Artificial Intelligence may be a concept unknown to a majority of consumers, but we unknowingly using AI in our everyday life. How? What about the smartphones with Google now and Siri, they help find information for you when you need it.
With real-time problem solving skills the only thing you have to worry about are your goals as you can leave the assistance to a computer that can think on it’s own but for your benefit. Many intelligent brains working in Artificial Intelligence to make our life comfortable. If you could have someone looking over your day to day needs it’s rather easy to focus on more important things in life. Implementing AI into our lives has been studied for years and now things are getting more real and Mantra Labs is well invested into it.
From consulting on niche technologies, to completely owning your AI initiative – Mantra Labs help you solve complex real world problems, leveraging their expertise in various aspects of AI.
• Data Science: It is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies.
• Natural Language: Natural Language Processing (NLP) refers to AI method of communicating with an intelligent system using a natural language such as English. Processing of Natural Language is required when you want an intelligent system like a robot to perform as per your instructions, when you want to hear a decision from a dialogue based clinical expert system, etc.
• Machine Learning: It is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
• Integrations: Most artificial intelligence systems involve some sort of integrated technologies, for example, the integration of speech synthesis technologies with that of speech recognition.
• Deep Learning: Deep learning refers to artificial neural networks that are composed of many layers. It is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations
• Computer Vision: It is the science that aims to give a similar, if not better, the capability to a machine or computer. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images.
Making an approach to pursue the most advanced technology takes a lot of innovation and it is exactly what Mantra Labs has been doing.
If you are keen to solve real world problem using AI, Drop us a line email@example.com
Across the Insurance ecosystem, a special fraction within the industry is noteworthy for its adoption of new technologies ahead of others. However slow but sure, uberization of insurance has conventionally demonstrated a greater inclination towards digitization. Insurers now more than ever, need big data-driven insights to assess risk, reduce claims, and create value for their customers.
92% of the C-Level Executives are increasing their pace of investment in big data and AI.
Artificial Intelligence has brought about revolutionary benefits in the Insurance industry.
AI enriched solutions can remove the ceiling caps on collaboration, removes manual dependencies and report errors.
However, organizations today are facing a lot of challenges in reaping the actual benefits of AI.
5 AI Implementation Challenges in Insurance
Lack of Quality training data
AI can improve productivity and help in decision making through training datasets. According to the survey of the Dataconomy, nearly 81% of 225 data scientists found the process of AI training more difficult than expected even with the data they had. Around 76% were struggling to label and interpret the training data.
Clean vision, Process, and Support from Executive Leadership
AI is not a one time process. Maximum benefits can be reaped out of AI through clear vision, dedicated time, patience and guided leadership from industry experts and AI thought leaders.
Organizational silos are ill-advised and are proven constrictive barriers to operational productivity & efficiency. Most businesses that have data kept in silos face challenges in collaboration, execution, and measurement of their bigger picture goals.
Technology & Vendor selection
AI has grown sharp enough to penetrate through the organizations. As AI success stories are becoming numerous investment in AI is also getting higher. However big the hype is, does AI implementation suits your business process or not – is the biggest question. The insurtech industries have continued its growth trajectory in 2019; reaching a funding of $6B. With the help of these insurtech service firms, Insurance organizations have made progress, tackling the age-old insurance ills with AI-powered innovations.
People, Expertise and Technical competency
‘Skills and talent’ in the field of AI is the main barrier for AI transformation in their business.
Still playing catch-up to the US, China, and Japan — India has doubled its AI workforce over the past few years to nearly 72,000 skilled professionals in 2019.
Are you facing challenges with your Insurance process but have no idea where the disconnect is? Is your Insurance business process ripe for AI in the year 2020?
What is the right approach?
Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders on the 13th of February, 2020.
Register for the live webinar by Parag Sharma (AI Thought Leader & CEO Mantra Labs).
Ratemaking, or insurance pricing, is the process of fixing the rates or premiums that insurers charge for their policies. In insurance parlance, a unit of insurance represents a certain monetary value of coverage. Insurance companies usually base these on risk factors such as gender, age, etc. The Rate is simply the price per ‘unit of insurance’ for each unit exposed to liability.
