As the sun rose over the track, a team of engineers huddled around the sleek, silver race car. They had been working tirelessly for months, pouring over data, and fine-tuning every detail to ensure that their vehicle was the best it could be. As they prepared to take to the track, they knew that they were up against fierce competition. The world of Formula 1 was filled with teams who were all striving for the same goal – to be the fastest and the best.

Not too far away, a team of software developers were grouped around a computer screen, lines of code scrolling across the display. Today was the culmination of months of writing, testing, and debugging – they were finally ready to show off their new Artificial Intelligence (AI) system at a special press conference. The technology space was full of AI assistants and chatbots, so they knew they had to impress to stand out – they needed to be unique and the best.

While these two teams may seem to be worlds apart, Formula 1 racing and AI have a lot in common! They both rely on high performance and the constant need for improvement. Just like a Formula 1 team, an AI system is only as good as its current capabilities. If an AI system is not constantly learning and improving, it will fall behind and become obsolete.

Intrepid Formula 1 engineer teams are constantly analyzing data, testing new technologies, and adjusting their cars to gain a competitive edge. Similarly, AI systems also rely on data and the ability to learn and adapt to improve.

Back at the track, and the engineer team was confident in their car and their abilities. They knew that they’d put in the time to analyze, adjust, and test; improving and updating their car to stay ahead of the competition. As the race began, the team watched nervously from the pits as their driver hurtled around the track at breakneck speeds. They knew that every second counted, and that even the slightest mistake could mean the difference between victory and defeat.

In a nearby auditorium, the software developers were watching backstage as their Technology Lead spoke confidently to the crowd. They too felt nervous of what may happen during the demonstration, what if their chatbot AI failed at the last moment? But like the engineers with their car, the developers knew that the AI they’d be nurturing for months was ready. The team had been tweaking the code and testing for weeks, squashing every bug that wriggled out, and teaching the software with the best data.

Both AI and Formula 1 are also ever evolving industries. New technologies and techniques are constantly being developed and implemented, and those that do not keep up with the times risk falling behind. In both cases, success is dependent on the ability to continuously learn, adapt, and improve.

One key difference between AI and Formula 1 is that while a Formula 1 team may have a set goal or objective, such as winning a race or championship, the goals of an AI system can be more open-ended. AI systems can be used to solve a wide range of problems and can be trained to perform a variety of tasks.

With all this in mind, was the engineer’s and developer’s hard work successful? Over at the track, as the chequered flag waved, the sleek silver car triumphantly crossed the finish line in first place, not just a win for the driver but the whole team behind them as well. And in the auditorium, a standing ovation as the new chatbot AI answered questions on a range of subjects fluently and accurately, as if a second person were speaking on stage.

As you can see, the world of AI is not so different from the world of Formula 1. Both are high performance industries that require continuous improvement to stay at the top of their game. Whether it’s analyzing data, testing new technologies, or adapting to changing conditions, both AI and Formula 1 rely on the ability to learn and evolve in order to succeed.

Time to come clean – this article wasn’t entirely written by hand. In fact, a new chatbot AI technology from OpenAI, ChatGPT, generated the title and majority of the body as part of an experiment to see how the chatbot AI handles content creation. This new technology has been helping people with all manner of questions, troubleshooting, and research – even providing ideas and writing content for blogs and social media.

To find out more about this article writing experiment, head over to our sister site MICology to read more. 

We are building new insurance products that can be responsive and relevant to our customers. Part of this is using photos taken from mobile phones at the start of the policy and during a claim. The big challenge here is how to look at large volumes of images each day.

Lets consider some numbers – high volume is say 50,000 or 100,000 images a day.

Human Workload

Images per day ~100,000. Number of humans needed to look, just look – open-look-close-next – say 10 seconds each – that’s 1,000,000 seconds = 35-man days per day!! This is just to LOOK, nothing else. Day in day out – add in hiring, churn, 24 hour operations and soon you will have over 100 people employed doing this simple task, just looking at the pictures!! Add in data collection, quality review, report writing, audits and reports and you can easily double that to 200 people.

