Data has always played a central role in the insurance industry, and today, insurance carriers have access to more of it than ever before. We have created more data in the past two years than the human race has ever created. Insurers—like organisations in most industries—are overwhelmed by the explosion in data from a host of sources, including telematics, online and social media activity, voice analytics, connected sensors and wearable devices. They need machines to process this information and unearth analytical insights. But most insurers are struggling to maximize the benefits of machine learning.
The pace of cloud adoption and innovation is rapidly accelerating bringing compelling new opportunities as well as greater complexity and risk. What are the market forces driving change in cloud management and adoption both today and beyond?
A new era for the chemicals industry: Cloud computing changes the gameaccenture
80% of chemical company executives expect cloud to generate highest ROI among digital technologies. Find out how we make that happen and where we see the industry heading.
Apache Hadoop Summit 2016: The Future of Apache Hadoop an Enterprise Architec...PwC
Hadoop Summit is an industry-leading Hadoop community event for business leaders and technology experts (such as architects, data scientists and Hadoop developers) to learn about the technologies and business drivers transforming data. PwC is helping organizations unlock their data possibilities to make data-driven decisions.
Return on Digital Technologies: Insights for OFES Companiesaccenture
Getting a return on digital technology investments: Insights for oil field and equipment and services (OFES) companies
Digital technologies can have a disruptive impact on the landscape of how work is done in the oil field, with the potential to redraw the value chain, deliver a step change in productivity and create new channels to market, but services and machine and equipment manufacturing companies need robust strategies to succeed.
Companies can become enamored with technological advancements, failing to consider how best to apply them toward business goals—at times spending on ventures and business models that do not deliver.
Leaders understand where and how disruptions will impact them—accurately assessing maturity and gaps in the existing digital culture—and determine which solutions are appropriate to further the overall digital vision while delivering the highest return on investment.
Building more value with Capital Markets – Project Owner editionaccenture
1) Capital projects are becoming more digital but are still often too expensive and late. Data quality and use varies across companies with only 3 out of 10 reporting success on key performance indicators.
2) The top challenges preventing better digital transformation are a lack of strategy, understanding how to use available data, siloed teams, lack of skills to analyze data, and limited interest from project teams.
3) The document recommends two approaches to capture more value: instituting data sharing and building insights from shared data; and explicitly focusing on time and budget through data, technology, and decision making. It also introduces the "CAPSTONE" approach to increase returns through data-driven transformation.
The traditional approach to software and application testing is evolving. Learn how Accenture is ushering a new era of Quality Engineering
Find out more: https://www.accenture.com/us-en/insights/technology/quality-engineering-new
The Industrialist: Trends & Innovations - Sep 2021accenture
Schneider Electric and Wärtsilä created a sustainable power solution for remote lithium mines that reduces costs and emissions compared to diesel power. ABB acquired AMR maker ASTI Mobile Robotics to boost its robotics capabilities for industries moving from linear production. Sandvik acquired Cambrio to strengthen its design and planning automation division for clients increasing productivity. Epiroc bought Mining Tag to develop more intelligent mining solutions using their sensor technologies.
The pace of cloud adoption and innovation is rapidly accelerating bringing compelling new opportunities as well as greater complexity and risk. What are the market forces driving change in cloud management and adoption both today and beyond?
A new era for the chemicals industry: Cloud computing changes the gameaccenture
80% of chemical company executives expect cloud to generate highest ROI among digital technologies. Find out how we make that happen and where we see the industry heading.
Apache Hadoop Summit 2016: The Future of Apache Hadoop an Enterprise Architec...PwC
Hadoop Summit is an industry-leading Hadoop community event for business leaders and technology experts (such as architects, data scientists and Hadoop developers) to learn about the technologies and business drivers transforming data. PwC is helping organizations unlock their data possibilities to make data-driven decisions.
Return on Digital Technologies: Insights for OFES Companiesaccenture
Getting a return on digital technology investments: Insights for oil field and equipment and services (OFES) companies
Digital technologies can have a disruptive impact on the landscape of how work is done in the oil field, with the potential to redraw the value chain, deliver a step change in productivity and create new channels to market, but services and machine and equipment manufacturing companies need robust strategies to succeed.
Companies can become enamored with technological advancements, failing to consider how best to apply them toward business goals—at times spending on ventures and business models that do not deliver.
Leaders understand where and how disruptions will impact them—accurately assessing maturity and gaps in the existing digital culture—and determine which solutions are appropriate to further the overall digital vision while delivering the highest return on investment.
Building more value with Capital Markets – Project Owner editionaccenture
1) Capital projects are becoming more digital but are still often too expensive and late. Data quality and use varies across companies with only 3 out of 10 reporting success on key performance indicators.
2) The top challenges preventing better digital transformation are a lack of strategy, understanding how to use available data, siloed teams, lack of skills to analyze data, and limited interest from project teams.
