CHITETEZO PA MOYO: AI-DRIVEN MICRO-INSURANCE FOR HEALTHCARE IN MALAWI

p r e c i o u s s a k a Page 61 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 CHITETEZO PA MOYO: AI-DRIVEN MICROINSURANCE FOR HEALTHCARE IN MALAWI PRECIOUS SAKA 23323351002 Guide MR PEMPHO JIMU (BSC) Project Report Submitted In partial fulfillment of the requirements for the degree of BACHELOR OF SCIENCE IN COMPUTER SCIENCE NOVEMBER 2025 DMI ST JOHN THE BAPTIST UNIVERSITY SCHOOL OF COMPUTER SCIENCE LILONGWE, MALAWI SIGNATURE OF THE HOD p r e c i o u s s a k a Page 62 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 BIO-DATA OF THE PROPOSED GUIDE FOR PROJECT WORK 1. PERSONAL INFORMATION NAME (in block letters) : PEMPHO JIMU Date of Birth & Age : 15/12/1989 Sex : Male Academic Qualification : MSC-CS, BSC Official Address : DMISJBULL P.O BOX 2398, LILONGWE Phone No. and Fax. : 0995319446 Residential Address : Area 25 Phone No., and e-mail id : [email protected] 2. DETAILS OF EMPLOYMENT Designation : Lecturer II Field of Specialization : Programming and Data Analysis Teaching Experience (in years): 4 Experience (in years) : 6 Particulars of contribution / experience in the field of specialization: No. of Projects guided : 30 I Mr. PemphoJimudo hereby accept to guide JIMMY FREDRICK NKHUNDA student of the Bachelor of SCIENCE IN COMPUTER SCIENCE program of DMI – ST. JOHN THE BAPTIST UNIVERSITY. Signature of the Student Signature of the Guide with Seal p r e c i o u s s a k a Page 63 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 ACKNOWLEDGEMENT First and foremost, we are grateful to the almighty God for the strength, health, and ability to successfully complete of this project. We would like to thank the DMI – St. John the Baptist University Malawi, for providing us the opportunity to do the project work as part of our curriculum. I also like to thank our university founder Rev. Fr. Dr. J.E. ARULRAJ founder of MMI and DMI We are also thankful to our director of international operations Dr. T.X.A ANANTH and Dr. IGNATIUS A. HERMAN– Director of Education for having provided these facilities to carry on my project. We a cordially give thanks to Rev. Fr. G. Sundar, Secretory university council, Dr. AmalArajAmburoise, Vice Chancellor, Rev. Sr. Pradeeba Vice Principal Administrator, Ms. Agnes Msonda vice Principal Academics, Mr. MtendeMkandawire, Head of Computer Science and Information Technology for their kind help during out project work by providing an opportunity to enhance our career, Mr. PemphoJimu, my Guide in this project and also my lecture in so many modules during my degree program We also sincerely thank, Dr. Muthuvel Laxmi kathan Dean of Research Computer Science and Information Communication Technology, our internal guide and staff members of the department for their valuable support to finish our project. Finally, I thank my parents Mr. and Mrs. Nkhunda for their support on my studies financially and other needs, and also my siblings and friends who in one way or another have given us a hand of support throughout the time we have been working on this project. p r e c i o u s s a k a Page 64 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 LIST OF ACROYMNS ACROYMN MEANING AI Artificial Intelligence ML Machine Learning USSD Unstructured Supplementary Service Data API Application Programming Interface UI User Interface OTP One Time Password WHO World Health Organization UX User Experience NLP Natural Language Processing NGO Non-Governmental Organization DFD Data Flow Diagram CRUD Create, Read, Update, Delete (database operations) MoH Ministry of Health p r e c i o u s s a k a Page 65 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 TABLE OF CONTENTS DECLARATION BY CANDIDATE ............................................ Error! Bookmark not defined. PROFORMA FOR APPROVAL OF PROJECT PROPOSAL Error! Bookmark not defined. BIO-DATA OF THE PROPOSED GUIDE FOR PROJECT WORK .................................. 62 ACKNOWLEDGEMENT ............................................................................................................ 63 LIST OF FIGURES & TABLES .................................................. Error! Bookmark not defined. LIST OF ACROYMNS .............................................................................................................. 64 ABSTRACT ................................................................................................................................. 67 CHAPTER I ................................................................................................................................ 68 1. INTRODUCTION ............................................................................................................... 68 1.1 BACKGROUND OF STUDY ..................................................................................... 68 1.2 OBJECTIVES .............................................................................................................. 69 1.3 SYSTEM DESCRIPTION ........................................................................................... 