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Artificial Intelligence in China’s Technology Communication Industry (2016-2025): AI Spending with 20+ KPIs, Market Size Forecast Across 9+ Application Segments, AI Domains, and Technology - ResearchAndMarkets.com

Artificial Intelligence Spending Analysis in Technology and Communication Industry in China 3.1 Artificial Intelligence Spending Analysis in Technology and Communication Industry, 2016-2025 3.2 Artificial Intelligence Spending Analysis in Technology and Communication Industry by Application, 2016-2025 3.2.1 Spend Analysis on Project Planning and Control in Technology and Communication Industry 3.2.2 Spend Analysis on Development and Code Optimization in Technology and Communication Industry 3.2.3 Spend Analysis on Human Resource Management in Technology and Communication Industry 3.2.4 Spend Analysis on Design Optimization in Technology and Communication Industry 3.2.5 Spend Analysis on Multichannel Marketing Analytics in Technology and Communication Industry 3.2.6 Spend Analysis on Predictive Sales Intelligence in Technology and Communication Industry 3.2.7 Spend Analysis on Disruption Intelligence in Technology and Communication Industry 3.2.8 Spend Analysis on Fraud Mitigation in Technology and Communication Industry 3.2.9 Spend Analysis on Risk Management and Compliance in Technology and Communication Industry 3.3 Artificial Intelligence Spending Analysis in Technology and Communication Industry by AI Domain, 2016-2025 3.3.1 Spend Analysis on Machine Learning and Deep Learning in Technology and Communication Industry 3.3.2 Spend Analysis on Natural Language Processing (NLP) in Technology and Communication Industry 3.3.3 Spend Analysis on Robotics and Expert Systems in Technology and Communication Industry 3.3.4 Spend Analysis on Machine Vision &

How Artificial Intelligence is advancing engagement with Defined Contribution pensions

AI may be on the cusp of driving our cars and diagnosing our illnesses, but it is also about to enter the DC pensions market in a big way.

In this article, Mark French considers how behavioural science and machine learning are coming together to radically and powerfully change the way in which pension schemes can communicate and engage with their members.

These automated messages are designed and managed by an algorithm that is looking at your personal data and similar data produced by millions of other customers.

DC providers are aggregating customer data across multiple touch points and building up unprecedented insight into pension scheme member behaviour.

2017 study of UK workers, The savings psyche of the UK, concluded that there are 6 separate personas into which UK employees can be categorised when looking at their attitude to saving.

Behavioural science shows that individuals are far more likely to respond to personalisation and this can be further amplified with behavioural communication techniques such as ‘nudge’

For example, HMRC revealed that issuing people in arrears on their taxes reminders that contained phrases such as “9 out of 10 people in your area are up-to-date with tax payments”

Where personalisation is able to tailor communications towards characteristics such as pot sizes, age and risk appetite, hyper-personalisation is about using AI to adapt to an individual’s decisions in real-time.

In DC pensions, this means understanding an individual’s engagement with the employer’s scheme by recording and analysing multiple data points about them and their interactions in order to construct relevant content from the available communications collateral.

Scheme sponsors may wish to leverage this advanced capability to meet specific objectives, for example they may want members to review their contributions or investment choices, or perhaps consolidate pots from an old scheme into a new scheme.

They are the virtual assistants that, through online or voice interactions, can navigate choices and accurately select the appropriate response suited to the customer.

Combining proven behavioural science techniques with AI powered communications looks set to genuinely start to break down apathetic behaviour among DC members. Artificially

technology may be the key to solving some of the biggest challenges facing DC schemes, such as delays in engagement leading to poor retirement outcomes.

These automated messages are designed and managed by an algorithm that is looking at your personal data and similar data produced by millions of other customers.

DC providers are aggregating customer data across multiple touch points and building up unprecedented insight into pension scheme member behaviour.

Our 2017 study of UK workers, The savings psyche of the UK, concluded that there are 6 separate personas into which UK employees can be categorised when looking at their attitude to saving.

For example, HMRC revealed that issuing people in arrears on their taxes reminders that contained phrases such as “9 out of 10 people in your area are up-to-date with tax payments”

Where personalisation is able to tailor communications towards characteristics such as pot sizes, age and risk appetite, hyper-personalisation is about using AI to adapt to an individual’s decisions in real-time.

In DC pensions, this means understanding an individual’s engagement with the employer’s scheme by recording and analysing multiple data points about them and their interactions in order to construct relevant content from the available communications collateral.

Scheme sponsors may wish to leverage this advanced capability to meet specific objectives, for example they may want members to review their contributions or investment choices, or perhaps consolidate pots from an old scheme into a new scheme.

Artificial Intelligence (AI) Business Opportunities Outlook in the United States (2016-2025) - Spend on AI is Expected to Record a CAGR of 27.8% During 2019-2025 - ResearchAndMarkets.com

DUBLIN--(BUSINESS WIRE)--Mar 26, 2019--The “United States Artificial Intelligence (AI) Business Opportunities and Outlook Databook Series (2016-2025) - AI Market Size / Spending Across 18 Sectors, 140+ Application Segments, AI Domains, and Technology” report has been added to ResearchAndMarkets.com’s offering.

Artificial Intelligence (AI) spend in United States has increased at 77.3% during 2018 to reach US$ 4,180 million.

Over the forecast period (2019-2025), spend on AI is expected to record a CAGR of 27.8%, increasing from US$ 6,452.8 million in 2019 to reach US$ 35,996.4 million by 2025.

This business intelligence report aims to analyze market opportunities and risks in artificial intelligence (AI) industry and its applications in over 140+ areas across 18 industries in United States.

This is a data centric report, consisting of 340 charts and 285 tables, providing detailed understanding of market dynamics.

This report covers country level AI market size/spending forecast (2016-2025) by applications across banking and finance industry’s value chain, AI technology domains, and technology.

Report Scope Reason to Buy This research report provides detailed market opportunity trend analysis, offering AI spending/market size and forecast across 140+ application areas in 18 industries in United States.

Technology and Communication United States AI Spending/Market Size by Artificial Intelligence Domains 2016-2025 United States AI Spending/Market Size by Technology Development 2016-2025 United States AI Spending/Market Size by Application (2016-2025) 1.

Risk Management and Compliance For more information about this report visit https://www.researchandmarkets.com/research/75kkxm/artificial?w=4

CONTACT: ResearchAndMarkets.com Laura Wood, Senior Press Manager press@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470 For U.S./CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

Related Topics:Artificial

Intelligence KEYWORD: UNITED STATES NORTH AMERICA

INDUSTRY KEYWORD: TECHNOLOGY SOFTWARE

SOURCE: Research and Markets

PUB: 03/26/2019 01:10 PM/DISC: 03/26/2019 01:10 PM http://www.businesswire.com/news/home/20190326005802/en

Artificial Intelligence: Communication Project

Team Members: 1) Deep Desai 2) Shalini Priya 3) Jasmeet Singh 4) Faraz Khan.

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