3) The document recommends two approaches to capture more value: instituting data sharing and building insights from shared data; and explicitly focusing on time and budget through data, technology, and decision making. It also introduces the "CAPSTONE" approach to increase returns through data-driven transformation.
The traditional approach to software and application testing is evolving. Learn how Accenture is ushering a new era of Quality Engineering
Find out more: https://www.accenture.com/us-en/insights/technology/quality-engineering-new
The Industrialist: Trends & Innovations - Sep 2021accenture
Schneider Electric and Wärtsilä created a sustainable power solution for remote lithium mines that reduces costs and emissions compared to diesel power. ABB acquired AMR maker ASTI Mobile Robotics to boost its robotics capabilities for industries moving from linear production. Sandvik acquired Cambrio to strengthen its design and planning automation division for clients increasing productivity. Epiroc bought Mining Tag to develop more intelligent mining solutions using their sensor technologies.
Innovation Capability is an Architectural MatterCapgemini
The document discusses how innovation capability is an architectural matter that depends on both organizational structure and resources. It presents a model showing how innovation capability is influenced by properties related to resources, processes, basic organizational attributes, and the external environment. The model identifies "adjustment screws" within each of these areas that organizations can modify to increase their innovation capability, such as providing training to develop human resources, establishing networks to gain new knowledge, or allocating financial resources to research and development. Two case studies of innovative companies, ZPMC and Huawei, are presented to illustrate how adjusting different parts of the model led to their success.
The document discusses how IT leaders face pressure to reduce costs but also recognize the importance of innovation to business success. It notes that CIOs spend little time (11% of their time) on innovation planning due to responsibilities keeping current systems running. The document advocates adopting cloud strategies and cloud-based communication platforms like UCaaS to help CIOs focus more on strategic innovation while also reducing costs and improving operations. Adopting these new models and technologies can help CIOs achieve goals around improving experiences, simplifying communications, and reducing costs.
This document discusses how new SAP solutions and technologies can help businesses become intelligent enterprises. It outlines 5 key technology trends - Citizen AI, Extended Reality, Data Veracity, Frictionless Business, and Internet of Thinking - and provides examples of how Accenture is developing applications using SAP technologies like SAP Leonardo, SAP Cloud Platform, and SAP HANA to help clients leverage these trends and transform their businesses. The goal is to infuse intelligence everywhere by applying new SAP solutions to power real-time systems, improve customer experiences, and unleash the potential of new technologies like AI, analytics, IoT, and more.
Using Analytics for Market Analysis and Improved Procurementaccenture
There are many factors that dictate the cost of goods - from competition to market trends to margin. It's important to develop the analytical skills of your team members to better equip them with the business intelligence to play a more profound role on the procurement team.
Learn how to gather and interpret important market data and procurement analytics and put it to work for your organization. Here are six important data points to consider when looking to improve your sourcing and procurement training.
Digital Asset Management initiatives can provide utilities several benefits:
1) They can decrease capital and operational costs by 10-20% through more predictable asset insights that reduce maintenance costs and allow for more targeted capital investments.
2) They provide greater transparency of asset health and risk, improving asset lifetime.
3) They optimize grid capacity by reducing asset down-time.
The document discusses key trends in strategic sourcing, including the growing role of analytics, defining digital procurement, emerging digital technologies, and how procurement is changing. It notes that procurement will become more strategic and partner directly with business units using "touchless" processes. The COVID-19 pandemic has disrupted supply chains but also created opportunities to focus on resilience, zero-based category strategies, and an agile operating model. Organizations need to determine the right level of resilience and embed it throughout their supply chain strategies.
Warehouse automation investment has grown significantly in recent years due to e-commerce growth and labor constraints. However, companies often implement automation solutions without a strategic vision, focusing only on quick wins. This document recommends a new approach to maximize the value of warehouse automation investments by: 1) aligning automation with broader supply chain strategies, 2) categorizing warehouses and matching the right solutions to operations, 3) measuring non-traditional returns, and 4) integrating technologies to facilitate end-to-end connectivity and human-machine collaboration. This framework can help companies unlock untapped gains from their automation investments.
Media-Morphosis Transforming Media and Entertainmentaccenture
This document discusses how digital technologies are transforming the media and entertainment industry. It outlines three phases for companies to undergo a digital transformation: 1) Tune Up - transform supply chains and collect richer data, 2) Tune In - put consumers at the heart of growth strategies and invest in technologies, 3) Take Off - design and scale new content, services, and partnerships to drive value. The document advocates that media companies become "intelligent enterprises" that reinvent capabilities to engage audiences across platforms at speed.
