We consider Machine Learning as a subset of Artificial Intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. This goes clearly beyond the basic conventional algorithms which also form part of the Artificial Intelligence spectrum. We see further areas of focus in machine learning, covering deep learning and deep reinforcement learning. Both bring us closer to a human-like artificial intelligence. Deep Learning models are seeing wide adoption in real-world applications. However, deep reinforcement learning is still limited to academia. In essence, Machine Learning is a very potent approach to solving tough problems. This is one of the reasons why Gartner recently labelled Machine Learning as a “mega-trend”. And we agree: just as we will remember the 1990s as “the rise of the Internet”, so too we will remember the 2010s and 2020s as “the rise of machine learning”.
Retail insurance carriers have to respond to two opposing forces. On one hand, end-customers have ever-increasing expectations on quality and tailored service. On the other hand, margins have been eroding, less in niche markets but particularly in high-volume markets. Spending human expert time on individual cases creates a huge challenge for profitability - be it in underwriting, operations or claims.
Enter Machine Learning, the ideal tool to automate processes or services that previously could only be done manually. Think of an automated personal assistant answering questions via an app or phone call. Think of digital broker automatically combining the insurance solutions most adapted to the lifestyle of the customer. Think of filling out an insurance contract with a few photos and buttons rather than long boring forms.
We see Machine Learning as a key enabler from completing the transformation from case by case focus to flow business, while increasing customer centricity along the value chain.
The relevance of machine learning for small and even medium commercials is similar as for retail segments. By contrast, the larger Corporate segment provides new opportunities. Here, machine learning makes it possible to systematically sift through large amounts of data. The disruptive potential does not relate to efficiency gains as in Retail segments, but in increasing profitability. High-dimensional forecasting solutions enable taking the right decisions based on a dynamic and systematic understanding of risks and opportunities.
The main challenges to setting up machine learning at your company are not related to the technology. Machine Learning has been around for several years and it is a proven, stable and scalable platform.
The major hurdles we have encountered at our customers are internal, for example:
Faster machine learning models are moving beyond basic predictions and classification, and into the content generation realm. For example: instead of recognizing a painting as Leonardo Da Vinci or Picasso, ML networks are now learning to paint like Leonardo Da Vinci. We see these applications in constrained environments, for example “deep fake” videos, but it is only a matter of time before these algorithms are used in business environments.
For insurance, we predict this means the opportunity for a fundamental change in the business model. Moving away from responding to risk and towards predicting it.
Sound data combined with Machine Learning are the main tools that will enable insurers to (i) have a dynamic and data-driven understanding of risk, as well as (ii) set-up effective risk prediction activities. Those who achieve this change will completely out-class and out-price those who do not.
Most machine learning applications used in the industry are classification applications, including chatbots or solutions used for image recognition. Such classification algorithms are predominantly based on supervised learning and are well understood today.
Faster machine learning models are moving beyond basic predictions and classification, and into the content generation realm. These more potent algorithms apply unsupervised learning approaches— they respond to an input without having specifically been trained for it. This enables the machine learning model to train alone and respond in dynamic ways.
We see these applications in constrained environments. For example, instead of recognizing a painting as Leonardo Da Vinci or Picasso, machine learning networks are now learning to paint like Leonardo Da Vinci. It is only a matter of time before these algorithms are used in business environments.
For insurance, we predict this means the opportunity for a fundamental change in the business model: moving away from responding to risk and towards predicting it.
Sound data combined with Machine Learning are the main tools that will enable insurers to (i) have a dynamic and data-driven understanding of risk, as well as (ii) set-up effective risk prediction activities. Those market participants who achieve this change will completely out-class and out-price those who do not.
A one-stop platform to acquire, extract, synthesize and classify relevant information. At the core is our know-how in applying machine learning to unstructured data sources such as PDFs, emails, websites or Excel files. We empower our clients to tap into a wider breadth of available information and automate highly manual processes such as submission intake, quote comparison, or validation of contractual documents.
Creates a holistic customer view and suggests the logical next step to maximize customer life-time value. We combine out-of-the box modules and pre-trained algorithms with tailored approaches to meet our client’s specific needs. Results are triangulated based on a range of techniques including pattern recognition, machine learning, naïve Bayes, and deep neural networks. Our solution augments decision-making of the market-facing workforce by proposing relevant customer segments, personalized pricing, and cross-selling opportunities.
We break down silos, set the right incentives, reduce complexity, and build lean processes to deliver customer value.
We help our customers shift from to define effective operating models to reduce costs and to improve the experience of both clients and employees.
A siloed and technically focused workforce needs to transition to an interconnected, collaborative and customer– centric operating model.
Augmenting the workforce through intelligent automation and data-driven insights are key levers to improve the customer experience and improving the operational effectiveness.
We have developed our own solutions to help you drive automation and data-driven insights along your value chain.
Machine Learning is a very potent approach to solving tough problems covering areas such as smart automation and predictive complex forecasting. We offer consultancy service to help you build machine learning capabilities. Additionally, our suite of machine learning products helps you to accelerate your adoption journey.
We shape your Machine Learning vision and identify opportunities along your value chain as input for your transformation roadmap. Our focus lies on improving your customer focus and operational excellence. Additionally, we help you explore disruptive new products and services.
Our product line includes cutting-edge data analytics and machine learning solutions to help our customers automate unstructured data extraction and take the right decisions.