Robotic Process Automation - Insurance


Capgemini has developed a holistic approach to automation which can help help insurers to re-think, re-design and re-configure their core business operations. The approach combines Lean methodologies with Robotic Process Automation (RPA) and Artificial Intelligence (AI), and helps the insurer to work out where and how they should use these technologies – or not. Complete the registration form to receive our POV, which includes the key success factors for RPA projects in insurance. If you would like to speak with a Capgemini RPA for Insurance expert, email Carolee Dagenais at to schedule a meeting. Visit our Insurance Website to learn more about the services and solutions that Capgemini's Financial Services delivers to clients, which represent 70% of the world's largest insurers.

Planning for the Future of Work


Rather than focus on finding jobs in the gaps left by machines, individuals and organizations would be smart to prepare themselves to adapt to a changing digital business environment. Digital technologies are poised to disrupt how work is done. Consider the popular example of the impending arrival of autonomous vehicles. When self-driving vehicles are mainstream -- within the next decade or two (or less) -- the impact on work in the United States alone will be massive. According to the United States Bureau of Labor Statistics, 1.5 million people in the U.S. are commercial truck drivers, 800,000 work as delivery drivers, and another 1 million people make a living as other types of transportation professionals -- including bus drivers, taxi drivers, and Uber drivers.

Machine learning is the future of insurance and customer service - Tech-Talk by Martijn De Jong ET CIO


So, how do companies find ways to address the ever-increasing customer needs? While the adoption of new technologies might have been slower than desired initially, the Indian insurance sector is certainly awakening to its benefits now. Several insurers are now deploying these processes to understand their customers better and for product innovation. Of all these news processes, perhaps artificial intelligence and machine learning are proving to be the most potent! Information overload New data sources like third-party databases, social media activity, internet of things, and more are providing a steady stream of information.

AI, machine learning offer great opportunities and major risks for insurers


But with that promise comes risk – perhaps greater than the risks inherent with all new technologies – that the insurance industry needs to consider. The Financial Stability Board released a detailed report this month on the potential upsides and downsides of AI, most of which align with Novarica's research of the technology. Many of the risks highlighted in the report stem from the increasing reliance financial services companies will have on outside technology companies for key business components. Another source of risk is that the results of AI and machine learning may be too complex for humans to fully understand. As the FSB report puts it, "New trading algorithms based on machine learning may be less predictable than current rule-based applications and may interact in unexpected ways."

Will AI photo recognition change motor claims?


Start-up-developed technology is offering'touchless' claims handling to the motor insurance industry through the use of AI-powered photo recognition. Tractable, which already provides the first-wave of its technology to UK insurer Ageas, has today announced the roll out of a new development to its AI system, which will see it able to provide a full estimate repair cost using photos in minutes. The company, which was founded in 2015 and has attracted more than $10 million (£7.5 million) in funding, says that its new'AI Estimating' technology will save time, cut costs, and transform the claims experience on more than 60% of motor claims. The system will streamline the claims process, from First Notification of Loss (FNOL) to an insurer-approved estimate, without the need for human intervention. "Today when you have an accident and you call your insurance company, the process to manage your claim is extremely slow, its expensive and its very manual," Adrien Cohen, chief commercial officer at Tractable, told Insurance Business.

Time for the first AI insurance lawyer?


There have been ominous signs that artificial intelligence is ready to make a serious impact on the insurance world, but not many had expected it to arrive in the insurance law field. Top 100 law firm Keoghs has announced the introduction of it what it is deeming the "first true AI lawyer to hit the insurance market." Known as Lauri, it has been launched today and will initially handle avoidable litigation cases with the aim being that insurers can enjoy transformative speed and ease of service while reducing costs. The firm believes that Lauri can settle cases back to insurers within seconds. It communicates via email using natural language processing – it interacts with Keoghs' case management system and can be applied across a range of claims types.

15 insights from InsurTech Rising's AI Summit


With AI (Artificial Intelligence) being the one of the hottest topics of 2017 for #InsurTech, here is a quick round-up of some of the takeaway points from the AI Summit that I attended earlier as part of the 2017 InsurTech Rising event. This is interesting, because within the insurance industry, many understand this fact but the distinction between AI, Machine Learning (ML) and Deep Learning (DL) is still misunderstood. AI being so topical is fuelling the misbelief that it is new. ML which is a subset of AI, is where machines learn a function from the data, namely patterns and trends, which we as humans can't always determine ourselves and not as quickly. DL which is a subset of ML, and thus, a subset of AI, is where neural networks (on a much bigger scale) work to think like humans.

Scientists Look at How Humans Drive in Self-Driving Cars


But if you were being very precise--if you were a team of Massachusetts of Technology researchers who study human-machine interactions--you wouldn't say that all those Americans are "driving," exactly. The new driver assistance systems on the market--like Tesla's's Autopilot, Volvo's's Pilot Assist, and Jaguar Land Rover's InControl Driver Assistance--mean that some of those travelers are doing an entirely new thing, participating in a novel, fluid dance. The human handles the wheel in some situations, and the machine handles it in others: changing lanes, parking, monitoring blind spots, warning when the car is about to crash. We might need a new word. Fully autonomous cars won't swarm the roads en masse for decades, and in the meantime, we'll have these semiautonomous systems.

Why AI-driven insights are the new crown jewels of the insurance industry


The insurance industry – like many elements within Financial Services (FS) – has come under intense pressure over the past decade or so. The fintech revolution has meant that smaller and more agile startups are able to offer a variety of new services to consumers and businesses. These services are not only more interactive and based on the latest technologies, but they are also services that bigger insurance firms cannot easily offer. This increased competition from newer market entrants is a growing problem for more established insurance providers. A 2016 PwC survey revealed that 65 per cent of insurance chief executives see new market entrants as a threat to growth, while 69 per cent of insurance chiefs were concerned about the speed of technological change in their industry.

Finance firms near the moment of truth to truly embrace AI


In today's rapidly growing digital age, long-term survival and success are increasingly being linked with how "smart" a business can be. And for financial services firms that means using technology to become more intelligent, and processes and systems that talk to each other and learn from one another. Banks, insurers and wealth managers are pouring billions of dollars into artificial intelligence (AI), machine learning and other types of technologies that are already not only raising productivity levels, but reducing risk and actually creating new jobs. According to Accenture research, the financial services industry is now the third most impacted by productivity gains achieved with the implementation of AI, behind only the media and telecoms, and manufacturing sectors. The figures show that by 2035 its use should have resulted in an increase of US$1.2 trillion in gross value added, which measures the output value of all goods and services in a sector.