70 1.4 LITERATURE REVIEW ............................................................................................ 70 1.4.1 SUMMARY REVIEW.......................................................................................... 72 CHAPTER II ............................................................................................................................... 73 SYSTEM ANALYSIS ............................................................................................................. 73 2.1 PROBLEM STATEMENT ......................................................................................... 73 2.2 EXISTISNG SYSTEMS .............................................................................................. 74 2.3 FEASIBILITY STUDY ............................................................................................... 74 2.3.1 Executive Summary .............................................................................................. 74 2.3.2 Technical Feasibility ............................................................................................. 75 2.3.3 Operational Feasibility ......................................................................................... 75 2.4 FINDING AND RECOMMENDATIONS ................................................................. 75 2.5 PROPOSED SYSTEM ................................................................................................. 76 2.6 SYSTEM OBJECTIVE ............................................................................................... 76 2.7 SYSTEM SPECIFICATION ....................................................................................... 77 2.7.1 HARDWARE REQUIREMENTS ......................... Error! Bookmark not defined. 2.7.2 SOFTWARE REQUREMENTS ............................ Error! Bookmark not defined. CHAPTER III ............................................................................................................................. 78 p r e c i o u s s a k a Page 66 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 SYSTEM DESIGN .................................................................................................................. 78 3.1 INTRODUCTION ........................................................................................................ 78 3.2 SYSTEM ARCHITECTURE ...................................................................................... 78 3.3 FLOW CHART ............................................................................................................ 80 3.4 USE CASE DIAGRAM ............................................................................................... 80 3.5 INPUT DESIGN ........................................................................................................... 81 3.6 CIRCUIT DIAGRAM..................................................... Error! Bookmark not defined. 3.7 OUTPUT DESIGN ....................................................................................................... 81 CHAPTER IV.............................................................................................................................. 83 SYSTEM DEVELOPMENT .................................................................................................. 83 4.3 METHODOLOGY ....................................................................................................... 84 4.4 ALGORITHM .............................................................................................................. 85 CHAPTER V ............................................................................................................................... 86 SYSTEM TESTING ................................................................................................................ 86 5.1 INTRODUCTION ........................................................................................................ 86 5.2 TEST PLAN .................................................................................................................. 86 CHAPTER VI.............................................................................................................................. 88 SYSTEM IMPLEMENTANTION ........................................................................................ 88 CHAPTER VII ............................................................................................................................ 94 7.1 CONCLUSION ............................................................................................................. 94 SCREENSHOTS OF THE ANDROID MOBILE APPLICATION ................................... 94 REFERENCES ............................................................................................................................ 