How can a human look at all the pictures and collect a vast number of data points analysed in real time from a single image or multiple images, and then determine location, time, date, bike not bike, bounce not bounce, damaged or not and where.

Humans don’t have good memories when compared to a computer. How can a human retain the data and reproduce it in real time analytics for consumption and actionable reporting? Its no wonder that in the world of insurance doing sensible things like visual inspections of vehicles prior to taking out insurance is not done and the ownness of fraud detection etc is left to when a claim happens.

However in my world of micro insurance and high volume / low costs premiums and claims this is not an option.

Computers specialise in speed and volume and automation repays investment with data. Every photo consistently and accurately assed and the data put into actionable reports. Comparisons? Computers can easily compare large volumes of data providing information for the business to act upon.

Training a large human team is costly and time consuming. Once trained its difficult to update the process and even harder to completely change a system or process. Once trained humans leave, and churn gives rise to constant levels of training and support for core business operations. Training is easy for computers, once its understood the rule and command base they just keep going and going….

Biases and inconstancies are constant in human driven processes even after training. Computers have bias too but are consistent in this meaning that over time it can be driven out. Holiday’s, moods and sick days are prevalent in a human workforce, we all get sick and want days off, we don’t want to work 24/7, we love a good holiday and have moods which all effect the quality of work and the productivity, plus if we get a better offer – we leave!

The process of hiring a good member of staff on average takes 4 to 6 weeks this doesn’t include notice periods and gardening leave which drastically change from days to months and from industry to industry and location to location. If you want to double the capacity of a computer automation system it could all be done overnight, fully tested and ready for the new capacity challenge.

If your business relies on users taking photos to prove your business operation, then without an AI automation system you make huge compromises in your business that costs real money and real customer issues day in day out. A 1 in N approach to service is just not good enough in the personal digital world. 100% is the only way to guarantee success.

Humans vs GPU

Use case comparison for simple vehicle inspection…

Number plate extraction

Human: 10 to 15 seconds, 10 start of day 15 seconds towards end of day, higher possibility of missing and getting confused with characters. Max images per person in 8 hours = 2,400 – but more realistically 1,500 per day

GPU: 2 second all day every day lower probability of inconsistencies. = 21,000 in a typical day

Damage inspection

Human: 10 to 15 seconds, 10 start of day 15 seconds towards end of day maybe longer with a possibility of missing damage. Humans lack consistency within processes and come with biases. Max images per person in 8 hours = 2,400 – but more realistically 1,500 per day

GPU: 4 seconds all day every day = 11,000 in a typical day

Is it a car or bike in the photo?

Human: 5 to 10 seconds, 5 start of day 10 seconds towards end of day maybe longer with a possibility of missing damage. Humans lack consistency within processes and come with biases. Max images per person in 8 hours = 4,000 – but more realistically 3,000 per day

GPU: 4 seconds all day every day = 11,000 in a typical 12 hour day

So to complete a task of say 50,000 images for all three tasks over a 12 hour work day:

Costs are all local costs and people costs vary around the world, however there is less variables when costing GUP’s to do the work.

Using the image data to our advantage must not be undervalued. An automated AI system will return huge value per vehicle or journey. For a human based solution, the process will always be a compromise and the insurance company will never achieve the quality service or operational efficiencies that is needed in its business.

Without technology we will not be able to maintain service standards because of poor image standards, data quality and lack of actionable reporting across every policy.

This not about computers taking jobs, this is about the ability to server millions of customers with insurance and sharing in their risk each day. It’s about making insurance available to everyone.

At MIC Global we are focused on changing the way business insurance is developed and processed. We are insurance with AI built in with API’s. We are in the forefront of that change; developing policies by the season, job, by the hour, by the day and by the Km, thus fitting our model to that of the platforms and the way small and micro businesses see risk. We are unbundling business policies so that the cover offered fits with peoples and business needs or the actual job or process being undertaken. Making Business Insurance transactional.