Emergence and transformation of digital utilities in the “smart” era Capgemini
By Jonathan D Loretto and Michel van Zutphen
Oracle Open World 2013
Content:
The Emergence of the Digital Utility
Reinventing the Digital Customer Experience
This document discusses the digital transformation of high-tech industries. It notes that profit and market value are migrating away from hardware and components towards internet platforms. It identifies trends like artificial intelligence, internet of things, cloud computing and edge processing driving changes. Few product companies have fully transformed, with internet platform companies outpacing spending on research and development. The document outlines a framework for companies to transform their core business while growing new business models in areas like connected products, living products and services, and ecosystem platforms. It emphasizes the need for digital talent and factories to drive transformation.
Banks are facing a world where profit is being compressed by:
• Historically low interest rates
• COVID-19 related credit losses
• Patchy economic recovery
• Fintech competition
The good news? Research shows that elevating their operational maturity helps banks outmaneuver the threats that tomorrow poses.
No Pressure No Diamonds: Getting Nonprofit Right in Today's Digital Ageaccenture
Nonprofits face mounting pressures, including increased service demands, decreased fundraising potential and pressure to integrate the newest technologies.
100 insurance companies were surveyed to understand how they view their journey to operations maturity.
Our experience indicates that operations maturity can translate into tech-savvy ways to acquire customers faster or discover new revenue growth.
This means combining data, technology, processes and people into an intelligent, data-driven— and more resilient—operating model.
Alfresco Day Amsterdam 2015 - "Digital Transformation in the Netherlands", IDCAlfresco Software
Alfresco Day Amsterdam 2015 - "Digital Transformation in the Netherlands. Three Things to consider when launching ECM", Jan van Vonno, Senior Analyst, IDC
Media-Morphosis Transforming Media and Entertainmentaccenture
This document discusses how digital technologies are transforming the media and entertainment industry. It outlines three phases for companies to undergo a digital transformation: 1) Tune Up - transform supply chains and collect richer data, 2) Tune In - put consumers at the heart of growth strategies and invest in technologies, 3) Take Off - design and scale new content, services, and partnerships to engage future consumers. The roles of media companies are evolving from content creators and distributors to intelligent enterprises that reinvent production and monetization using data and platforms.
The Journey Towards AI: The Impact on European InsurersPeerasak C.
The document discusses how AI will impact the insurance industry workforce according to experts from SpareBank 1 Insurance, LV=, Markerstudy, and Direct Line Group. While AI may automate some roles, it will also create new roles requiring specialized skills like data science. Insurers see AI bringing large operational efficiencies through automation but warn not to replace all human expertise. AI is impacting all parts of the insurance organization from claims to sales. Managing expectations of AI's capabilities remains a key challenge for insurers.
The insurance industry – from product development to underwriting to claims – is being fundamentally transformed by AI technologies. Although some companies are investing aggressively in AI to slash costs while also enhancing the customer experience, most insurers will need to accelerate their efforts or risk discovering that it has become too late to catch up.
Innovation Capability is an Architectural MatterCapgemini
The document discusses how innovation capability is an architectural matter that depends on both organizational structure and resources. It presents a model showing how innovation capability is influenced by properties related to resources, processes, basic organizational attributes, and the external environment. The model identifies "adjustment screws" within each of these areas that organizations can modify to increase their innovation capability, such as providing training to develop human resources, establishing networks to gain new knowledge, or allocating financial resources to research and development. Two case studies of innovative companies, ZPMC and Huawei, are presented to illustrate how adjusting different parts of the model led to their success.
The document discusses how IT leaders face pressure to reduce costs but also recognize the importance of innovation to business success. It notes that CIOs spend little time (11% of their time) on innovation planning due to responsibilities keeping current systems running. The document advocates adopting cloud strategies and cloud-based communication platforms like UCaaS to help CIOs focus more on strategic innovation while also reducing costs and improving operations. Adopting these new models and technologies can help CIOs achieve goals around improving experiences, simplifying communications, and reducing costs.
This document discusses how new SAP solutions and technologies can help businesses become intelligent enterprises. It outlines 5 key technology trends - Citizen AI, Extended Reality, Data Veracity, Frictionless Business, and Internet of Thinking - and provides examples of how Accenture is developing applications using SAP technologies like SAP Leonardo, SAP Cloud Platform, and SAP HANA to help clients leverage these trends and transform their businesses. The goal is to infuse intelligence everywhere by applying new SAP solutions to power real-time systems, improve customer experiences, and unleash the potential of new technologies like AI, analytics, IoT, and more.
Using Analytics for Market Analysis and Improved Procurementaccenture
There are many factors that dictate the cost of goods - from competition to market trends to margin. It's important to develop the analytical skills of your team members to better equip them with the business intelligence to play a more profound role on the procurement team.
Learn how to gather and interpret important market data and procurement analytics and put it to work for your organization. Here are six important data points to consider when looking to improve your sourcing and procurement training.
Digital Asset Management initiatives can provide utilities several benefits:
1) They can decrease capital and operational costs by 10-20% through more predictable asset insights that reduce maintenance costs and allow for more targeted capital investments.