97 p r e c i o u s s a k a Page 67 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 ABSTRACT The outcome is that high quality health care services highly limited in Malawi has been affordability, insufficient infrastructure and very little insurance coverage. Most of the traditional health insurance companies charge services that are absolutely unaffordable for the low income and rural communities in these isolated areas where digital exclusion is compounded with a lack of Smartphone ownership and poor internet connectivity. A lack of tailored services for various health needs, and delays in emergency response, coupled with inefficiencies in manual claim processing, worsen financial vulnerability. Importantly, these, therefore, emphasize the need for innovative, tech-enabled solutions. Evidence-Driven Health furthermore constrains meaningful policy formulation and strategic resource mobilization. Without smart systems, insurance companies do not have the competence to evaluate risks adequately, predict health trends or cut survey costs but fail to deliver sufficiently their output. The aforementioned gaps can adequately be filled through introducing Artificial Intelligence and Machine Learning within the Chitetezo Pa Moyo platform to provide efficient real-time risk assessments, automated claims adjudications, policy recommendations for particular issues in addition to deep user engagement. Such development holds the potential of bridging the digital divide and transforming access for poorer Malawians. Keywords: Insurtech, micro-health insurance, mobile money, access to healthcare, telemedicine, financial inclusion, Malawi. p r e c i o u s s a k a Page 68 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 CHAPTER I 1. INTRODUCTION Access to quality and affordable healthcare remains one of the most pressing challenges in Malawi, particularly for people living in rural areas. Similar to the way solar energy has transformed energy access in low-resource settings, digital technology and intelligent systems are now emerging as powerful tools to bridge gaps in healthcare. Malawi has one of the lowest health insurance coverage rates in sub-Saharan Africa, with a majority of citizens, especially in rural regions, relying on out-of-pocket expenses or under-resourced public health facilities. This has left many families vulnerable to health-related financial shocks and has led to delays in treatment, worsening health outcomes, and an increased burden on already stretched healthcare systems.The Chitetezo Pa Moyo (Safeguarding Life) initiative proposes an innovative microinsurance solution powered by Artificial Intelligence (AI) and Machine Learning (ML) to tackle these longstanding problems. Just like how solar energy is considered a clean, reliable, and lowcost alternative for households and agriculture, AI-powered micro-insurance can be a sustainable and inclusive model for delivering health coverage to the underserved. Through intelligent risk assessment, automated claims processing, and offline-compatible access channels like USSD and SMS, this platform aims to make health insurance accessible even to people without smartphones or internet connectivity. Moreover, Malawi’s rural health infrastructure is not only underdeveloped but also lacks integration with digital tools for proactive care and efficient service delivery. Chitetezo Pa Moyo addresses these gaps by offering personalized insurance plans based on a person’s medical history, location, and income level, while ensuring real-time system monitoring—similar to how smart solar energy systems detect faults and inefficiencies to improve energy yield. The AI and ML models will help assess individual risk levels, suggest policy recommendations, and trigger early alerts in medical emergencies through automated messages. By adopting this technology, Malawi can take a major step toward universal health coverage, reducing dependency on traditional systems that are often inefficient, expensive, and inaccessible to many. Chitetezo Pa Moyo is therefore not just a project, but a life-changing platform designed to improve health security, promote financial inclusion, and ultimately safeguard the lives of Malawians through the power of intelligent and inclusive micro-insurance. 1.1 BACKGROUND OF STUDY Malawi, like many sub-Saharan African countries, is burdened by multiple challenges in the healthcare sector. These challenges include a high prevalence of communicable and noncommunicable diseases, insufficient health infrastructure, poor funding, and a lack of skilled medical personnel. According to the World Health Organization, the doctor-to-patient ratio in Malawi is significantly lower than the recommended average, and over 70% of Malawians rely p r e c i o u s s a k a Page 69 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 on out-of-pocket payments to access healthcare services. This reliance on out-of-pocket expenditure forces many families to choose between health and other essential needs, often leading to catastrophic financial consequences. One of the most pressing issues is the lack of affordable and accessible health insurance. The health insurance penetration rate in Malawi remains low, especially in rural areas, due to high premiums, lack of awareness, and