Artivatic team did study for the problems considering the output, need and processes to identify the best solution for anomaly detection based on time series data and fraud detection in multiple sectors.
Introduction
Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. The goal of anomaly detection is to identify unusual or suspicious cases based on deviation from the norm within data that is seemingly homogeneous.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.
Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
What you will learn:
Anomaly Detection: An introduction
Graphical and Exploratory analysis techniques
Statistical techniques in Anomaly Detection
Machine learning methods for Outlier analysis
Evaluating performance in Anomaly detection techniques
Detecting anomalies in time series data
Case study 1: Anomalies in Freddie Mac mortgage data
Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
So, you've heard about adaptive testing, and wondering what it takes to develop a valid one? This presentation is made for you. It outlines a 5 step process, starting with feasibility studies and business case evaluation. More info at www.assess.com and http://pareonline.net/getvn.asp?v=16&n=1.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.
Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
What you will learn:
Anomaly Detection: An introduction
Graphical and Exploratory analysis techniques
Statistical techniques in Anomaly Detection
Machine learning methods for Outlier analysis
Evaluating performance in Anomaly detection techniques
Detecting anomalies in time series data
Case study 1: Anomalies in Freddie Mac mortgage data
Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
So, you've heard about adaptive testing, and wondering what it takes to develop a valid one? This presentation is made for you. It outlines a 5 step process, starting with feasibility studies and business case evaluation. More info at www.assess.com and http://pareonline.net/getvn.asp?v=16&n=1.
Types of Machine Learning- Tanvir Siddike MoinTanvir Moin
Machine learning can be broadly categorized into four main types based on how they learn from data:
Supervised Learning: Imagine a teacher showing you labeled examples (like classifying pictures of cats and dogs). Supervised learning algorithms learn from labeled data, where each data point has a corresponding answer or label. The algorithm analyzes the data and learns to map the inputs to the desired outputs. This is commonly used for tasks like spam filtering, image recognition, and weather prediction.
Unsupervised Learning: Unlike supervised learning, unsupervised learning deals with unlabeled data. It's like being given a pile of toys and asked to organize them however you see fit. The algorithm finds hidden patterns or structures within the data. This is useful for tasks like customer segmentation, anomaly detection, and recommendation systems.
Reinforcement Learning: This is inspired by how humans learn through trial and error. The algorithm interacts with its environment and receives rewards for good decisions and penalties for bad ones. Over time, it learns to take actions that maximize the rewards. This is used in applications like training self-driving cars and playing games like chess.
Semi-Supervised Learning: This combines aspects of supervised and unsupervised learning. It leverages a small amount of labeled data along with a larger amount of unlabeled data to improve the learning process. This is beneficial when labeled data is scarce or expensive to obtain.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
Machine Learning presentation. Helps you to have a brief idea about what machine learning is and gives you direction to go deep into it. It covers the idea of Supervised learning and unsupervised learning and examples of how to use different models.
Using Python library such as numpy, scipy and pandas to carry out supervised learning operations like Support vector machine, decision tree and K-nearest neighbor.
Data Analyst Interview Questions & AnswersSatyam Jaiswal
Practice Best Data Analyst Interview Questions for the best preparation of the data analyst interview. these interview questions are very popular and asked various times in data analyst interview.
The ALFRED Health Claims Platform by Artivatic Health leverages advanced AI and ML technologies to streamline the health claims process, ensuring compliance with the IRDAI's guidelines.
Enabling Pre-auth in Less Than 2 Minutes & Final Claims Discharge Under 15 Minutes: Artivatic Health
The ALFRED Health Claims Platform by Artivatic Health (Artivatic.ai) is designed to streamline and expedite the health insurance claims process, ensuring compliance with the IRDAI's stringent guidelines. Here’s a comprehensive look at how ALFRED HEALTH not only meets but exceeds regulatory requirements, delivering a seamless experience for both insurers and policyholders.
