPA is setting new benchmarks for operational excellence in insurance claims management

Knowledge Aware Automated Health Claims Processing with Medical Ontologies and Large Language Models

One of the major challenges in automating health insurance claims processing lies in the complexity involved in validating an incoming claim's medical diagnoses against its policy Underwriting (UW) exclusions. Termed UW Exclusion Detection, this process ensures claims are only paid out if their diagnoses are not medically associated with conditions excluded under the policy. Medical diagnoses in health insurance claims are typically represented by the International Classification of Disease (ICD) codes, established by the World Health Organization. For example, given a policy that excludes "all respiratory illness". A claim with the ICD code J45 (Asthma) will be subject to rejection as J45 is a respiratory-related diagnosis that falls within the scope of the policy's UW exclusion. The key challenge in automating this process lies in the wide range of available ICD codes. The ICD-10-CM coding scheme consists of over 40,000 codes, which often results in scenarios where codes encountered during inference are absent from the training data. These unseen ICD codes limit the effectiveness of data-driven approaches, which depend on the training data to discern medically relevant associations between UW exclusions and ICD codes. This underscores the need to supplement data-driven approaches with additional domain knowledge. We hypothesize that integrating implicit medical domain knowledge inherent in Large Language Models (LLMs) with explicit domain knowledge from medical ontologies, will enhance data-driven approaches for UW Exclusion Detection. Thoroughly validated on real-world health insurance claims data, our proposed approach proved effective in accurately establishing medically relevant associations between UW exclusions and ICD codes.

Analisis Penyebab Penolakan Klaim Nasabah di PT Asuransi Umum Bumiputra Muda 1967 (Cabang Medan)

The aim of this research is to analyze the reasons behind the rejection of claims at PT Asuransi General Bumiputra Muda Medan Branch. The analytical method used is a qualitative descriptive technique that uses interviews to collect data on ten informants who work at PT Asuransi General Bumiputra Muda Medan Branch. Based on research findings, the causes of lost claims at PT Asuransi Jenderal Bumiputra Muda Medan Branch include incomplete documentation, inaccurate information in insurance claims, police investigations, delays in processing claims, and handling errors. The implications of this research include that PT Asuransi General Bumiputra Muda Medan Branch must improve communication with customers regarding the insurance claims process and strengthen internal processes to prevent fraud.

Modelling Motor Insurance Claims Using Arma Model And Appropriate Statistical Distribution Of Insurance Companies, Machakos County, Kenya

The prediction of future claim amounts based on past or present claims is a challenging task for insurance companies. According to research carried out by Michael Barth and David L. Eccles (2009); claim costs constitute large proportion of insurers expenses in evaluating the premium growth on the loss ratio of property causality insurers. Various statistical techniques have been employed to analyze claims data and assess its impact on productivity. Autoregressive (AR), Moving Average (MA), and Auto Regressive Moving Average (ARMA) processes are commonly used to model the temporal dependencies and trends in the data. These techniques enable insurance companies to understand the dynamics of claim incidence and their effects on productivity over time (Philip J, 2016). Advanced modeling techniques such as generalized additive models, negative binomial regression, and multinomial logit models have been used to evaluate claims processes, predict claim occurrence, and model claim severity (Renshaw A.E, 1994; Edward. W Frees et al., 2008; Daiane Aparecida Zanetti et al., 2006). These models consider various factors such as driver age, gender, vehicle type, and no claim discount to assess their impact on claim incidence and productivity. Previous studies have overlooked the comprehensive examination of the impact of insurance claims on productivity on insurance companies. Therefore, this research aims to address this gap by investigating the factors associated with insurance motor claims and their effects on the productivity of insurance companies by employing statistical techniques like appropriate probability distribution, incorporation of moving average technique, Autoregressive process and ARMA process, as identified by industry professionals and experts. The results obtained from the real-life data for distinctive 11 years indicated the potential strength of ARMA models to provide insurance company's claim amount prediction that could assist the companies to have better planning. The findings of this study, if adopted by various keynote players in the insurance and regulatory sectors, can be highly beneficial in formulating appropriate policies to enhance the management of motor insurance claims and improve the productivity of insurance companies.