limited infrastructure to support insurance operations. Traditional insurance models are often designed without the needs of low-income earners in mind and fail to account for the socioeconomic realities of people living in rural areas. Microinsurance has emerged as a viable alternative, providing low-cost, accessible insurance options tailored for low-income populations. However, the micro-insurance models currently available in Malawi are either unsustainable or fail to meet the real needs of their target users. These systems are often paper-based, difficult to manage, and prone to fraud and inefficiency. The application of artificial intelligence (AI) and machine learning (ML) in micro-insurance introduces new possibilities for addressing these challenges. AI and ML can be used to personalize insurance policies, automate claims processing, detect fraudulent activities, and provide insights that help in designing effective health programs. The "Chitetezo Pa Moyo: Safeguarding Life" project aims to build an AIand ML-driven micro-insurance platform that ensures inclusive access to healthcare in Malawi. It combines mobile technology (including USSD and SMS support), multilingual interfaces (English and Chichewa), and real-time emergency response features to provide comprehensive, affordable, and accessible health insurance services. The system is designed to function even in remote areas with limited internet access, making it suitable for Malawi's diverse population. 1.2 OBJECTIVES The main objective of this project is to design, develop, and deploy a comprehensive AIand ML-powered micro-insurance platform that improves access to inclusive, affordable healthcare services in Malawi, particularly for underserved communities, by leveraging mobile technologies, intelligent systems, and multilingual support. To design and implement a micro-insurance system accessible through mobile (USSD and SMS) and web platforms, ensuring wide reach including users without smartphones or internet access. To develop and integrate artificial intelligence (AI) and machine learning (ML) algorithms capable of profiling individuals, assessing health risks, and dynamically generating affordable and personalized insurance policies based on data-driven models. To automate the insurance claims process using intelligent systems that can validate, prioritize, and process claims efficiently, reducing manual intervention and turnaround times. p r e c i o u s s a k a Page 76 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 Reach and Accessibility: Current healthcare systems and insurance schemes in Malawi are inaccessible to a majority of the rural population. The proposed system bridges this gap by leveraging mobile technologies (USSD/SMS) and local language support (Chichewa), ensuring inclusiveness even for non-smartphone users. Secondly Lack of Intelligence in Risk Profiling: Existing insurance platforms do not utilize AI/ML for personalized risk assessments and premium adjustments. Chitetezo Pa Moyo will introduce intelligent risk assessment using machine learning algorithms, improving fairness and affordability. Thirdly Manual Claims and Policy Management: Current systems involve a lot of manual work in claim validation and policy design. The proposed system will automate claims processing and policy recommendations using data analytics and decision support algorithms. Lastly Absence of Localized Health Education: There is a lack of systems that integrate health awareness and education as part of the insurance service. Chitetezo Pa Moyo includes multilingual health tips, maternal advice, and disease prevention information to empower users with knowledge. 2.5 PROPOSED SYSTEM The proposed system, Chitetezo Pa Moyo, is a comprehensive, AI and machine learningpowered micro-insurance platform designed to improve access to affordable healthcare services in Malawi. It leverages mobile communication channels (USSD, SMS, WhatsApp), intelligent risk modeling, and local language support to provide inclusive health insurance for underserved populations. Key functionalities include:  User registration and profile creation  AI-driven risk scoring and premium recommendation  Digital insurance policy management  Smart claims processing  Integration with mobile money for payments  Offline access via USSD/SMS  Localized health education (in English and Chichewa) 2.6 SYSTEM OBJECTIVE The main objective of the Chitetezo Pa Moyo project is to develop an intelligent, scalable, and inclusive micro-insurance health platform that leverages artificial intelligence (AI) and mobile technologies to improve access to affordable healthcare services, especially for underserved and rural populations. The system aims to utilize machine learning algorithms for accurate health risk p r e c i o u s s a k a Page 77 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 profiling and personalized insurance policy generation tailored to individual needs. It will feature an AI-powered claims processing engine to enable fast, fair, and automated claim evaluations, reducing manual inefficiencies and fraud. The platform is designed to ensure digital inclusion by supporting USSD and SMS-based interfaces, allowing users with basic feature phones to access services seamlessly. Additionally, it will integrate with local mobile money platforms for convenient premium payments, offer real-time alerts, and provide multilingual health education content to enhance user engagement and awareness. Lastly, the system will include an admin dashboard to enable insurance administrators and healthcare partners to monitor policies, user activity, and claims securely and efficiently. 