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The use of artificial intelligence (AI) in the insurance industry has gained considerable momentum in recent years, revolutionizing various aspects of insurance operations and customer experience. AI technologies, such as machine learning, natural language processing, and predictive analytics, are being employed to streamline processes, enhance risk assessment, automate claims processing, detect fraud, and personalize policies, among other applications. By leveraging AI, insurance companies can make data-driven decisions, improve operational efficiency, mitigate risks, and deliver more tailored products and services to policyholders also utilizing AI in these insurance processes can enhance precision and result in cost savings.
However, there are some obstacles that currently make it difficult to fully integrate AI into existing insurance systems.
Challenges to incorporate AI into the insurance industry
Managing Data Quality and Availability: ML models and algorithms heavily rely on high-quality and diverse data. However, insurance companies may encounter challenges in ensuring the accuracy, completeness, and accessibility of data. Inconsistent or fragmented data sources can hinder models' effectiveness.
Data Privacy and Security: Data privacy and security pose concerns in the insurance industry due to the sensitive customer information involved. Integrating AI requires robust measures to protect data and comply with regulatory requirements, such as HIPAA and GDPR, to maintain trust and confidentiality.
Domain Expertise: Insurance-specific knowledge and expertise are required to label insurance-related documents and data correctly. It can be challenging to find labeling resources with deep understanding of insurance terminology, policies, and regulations.
Scalability and Volume: Insurance companies handle large volumes of data and documents that need to be labelled. Scaling labeling operations to handle such volumes while maintaining accuracy and efficiency can be a significant challenge.
Adapting to Evolving Insurance Requirements: Insurance practices, regulations, and products continuously evolve. Keeping the practices up to date and adaptable to changing insurance requirements can be a challenge. Continuous monitoring, feedback loops, and iterative improvements are essential to address evolving needs.
Managing the cost: Building infrastructure and technology costs, data acquisition and management expenses, talent acquisition, training and model development costs (the time and costs of labeling data, and slow iteration cycles)
Ethical and Bias Concerns: A growing challenge is the ethical considerations surrounding AI. The effectiveness of AI algorithms heavily relies on the quality of the data used for training, as biased data can result in biased outcomes that perpetuate discrimination and can impact the lives and financial security of people and their businesses.
Best practices to manage insurance AI projects
To overcome these challenges requires a combination of industry and domain expertise, quality control measures, and optimizing labeling processes to strike a balance between accuracy, cost-efficiency, and scalability of datasets
At Objectways we follow the Best Practices in labeling which include
Adherence to precise guidelines: We establish well-defined guidelines that outline the criteria and instructions for labeling data. Clearly communicate the labeling process, including the types of labels required, the context in which labels should be applied, and any specific rules or conventions to follow.
Executing KPT (Knowledge, Process, Test): Firstly, we establish a deep understanding of the insurance domain and its terminology to our team. Next, we define a standardized labeling process with clear guidelines for consistent and accurate labeling. Then there is regular testing and quality assurance to ensure the effectiveness of the process, allowing for continuous improvement and adaptation to changing insurance requirements.
Strong Team Organization: Our team has implemented a well-organized structure consisting of labeling personnel, spot Quality Assurance (QA) reviewers, and dedicated QA professionals. This framework promotes responsibility, streamlined workflow, and unwavering quality.
Performance Measures: We have established suitable quality indicators, including precision, recall, and F1 score, to evaluate the accuracy of labeling. We consistently monitor and analyse these measures to identify areas for enhancement and uphold exceptional quality standards.
Continuous Feedback Loop: We have implemented a system for providing consistent feedback to labeling teams regarding their performance. This facilitates the resolution of any discrepancies, clarifies guidelines, and enhances the overall accuracy of labeling.
Quality Control and Spot QA: By implementing robust quality control measures, including periodic spot QA reviews by experienced reviewers, helps identify and rectify any labeling errors, ensures adherence to guidelines, and maintains high labeling quality.
Data Security and Privacy: Data security and privacy are of paramount importance in the insurance industry, necessitating stringent measures to safeguard sensitive information and protect customer confidentiality. Therefore, to validate our commitment to security and privacy controls, we have obtained the following formal certifications SOC2 Type2, ISO 27001, HIPAA, and GDPR. These certifications affirm our dedication to safeguarding customer data and they continue to expand, adhering to Privacy by Design principles and incorporating industry standards and customer requirements from various sectors.