2) They provide greater transparency of asset health and risk, improving asset lifetime.
3) They optimize grid capacity by reducing asset down-time.
The document discusses key trends in strategic sourcing, including the growing role of analytics, defining digital procurement, emerging digital technologies, and how procurement is changing. It notes that procurement will become more strategic and partner directly with business units using "touchless" processes. The COVID-19 pandemic has disrupted supply chains but also created opportunities to focus on resilience, zero-based category strategies, and an agile operating model. Organizations need to determine the right level of resilience and embed it throughout their supply chain strategies.
Warehouse automation investment has grown significantly in recent years due to e-commerce growth and labor constraints. However, companies often implement automation solutions without a strategic vision, focusing only on quick wins. This document recommends a new approach to maximize the value of warehouse automation investments by: 1) aligning automation with broader supply chain strategies, 2) categorizing warehouses and matching the right solutions to operations, 3) measuring non-traditional returns, and 4) integrating technologies to facilitate end-to-end connectivity and human-machine collaboration. This framework can help companies unlock untapped gains from their automation investments.
Media-Morphosis Transforming Media and Entertainmentaccenture
This document discusses how digital technologies are transforming the media and entertainment industry. It outlines three phases for companies to undergo a digital transformation: 1) Tune Up - transform supply chains and collect richer data, 2) Tune In - put consumers at the heart of growth strategies and invest in technologies, 3) Take Off - design and scale new content, services, and partnerships to drive value. The document advocates that media companies become "intelligent enterprises" that reinvent capabilities to engage audiences across platforms at speed.
Emergence and transformation of digital utilities in the “smart” era Capgemini
By Jonathan D Loretto and Michel van Zutphen
Oracle Open World 2013
Content:
The Emergence of the Digital Utility
Reinventing the Digital Customer Experience
This document discusses the digital transformation of high-tech industries. It notes that profit and market value are migrating away from hardware and components towards internet platforms. It identifies trends like artificial intelligence, internet of things, cloud computing and edge processing driving changes. Few product companies have fully transformed, with internet platform companies outpacing spending on research and development. The document outlines a framework for companies to transform their core business while growing new business models in areas like connected products, living products and services, and ecosystem platforms. It emphasizes the need for digital talent and factories to drive transformation.
Banks are facing a world where profit is being compressed by:
• Historically low interest rates
• COVID-19 related credit losses
• Patchy economic recovery
• Fintech competition
The good news? Research shows that elevating their operational maturity helps banks outmaneuver the threats that tomorrow poses.
No Pressure No Diamonds: Getting Nonprofit Right in Today's Digital Ageaccenture
Nonprofits face mounting pressures, including increased service demands, decreased fundraising potential and pressure to integrate the newest technologies.
100 insurance companies were surveyed to understand how they view their journey to operations maturity.
Our experience indicates that operations maturity can translate into tech-savvy ways to acquire customers faster or discover new revenue growth.
This means combining data, technology, processes and people into an intelligent, data-driven— and more resilient—operating model.
Alfresco Day Amsterdam 2015 - "Digital Transformation in the Netherlands", IDCAlfresco Software
Alfresco Day Amsterdam 2015 - "Digital Transformation in the Netherlands. Three Things to consider when launching ECM", Jan van Vonno, Senior Analyst, IDC
Media-Morphosis Transforming Media and Entertainmentaccenture
This document discusses how digital technologies are transforming the media and entertainment industry. It outlines three phases for companies to undergo a digital transformation: 1) Tune Up - transform supply chains and collect richer data, 2) Tune In - put consumers at the heart of growth strategies and invest in technologies, 3) Take Off - design and scale new content, services, and partnerships to engage future consumers. The roles of media companies are evolving from content creators and distributors to intelligent enterprises that reinvent production and monetization using data and platforms.
The Journey Towards AI: The Impact on European InsurersPeerasak C.
The document discusses how AI will impact the insurance industry workforce according to experts from SpareBank 1 Insurance, LV=, Markerstudy, and Direct Line Group. While AI may automate some roles, it will also create new roles requiring specialized skills like data science. Insurers see AI bringing large operational efficiencies through automation but warn not to replace all human expertise. AI is impacting all parts of the insurance organization from claims to sales. Managing expectations of AI's capabilities remains a key challenge for insurers.
The insurance industry – from product development to underwriting to claims – is being fundamentally transformed by AI technologies. Although some companies are investing aggressively in AI to slash costs while also enhancing the customer experience, most insurers will need to accelerate their efforts or risk discovering that it has become too late to catch up.
Modernizing Insurance Data to Drive Intelligent DecisionsCognizant
To thrive during a period of unprecedented volatility, insurers will need to leverage artificial intelligence to make faster and better business decisions - and do so at scale. For many insurers, achieving what we call "intelligent decisioning" will require them to modernize their data foundation to draw actionable insights from a wide variety of both traditional and new sources, such as wearables, auto telematics, building sensors and the evolving third-party data landscape.