2.7 SYSTEM SPECIFICATION It shows the specifications of the requirements of the system to be developed or to be used after production. It’s how both the software and hardware requirements. Hardware Requirements To effectively deploy and run the Chitetezo Pa Moyo platform, the following hardware components are recommended:  Server: Cloud or on-premise server with a minimum of 8GB RAM, 4-core processor, 50GB SSD storage  Smartphones/Tablets (Admin Use): Android 8.0+, minimum 2GB RAM  USSD Gateway Device: GSM modem or hosted USSD API platform  Mobile Phones (User Access): Compatible with USSD/SMS  Modem for SMS Service (optional): USB GSM modem for SMS integration Software Requirements  Backend: Python Flask or Node.js  Frontend: React for web dashboard, React Native for mobile app (admin version)  Database: PostgreSQL / MongoDB  AI/ML: Scikit-learn, TensorFlow or PyTorch for risk and fraud analysis  Communication APIs: Africa's Talking (USSD/SMS), Twilio (optional)  Mobile Payments: Mpamba and Airtel Money APIs (integration modules)  Other Tools: Docker for containerization, GitHub for version control, Firebase/OneSignal for real-time alerts p r e c i o u s s a k a Page 78 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 CHAPTER III SYSTEM DESIGN 3.1 INTRODUCTION System design shows the design and data allocation on the system. the process of designing the elements of a system such as the architecture, modules, and components, the different interfaces of those components, and the data that goes through that system all this is done according to the requirements provided. It acts as a guideline or blueprint for the developers when making the system. Once the software requirements have been analyzed and specified the software design involves three technical tasks which are design, coding, implementation and testing which are required for the building and verification of the software. The design activities are mostly critical important in this phase, because in this activity, decisions ultimately affecting the success of the software implementation and its ease of maintenance are made. These decisions have the final bearing upon reliability and maintainability of the system. Design phase is when user requirements are accurately translated into a software or system and if they are miss translated it can lead to development of a system which does not meet user standards(requirements). Creating a system which can work efficiently providing the required output is done in the design phase, a system should give or provide accurate and correct output and results if commanded to process a certain task. 3.2 SYSTEM ARCHITECTURE The architecture of the Chitetezo pa Moyo system is based on a modular, cloud-enabled design that integrates mobile technologies, real-time data processing, and health service coordination to facilitate rapid emergency response in low-resource environments. The frontend is developed using React, providing a responsive, user-friendly web interface for healthcare professionals and administrators to manage reported cases, monitor patient status, and coordinate response actions. For patients and community health workers, the system offers a mobile application and a USSD/SMS interface to ensure inclusivity for users without smartphones or internet access. Reported symptoms and incidents are processed through a backend triage engine, which uses rule-based algorithms to assess severity and trigger alerts based on urgency and geographic proximity. These alerts are delivered to medical personnel and emergency responders via the React dashboard and integrated messaging services. All case data is stored securely in cloudhosted relational and NoSQL databases, enabling real-time analytics, historical tracking, and efficient resource allocation. The system also exposes APIs to support integration with national p r e c i o u s s a k a Page 79 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 health information systems, ambulance services, and third-party health platforms. Security is enforced through modern protocols, including user authentication, role-based access control, and end-to-end encryption to protect sensitive health information and ensure compliance with data protection standards. DATA FLOW DIAGRAM Figure 2: p r e c i o u s s a k a Page 80 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 3.3 FLOW CHART A flow chart is a diagram which is used to demonstrate the flow of data in the system from input to output processes, it includes a lot of processes depending on the type and size of the system. 