We are working with many clients and platforms to provide insurance and insurance services and we have noticed a sea change in the last few months. This sea change is that AI is coming. AI became generally available in the insurance industry around 4 years ago (2015) with the funding of a few Insurtechs and the likes of IBM trying to gather data into their Watson AI engine. The big promise is that it will be used to improve the process of dealing with claims and placing insurance and pricing.

Tasks such as measuring the ground floor distance to the surrounding ground level for flood, looking at the pitch of the roof of a building, answering questions through a bot, looking at car dealerships for hailstone risks, determining damage to cars and phones via computer vision or viewing crop growth via satellite images, These are all things that AI can do – AI is not one thing, it is many things.

At MIC Global we have a vision for our AI process and use of AI and Machine Learning is central to this. These technologies will power our vision. The vision is to turn the human effort around – the processes starts with our customers and ends with customer satisfaction.

Customers enter data – take pictures, answer questions, upload documents, integration with Apps. This data is then used in the claims or policy process to speed up and give accurate results.

The AI processes the data, aligns the results, completing a recommendation, gaining approval, sending a policy or closing the claim.

This is all based on zero human processing by MIC Global. This is the vision, speed, accuracy and transparency.

Data Entry; AI Processing; No humans involved

Each product we develop will have a profile of Easy Customer Data Input; AI processing; Zero human input for MI.

Why is this important?

Our insurance products are integrated into our client platforms and operations. Because our insurance products back client service operations it’s essential that our business can scale through tech. Our vision fully supports our clients growth ambitions by limiting the impact of our products and services on their processes, whilst delivering essential insurance cover for their customers.

At MIC Global we are focused on changing the way business insurance is developed and processed. We are insurance with AI built in, API. We are in the forefront of that change; developing policies by the season, job, by the hour, by the day and by the Km, thus fitting our model to that of the platforms and the way small and micro businesses see risk. We are unbundling business policies so that the cover offered fits with peoples and business needs or the actual job or process being undertaken. Making Business Insurance transactional.

We hear the fear in people’s voices when we mention AI and ML, blue collar jobs going, eating into white collar jobs, autonomous cars, lorries, taxis.

The fear is what will we all be doing with our time? How will we earn money? Will we hear ‘they took our jobs….’ But this time it’s not some other country – it’s tech soaking up work like blotting paper. This ‘fear’ could even be hampering adoption of even the most basic AI and ML.

However, the truth is that things like autonomous cars will take time to fully come about, in a recent article in the WSJ it pointed to augmenting drivers in the here and now. Using the best AI tech, implemented in human-driven cars, to augment the human driver to reduce or even nearly eliminate road deaths in the USA as the quickest benefit. This could be the real competition to Autonomous cars, Human Augmented Driven Cars.

That could have huge implications for the fortunes of companies like Tesla. It could also spell doom for companies such as Uber and Lyft, which aren’t yet profitable and might not be until they can cut out their high human costs—that is, removing drivers from vehicles.

I think there will be a mix of driverless and human augmented cars. In the confines of a city, all the sensors and additional tech needed to support fully driverless cars could be implemented and hence I keep coming across this term, and we use it within our tech pitches, Augmented. Human and tech is deliverable, today.

All this doom and gloom for tech companies won’t come about. The issue is we do not want to be driving in cities and towns, we don’t want to be doing repetitive jobs, convenience has a value. Over time there will be a transition. The AI Augmented Human will take many forms and will be stiff completion for robots.

For insurance we use the term Augmented Insurance, this is AI for insurance. Adding AI Tech into a process and making the humans better. Adding AI Tech to human process is the way to improve, to offer new services, do better customer services and provide an overall better user experience.

I believe in insurance on-demand, insurance as a service, matching usage with payment. These can only be provided with AI and ML.