Revolutionizing Health Claims Management with GPTArtivatic.ai
Revolutionizing Health Claims Management with GPT
Transforming the Health Insurance Industry
The Current State of Health Claims Management
Traditional methods
Challenges faced by insurers and policyholders
High administrative costs and inefficiencies
Slide 3:
Title: Introduction to GPT
Brief overview of GPT (Generative Pre-trained Transformer)
How it works
Advantages of using GPT in various industries
Slide 4:
Title: GPT in Health Claims Management
Improved data processing and analysis
Faster and more accurate claim processing
Enhanced fraud detection and prevention
Slide 5:
Title: Benefits for Insurers
Reduced operational costs
Improved customer satisfaction
Streamlined workflows
Better decision-making
Slide 6:
Title: Benefits for Policyholders
Faster claim settlements
Enhanced transparency
Personalized customer experience
Easier communication with insurers
Slide 7:
Title: Case Study: Successful Implementation of GPT in Health Claims Management
Company background
Challenges faced
GPT implementation process
Results and benefits
Slide 8:
Title: Future Prospects
Continuous improvement in GPT technology
Integration with other AI tools
Broader adoption in the health insurance industry
Potential impact on global healthcare systems
Slide 9:
Title: Conclusion
GPT's significant role in revolutionizing health claims management
Positive outcomes for insurers and policyholders
A brighter future for the health insurance industry
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Types of Machine Learning- Tanvir Siddike MoinTanvir Moin
Machine learning can be broadly categorized into four main types based on how they learn from data:
Supervised Learning: Imagine a teacher showing you labeled examples (like classifying pictures of cats and dogs). Supervised learning algorithms learn from labeled data, where each data point has a corresponding answer or label. The algorithm analyzes the data and learns to map the inputs to the desired outputs. This is commonly used for tasks like spam filtering, image recognition, and weather prediction.
Unsupervised Learning: Unlike supervised learning, unsupervised learning deals with unlabeled data. It's like being given a pile of toys and asked to organize them however you see fit. The algorithm finds hidden patterns or structures within the data. This is useful for tasks like customer segmentation, anomaly detection, and recommendation systems.
Reinforcement Learning: This is inspired by how humans learn through trial and error. The algorithm interacts with its environment and receives rewards for good decisions and penalties for bad ones. Over time, it learns to take actions that maximize the rewards. This is used in applications like training self-driving cars and playing games like chess.
Semi-Supervised Learning: This combines aspects of supervised and unsupervised learning. It leverages a small amount of labeled data along with a larger amount of unlabeled data to improve the learning process. This is beneficial when labeled data is scarce or expensive to obtain.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
Machine Learning presentation. Helps you to have a brief idea about what machine learning is and gives you direction to go deep into it. It covers the idea of Supervised learning and unsupervised learning and examples of how to use different models.
Using Python library such as numpy, scipy and pandas to carry out supervised learning operations like Support vector machine, decision tree and K-nearest neighbor.
Data Analyst Interview Questions & AnswersSatyam Jaiswal
Practice Best Data Analyst Interview Questions for the best preparation of the data analyst interview. these interview questions are very popular and asked various times in data analyst interview.
The ALFRED Health Claims Platform by Artivatic Health leverages advanced AI and ML technologies to streamline the health claims process, ensuring compliance with the IRDAI's guidelines.
Enabling Pre-auth in Less Than 2 Minutes & Final Claims Discharge Under 15 Minutes: Artivatic Health
The ALFRED Health Claims Platform by Artivatic Health (Artivatic.ai) is designed to streamline and expedite the health insurance claims process, ensuring compliance with the IRDAI's stringent guidelines. Here’s a comprehensive look at how ALFRED HEALTH not only meets but exceeds regulatory requirements, delivering a seamless experience for both insurers and policyholders.