3.4 USE CASE DIAGRAM Use case diagram explains how system components are interacting, its main focus is how a system user is interacting with the system inform of an actor and how the system is responding to such interactions or requests, both the system and the user are actors to the system. The system will allow users to create an account to access the user dash board and the system will receive and create the user requests, below is a use case diagram explaining the processes. Figure 5 1: Use Case Diagram p r e c i o u s s a k a Page 81 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 3.5 CLASS DIAGRAM 3.5 INPUT DESIGN In the Chitetezo Pa Moyo systeman AI-driven micro-insurance platform for healthcare in Malawiinput design is critical to ensuring accurate capture of health, environmental, and userrelated data. The system supports both automated data input (from devices/sensors) and manual input (from users or health agents). Proper input design ensures clean, validated data that supports timely decision-making in health insurance underwriting, claims approval, and risk assessment 3.7 OUTPUT DESIGN The output design is the most important task of any system. During output design, developers identify the type of outputs needed, and consider the necessary output controls and prototype report layouts. Referring to our system output design consists the user interface/ dashboard which will show the user all what the sensors are observing on the solar plant, and also suggested solutions to some issues. p r e c i o u s s a k a Page 82 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 Figure 7 1: System Dashboard 3.7 TABLE DESIGN Users Collection Policies Collection Claims Collection Field Type Description AiFraudScore Number 0-100 (Gemini-genarated) medicalDocuments Array Cloudinary URLs Status String Pending, Approed, Rejected p r e c i o u s s a k a Page 83 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 CHAPTER IV SYSTEM DEVELOPMENT 4.1INTRODUCTION System development for Chitetezo Pa Moyo involves the detailed process of designing, developing, testing, and implementing a digital micro-insurance platform tailored for Malawians, particularly those in rural and underserved areas. The system integrates AI for health risk assessments, mobile money for transactions, and USSD/SMS for accessibility. This development phase captures the translation of user and system requirements into a robust, scalable, and user-friendly software solution. 4.2 MODULE DESCRIPTION Modules in Chitetezo Pa Moyo represent individual components that carry out specific tasks and work together to deliver the intended health micro-insurance services. Below are the core modules: 4.2.1 Module 1 User Registration and Profile Management: This module handles new user sign-up and login. It captures personal details such as name, phone number, gender, location, and language preference. Each user has a profile with editable health and policy information. Bi-lingual support (English and Chichewa) ensures inclusivity. 4.2.1 Module 2 Policy Management: This module enables users to view, select, and manage micro-insurance health policies. It integrates AI to recommend the most suitable plans based on risk profiling. Admins can create and update policies and pricing structures. 4.2.4 Module 3 Claims Management: Handles user-submitted claims for medical coverage. AI is used to detect anomalies or fraudulent claims before submission to healthcare partners. Claims can be tracked by users via USSD or mobile app. p r e c i o u s s a k a Page 84 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 4.2.4 Module 4 Admin Dashboard: Provides a secure interface for system admins and health partners to monitor users, policies, claims, and analytics. Features role-based access and real-time reporting. METHODOLOGY Methodology in research is defined as the systematic method to resolve a research problem through data gathering using various techniques, providing an interpretation of data gathered and drawing conclusions about the research data. The methodology for developing the IoT-based solar power monitoring system is a step-by-step process that requires careful planning and execution. It involves selecting appropriate components, developing and integrating these components, and testing the functionality of the system. By following this methodology, it is possible to develop a comprehensive monitoring system capable of improving the efficiency and reliability of solar power systems. Below is the methodology that will be used to implement the project. 