AI in Insurance: How to Automate Insurance Claims Processing with Machine Learning
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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 teams, 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 will learn: . Deep dive into how insurance companies are adopting AI . Discuss prominent industry use cases . Live demo of vehicle damage assessment for insurance claims management
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AI in Insurance: How to Automate Insurance Claims Processing with Machine Learning
- 1. AI in Insurance How to Automate Insurance Claims Processing with Machine Learning?
- 2. 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
- 3. Solutions Consultant with experience working at the forefront of cutting-edge technology and leading innovative projects. Areas of expertise include solutions analysis and design. Fahid Basheer Solutions Consultant 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. ...In the next 45 minutes How Organizations are Leveraging AI & Machine Learning in Insurance 1 2 Live Demo of Vehicle Damage Detection for Smart Claims Management
- 7. 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
- 8. POLL #1 At what stage of Machine Learning adoption is your organization? ⊚ 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
- 14. Risk Analysis Faster fraud identification & risk prediction ⊚ Transaction analysis to identify, predict & prevent fraudulent claims ⊚ Reaffirmation with AI to verify if the asserted claims are true or not ⊚ Anticipate health risks 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 Claims Management For Automotive Insurance
- 17. Traditional Time-Consuming Manual Claims Process 1 2 3 4 5 6 Claim Submission Insurance Payment Original Receipt Submission Manual Data Transfer Claim Assessment Claim Approval
- 18. Smart Claims Management 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
- 19. Live Demo of Vehicle Damage Assessment for Automotive Insurance 02
- 20. 8 Stages of Machine Learning Workflow
- 21. Live Demo on Vehicle Damage Assessment for Automotive Insurance
- 22. What we help our clients with ⊚ AI Adoption Assessment ⊚ AI Systems Integration ⊚ AI Performance Evaluation ⊚ AI-Enabled Software Development Our AI Consulting Services www.skyl.ai contact@skyl.ai
- 23. ⊚ Free 1 month Trial + POC ⊚ Complimentary 30 min consultation ⊚ AI Implementation Playbook www.skyl.ai contact@skyl.ai What we offer our clients
- 24. Questions? ?
- 25. 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!
Editor's Notes
- Hello everyone and welcome. Thank you for joining today’s webinar on AI In Insurance. My name is Edwin Martinez and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
- First we have Shruti Tanwar - Shruti is an expert in data science and 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!
- Next we have Fahid Basheer - Fahid is a Solutions Consultant with experience working at the forefront of cutting-edge technology and leading innovative projects. His expertise include solutions analysis and design. Welcome, Fahid!
- Finally, we have Bikash Sharma joining as a panelist 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 Fahid.
- Thank You Edwin and welcome everyone, my name is Fahid and Ill be one of the presenters today Now Lets take a look at what we are going to cover in the next 45 minutes so the first part of this webinar will be presented by me and it will be about the why of AI in the insurance sector or how exactly insurance organizations are using machine learning models or AI applications in the insurance space, so we will be taking a look at some example on where AI is increasing business value, and the second section of this webinar would be a live demo by Shruthi on how you can do vehicle damage detection for automating claims management of automotive insurance solutions using AI and machine learning. Like Edwin mentioned earlier, we will have a QnA session at the end of the webinar, so you dont have to worry if you have any questions about the sections we cover in the webinar, we will address your questions at that time.
- Let me start with a quick intro about the Skyl.ai platform and its capabilities, As this is the platform we have used to build the vehicle damage detection AI model. It is a ML automation platform for unstructured data which includes text, images, audio etc. And using Skyl.ai’s platform business can build and deploy high quality NLP, Computer Vision models in hours rather than days or weeks. So how does Skyl.ai do that? So Skyl.ai provides an easy to use unified platform for the entire machine learning workflow which includes data collection, data labeling, feature engineering, training the model by choosing out of the box algorithms at scale, once model is trained, carrying out model evaluation and finally one click deployment and monitoring the model in production. So with Skyl.ai Platform you can basically. Manage your ML projects in one place. And allows you to take your AI experiments to production in no time with scale and leads to faster model release iteration cycles. The best part doing all this with no infrastructure or MLops effort required. before we start you know we go more deeper into the subject of today which is AI and insurance I'd like to give you a very brief intro about our platform which is called Skyl.ai which is a platform that we have used to build out the models that Shruti is going to give you a live demo today so what is called Skyl.ai AI is a automation platform for machine learning and specifically its specialized in unstructured data so when I say unstructured data if you see this this diagram on the screen and if you see on the left side when we sound structured data you can see all kinds of unstructured data the picture there like a text image audio messages and and such data and what's Kyle does is it automates the entire machine learning workflow which includes a collection of data labeling of data and we also provide a lot of different visualizations of this data and through scale you can you know define your feature sets your data sets all of that and we also give you a one-click training so you can in fact plug in your own algorithm or use out-of-the-box algorithms and then there there are model there are modules for evaluations as well just quickly to recap on Skyl.ai’s platform so scale gives you the entire machine learning automation for unstructured data all the way from labeling to model creation as well as post deployment model monitoring.