Artificial intelligence (AI) currently being used by insurance companies has failed to remove gender bias from the profession’s claims, underwriting and marketing processes.
A Chartered Insurance Institute (CII) report tells insurers they must tackle these gender biases. The report found that the datasets used to train the algorithms which support AI systems are rooted in outdated gender concepts. Algorithms learn by being trained on historic data but the report notes more and more of that data is now unstructured, coming from text, audio, video and sensors.
Yet the report warns embedded in that historic data are decisions based upon historic biases, particularly around gender. The report concluded insurance firms need to prepare a structured response to this issue, starting with visible leadership on tackling gender bias in AI.
The document discusses how artificial intelligence and machine learning are becoming increasingly important in the financial services industry. It provides examples of how AI/ML can be used for applications like customer experience enhancement, credit decisioning, fraud detection, intelligent document processing, predictive analytics, and personalized recommendations. The document also summarizes some key AWS machine learning services that financial institutions can use to build impactful AI/ML solutions and accelerate their adoption of these technologies.
How Insurers Can Tame Data to Drive InnovationCognizant
To thrive among entrenched rivals and compete more effectively with digital natives, insurers will need to get their data right. That will mean moving to more responsive, AI-enabled architectures that accelerate data management and deliver insights that drive business performance.
Insurers expect artificial intelligence to completely transform the way they run their businesses.
Read more: https://www.accenture.com/in-en/insight-ai-redefines-insurance
Generative AI in insurance- A comprehensive guide.pdfStephenAmell4
Generative AI introduces a new paradigm in the insurance landscape, offering unparalleled opportunities for innovation and growth. The ability of generative AI to create original content and derive insights from data opens doors to novel applications pertinent to this industry.
6 use cases of machine learning in Finance Swathi Young
The use of Artificial Intelligence and Machine learning is increasingly adopted in multiple industries. Question is, does a regulated industry like Finance adopt AI/ML? the answer is a huge YES! Here we take a look at 6 different use cases:
* Chatbots
* RoboAdvisors
* Risk scoring
* Fraud Detection
* Insurance claims
* Underwriting
* regulatory compliance
In the year 2014, while e-commerce was majorly a business-to-consumer (B2C) game a platform best constructed for consumer brands and retail transactions, business-to-business (B2B) was barely on the limelight. B2B ordering solutions were very few, pricey, and complex in nature. Because of this, it was difficult for small wholesale distributors and retailers to implement B2B ordering solutions in their businesses.
Disruptive Impact of Big Data Analytics on Insurance- Capgemini Australia Poi...dipak sahoo
The document discusses how big data and analytics are disrupting the insurance industry. It outlines that:
1) Insurers are now able to access vast new sources of data like social media, wearables, connected devices and more to better understand risks and strengthen customer relationships.
2) Technologies like telematics allow insurers to access real-time driver behavior data to more accurately price and manage risk.
3) Insurers must adopt a proactive, data-driven approach to predict events rather than just react, in order to remain competitive in this new environment of abundant data and advanced analytics.
Id insurance big data analytics whitepaper 20150527_lo resPrakash Kuttikatt
The document discusses how big data and analytics are disrupting the insurance industry. It provides background on the authors and describes the Australian insurance landscape, noting challenges like an aging population and increased natural disasters. It then discusses how big data is transforming the insurance value chain by enabling more accurate risk assessment and pricing through analysis of diverse new sources of data like telematics and social media. Insurers who leverage big data and analytics to gain insights and improve customer relationships will have a competitive advantage over those who do not adapt to this new digital environment.
Id insurance big data analytics whitepaper 20150527_lo resPrakash Kuttikatt
1) The document discusses how big data and analytics can disrupt the insurance industry. It provides examples of how various parts of the insurance value chain can leverage big data, from underwriting and risk assessment to claims processing and fraud detection.
2) The insurance industry in Australia faces challenges like an aging population, natural disasters, and increasing digital disruption. Big data analytics can help insurers address these challenges by gaining deeper customer insights and improving processes.
3) New sources of data from sensors, wearables, connected devices, and social media can provide insurers with more accurate individual risk profiles and enable more customized products and pricing. This moves the industry from pooling risk to the "segment of one".
ID_Insurance Big Data Analytics whitepaper_ 20150527_lo resPrakash Kuttikatt
1) The document discusses how big data and analytics are disrupting the insurance industry. It provides examples of how data from sources like social media, wearables, connected devices and more can be used across the insurance value chain, from pricing to claims processing.
2) The insurance landscape in Australia is facing challenges like an aging population, natural disasters, and increasing digital disruption. However, big data presents opportunities to better understand customer risks and needs.