Below are some of the representative use cases for our clients:
Expediting and streamlining claims processing: Developing AI-powered claims processing and adjustment systems for insurance companies face challenges such as establishing human in the loop system, dealing with the time and cost-intensive process of labeling data, and facing slow iteration cycles. At Objectways we offer comprehensive support for all aspects of claims adjustment AI projects, offering solutions that enhance model performance and accelerate time to market, thereby helping insurers overcome these obstacles.
Accelerating insurance documents faster with AI and human in the loop: Expediting the processing of insurance documents by utilizing AI in combination with human expertise. AI technologies like OCR and NER enable efficient information retrieval, document understanding, and automated decision-making. Objectways human-in-the-loop workflows ensures accuracy, quality control, in the above cases, enabling faster processing while maintaining precision and compliance.
Fast and Precise Underwriting: By training computer vision models on geospatial data, underwriters can assess risks and property values without the need for human inspection. At Objectways we offer comprehensive support for geospatial data within all its products, enabling teams to visualize raw data, annotate information, and curate location data for spatial analysis. This native support empowers teams to leverage geospatial data effectively and make informed decisions based on accurate insights.
Driving language and text AI development: At Objectways we offer cutting-edge text labeling services to insurers, enabling them to harness the power of large language models for enhancing recommendations, chatbots (Providing automated customer service, answering frequently asked questions with personalization and empathy. Enabling multi-lingual customer service by translating customers queries and responding in the customers preferred language), risk assessments, and other applications. With our services, insurers can accelerate and optimize the development of NLP-based AI, propelling advancements in language and text processing.
Summary
At Objectways, our team consists of over 1000 experts specializing in Computer Vision, Natural Language Processing (NLP), and prompt engineering. They bring extensive experience in tasks like object detection, such as image segmentation and classification, as well as common language tasks like named entity recognition (NER), optical character recognition (OCR), and LLM prompt engineering.
In summary while there are challenges to fully integrating AI into the insurance industry, Objectways follows best practices in labeling to overcome these obstacles and ensure accuracy, cost-efficiency, and scalability of datasets. To learn more about how Objectways can revolutionize your insurance processes with AI, contact sales@objectways.com to provide feedback or have any questions.
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Ο Κύκλος Ιδεών για την Εθνική Ανασυγκρότηση σε συνεργασία με το Ίδρυμα Διεθνών Νομικών Μελετών- Καθηγητού Ηλία Κρίσπη και με την υποστήριξη της Ελληνοαμερικανικής Ένωσης, πραγματοποίησε ανοιχτή συζήτηση με θέμα:
«Προστασία Προσωπικών Δεδομένων - Ηλεκτρονική Ταυτοποίηση»
Διαχείριση Προσωπικών Δεδομένων μετά την υιοθέτηση του νέου Γενικού Κανονισμού (GDPR) και Ηλεκτρονική Ταυτοποίηση με τη χρήση του δικτύου eIDAS (eID_EU): Επιχειρησιακές, τεχνικές και θεσμικές συνέπειες
την Τετάρτη 14 Μαρτίου 2018, στο Θέατρο της Ελληνοαμερικανικής Ένωσης
Στη συζήτηση συμμετέχουν:
Λίλιαν Μήτρου, Πανεπιστήμιο Αιγαίου - Πολυτεχνική Σχολή
Κωνσταντίνος Χριστοδούλου, Πανεπιστήμιο Αθηνών - Νομική Σχολή
Αντώνης Στασής, Υπουργείο Διοικητικής Ανασυγκρότησης - Διεύθυνση Ηλεκτρονικής Διακυβέρνησης
Χρυσούλα Μιχαηλίδου, ΕΕΤΤ, Νομική Υπηρεσία
Γιώργος Παπασταματίου, FORTH-CRS
Κώστας Γκρίτσης, MICROSOFT
Φερενίκη Παναγοπούλου-Κουτνατζή, Πάντειο Πανεπιστήμιο – Σχολή Δημόσια Διοίκησης
Συντονίζει ο Πέτρος Καβάσαλης, Πανεπιστήμιο Αιγαίου - Πολυτεχνική Σχολή & Κύκλος Ιδεών για την Εθνική Ανασυγκρότηση
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The healthcare industry has witnessed the remarkable growth of artificial intelligence (AI), which has found diverse applications. As technology advances, AI’s potential in healthcare continues to expand. Nevertheless, certain limitations currently hinder the seamless integration of AI into existing healthcare systems.
AI is used in healthcare datasets to analyze data, provide clinical decision support, detect diseases, personalize treatment, monitor health, and aid in drug discovery. It enhances patient care, improves outcomes, and drives advancements in the healthcare industry. Many AI services such as Amazon Comprehend Medical, Google Cloud Healthcare API, John Snow Labs provide pre-built models. Due to the variety of medical data and requirements for accuracy human in the loop techniques are important to safeguard accuracy. However, the success of AI and ML models largely depends on the quality of the data they are trained on, necessitating reliable and accurate data labelling services.