4.3.1 AGILE METHODOLOGY The Chitetezo Pa Moyo system was developed using the Agile methodology to support a flexible and iterative software development process. Agile was chosen for this project due to its suitability for dynamic environments where user needs and system requirements evolve over time. Given the nature of this health-focused micro-insurance platform—which must address the needs of users in rural, urban, and underserved areas—Agile offered the ideal balance of responsiveness and adaptability. Development was broken down into short, time-bound iterations known as sprints, each lasting two weeks. Each sprint had a clearly defined goal and deliverables, allowing developers to implement features incrementally while maintaining quality and speed. For example, one sprint focused entirely on mobile money payment integration, ensuring compatibility with Airtel Money and TNM Mpamba APIs. Another sprint dealt exclusively with AI-powered risk profiling, leveraging Gemini's language and analytical capabilities. A critical aspect of Agile is continuous stakeholder engagement. During each sprint, feedback from project advisors, target users, and healthcare partners was gathered and used to refine the system. This iterative approach ensured that the project remained aligned with user needs, particularly around language accessibility (English/Chichewa), rural usability (USSD/SMS), and healthcare policy modeling. Furthermore, Agile supports parallel testing and development, which minimized bugs and enhanced modular reliability. Each completed module was tested before the next sprint began. This method not only reduced overall project risk but also ensured smoother integration of components such as user registration, claims management, and AI predictions. p r e c i o u s s a k a Page 85 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 4.4 ALGORITHM At the heart of the Chitetezo Pa Moyo system lies a robust Artificial Intelligence (AI) engine powered by Artificial Neural Networks (ANNs). These networks mimic the structure and function of the human brain, making them well-suited for detecting complex, non-linear relationships in health and financial datasets. The ANN models are trained using health histories, demographic data, and historical claim outcomes, enabling the system to learn patterns that indicate risk and fraudulent behavior.  Health Risk Prediction: By analyzing data such as age, existing conditions, lifestyle, and geographic health trends, ANN models are able to estimate a user's health risk score. This score is then used to offer personalized insurance policies that reflect the individual's true risk profile.  Fraud Detection in Claims: ANN models are trained to identify anomalies in claim submissions—such as inconsistent treatment costs or suspicious timing—that may indicate fraud. When an outlier is detected, the system flags the claim for manual review.  AI-Driven Policy Recommendations: The platform uses AI to recommend affordable and suitable policies based on the user’s medical history, risk category, and payment capability. This supports inclusiveness by matching people with appropriate plans they can understand and afford. p r e c i o u s s a k a Page 92 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 constfacilitySchema = new mongoose.Schema({ governmentId: String, // Malawi MOH Facility ID name: { type: String, required: true }, type: { type: String, enum: ["clinic", "hospital", "pharmacy"], required: true }, location: { district: String, gps: { lat: Number, lng: Number } }, contact: { phone: String, email: String } },{ timestamps:true }); const Facility = mongoose.model('Facility', facilitySchema); export default Facility; / audit.model.js import mongoose from "mongoose"; constauditSchema = new mongoose.Schema({ action: { type: String, enum: ["login", "claim_approval", "payout", "user_registration" ,"login_attempt","facility_registration", "facility_update","policy_creation","policy_update","claim_submission"], required: true }, performedBy: { type: mongoose.Schema.Types.ObjectId, ref: 'User' }, targetEntity: { type: String, enum: ["user", "claim", "policy","facility"] }, targetId: mongoose.Schema.Types.ObjectId, // Reference to affected document timestamp: { type: Date, default: Date.now }, ipAddress: String, metadata: mongoose.Schema.Types.Mixed // Additional data about the action p r e c i o u s s a k a Page 93 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 }); const Audit = mongoose.model('Audit', auditSchema); export default Audit; / facilities.model.js import mongoose from "mongoose"; constfacilitySchema = new mongoose.Schema({ governmentId: String, // Malawi MOH Facility ID name: { type: String, required: true }, type: { type: String, enum: ["clinic", "hospital", "pharmacy"], required: true }, location: { district: String, gps: { lat: Number, lng: Number } }, contact: { phone: String, email: String } },{ timestamps:true }); const Facility = mongoose.model('Facility', facilitySchema); export default Facility; p r e c i o u s s a k a Page 94 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 CHAPTER VII 7.1 CONCLUSION The Chitetezo Pa Moyo: Safeguarding Life system has been successfully developed as an inclusive, AI-assisted micro-insurance platform that addresses critical gaps in healthcare access and financial protection for underserved populations in Malawi. Through the use of modern web technologies such as