3) Early insurance industry adopters of big data are transforming their business through more personalized pricing, real-time policy management enabled by telematics, and predictive analytics. Organizations that effectively leverage data will have an advantage over competitors.
The Work Ahead in Insurance: Vying for Digital SupremacyCognizant
Insurers expect dramatic changes to their work by 2023 as a result of adopting digital technologies and mindsets, according to our study. Speeding processes, harnessing data and forming new collaborations will be key to winning the digital arms race ahead.
The document discusses how data and technology are transforming the insurance industry. It covers topics like how insurers are using data from telematics, health apps, and other sources to better assess risk and offer more personalized premiums. This allows for pricing tailored to individuals based on their behavior rather than just demographics. However, increased data collection also raises privacy concerns for consumers about what data is being collected and how it will be used and secured. Insurers are aiming to address these concerns through transparency about their data practices while harnessing new sources of data to improve their business.
1. Smart cards are credit card sized cards with embedded integrated chips that act as security tokens. They connect to readers through direct contact or wireless technologies like RFID.
2. Smart cards have various applications including use in telecommunications, identification, government, financial, healthcare, loyalty programs, and transportation.
3. Business intelligence refers to collecting, storing, and analyzing business data to inform management decisions. It includes tools like spreadsheets, reporting software, data visualization, data mining, and online analytical processing.
Shaping the right strategy, managing thebiggest risk.Until recently, the Internet of Things (IoT) was on the strategic agenda of only the largest and most progressive insurers. The IoT was largely viewed as a futuristic concept, and many insurers adopted a “wait and see” attitude.
Get Ready For What's New In Insurance Technology Trends For 2021Mindbowser Inc
The Insurance technology trends will streamline various processes and smoothen up the road to developing various products catering to the current times and users. Some of the Insurance technology trends that can alter and transform the insurance industry are shared here.
Read More about the latest insurance technology trends 2021 on https://success.mindbowser.com/insurance-technology-trends
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
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AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
2. CONTENT
• DATA JOURNEY SO FAR
• KEY FACTORS DRIVING MACHINE LEARNING IN INSURANCE
• UNLOCKING THE POWER OF DATA
• POTENTIAL FOR MACHINE LEARNING IN INSURANCE VALUE CHAIN
o Insurance advice
o Claims processing
o Fraud prevention
o Risk management
o Other applications
• CHALLENGES IN IMPLEMENTING MACHINE LEARNING
• PROVIDING A STEPPING-STONE TO CHANGE
• ACCENTURE VIEWPOINT
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5
6
9
11
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3. DATAJOURNEY
SO FAR
Data has always played a central role in the insurance industry, and today,
insurance carriers have access to more of it than ever before. We have
created more data in the past two years than the human race has ever
created. Insurers—like organisations in most industries—are overwhelmed
by the explosion in data from a host of sources, including telematics, online
and social media activity, voice analytics, connected sensors and wearable
devices. They need machines to process this information and unearth
analytical insights. But most insurers are struggling to maximise the benefits of
machine learning.
This situation is seeing a gradual but steady change, driven by an environment
characterised by increased competition, elastic marketplaces, complex claims
and fraud behaviour, higher customer expectations and tighter regulation.
Insurers are being forced to explore ways to use predictive modelling
and machine learning to maintain their competitive edge, boost business
operations and enhance customer satisfaction.
They are also examining how they can take advantage of recent advances in
artificial intelligence (AI) and machine learning to solve business challenges
across the insurance value chain. These include underwriting and loss
prevention, product pricing, claims handling, fraud detection, sales and
customer experience.
2
4. AI and advanced machine learning are among the top 10 strategic
technology trends leading organisations are currently using to
reinvent their business for a digital age.
The key market forces driving the adoption of AI and advanced
machine learning in 2018 and beyond are:
1. Smart everything – Enterprises are looking to use advanced
machine learning to drive smart, automated applications in fields
such as healthcare diagnosis, predictive maintenance, customer
service, automated data centres, self-driving cars and smart homes.
2. Open source everywhere – As data becomes omnipresent, open
source protocols will emerge to ensure data is shared and used
across. Different public and private entities will come together to
create ecosystems for sharing data on multiple use cases under a
common regulatory and cybersecurity framework.
3. Harnessing Internet of things (IoT) data – The volume and
velocity of data from IoT will drive the need to automate the
generation of actionable insight using advanced machine learning
tools. According to Gartner, by 2020, 20 percent of enterprises will
employ dedicated people to monitor and guide machine learning
(such as neural networks). The notion of training rather than
programming systems will become increasingly important.
4. Ability to talk back – Natural-language processing algorithms are
continuously advancing. AI is becoming proficient at understanding
spoken language and at facial recognition, helping to make it more
useful and intuitive. These algorithms are evolving in unexpected
ways, as Google found when Google Translate invented its own
language to help it translate more effectively.
