Ce diaporama a bien été signalé.
Le téléchargement de votre SlideShare est en cours. ×

Ai in insurance how to automate insurance claim processing with machine learning

Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Chargement dans…3
×

Consultez-les par la suite

1 sur 39 Publicité

Ai in insurance how to automate insurance claim processing with machine learning

Explore more at https://skyl.ai/form?p=start-trial

About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.

Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.

In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.

Explore more at https://skyl.ai/form?p=start-trial

About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.

Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.

In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.

Publicité
Publicité

Plus De Contenu Connexe

Diaporamas pour vous (20)

Similaire à Ai in insurance how to automate insurance claim processing with machine learning (20)

Publicité

Plus par Skyl.ai (13)

Plus récents (20)

Publicité

Ai in insurance how to automate insurance claim processing with machine learning

  1. 1. AI in Insurance How to Automate Insurance Claim Processing with Machine Learning?
  2. 2. Technology leader with 20+ years expertise in Product Development, Business strategy and Artificial Intelligence acceleration. Active contributor in the New York AI community Extensively worked with global organizations in BFSI, Healthcare, Insurance, Manufacturing, Retail and Ecommerce to define and implement AI strategies Nisha Shoukath Co-founder, People10 & Skyl.ai The Speaker
  3. 3. Shruti Tanwar Lead - Data Science Extensive experience building future tech products using Machine Learning and Artificial Intelligence. Areas of expertise includes Deep Learning, Data Analysis, full stack development and building world class products in ecommerce, travel and healthcare sector. The Speaker
  4. 4. CTO & Software Architect with 15 years of experience working at the forefront of cutting-edge technology leading innovative projects Areas of expertise include Architecture design, rapid product development, Deep Learning and Data Analysis The Panelist Bikash Sharma CTO and Co-founder at Skyl.ai
  5. 5. All dial-in participants will be muted to enable the presenters to speak without interruption Getting familiar with ‘Zoom’ Questions can be submitted via Zoom Questions chat window and will be addressed at the end during Q&A The recording will be emailed to you after the webinar Please familiarize yourself with the Zoom ‘Control Panel’ on your screen
  6. 6. A quick intro about Skyl.ai ML automation platform for unstructured data Guided Machine Learning Workflow Build & deploy ML models faster on unstructured data Collaborative Data Collection & Labelling Easy-to-use & scalable AI SaaS platform
  7. 7. Live Demo of Smart Claim Management ...In the next 45 minutes How organizations are leveraging AI & Machine learning in Insurance Best practices to automate machine learning models 1 2 3
  8. 8. POLL #1 At what stage of Machine learning adoption your organization is at? ⊚ Exploring - Curious about it ⊚ Planning - Creating AI/ML strategy ⊚ Experimenting - Building proof of concepts ⊚ Scaling up - Some departments are using it ⊚ In production - Using it in product features ⊚ Transforming - AI/Ml driven business
  9. 9. How organizations are leveraging AI & Machine learning in Insurance01
  10. 10. Power users of AI with a strong digital base can boost the profits by 1-5% above industry average. Mckinsey Insights “Why a digital base is critical”
  11. 11. How AI is transforming Insurance Sales & Marketing Claim Management Risk Analysis Customer Engagement
  12. 12. Enable Sales & Marketing Focused efforts, Tailored products ⊚ Prospect Pre-qualification ⊚ Relevant product recommendations ⊚ Virtual agents for guided online buying process Spixii featured in The digital insurer
  13. 13. Claim Management Reduce claim settlement time and increase accuracy ⊚ Car damage recognition ⊚ Healthcare claim settlement ⊚ Anticipate health risks ICICI Lombard app - Insure
  14. 14. Risk Analysis Faster fraud identification & prediction ⊚ Transaction analysis to identify, predict & prevent fraudulent claims ⊚ Reaffirmation with AI to verify if the asserted claims are true or not ICICI Lombard app - Insure