React.js, Node.js, Express.js, and MongoDB, the system delivers a responsive, secure, and scalable solution that empowers users to register, manage insurance policies, submit claims, and access emergency services through both online and offline channels. Notably, the integration of multilingual support (English and Chichewa), mobile money platforms (Airtel Money and TNM Mpamba), and USSD/SMS functionality ensures that even those in rural areas without smartphones or internet access can participate in the healthcare ecosystem. User testing confirmed the platform’s ease of use, relevance, and potential impact on healthcare equity. As a result, the project meets its objectives of improving healthcare accessibility, promoting digital inclusion, and introducing affordable micro-insurance to marginalized populations. SCREENSHOTS User Dashboard p r e c i o u s s a k a Page 95 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 User claims p r e c i o u s s a k a Page 96 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 p r e c i o u s s a k a Page 97 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 REFERENCES 1. World Health Organization (2021). Universal Health Coverage: Moving Together to Build a Healthier World. Retrieved from https://www.who.int 2. Ministry of Health – Malawi (2023). Digital Health Strategy 2020–2025. Retrieved from https://www.health.gov.mw 3. GSMA (2022). State of the Mobile Money Industry in Sub-Saharan Africa. Retrieved from https://www.gsma.com 4. Africa's Talking. (n.d.). SMS & USSD API Documentation. Retrieved from https://developers.africastalking.com 5. MongoDB Inc. (2023). MongoDB Documentation. Retrieved from https://www.mongodb.com/docs 6. React.js (Meta). (n.d.). React Official Documentation. Retrieved from https://reactjs.org 7. Node.js Foundation. (n.d.). Node.js Documentation. Retrieved from https://nodejs.org 8. Express.js. (n.d.). Express.js Web Framework Documentation. Retrieved from https://expressjs.com 9. Stripe. (n.d.). Stripe API for Payment Integration. Retrieved from https://stripe.com/docs/api 10. OpenAI. (2024). AI in Healthcare Applications: A Survey. Retrieved from https://openai.com 11. United Nations (2022). Sustainable Development Goals – Goal 3: Good Health and Well-being. Retrieved from https://sdgs.un.org/goals/goal3 12. Airtel Malawi. (n.d.). Airtel Money Developer Portal. Retrieved from https://airtel.africa 13. TNM Malawi. (n.d.). Mpamba API Integration Guide. Retrieved from https://www.tnm.co.mw 14. Chichewa Dictionary Project. (n.d.). English–Chichewa Translation Support. Retrieved from https://www.translate.chichewadictionary.org 15. Malawi Communications Regulatory Authority (MACRA). (2022). Guidelines on Mobile Communication Services and Data Privacy. Retrieved from https://www.macra.org.mw 16.  World Bank. (2023). Health Financing in Africa: Trends and Challenges. Retrieved from https://www.worldbank.org 17.  BMC Health Services Research. (2022). The Role of Micro-Insurance in Improving Access to Healthcare in Rural Africa. https://bmchealthservres.biomedcentral.com 18.  IBM Research. (2021). AI for Social Good: Advancing Health Equity in Low-Resource Settings. Retrieved from https://www.research.ibm.com 19.  MIT Technology Review. (2023). How AI is Transforming Insurance. Retrieved from https://www.technologyreview.com 20.  Digital Impact Alliance. (2020). Designing Inclusive Digital Services for Developing Countries. Retrieved from https://digitalimpactalliance.org p r e c i o u s s a k a Page 98 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 61-98 November 2025 21.  JICA. (2022). Malawi Energy Sector: Sector Position Paper. Retrieved from https://www.jica.go.jp 22.  WHO Malawi. (2023). Community Health Strategies and Local Innovations. Retrieved from https://www.afro.who.int/countries/malawi 23.  IEEE Access. (2024). Internet of Things (IoT) Applications in Micro-Insurance Systems. https://ieeexplore.ieee.org 24.  Google AI. (2022). Ethical Use of AI in Emerging Markets. Retrieved from https://ai.google/responsibility 25.  Helix Institute. (2021). Designing Mobile Money Services for the Poor in Africa. Retrieved from https://www.helix-institute.com 26.  UNICEF Malawi. (2023). Mobile Innovations for Health and Insurance in Malawi. Retrieved from https://www.unicef.org/malawi 27.  Nature Digital Medicine. (2023). Machine Learning in Community Health Systems: A Review. https://www.nature.com/npjdigitalmed 28.  Code for Africa. (2024). Building Scalable Digital Public Infrastructure. Retrieved from https://codeforafrica.org 29.  McKinsey& Company. (2022). The Future of Health Tech in Africa. Retrieved from https://www.mckinsey.com 30.  TechCrunch Africa. (2025). Fintech Driving Health Access in Africa. Retrieved from https://techcrunch.com/tag/africa 31.  National Statistical Office Malawi. (2023). Digital Penetration and Mobile Usage Report. Retrieved from https://www.nsomalawi.mw 32.  WHO Innovation Hub. (2024). Leveraging Digital Health Tools in Low-Resource Settings. Retrieved from https://www.who.int/initiatives/digital-health