KEY FACTORS
DRIVING
MACHINE
LEARNING
ININSURANCE
3
5. Figure 1
10000
0
20000
30000
Figure 1 illustrates the growth of the AI/machine learning market in
different geographical regions over 10 years. It shows the accelerating
adoption of AI and the critical importance of this technology trend.
Global AI market, by
geography
2017–2024 (in US$ M)
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
North America
Europe
Asia Pacific
Rest of the world
4
6. UNLOCKING
THE POWER
OF DATA
Most insurance companies process only 10–15
percent of the data they have access to—most of
which is structured data they house in traditional
databases. That means they are not only failing to
unlock value from their structured data, but also
overlooking the valuable insights hidden in their
unstructured data.
Analysing this unstructured data and using it to
drive better business decisions requires advanced
data science techniques. Emerging data analytics
technologies centred on machine learning bring
order and purpose to this unstructured data so that it
can be more effectively mined for business insights.
One major benefit of machine learning is that it
can be effectively applied across structured, semi-
structured or unstructured datasets. It can be
used right across the value chain to understand
risk, claims and customer behaviour, with higher
predictive accuracy.
The potential applications of machine learning
in insurance are numerous: from understanding
risk appetite and premium leakage, to expense
management, subrogation, litigation and
fraud identification.
5
7. POTENTIAL
FORMACHINE
LEARNING IN
INSURANCE
VALUE CHAIN
Some of the potential use cases are as follows:
INSURANCE ADVICE
Machines will play a significant role in customer service, from managing the
initial interaction to determining which cover a customer requires. According
to a recent survey, a majority of consumers are happy to receive such
computer-generated insurance advice. Consumers are seeking personalised
solutions—made possible by machine learning algorithms that review their
profiles and recommend tailor-made products. At the front end, insurers are
making wider use of chatbots on messaging apps to resolve claims queries
and answer simple questions.
New Business/Underwriting
Product Development
Policy Servicing
Claims
39%
26%
26%
26%
26%
Customer Experience
LIFE/ANNUITY
New Business/Underwriting
Claims
Product Development
Policy Servicing
Distribution
Customer Experience
56%
40%
36%
32%
32%
32%
PROPERTY/CASUALTY
SMA Research, 2016 Innovation
and Emerging Technologies, n=84
Figure 2: Insurance business areas where machine learning can be leveraged
Machine learning is extensively used across the insurance value chain.
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8. One such example is that of Allstate, which partnered with EIS (Earley
Information Science) to develop a virtual assistant, called ABle (the Allstate
Business Insurance Expert). ABIe assists Allstate agents seeking information
on Allstate Business Insurance (ABI) commercial insurance products. Before
ABle was deployed, agents were accustomed to selling personal lines
products such as health or homeowners insurance. However, when the
company decided to shift its focus to selling commercial insurance, many
agents had a slow learning curve and encountered challenges in accessing
the information they needed to effectively communicate with potential
clients. As a result, Allstate’s sales support call centre was consistently
flooded with inquiries from agents. Ultimately, “long wait times” translated
to “lost business opportunities.” ABle provides agents with step-by-step
guidance on “quoting and issuing ABI products,” using natural language. EIS
claims that ABle processes 25,000 inquiries per month.
CLAIMS PROCESSING
Insurers are using machine learning to improve operational efficiency, from
claims registration to claims settlement. Many carriers have already started to
automate their claims processes, thereby enhancing the customer experience
while reducing the claims settlement time. Machine learning and predictive
models can also equip insurers with a better understanding of claims costs.
These insights can help a carrier save millions of dollars in claim costs
through proactive management, fast settlement, targeted investigations and
better case management. Insurers can also be more confident about how
much funding they allocate to claim reserves.
Tokio Marine has an AI-assisted claim document recognition system that
helps to handle handwritten claims notice documents using a cloud-based
AI optical character recognition (OCR) service. It reduces 50 percent of the
document input load as well as complies with privacy regulations. AI is used
to read complicated, ambiguous Chinese characters (Kanji), and the “packet-
like” data transfer system protects customer privacy. The results: over 90
percent recognition rate, 50 percent reduction in input time, 80 percent
reduction in human error, and faster and hassle-free claims payments.
Insurance companies lose an estimated US$30 billion a year to fraudulent
claims. Machine learning helps them identify potential fraudulent claims
faster and more accurately, and flag them for investigation. Machine learning
algorithms are superior to traditional predictive models for this application
because they can tap into unstructured and semi-structured data such as
claims notes and documents as well as structured data, to identify
potential fraud.
FRAUD PREVENTION
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9. Insurers use machine learning to predict premiums and losses for their
policies. Detecting risks early in the process enables insurers to make better
use of underwriters’ time and gives them a huge competitive advantage.