At MIC Global we are focused on changing the way insurance is developed and processed. We are insurance with AI built in, a digital/ augmented insurance company. We are in the forefront of that change; developing policies by the season, job, by the hour, by the day and by the Km, fitting our model to that of the platforms and the way small and micro businesses see risk. We are unbundling business policies so that the cover offered fits with peoples and business needs or the actual job or process being undertaken. Making Business Insurance transactional, the digital insurer for the new economy.

What does it take to be a Digital Insurer? Well one of the basics is to have a deep and a sound understanding of Machine Learning (ML) and Artificial Intelligence (AI) with the capabilities to actually develop these for ‘insurance’ case studies.

Our main AI product is a set of several AI modules, we design very robust AI modules for individual requirement. While at the same time, users/products can do multiple analysis efficiently.

MIC used Auto-Machine Learning (Auto-ML) approach in our AI product, where Auto-ML decides the best ML Algorithm. We used best ML Algorithm as suggested by Auto-ML which enhances our product capability.

We see other companies develop generic AI products, then focus on insurance domain. However, our products are designed specially for Insurance case studies.

Typically companies say that they use AWS or Google or TensorFlow. We don’t use single ‘AI’ from the likes of AWS or Google.

What we do is use a ranges of tools and our own algorithms and code, the core being made up as follows:

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications

Keras is a high-level neural networks API that works as a layer.

Deep Learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.

Natural Language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Optical Character Recognition or optical character reader (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image

Machine Learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead.

Tensor Flow and Keras takes maximum memory and computing time, it is very big challenges for companies to deploy deep learning-based application. We over come this using our embedded application, this takes less memory and less computing time (0.5-1.2 sec per image) and can run on any operating system.

Our AI product allows us to focus on accuracy, for our use cases and successfully identifies body parts, damage, colour with user input images with 80-90 percent accuracy. Furthermore, we developed robust algorithm to calculate damage severity with very good accuracy 75-80 percent. This is growing as more data is applied and as we add features.

Our AI product takes less computing time with highly mechanized deep learning neural network architecture, this enables us to provide faster solutions suitable for insurance. Additionally we deal with error tolerance and to handle outlier/noise. This helps support multi-tasking with error less environments.

We use our team and they have their own definitions of our licensed tech.

Penetration and adoption of a new technology, whether mobile phones, TV, credit cards, or now Artifical Intelligence (AI) for insurance typically follow a ‘S’ curve.

Wikipedia

The path of tech adoption follow this path. Early on, a few users bet heavily on the innovation, these people and companies are looking to change quickly and take advantage – these companies maybe very disruptive. Then, over time, as more companies ‘rush’ to invest and embrace the technology to capture the potential gains, protecting their markets and customers. As the cycle moves on the market opportunities for nonadopters dwindle. The cycle draws to a close with slow movers suffering damage and potentially going our of business.

This timeline shows how Artifical Intelligence has come together and pulled systems and tools from many different tech innovations to get to where we are today, a place where AI tech is finally useful to businesses.

A study by McKinsey suggests that faced with AI-fuelled competitive threats, companies are twice as likely to embrace AI as they were to adopt other new technologies in past technology cycles. This means that the pace of change for AI adoption will be fast.

Artifical Intelligence, AI, or as we call it Augmented Insurance, is here. AI tech helps, and augments companies and process in all areas. The insurance industry is one based on data yet has not kept up with tech investment. This makes it open for AI. Companies like MIC Global, who adopt AI will be here in 5 or 10 years time, others may not be.

Is ‘augmented insurance’ the way forward?

Are we too late? No. Only a fraction of companies have tried AI across their whole enterprise, i.e. are a ‘power user’. Another big block of companies have tested AI to a limited extent. The majority of companies have yet to adopt any AI technologies at all. This is the normal position, think about where we were in early 2000 with the adoption of the internet as a sales process. There were only a few companies emerging like Amazon. Today we see the results – Amazon and the like are strong and growing, many companies have fallen by the wayside and everyone can agree that using the internet is a must for any company.