Claim Pricing in Claim Adjudication Process

The claim adjudication process is a critical function within healthcare organizations, determining the legitimacy and value of claims submitted for payment. This paper explores the pricing logic applied during claim adjudication, focusing on the algorithms and methodologies used to evaluate claims effectively. By analyzing the impact of pricing logic on the overall efficiency and accuracy of claim processing, this study aims to provide insights that can enhance healthcare reimbursement practices and improve operational efficiency. Keywords – Claim Adjudication, Claim Pricing, Healthcare Organization, Reimbursement, Algorithms.

Leveraging Artificial Intelligence for Early Fraud Detection in Insurance: Focusing on Intake and Claims Processing

Leveraging Artificial Intelligence for Early Fraud Detection in Insurance: Focusing on Intake and Claims Processing

Service Delivery In Insurance Companies: Does Cultural Alignment Matter?

Background: In the last decade, Kenyan insurance companies have faced increased competition, changing consumer needs, and globalization which have potential adverse effects on service delivery. Despite the various initiatives that have been undertaken to resolve such challenges, there is evidence of delays in the processing of insurance claims, low claims settlement ratio and policy processing time. Therefore, this research focused was examining how cultural alignment impact service delivery within the Nairobi City County insurance industry in Kenya. It was anchored on, theory of dynamic capabilities, and service delivery dynamic theory. Employing a descriptive research design, the study encompassed 56 insurance companies, with a target population of 203 functional heads in finance, marketing, human resources, and customer service across these firms. Materials and Methods: Slovin's formula was employed to help determine the appropriate sample size while a combination of simple random and stratified random sampling were utilized in drawing the respondents. For gathering of data, structured questionnaire was deployed for both the piloting and actual undertaking of the research. The piloting was conducted in Britam Insurance Company with a sample size of 10%. The research instruments yielded both quantitative and qualitative data that was then analyzed appropriately for a comprehensive exploration of the possible statistical links among the variables. Descriptive results were then presented through frequency distributions, means, percentages, standard deviations and means. Deeper analyses involving inferential tests like the regressions tests were then performed to examine the presence or absence of statistical associations among the variables. Findings were summarized in bar charts, tables and pie charts. Results: The study found that cultural alignment was established to positively influence delivery of services among the insurance companies. Conclusion: The study therefore made recommendations on the need to conduct regular training sessions to ensure clarity and transparency in decision-making, fostering a collaborative culture that empowers employees at all levels. In addition, leaders should actively model desired behaviors and traits, creating a role model effect that encourages employees to embody these values

Unlocking the potential of digital therapeutics: The need for consistent and granular inclusion in drug compendia for managed care.

The field of digital therapeutics (DTx), software programs that prevent, manage, and treat medical conditions, continues to grow. DTx offers new treatment options and has the potential to close gaps in care caused by unmet patient needs, provider shortages, or socioeconomic or geographical disparities. However, the field of DTx has not seen steady adoption owing to barriers, particularly related to coverage, payer acceptance of the category, provider use, and integration within existing health care delivery tools. One challenge for payers to effectively evaluate and cover DTx products is ensuring that consistent data elements are listed for these products in traditional drug compendia databases. Managed care organizations will need similar information about DTx product features as are available for traditional medications to inform coverage and reimbursement decisions. The Academy of Managed Care Pharmacy DTx Advisory Group developed and distributed a request for information to the 5 top drug compendia companies to assess how compendia products incorporate DTx and prescription DTx. This article summarizes how DTx are listed within different compendia products and offers insights on future data needs to adequately inform payers. As the DTx sector grows and consumer demand rises, compendia listing services will need to evolve to accommodate these new therapies and treatment modalities and facilitate patient access and efficient claims processing. Recommendations for how compendia companies can support managed care in these efforts are outlined.

Importance of variables from different time frames for predicting self-harm using health system data.