- Now I'd like to launch a quick poll if I may that would be great if you could answer that and that will give us a good idea about you know at which stage and machine learning adoption you're at and that will help us to kind of talk at that level So I'm just launching the poll please go ahead and vote just failing for a few more people if you could complete it in a few seconds before I close the poll that that would be great. okay I'm about to close the poll alright interesting so we have about one third of our attendees in the mid stage like they're experimenting and building proof of concepts which is amazing and followed by that we have about 22% of the our attendees are exploring or scaling up so they're kind of like a bow and below that level and we have about 11 percent of attendees having their models used in production so we have you know people at various stages and 11 percent of people are in the planning stage so we have more or less an equal distribution of the level of failure stage that you are at and that actually helps because you know that we know that you know we are at your are different levels and we'll try to keep it as generic as you can but if you have more advanced questions or even basic questions you know please feel free to put that in the chat window and we'll be taking those questions in the end. 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
- Great now moving on, in this section of the webinar I'll quickly touch upon how organizations like yours are using AI in machine learning in the insurance sector
- so what the study say is that see if you are a leader or if you are an early adopter of AI and have a strong digital base in the AI space and you can expect your profits are gonna go up anywhere by 1 to 5 percent that's your bottom line right, so your profits are going to up by upto five percent more than the industry average which means that AI is going to be a clear differentiator for you to be become a market leader in the coming decades so and leveraging AI will definitely give you that competitive edge over your counterparts, so investing your time in implementing AI in the right manner is definitely something you should keep in mind So these are some of the interesting facts so if you use AI and machine learning McKinsey has done this study and they say that you are able to make more profits right you know as compared to be the other companies in your industry so which means that there's really a good edge for you to leverage AI and in this tough times I know AI is not something that people have at the top of their mind but I would say it's also a time for us to kind of you know look you know take a step back and look at things and see that okay this is how I've been doing things and this is commercially you know these are not really the best times and it's respect to market conditions and economy and stuff like that so now how can we do better you know so things can definitely be done better by investing a little bit of time into these things and then obviously you know it helps the bottom bottom line and the top line as well and would give you more examples to that so
- So let's take like a look at it from all angles, you want to see the different areas where AI can actually add value in the insurance sector, so at the frontline we have sales and marketing , where your agent are essentially selling your policies and products, so you want AI to assist you in improving your sales number and your marketing initiatives, essentially increase your business Then we will take at claims processing or claims management, which is the operating effort that'll have to be undertaken by your organization or resources, here you want AI to make your operation efforts faster, or lets say more efficient and then you also have risk analysis operations, a very crucial part of the business cycle here where youre managing the payouts from your policies, and ensuring that there arent any fraudulent claims, and that you are servicing the right customers and there is a customer engagement, the most important bit in the business cycle and how you can use AI to better service your customers, how to upsell and cross-sell different policies and services, and how we retain your customers by keeping them satisfied with your service, so it is sort of entire lifecycle for the insurance sector so we'll take a look at some of the applications of AI for all of these functions throughout the entire lifecycle and insurance is one of the industries where there is so much information or so much data available on hand that is just lying around without providing any business value, AI becomes so much more relevant here as it can provide that insight or create that inference which wasn't possible before, and it doesnt have to be just about transactional data, or processing data,
- Now lets talk about enabling sales and marketing through AI, So you are looking at how do you bring in more business through the door, so some of the ways you can do that is through better prospect pre-qualification, if you're spending time prospecting a potential insurance customer you can using AI or different AI implementations to understand if that person is a right prospect, do you want to spend your time and effort on that person So one way AI does that is by monitoring or listening for Important and typically unique life events, such as property acquisition or the birth of a child, which can be monitored using various channels such as data from social media, and then use that data to pre-qualify and approach the right kind of customers. And it doesnt have to stop there, you can also use this data for targeted measures. The 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. or if you are if they're living in a more you know vulnerable area with respect to weather conditions and things like that how you can upsell some of those products. so there's so much of pre-qualification that you can do with respect to demography or other attributes if you know that so a I can really play a big role in it in recommending you know products, as well as anticipating what kind of products or policies can be sold in the future. Even the selling process can be enhanced using AI, wherin you can use virtual agents like chat podsm which can provide a very human like interaction with your customers using Natural Language intelligence, but at a much faster pace than a human can, and or you know to have them get to the right product very quickly which which means that we can have like a very better guided online buying process 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.
