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3 Challenges of Adopting AI in Claims Processing

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Many insurance companies are automating their claims processing as it minimizes turnaround time, regulation and claims processing cost leading to enhanced customer services and business profitability. FREMONT, CA: Claims processing cost and fraudulent claims payment raise operation cost of the companies and cause a lot of hassle for customers. This is why claims management has become a priority for insurance companies as it influences the bottom line and customer retention strategies. Digital transformation and artificial intelligence (AI) can optimize claims management practices and provide improved customer satisfaction. However automation in the insurance industry can face some challenges.


How AI and ML are changing insurance for good

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The Insurance industry has been dealing with vast volumes of data for years, but analytics, Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being used to help insurance providers make faster data driven decisions. Given the exponential level of data available today with AI/ML, insurance providers can now efficiently extract new insights into their customer's needs and create stronger long-term value. Starting with how the market calculates premiums, the insurance sector now has access to thousands of data points to help them calculate premiums. Machine learning algorithms expedite the identification of the most predictive attributes driving claims losses – the most recent data points being historical cancellation data and gaps in cover. This helps insurers become more competitive, match their risks to the most appropriate pricing strategies and write the risks that meet their underwriting appetite.


How Artificial Intelligence and Machine Learning Can Help Insurers - Agency Nation

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The insurance industry is reliant on a strong digital presence and detailed analytics, whether for marketing, risk analysis or the prediction of future events. An insurer who can adopt Artificial Intelligence (AI) and Machine Learning (ML) will gain an advantage over their competitors that will last for years to come. Artificial Intelligence involves using computers to complete tasks such as learning and problem solving that traditionally require human intelligence. Machine Learning is an application of artificial intelligence that provides the ability to automatically learn from the environment and applies that learning to make better decisions. If we take the first example of marketing AI, and in this case, Natural Language Processing (NLP) is able to extract social media posts, reviews and threads that surround your company and your competitors.


Intelligent IoT: Bringing the power of AI to the Internet of Things - ELE Times

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The IoT is getting smarter. Companies are incorporating artificial intelligence--in particular, machine learning--into their Internet of Things applications and seeing capabilities grow, including improving operational efficiency and helping avoid unplanned downtime. WITH a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy, evaluating a potential new IoT project, or seeking to get more value from an existing IoT deployment may want to explore a role for AI. Artificial intelligence is playing a growing role in IoT applications and deployments, a shift apparent in the behavior of companies operating in this area.


Swiss Re leveraging machine learning to predict motor frequency developments - Reinsurance News

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By utilising machine learning and numerical text processing techniques, Swiss Re has been able to generate a "predictive view" of motor frequency developments in several markets. In a recent conversation with Nikita Kuksin, Hhead of modelling within Casualty R&D, Miriam Hook, vice president Global clients and Surbhi Gupta, assistant vice president, casualty R&D at Swiss Re, it was explained to us how these alternative approaches were able to provide added granularity to existing data. "We intended to develop an alternative to traditional actuarial calculation methods that would give us an "external perspective" on claims frequency within our motor portfolio and allow us to predict motor frequency developments in several motor markets," said Kuksin, who leads the modelling team within the casualty research and development department at the Swiss Re Institute. Gupta, who prior to her current role served at Swiss Re for three years' as a data scientist, explained how these methods were brought into fruition by first checking the status quo of frequency developments against external data, before then explaining motor frequency using external data to generate factors that could be projected into the future. "These are complex objectives, requiring solid data sets and robust analytics," Gupta explained.


How to create innovation in your insurance business

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It is no surprise that the insurance sector at large has been guilty of negligence at one particular front, industry Innovation. Although, Insurtech startups have come thick and fast in the nick of time, yet their broad impact on the industry is marginal at best and meager otherwise. Given the era of uncertainty that we are scraping through, the rate of innovation in insurance segments needs to pick up if the underwriters of today are to thrive tomorrow. In this article, we'll be focusing on the possible measures they can implement to do so. Insurance is not the ball game it once used to be when the incoming customer used to feel indebted to a company for assured support in challenging times.


Interpretable vs Explainable Machine Learning

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From medical diagnoses to credit underwriting, machine learning models are being used to make increasingly important decisions. To trust the systems powered by these models we need to know how they make predictions. This is why the difference between an interpretable and explainable model is important. The way we understand our models and degree to which we can truly understand then depends on whether they are interpretable or explainable. Put briefly, an interpretable model can be understood by a human without any other aids/techniques.


Deep Claim: Payer Response Prediction from Claims Data with Deep Learning

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Content provided by Byung-Hak Kim, the first author of the paper Deep Claim: Payer Response Prediction from Claims Data with Deep Learning. Peer-review research has been the cornerstone of advancing the practice of medicine, it's time to apply this same scientific rigor to improving the back office of healthcare. Alpha Health is proud to have our research featured at ICML2020. The paper outlines a predictive model we've developed that has the potential to help significantly reduce wasteful healthcare spending. What's New: The paper describes one of the company's machine learning models believed to be the first published deep learning-based system that successfully predicts how a claim will be paid in advance of submission to a payer.


AI and Machine Learning: Propelling the Fintech Industry to New Heights

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The Fintech industry, with its focus on efficiency and consumer-centricity, is a disruptive force in the traditionally staid and frequently complacent financial services market. In certain areas, the pace of Fintech disruption has been so dramatic it has forced incumbent institutions to scramble to adapt their offerings to meet changing consumer demands. Increasingly, artificial intelligence and machine learning are the key technologies that enable Fintechs to compete aggressively with legacy players. Below are some of the key ways AI and machine learning are powering continued innovation in the Fintech sphere. One issue with many financial products and services is the fact that they are often designed to meet the needs of large population groups but fail to address more individualized needs and desires.


📐 Size Matters

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The recent emergence of pre-trained language models and transformer architectures pushed the creation of larger and larger machine learning models. Google's BERT presented attention mechanism and transformer architecture possibilities as the "next big thing" in ML, and the numbers seem surreal. OpenAI's GPT-2 set a record by processing 1.5 billion parameters, followed by Microsoft's Turing-NLG, which processed 17 billion parameters just to see the new GPT-3 processing an astonishing 175 billion parameters. To not feel complacent, just this week Microsoft announced a new release of its DeepSpeed framework (which powers Turing-NLG), which can train a model with up to a trillion parameters. That sounds insane but it really isn't.