AI in Insurance: How to Automate Insurance Claim Processing with Machine Learning
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About the webinar Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and 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. What you'll learn - How Insurance companies are using ML to drive more efficiency and business gain - Best practices to automate machine learning models - Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai
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AI in Insurance: How to Automate Insurance Claim Processing with Machine Learning
- 1. AI in Insurance How to Automate Insurance Claim Processing with Machine Learning?
- 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. 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. Shruti Tanwar Lead - Data Science The Speaker
- 4. Bikash Sharma CTO and Co-founder at Skyl.ai 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
- 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. 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. 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. 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. How organizations are leveraging AI & Machine learning in Insurance 01
- 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. How AI is transforming Insurance Sales & Marketing Claim Management Risk Analysis Customer Engagement
- 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. ICICI Lombard app - Insure Claim Management Reduce claim settlement time and increase accuracy ⊚ Car damage recognition ⊚ Healthcare claim settlement ⊚ Anticipate health risks
- 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. 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. Smart Claim Management For Automotive Insurance
- 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. 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. 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. Live Demo of smart claim management for automotive insurance 02
- 21. 8 stages of Machine Learning workflow
- 22. Live Demo on Smart Claim Management for Auto Insurance
- 23. Best practices to automate machine learning models 03
- 24. POLL #2 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
- 25. Data Collection - Flexible options (CSV bulk upload, APIs, Mobile capture, Form based…)
- 26. Data Labeling - Simple 4 steps process (collaboration jobs, guided workflow…)
- 27. Data Labeling - Real-time early visibility (class balance, missing data…)
- 28. Data Labeling - Early Visibility (data frequency, data intuition, outliers, trends, labeling accuracy…)
- 29. Data Labeling with Effective Collaboration (Job allocation, trend, statistics, interactive messaging…) Analyse trends and progress of your data labeling job in real time with statistics and interactive visualizations Manage collaborator progress, activity, interactive messaging
- 30. Data Visualization to build strong data intuition ( visuals for data composition, data adequacy)
- 31. 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.
- 32. 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.
- 33. 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.
- 34. No upfront cost in Infrastructure set up (no DevOps needed, auto-deploy, SaaS & On-prem models…) No DevOps required - Incorporates automatic deployment and dockerization 01 Scalable tech with latest stack 02 Domain agnostic build by data type 03 Scalable On demand 04 On premise and saas models 05
- 35. Skyl.ai - as ML automation platform
- 36. Try out 15 days free trial with complimentary consultation on pilot project Register https://skyl.ai/form?p=start-trial
- 38. 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!
Notas del editor
- Hello everyone and welcome. Thank you for joining today’s webinar on How to Automate Insurance Claim Processing with Machine Learning. My name is Ethan and I’ll be your host today. First off, I’d like to introduce 3 AI-expert speakers for today’s webinar..
- First we have Nisha Shoukath - Nisha is a technology entrepreneur with background in investment banking. She’s co-founded two successful technology startups and has worked with wide variety of global organizations from different industries. She helps enterprises with defining AI strategy, and AI adoption roadmaps. Welcome, Nisha!
- Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI. Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
- Finally, we have Bikash Sharma joining today. Bikash is CTO and Software Architect with 15 years of experience in leading innovative software projects and solutions. He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
- Now before we begin, I’d like to briefly talk about Zoom features. All participants in the webinar will be muted to avoid any interruptions during the session. Any questions you might have can be submitted to the Zoom Questions chat window in the control panel which is located on the bottom of the screen And we’ll make sure to address them towards the end during the Q&A session. Also, the recording of the webinar will be emailed to you afterwards, so don’t worry if you’ve missed any points during the session or wish to view it again So that’s all for the introduction - now, we’ll get started with the webinar and I’ll hand over the session to Nisha
- Change Medical imaging image - different types of scans /CTs
- 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
- 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
- Prospect pre-qualification and showcasing relevant products - How do you know if the prospect is worth marketing to? How likely are they going to respond to an offer and buy? Important and typically unique life events, such as property acquisition or the birth of a child, can be predicted using various channels such as data from social media, and then used for targeted measures. Thus the respective insurance company can arrange for consultation that is tailored to the particular life event or offer additional products like household contents insurance or private liability insurance. 2. With the availability of thousands of products and policies, insurers can target products based on individual needs and lifestyle.