- Now we will talk about enabling claims management using AI, Here we are using AI to reduce the claims settlement time and make it much more efficient, And lets take the example of an automobile accident and filing for claims after an accident can be a difficult process to say the least, because youve been through a horrible incident and it can be a really tough time for a lot of customers so they might not be able to file for claims as soon as they want to, so how can you make this more easy for the customer as well as for the insurance company. Since visual inspection is a key part of while assessing claims for automotive insurance, there are AI implementations where you can actually click a picture of the damage and upload it to the to an insurance portal backed by AI, which can automatically recognize what parts of the vehicle is damaged, what kind of damage it is, as in is it a scratch or dent, then it can even recognize the extent of the damage so that it can and then it can pull up a repair estimate automatically, which can be used for claims settlement. Another example would be in healthcare claims settlements, where sometimes youve to deal with a lot of unstructured data in the form of healthcare reports, such as diagnostics information, operational and drug information, even the claims notes, which are sometimes printed on pieces of paper and someone has to go through them to approve the insurance payout at the end. So instead you can use AI and specifically Natural Language Processing and capture information from these healthcare reports, understand what is being detailed in these reports, and use its intelligence to understand the admissibility of the claim. So you wouldnt need to have someone manually do this process for you. So you can instead use AI to make this process much faster. So 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 and the second stage is while you are doing the processing for claims now they're actually encouraging especially health insurance companies are encouraging their users to use varible devices and they are pulling that data in to predict any kind of health hazards any anomalies and they are able to predict some some issues to the health and they're able to prevent and proactively prevent those incidents and and to again minimize the damage to the person and also the overall cost of insurance as well so all these things can be done very effectively using different AI and machine learning
- Lets take a look at using AI for Risk Analysis, And some of the examples we are going to look at is in fraudulent claims identification as well as risk prediction. So it is said that in the US alone there are about forty billion dollars spent on fraudulent claims and this is not even in the healthcare sector and insurance companies are not able to manage all of these fraudulent claims well enough currently. So to attend to these fraudulent claims, AI solutions can be implemented and can actually identify quickly the claims that are suspicious claims or have a higher likeliness of being insurance fraud, For example, if a customer claims that my automobile accident was caused due to bad weather or because the climate wasnt ideal, or the traffic conditions were poor, so you can actually have AI weather time series data or traffic reports of that particular area in that period of time and analyze if what is mentioned in the claim is true or not, so in these situations where it would be practically impossible for humans to quickly assess all those conditions and and make sure that it's all truthful you can instead implement AI and machine learning to very quickly analyze the situation using historic and current data, identify patterns in this data, and reaffirm whether the claims are a really genuine or not before you pay them out so once again AI systems make the effort to handle all the suspicious claims that come through, while your organization can attend to legitimate claims that need your attention. Now taking a look at anticipating risks, there is also usage of AI and machine learning to anticipate health risks in an individual, So how would it do that exactly? So in this instance you can use AI to capture data from health records or even from health monitoring device and wearable devices like an Apple watch for instance or IoT sensors which allow you price coverages based on real events, in real time, using data linked to individuals rather than samples of data linked to groups. Essentially you are using this data to anticipate and predict what kind of ailments or diseases can incur in an individual based on the real-time data that you are getting from them. So you can either provide higher or lower premiums based on this telemetric data that you have. 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. and things like that to actually analyze and see if the is this game was really you know legit or not so that's one example and then then other thing you can do is you can actually predict you know one is that you can check and analyze and you can verify the other is that how do you predict that you know in this time or like in the season or in the from this area there's so such and such things have happened so there is the area that is you know you can actually predict within which from which side you're gonna get more fraudulent claims from what what kind of people or what kind of you know areas where you can have such claims coming from so these can be very difficult for humans and analyzing the data finding patterns and fire finding relations this is something that is very very difficult for humans but if you train the machines this is going to be something that is going to be very interesting and we would see you know such patterns emerging that we could never hope to do just by like having humans do it so risk is a very very interesting area where you can actually apply ai
- So the next area where we can implement AI is in customer engagement and customer support, which very important to retain your customers and ensure that they have a really good service experience. So one of the ways AI is being implemented in this aspect, is by using facial recognition and voiceprint recognition to speed up the identification and pullup all the information about the customer as soon as possible, so you dont have to spend your time asking a hundred different questions and identification proofs to start servicing your customer in the first place. So how great would it be to know as soon as the customer walks in to your establishment what products and policies theyve bought, then you can have a really personalized interaction with every customer that walks in. And another applications of AI would be in monitoring the sentiment of your customer throughout their engagement with you, if they have been happy or they've had complaints. Once example would be, Using customer data and transaction data as well as other information, these AI systems are able to determine which customers are likely to cancel contracts in the near future. Text mining can be used to analyse messages when they chat with you from all input channels, and the AI system evaluates the customer’s mood and detect changes in mood over the course of the interaction period. This allows conclusions to be drawn about customer satisfaction and the likelihood of churn as in, are they going to stay on with you as a customer or are they going to fall out with your organization, so AI helps you keep a tab on your customers, and if it detects that a customer is probably going through a bad experience, you can deploy corrective measures to rectify the situation and retain them. You will also be able to use AI to calculate the probability of cross-selling or upselling various other products, based on the previous purchases of your customers, in addition to the insurance portfolio that has already been arranged for them. Even at an interaction level, like with our example of the intelligent assistant or AI-enabled chatbot, you can address customer queries about policies or procedures much much faster with an omnipresent virtual assistant, who is available 24/7 to address all their questions. So thats it for that part of the webinar. 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.