We, MIC Global, are developing AI tools and solution to help power our sales and claims processes and to build the payment of claims into the whole process. MIC Global has a strong base in digital capabilities giving us a huge benefit, since we can move more quickly to adopt AI. We work with partners and teams that can work with us and are focused on being digital, bringing our brand of Augmented Insurance to them in innovative ways.

How will things change going forward? We believe that as the world move towards 2022-25; customers, business and consumers will be demanding more open and digitally aware businesses to be dealing with their insurance and finances.

This will be driven by the development of innovative propositions, such as blockchain, voice tech and AI, whose benefits will outweigh current concerns around sharing data, infact the idea of ‘owning’ and sharing data itself will change.

New customer propositions that are enabled or enhanced by AI and open business will include:

• Policy aggregation to provide single view of clauses, limits and risks across different policies.
• Risk management tools using data analytics to identify risk patterns to enable people and businesses to be more in charge of their own risk profiles.
• Parametric policies and tools to work with customer and companies.
• Tailored product and customization of products based on risk, profiles & transaction history, such as specific event-based or time-based policies.
• Increased access to insurance for micro customers due to improved access to data and micro-payments.
• Internet of Things (IoT) allowing passive and active collection of data and turning policy cover on and off as the profile of use changes.

I hear every day of Artificial Intelligence (AI) startups that are or about to revolutionise this business or that. Today, for example, I was reading about “robot investigators” being widely used to examine documents in complex Serious Fraud Office (SFO) cases. Normally the SFO would use around 30 lawyers over many weeks for a case. Using an algorithm instead, the SFO said it took a tenth of the time to sift through this very complex case and, as bonus, the SFO reported that the algorithm is much more accurate.

Does this mean 100’s of lawyers will be out of a job? That AI will take all our jobs? Possibly.

I take a different view of this new technology when I think of the future, and I use the game of Chess as an example.

IBM’s Deep Blue computer won its first game against a world champion in 1996, in that match Deep Blue whilst it won a match it was overall defeated by a score of 4–2. Over the next year Deep Blue was upgraded, and played again in 1997. Deep Blue won the six-game rematch 3½–2½ and became the first computer system to defeat a reigning world champion.

Recently AlphaZero, the game-playing AI created by Google sibling DeepMind, beat the world’s best chess-playing computer program, having taught itself how to play in under four hours!

So, chess is over right – I mean what’s the point?

Obviously, this is not true, chess is still a great game and more people can play chess on phones and other devises, both learning the game and pitting themselves against chess software. Tournaments are held where Chess Masters, each with computers, do battle in games that are every bit as close and exciting as they ever were. It’s just that the humans have been upgraded, a kind of chess cyborg that elevates even novice players to greatness.

Using this as a template we can return to the use of AI for business. I believe that AI will change nearly every job and wipe out some along the way. It will also create jobs, enabling businesses to become far more insightful and smart and customer facing that ever before. Products, service and insight like never before will be available and delivered at a cost that today we cannot imagine.

Looking at my industry, insurance, data has always been at its heart but poorly used. Data is traditionally split, rekeyed and divided as the customer’s policy is processed through agents, wholesalers, brokers, underwriters, reinsurance, claims and on and on.

Today, there is an ability to create and uncover insights at speed, in real time, and this is pulling process, data and information together. AI and machine learning is and will be used in many ways – the insurance cyborg is coming our way! This is critical for shared economy insurance to work.

This is a BIG win for customers and where they will notice the most is around customer service. Insurance is known to be one of, if not the worst industry when it comes to Customer Service, so it won’t be too hard to be impressive!

What will customers notice? The successful new insurance companies and intermediaries will focus on instant customization, instant claims payments, more frequent relevant touch points to the customer and transactional short-term event based policies that customers will be demanding to meet their event based work and leisure lifestyles.

AI will allow the industry to move away from the constraints of the product and focus on service and doing what insurance is all about – managing risk and paying claims when certain conditions are met, all at a reasonable and understandable cost.