Challenges in applying AI in Healthcare
Extensive testing of AI is necessary to prevent diagnostic errors, which account for a significant portion of medical errors and result in numerous deaths each year. While AI shows promise for accurate diagnostics, concerns remain regarding potential mistakes. Ensuring representative training data and effective model generalization are crucial for successful AI integration in healthcare.
In the healthcare sector, ensuring the privacy and security of patient data is paramount, as it not only fosters trust between healthcare providers and patients, but also complies with stringent regulatory standards such as HIPAA, promoting ethical, responsible data handling practices.
Lack of High-Quality Labeled datasets
Achieving a high quality labeled medical dataset poses several challenges, including:
Medical Labeling Skills: -Properly labeling medical data requires specialized domain knowledge and expertise. Medical professionals or trained annotators with a deep understanding of medical terminology and concepts are necessary to ensure accurate and meaningful annotations.
Managing Labeling Quality: -Maintaining high-quality labeling is crucial for reliable and trustworthy datasets. Ensuring consistency, accuracy, and minimizing annotation errors is challenging, as medical data can be complex and subject to interpretation. Robust quality control measures, including double-checking annotations and inter-annotator agreement, are necessary to mitigate labeling inconsistencies.
Managing the Cost of Labeling: -Labeling medical datasets can be a resource-intensive process, both in terms of time and cost. Acquiring sufficient labeled data may require significant financial investment, especially when specialized expertise is involved. Efficient labeling workflows, leveraging automation when feasible, can help manage throughput and reduce costs without compromising data quality.
Data Privacy and Security: -Safeguarding patient privacy and ensuring secure handling of sensitive medical data is crucial when collecting and labeling datasets.
Data Diversity and Representativeness: -Ensuring that the dataset captures the diversity of medical conditions, demographics, and healthcare settings is essential for building robust and unbiased AI models.
Best practices to manage medical labeling projects
Addressing these challenges requires a combination of domain expertise, quality control measures, and optimizing labeling processes to strike a balance between accuracy, cost-effectiveness, and dataset scale.
At Objectways we follow the Best Practices in medical labeling which include
Adherence to Guidelines: -Familiarize labeling teams with clear and comprehensive guidelines specific to the medical domain. Thoroughly understanding the guidelines ensures consistent and accurate labeling.
Conducting KPT (Knowledge, Process, Test): -Provide comprehensive training to labeling teams on medical concepts, terminology, and labeling procedures. Regular knowledge assessments and testing help evaluate proficiency of labeling teams and ensure continuous improvement.
Robust Team Structure: -We have established a structured team comprising labeling personnel, spot Quality Assurance (QA) reviewers, and dedicated QA professionals. This structure promotes accountability, efficient workflow, and consistent quality.
Quality Metrics: -We have implemented appropriate quality metrics such as precision, recall, and F1 score to assess labeling accuracy. We regularly monitor and track these metrics to identify areas for improvement and maintain high-quality standards.
Continuous Feedback Loop: -We have established a feedback mechanism where labeling teams receive regular feedback on their performance. This helps address any inconsistencies, clarify guidelines, and improve overall labeling accuracy.
Quality Control and Spot QA: - By implementing robust quality control measures, including periodic spot QA reviews by experienced reviewers, helps identify and rectify any labeling errors, ensures adherence to guidelines, and maintains high labeling quality.
Data Security and Privacy: - To validate our commitment to security and privacy controls, we have obtained the following formal certifications SOC2 Type2, ISO 27001, HIPAA, and GDPR. These certifications affirm our dedication to safeguarding customer data. Our privacy and security programs continue to expand, adhering to Privacy by Design principles and incorporating industry standards and customer requirements from various sectors.
Summary
At Objectways we have a team of certified annotators, including medical professionals such as nurses, doctors, and medical coders. Our experience includes working with top Cloud Medical AI providers, Healthcare providers and Insurance companies, utilizing advanced NLP techniques to create top-notch training sets and conduct human reviews of pre-labels across a wide variety of document formats and ontologies, such as call transcripts, patient notes, and ICD documents. Our DICOM data labeling services for computer vision cover precise annotation of medical images, including CT scans, MRIs, and X-rays and expert domain knowledge in radiology to ensure the accuracy and quality of labeled data.
In summary, the effectiveness of AI and ML models hinges significantly on the calibre of the data used for training, underscoring the need for dependable and precise data labeling services. Please contact sales@objectways.com to enhance your AI Model Performance
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