Revolutionizing Health Claims Management with GPTArtivatic.ai
Revolutionizing Health Claims Management with GPT
Transforming the Health Insurance Industry
The Current State of Health Claims Management
Traditional methods
Challenges faced by insurers and policyholders
High administrative costs and inefficiencies
Slide 3:
Title: Introduction to GPT
Brief overview of GPT (Generative Pre-trained Transformer)
How it works
Advantages of using GPT in various industries
Slide 4:
Title: GPT in Health Claims Management
Improved data processing and analysis
Faster and more accurate claim processing
Enhanced fraud detection and prevention
Slide 5:
Title: Benefits for Insurers
Reduced operational costs
Improved customer satisfaction
Streamlined workflows
Better decision-making
Slide 6:
Title: Benefits for Policyholders
Faster claim settlements
Enhanced transparency
Personalized customer experience
Easier communication with insurers
Slide 7:
Title: Case Study: Successful Implementation of GPT in Health Claims Management
Company background
Challenges faced
GPT implementation process
Results and benefits
Slide 8:
Title: Future Prospects
Continuous improvement in GPT technology
Integration with other AI tools
Broader adoption in the health insurance industry
Potential impact on global healthcare systems
Slide 9:
Title: Conclusion
GPT's significant role in revolutionizing health claims management
Positive outcomes for insurers and policyholders
A brighter future for the health insurance industry
Alfred Health Platform - AI Health Claims Artivatic.ai
Alfred AI Health Claims: Revolutionizing Healthcare Through Artificial Intelligence
Slide 1: Introduction
Introducing Alfred AI: A cutting-edge AI solution designed to transform healthcare
Objective: Streamline health claims management, optimize efficiency, and enhance patient experience
Slide 2: The Need for AI in Healthcare
Rising healthcare costs and complexity
Increasing demand for personalized care
Challenges in manual health claims processing
Slide 3: Alfred AI's Key Features
Automated Claims Processing: Faster, accurate, and error-free claims processing using advanced AI algorithms
Personalized Healthcare Plans: AI-driven analytics to create tailored healthcare plans for individuals
Fraud Detection & Prevention: Identifying suspicious claims patterns and reducing healthcare fraud
Real-Time Analytics & Reporting: Easy access to insights and analytics for data-driven decision making
Slide 4: Benefits of Alfred AI Health Claims
Improved Operational Efficiency: Streamlined claims processing, reducing manual effort and administrative costs
Enhanced Patient Experience: Faster claims resolution, personalized care, and transparent communication
Reduced Fraud & Financial Loss: Proactive fraud detection, safeguarding against financial risks
Data-Driven Decision Making: Informed strategic decisions based on real-time data insights
Slide 5: Success Stories
Highlighting successful implementations of Alfred AI in various healthcare settings
Demonstrating measurable improvements in efficiency, patient satisfaction, and financial outcomes
Slide 6: Future of Healthcare with Alfred AI
Continuous innovation for improved patient care
Expansion into new healthcare segments and applications
Promoting a data-driven, patient-centric healthcare ecosystem
Slide 7: Conclusion
Embracing Alfred AI as a solution to revolutionize healthcare claims management and enhance patient experience
A step towards more efficient, personalized, and sustainable healthcare systems
Healthcare Expenses in India: How Indians Pay for Medical TreatmentArtivatic.ai
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🚀 Exciting news in the world of #insurance! GPT-4, the latest AI language model, is transforming the industry with its innovative applications and capabilities. 🧠
As a powerful AI model, GPT-4 offers incredible potential in streamlining processes, enhancing decision-making, and improving customer experience across various aspects of insurance. Here are some notable use cases:
1️⃣ Automated customer support: AI-powered chatbots can now handle customer queries, provide policy information, and assist in policy purchasing and claims processes, significantly improving customer satisfaction.
2️⃣ Personalized policy recommendations: GPT-4 can analyze customer data and preferences to recommend tailored insurance policies, leading to higher conversion rates and better customer satisfaction.
3️⃣ Underwriting and risk assessment: GPT-4 can help underwriters make more informed decisions by analyzing large volumes of historical data and identifying patterns, leading to more accurate pricing and improved risk management.
4️⃣ Fraud analysis and prevention: GPT-4 can identify unusual patterns or inconsistencies in policy applications and claims, flagging potential fraud or misrepresentation for further investigation.
5️⃣ Innovative product design: GPT-4 can analyze market trends and customer preferences to help insurance companies develop new, innovative products that meet the evolving needs of their customers.
The potential of GPT-4 in the insurance industry is immense! By leveraging this advanced technology, we can revolutionize the way insurance companies operate and deliver value to customers. 🌟
Are you excited about the role of AI in the insurance industry? Share your thoughts and experiences in the comments below! 👇
#GPT4 #AI #Insurtech #Innovation #CustomerExperience
How technology is helping in faster claim settlements in health insurance.pdfArtivatic.ai
It is said that technology can be a great leveler as it ensures that improved products and services are available to society at large. The insurance industry can definitely leverage advancements in technology for the benefit of its customers.
For a long time, both the insurance and banking industries have faced criticism for being profit-making enterprises. They have been changing this image by utilizing the decentralized and transparent nature of Blockchain.
Web 3.0 is primarily concerned with connecting data in a decentralized manner rather than storing it in centralized repositories, with computers capable of interpreting information as intelligently as humans.
Artivatic.ai is leveraging the future power of web 3.0 to transform legacy insurance into digital, personalized, and customer-centric products while keeping our clients' budgets in mind.
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Gopinath Rebala
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Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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2. Heatmap utilization for testing
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Orchestrator execution result
Defect reporting
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In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
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But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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And...
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Charlie Greenberg, Host
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
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Sectoral targets and attacks as well as the cost of ransom
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Major cyber events in 2024
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https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
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- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
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Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.