Typically, a unit of insurance (both in life and non-life) is equal to $1,000 worth of liability coverage. By that token, for 200 units of insurance purchased the liability coverage is $200,000. This value is the insurance ‘premium’. (This example is only to demonstrate the logic behind units of exposure, and is not an exact method for calculating premium value)
The cost of providing insurance coverage is actually unknown, which is why insurance rates are based on the predictions of future risk.
Actuaries work wherever risk is present
Actuarial skills help measure the probability and risk of future events by understanding the past. They accomplish this by using probability theory, statistical analysis, and financial mathematics to predict future financial scenarios.
Insurers rely on them, among other reasons, to determine the ‘gross premium’ value to collect from the customer that includes the premium amount (described earlier), a charge for covering losses and expenses (a fixture of any business) and a small margin of profit (to stay competitive). But insurers are also subject to regulations that limit how much they can actually charge customers. Being highly skilled in maths and statistics the actuary’s role is to determine the lowest possible premium that satisfies both the business and regulatory objectives.
Actuaries are essentially experts at managing risk, and owing to the fact that there are fewer actuaries in the World than most other professions — they are highly in demand. They lend their expertise to insurance, reinsurance, actuarial consultancies, investment, banking, regulatory bodies, rating agencies and government agencies. They are often attributed to the middle office, although it is not uncommon to find active roles in both the ‘front and middle’ office.
Recently, they have also found greater roles in fast growing Internet startups and Big-Tech companies that are entering the insurance space. Take Gus Fuldner for instance, head of insurance at Uber and a highly sought after risk expert, who has a four-member actuarial team that is helping the company address new risks that are shaping their digital agenda. In fact, Uber believes in using actuaries with data science and predictive modelling skills to identify solutions for location tracking, driver monitoring, safety features, price determination, selfie-test for drivers to discourage account sharing, etc., among others.
Within the General Actuarial practice of Insurance there are 3 main disciplines — Pricing, Reserving and Capital. Pricing is prospective in nature, and it requires using statistical modelling to predict certain outcomes such as how much claims the insurer will have to pay. Reserving is perhaps more retrospective in nature, and involves applying statistical techniques for identifying how much money should be set aside for certain liabilities like claims. Capital actuaries, on the other hand, assess the valuation, solvency and future capital requirements of the insurance business.
New Product Development in Insurance
Insurance companies often respond to a growing market need or a potential technological disruptor when deciding new products/ tweaking old ones. They may be trying to address a certain business problem or planning new revenue streams for the organization. Typically, new products are built with the customer in mind. The more ‘benefit-rich’ it is, the easier it is to push on to the customer.
Normally, a group of business owners will first identify a broader business objective, let’s say — providing fire insurance protection for sub-urban, residential homeowners in North California. This may be a class of products that the insurer wants to open. In order to create this new product, they may want to study the market more carefully to understand what the risks involved are; if the product is beneficial to the target demographic, is profitable to the insurer, what is the expected value of claims, what insurance premium to collect, etc.
There are many forces external to the insurance company — economic trends, the agendas of independent agents, the activities of competitors, and the expectations and price sensitivity of the insurance market — which directly affect the premium volume and profitability of the product.
Dynamic Factors Influencing New Product Development in Insurance
To determine insurance rate levels and equitable rating plans, ratemaking becomes essential. Statistical & forecasting models are created to analyze historical premiums, claims, demographic changes, property valuations, zonal structuring, and regulatory forces. Generalized linear models, clustering, classification, and regression trees are some examples of modeling techniques used to study high volumes of past data.
Based on these models, an actuary can predict loss ratios on a sample population that represents the insurer’s target audience. With this information, cash flows can be projected on the product. The insurance rate can also be calculated that will cover all future loss costs, contingency loads, and profits required to sustain an insurance product. Ultimately, the actuary will try to build a high level of confidence in the likelihood of a loss occurring.
This blog is a two-part series on new product development in insurance. In the next part, we will take a more focused view of the product development actuary’s role in creating new insurance products.
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