Self-harm risk prediction models developed using health system data (electronic health records and insurance claims information) often use patient information from up to several years prior to the index visit when the prediction is made. Measurements from some time periods may not be available for all patients. Using the framework of algorithm-agnostic variable importance, we study the predictive potential of variables corresponding to different time horizons prior to the index visit and demonstrate the application of variable importance techniques in the biomedical informatics setting. We use variable importance to quantify the potential of recent (up to three months before the index visit) and distant (more than one year before the index visit) patient mental health information for predicting self-harm risk using data from seven health systems. We quantify importance as the decrease in predictiveness when the variable set of interest is excluded from the prediction task. We define predictiveness using discriminative metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. Mental health predictors corresponding to the three months prior to the index visit show strong signal of importance; in one setting, excluding these variables decreased AUC from 0.85 to 0.77. Predictors corresponding to more distant information were less important. Predictors from the months immediately preceding the index visit are highly important. Implementation of self-harm prediction models may be challenging in settings where recent data are not completely available (e.g., due to lags in insurance claims processing) at the time a prediction is made. Clinically derived variables from different time frames exhibit varying levels of importance for predicting self-harm. Variable importance analyses can inform whether and how to implement risk prediction models into clinical practice given real-world data limitations. These analyses be applied more broadly in biomedical informatics research to provide insight into general clinical risk prediction tasks.

Efektivitas Pemberian Jaminan Sosial kepada Pegawai

Social security plays an essential role in human resource (HR) management, particularly in improving employee welfare, as employees are key assets in any organization. This study aims to analyze the effectiveness of implementing the social security program at PT. Bank Sumut KCP Lahewa, North Nias Regency, and its impact on employees. A qualitative method was used, with interviews conducted to gain employees' views on the program's benefits. The results show that the program has been implemented effectively. Employees feel their well-being is supported through two health insurance options, Mandiri Inhealth and BPJS, which offer sufficient protection. This program enhances employees' sense of security and comfort, positively influencing their motivation and productivity. However, there are technical issues in the claims process that require further assessment. The study recommends that the company improve social security outreach, streamline the claims process, and consider additional relevant coverage options. Thus, social security is expected to further support employee welfare and foster a more productive work environment.

Bridging India's Financial Divide: The Power of Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are transforming India's financial sector, enhancing decision-making, risk assessment, and personalized services. They extend financial services to underserved populations through scalable solutions like digital lending and mobile banking, fostering financial inclusion and bridging urban-rural gaps. Notable progress has been made through initiatives like the Pradhan Mantri Jan Dhan Yojana (PMJDY), which opened over 53 crore accounts by August 2024. However, challenges such as low literacy rates, inadequate digital infrastructure, and a large informal economy persist. AI and ML address financial inclusion gaps by automating KYC processes and improving onboarding through facial recognition and identity verification. Initiatives like Aadhaar provide digital identities, enhancing access to banking. ML algorithms expand financial access by analyzing alternative data for credit scoring. Companies like Lenddo and Tala leverage ML for loans, and AI revolutionizes fraud detection by analyzing transaction data in real-time. AI integration with digital payment platforms like UPI has transformed India's financial ecosystem, processing over 15 billion transactions monthly by August 2024 (NPCI). AI-driven micro-lending platforms like Capital Float and Aye Finance address a $380 billion credit gap (BCG) by assessing creditworthiness using alternative data. AI-driven chatbots and financial products tailored to rural customers improve financial literacy and services. In agricultural finance, AI introduces lending models and enhances crop insurance through satellite data and weather patterns. Programs like PMFBY and companies like Skymet use AI for faster claims processing. AI-driven solutions in RegTech simplify compliance and improve fraud detection. Fintech collaborations and government initiatives like IndiaStack, Aadhaar, and DigiLocker promote financial inclusion. Overcoming barriers like infrastructure challenges, data privacy concerns, and AI bias is essential for leveraging AI to provide equitable financial services in India. The constructive interaction between AI and blockchain further enhances financial inclusion, creating a secure, transparent ecosystem that drives economic growth and social well-being.