Progressive Insurance is reportedly leveraging machine learning algorithms
for predictive analytics based on data collected from client drivers. The car
insurer claims that its telematics (integration of telecommunications and IT to
operate remote devices over a network) mobile app, Snapshot, has collected
14 billion miles of driving data. To encourage the use of Snapshot, Progressive
offers “most drivers” an auto insurance discount averaging US$130 after six
months of use.
RISK MANAGEMENT
These are just some examples of potential use cases. Insurers are also seeing
significant benefits from using machine learning across functions such as
direct marketing, audits, claims prediction and customer retention.
OTHER APPLICATIONS
Chola MS, one of India’s fastest-growing insurance companies, has adopted
mobile technology for its claims survey process. The company’s vehicle
surveyor application uses the voice, camera and data connectivity capabilities
of the Samsung Galaxy Tablet to capture and store auto survey data in one
database. In the past, loss adjusters had to manually match survey notes
with e-mail and photos saved in other databases before making a decision
on a claim. This initiative helped to speed up the claims settlement process,
increased surveyor productivity and improved fraud prevention.
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10. Most insurers recognise the value of machine learning in driving better
decision-making and streamlining business processes. Research for the
Accenture Technology Vision 2018 shows that more than 90 percent of
insurers are using, plan to use or considering using machine learning or
AI in the claims or underwriting process.
CHALLENGESIN
IMPLEMENTING
MACHINE
LEARNING
9
11. 1. Training requirements
AI-powered intellectual systems must be trained in a domain, e.g., claims
or billing for an insurer. This requires a separate training system, which
insurers find hard to provide for training the AI model. Models need to
be trained with huge volumes of documents/transactions to cover all
possible scenarios.
2. Right data source
The quality of data used to train predictive models is equally important
as the quantity, in the case of machine learning. The datasets need to be
representative and balanced so that they can give a better picture and
avoid bias. This is important to train predictive models. Generally, insurers
struggle to provide relevant data for training AI models.
3. Difficulty in predicting returns
It’s not very easy to predict improvements that machine learning can
bring to a project. For example, it’s not easy to plan or budget a project
using machine learning, as the funding needs may vary during the project,
based on the findings. Therefore, it is almost impossible to predict the
return on investment. This makes it hard to get everyone on board the
concept and invest in it.
4. Data security
The huge amount of data used for machine learning algorithms has
created an additional security risk for insurance companies. With such an
increase in collected data and connectivity among applications, there is a
risk of data leaks and security breaches. A security incident could lead to
personal information falling into the wrong hands. This creates fear in the
minds of insurers.
Some of the challenges
insurers typically encounter when
adopting machine learning are:
10
12. PROVIDINGA
STEPPING-STONE
TOCHANGE
Accenture is a proven partner for implementing New IT solutions, having
made extensive investments in a dozen research labs worldwide. We have
already delivered more than 50 machine learning and AI projects globally
in the insurance industry and are active in more than 100 AI engagements.
Accenture owns five patents for AI technology for insurance applications
and has two more that are patent pending.
• Strategy-led framework that
focuses on driving business value
• Industry expertise to design
optimised processes
• Independently test
technology components
• Develop integrated solutions
that leverage best-of-
breed products
• Design scalable,
future-proof solutions
• Resources and technology
platforms available to prototype
and scale
• Industrialised services and
cloud capabilities optimised
for delivery
• Research and thought
leadership dedicated to
responsible AI
• Robust service design approach
that puts humans at the centre
of the solution
• Change management expertise
to ensure smooth adoption
• Partnerships with academia to
deliver thought leadership and
innovative solutions
• Relationships with key technology
partners and startups
Technology
agnostic
Business
value
focused
Positioned
to scale
Diverse
ecosystem
Put
humans first
OUR UNIQUE RANGE OF CAPABILITIES
WE CAN PROVIDE END-TO-END MACHINE LEARNING OFFERINGS
Figure 3
11
13. As rapid technological advances reshape the insurance landscape, carriers
must become more customer-centric, enhance customer service, create better
solutions for operational efficiency and build ever more accurate underwriting
models. Insurers have no option but to embrace machine learning to remain
competitive, drive operational excellence and boost growth.
Although machine learning used to be the exclusive domain of data scientists,
it is now possible for business users to build data models and make accurate
predictions faster. Insurers already have domain experts: actuaries, claims
managers and underwriters, who can contribute to machine learning projects
with the right training and tools.
As insurers consider and evaluate machine learning for their organisations,
they should bear in mind the importance of automation and seek platforms
that automate the entire workflow. However, the journey begins with a pilot
model: develop a proof of concept, test the derived machine learning benefits
and extend deployments once successful.
ACCENTURE
VIEWPOINT
REFERENCES
https://www.forbes.com/forbes
https://hortonworks.com
http://www.propertycasualty360.com
http://www.tellius.com
https://channels.theinnovationenterprise.com /articles
https://www.gartner.com
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