- Insurance claim involves a lot of unstructured data such as diagnostics, drug information and claim notes. While filing for insurance with this, the AI system can assess early indicators and determine that a certain claim might be denied. It can then provide an alert to users. A claims representative can figure out how to intervene and give a particular claim more care to prevent the claimant’s attorney from getting involved (typically, denied claims wind up involving an attorney, which gets very expensive and takes a long time to resolve). Example 1: Upload the photo of damaged part of the car and get an approx estimate for the cost of damage - image is from ICICI app Example 2: with Intelligent Character Recognition (ICR) & Optical Character Recognition (OCR), the decision on the health claim authorisation can be updated. Once the data is uploaded in the system, the AI based technology evaluates the admissibility of the claim. A deep learning module is deployed, which automatically provides the amount to be approved using defined algorithms. As a result the time required for reading and then subsequently approving the form turns to a matter of seconds. Example 3: Anticipate health risks based on data from your fitness tracker and apps and recommend relevant products
- According to the FBI, non-health insurance fraud in the US is estimated at over $40 billion per year, which can cost families between $400–700 per year in extra premiums Artificial intelligence can help to query the alleged events of an accident while claims processing. If a car driver claims their vehicle broke down due to bad weather, it can reaffirm weather reports. Fraud claims can be prevented as AI will confirm if the asserted claims are true or not. A human insurance agent can then dig a claim request further if needed.
- Face recognition & voiceprint to reduce customer verification time Using customer and transaction data as well as other information, algorithms are able to determine which customers are likely to cancel contracts in the near future. Text mining can be used to analyse messages from all input channels. Algorithms evaluate the customer’s mood and detect changes in mood over the course of time. This allows conclusions to be drawn about customer satisfaction and the likelihood of churn. With individually determined purchase probabilities, AI offers customers in the area of upselling and cross-selling tailor-made, additions to the insurance portfolio that has already been arranged.
- In today’s webinar let’s take an example of smart claim management using ML for Auto Insurance. Many a consumer embarking on the cumbersome path of filing an insurance claim has already had the harrowing experience of being in a car accident. The last thing he or she wants to deal with is a process that typically requires waiting days or weeks for appointments with appraisers before being able to file. The wait continues until consumers finally receive funds from their providers to get their vehicles repaired.
- This is an experience many consumers are all too familiar with. The cost of the accidents costs individual countries 1-2% of their annual GDP.
- Imagine a scenario - When a vehicle is damaged in an accident, the person gives it to a service center so a service engineer can assess the damage and provide an estimate for repair. Then, an insurance personnel examines both the car and the estimate, and either approves, rejects or modifies individual parts of the estimate. The process goes like this - Today’s tech-savvy customers can find manual evaluation processes frustrating.
- Compare that to the new, AI-driven process; a customer will use the app to take photographs of car’s damage. Once uploaded, the system’s deep learning model and computer vision identifies in real time all the parts of the vehicle, like roof, window or bumper and then spots all the different types of damage – be it scratch, dent, crack, and so on. Most importantly, the app replies with an estimated cost quickly using historical data. But what’s in it for insurance companies? Automating the process reduces the possibility of inaccurate assessments due to human error. And, increased the efficiency and productivity improves the bottom line.
- How
- Benefit
- Not started yet, so no challenges Data collection Data Labeling Data Bias 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 Monitoring the model performance
- Now, we
- Thank you Nisha and Shruti, for the wonderful presentation and demo. If you’re interested in incorporating powerful machine learning solutions to your business, or just interested in learning more about machine learning in general, Skyl offers free trial for 15 days with complimentary consultation, you can follow the link on the screen to register
- Now we will go ahead and take some time for questions. Once again as a reminder, if you have any questions, you can type your questions in the question box in your control panel - located on the bottom of your Zoom screen and I’ll try to address them as many as possible if we have enough time. Sample questions: Victor How can I keep track of my model’s performance and fairness? Christina How does skyl handle scalability? Mikael Apart from images, can we use Skyl for classifying text data or extracting data from documents like pdfs? Davon What if I have sensitive data, that I do cannot use outside my org for security reasons, how can Skyl help in that situation?
- All right, so that’s it for today’s webinar, I hope you enjoyed it We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries, So don’t miss out and make sure you sign up for upcoming ones as well Thank you for joining and I hope you have a wonderful day.