- Now lets take a look at smart claims management, in automotive insurance, So why smart claims management, So consumers have to go through the traditional manual claims management process 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.
- So lets quickly go through what it means when we talk about the traditional claims management process and then compare it to a smart claims management process. So lets 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, when you submit your claim after that and provide all the necessary documentations, proofs, receipts and things like that, an insurance personnel examines both the car and the estimate, sometimes they ask questions so that can go again back and forth and all this is done manually so somebody at their insurance company would capture all this and either approves, rejects or modifies individual parts of the estimate. Then once the claim is approved, the customer get the payout at the end , after quite a bit of time. And this entire manual process can be very frustrating for today’s tech-savvy customers, who are more used to an automated approach of doing a lot of their operational activities. So if the person was in a automobile accident they cant wait around for for too long when expecting a payout from their provider, because they need to get their car you know back on the road and soon as possible. Your customers are not going to be happy at all with delayed payouts at all and they expect a more seamless experience with their providers.
- So how does a smart claims management system compare to this? Well when we introduce an AI-driven process; it can simplify all the aspects of the claims process that I had presented earlier. In our instance of the car accident, to submit a claim, the customer will just have to use an AI-enabled application that will allow them to take images of the damage to the car, and they can do this even using their mobile phone and once these images are uploaded onto the AI system, its 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 after assessing the damage. So the AI system will be doing the heavy lifting and the manual work that is required for claims submissions and evaluation of damages and estimation of cost. And since all of this happening through the AI system, you dont have to worry about manually documenting each aspect of the claims, because the customer are doing it through this portal of yours, you can set up an automated documentation system where this data or anything that is required for the claims process can automatically be captured for reference later on. Now you've had the system assess the damages, estimate the costs incurred and the payout that is applicable for this particular situation, so you have a complete streamlined approach to claims management. And you don't always have to leave it to just AI automate the entire process, maybe you want to take a look at the estimates and the costs and have the final word in what the payout will be. So you can have AI systems do almost 90 percent of your work for you and you can just sign off at the end of decide that maybe this case needs human intervention as well. So AI can either completely automate this process for you, or assist in the claims management process and do all of the heavy lifting. Now you may be thinking, okay so its great that the customers claims process is more streamlined and efficient, but whats in it for insurance organizations? Using AI to Automating THIS process reduces the possibility of inaccurate assessments due to human error. So the assessment will be completely objective in nature and there will always be a fair assessment for the damages and cost estimates, as provided by the AI system. So now your customers are going to have a really good experience with their claims being processed so smoothly and they will refer your organization and this experience to other people as well, driving more business for you.
- Okay that was the end of the first part of the webinar, thank you so much for listening to me, and now Shruti will present a live demo of building a vehicle damage assessment ML model for automated claims processing in automotive insurance. Thank you and over to your Shruti.
- Thank you Shruti and Fahid, for the wonderful presentation and demo. I’d like to mention that Skyl.ai is dedicated to helping people with their Machine Learning journey by offering consulting services. Services such as: AI Adoption Assessment, Skyl will help find key areas in your organisation where AI is beneficial. AI Systems Integration, Skyl will help find the best ways to integrate AI models with your current software systems AI Performance Evaluation, Skyl will assess your AI workflow and help find ways to improve your AI system’s performance And AI-Enabled Software Development, The team at Skyl can develop highly customized, AI-enabled software solutions catered towards your organisation’s needs. If you’d like to find out more, please check out the skyl.ai website or you can send an email directly to contact@skyl.ai.
- Skyl also has special offers for those of you that are curious about incorporating Machine Learning to your business. Skyl offers a free 1 month trial, plus Proof of Concept. You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model! Skyl offers a complimentary 30 min consultation and the AI Implementation Playbook to go along. This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges.
- 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: Steve Q: Can the AI solution be integrated to a mobile application? A: Robert Q: How much time does it take to typically build the AI solution? A: Using Skyl.ai 1-2 months, first exploration will be done within weeks. Amar Q: What does it cost to use a platform like Skyl.ai? A: Its subscription type, based on usage.
- All right, so that’s it for today’s webinar, we 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.