  15. 15. Customer Engagement Increase customer lifetime value & satisfaction ⊚ Face recognition & voiceprint to reduce customer verification time ⊚ Churn prediction & reduction ⊚ Upsell & Cross-sell products ⊚ Use NLP to address queries on policy Facial Recognition
  16. 16. Smart Claim Management For Automotive Insurance
  17. 17. 20-50 million people Get Injured in accidents globally 1.25 million people Die in road crashes every year $518 billion Cost accrued globally Assocition for safe international travel https://www.asirt.org/safe-travel/road-safety-facts/
  18. 18. Traditional time consuming manual claim process 1 2 3 4 5 6 Claim Submission Insurance payment Original receipt submission Manual data transfer Claim assessment Claim approval
  19. 19. Car damage recognition solution with Machine Learning 1 2 3 4 Digital Claim submission Auto evaluation and cost estimation Automated document workflow guided by Machine learning system Insurance payment
  20. 20. Live Demo of smart claim management for automotive insurance02
  21. 21. 8 stages of Machine Learning workflow
  22. 22. Live Demo on Smart Claim Management for Auto Insurance
  23. 23. POLL #2 State your role in the AI initiatives/ projects in your organization ⊚ We don’t have any AI projects yet ⊚ Practitioner - Data Science / Engineering background ⊚ Sponsor/Executive ⊚ Product Manager ⊚ Project Manager ⊚ Student ⊚ Others
  24. 24. Best practices to automate machine learning models 03
  25. 25. POLL #3 Some challenges that you are facing while implementing AI & Machine Learning ⊚ Not started yet, so no challenges ⊚ Data collection ⊚ Data Labeling ⊚ Large volumes of data ⊚ Identifying the right data set to train ⊚ Lack of knowledge of ML tools ⊚ Lack of end to end platform ⊚ Lack of expertise ⊚ Choosing the right algorithms
  26. 26. Data Collection - Flexible options (CSV bulk upload, APIs, Mobile capture, Form based…)
  27. 27. Data Labeling - Simple 4 steps process (collaboration jobs, guided workflow…)
  28. 28. Data Labeling - Real-time early visibility (class balance, missing data…)
  29. 29. Data Labeling - Early Visibility (data frequency, data intuition, outliers, trends, labeling accuracy…)
  30. 30. Data Labeling with Effective Collaboration (Job allocation, trend, statistics, interactive messaging…) Manage collaborator progress, activity, interactive messaging Analyse trends and progress of your data labeling job in real time with statistics and interactive visualizations
  31. 31. Data Visualization to build strong data intuition ( visuals for data composition, data adequacy)
  32. 32. One click training at scale (Easy feature sets, out of the box algorithms, API integration, hyper parameter tuning, auto scaling…) ● Train, Deploy and Version your models by creating feature-sets in no time with our easy feature selection provision. ● Choose from state-of-art neural network algorithms, tune hyperparameters and see logs for your training in real time. ● Integrate our powerful inference API with your application for AI-driven actionable intelligence. ● Auto scaling of model training based on data and hyperparameters
  33. 33. Model Monitoring of metrics in real-time (inference count, execution time, accuracy…) ● Monitor your deployed models and analyse inference count, accuracy and execution time. ● See how your models are performing in real-time. No black boxes here.
  34. 34. Model Evaluation - Release Confidently (Accuracy, Precision, Recall, F1 Score) ● Monitor your deployed models and analyse inference count, accuracy and execution time. ● See how your models are performing in real-time. No black boxes here.
  35. 35. No upfront cost in Infrastructure set up (no DevOps needed, auto-deploy, SaaS & On-prem models…) 1. No DevOps required - Incorporates automatic deployment and dockerization 2. Scalable tech with latest stack 3. Domain agnostic build by data type 4. Scalable on demand 5. On premise and saas models
  36. 36. Skyl.ai - as ML automation platform
  37. 37. Try out 15 days free trial with complimentary consultation on pilot project Register https://skyl.ai/form?p=start-trial
  38. 38. Questions? contact@skyl.ai https://skyl.ai/ ?
  39. 39. 85 Broad Street, New York, NY, 10004 +1 718 300 2104, +1 646 202 9343 contact@skyl.ai We hope to hear from you soon Thank you for joining!

×