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IBM Mobile Customer Engagement Sales Mastery Test v1
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Killexams : IBM Engagement learner - BingNews https://killexams.com/pass4sure/exam-detail/M2080-713 Search results Killexams : IBM Engagement learner - BingNews https://killexams.com/pass4sure/exam-detail/M2080-713 https://killexams.com/exam_list/IBM Killexams : Copy Hollywood's 3-Act Structure To Make Your Pitch Irresistible

“Story is everything. Good content making is not a crap shoot. We know how this works,” the actor Kevin Spacey told a meeting of business marketers. Spacey is right. Thanks to neuroscience we’ve learned more about storytelling in the last 10 years than we’ve ever known. We now know what brain chemicals make us pay attention (cortisol), what causes to feel empathy (oxytocin) and what make us feel good (dopamine). We know exactly which type of stories trigger these chemicals, why they work and we can prove it scientifically.

Whether your goal is to pitch an idea, ace a job interview, sell a product, build a company, Improve teamwork and employee engagement or give the presentation of a lifetime, If you want to learn to sell your ideas more effectively turn to the people who know how to do storytelling best—hit filmmakers.

Nearly all successful modern movies follow the hero’s journey in a three-act structure: The Set-up, The Confrontation and The Resolution. The set-up describes the hero’s world, the status quo. The confrontation introduces the stuff of narrative—tension and battle between hero and villain. The resolution ends with the hero’s conquest over the villain and, most important, shows how the hero’s world is transformed for the better.

George Lucas is a student of mythology and followed the three-act structure perfectly for all six of the Star Wars movies he created (and Disney followed the template for Star Wars: The Force Awakens). Ridley Scott’s The Martian, nominated for an Academy Award for Best Picture, is also a perfect illustration of the structure. In act one we’re introduced to Mark Watney (Matt Damon) and his team of astronauts on Mars. We like Mark and that makes it easier for us to root for him, which is nice since we’ll be spending a lot of time with him (he just doesn’t know it yet). In act two Watney makes a decision that he’ll survive on Mars until he’s rescued. In act three a decision is made that will resolve Watney’s predicament.

The structure is well established in Hollywood because it’s classic narrative and has worked for centuries. Some screenwriters believe there is no story that hasn’t been told, just fresh takes on old stories. Game-changing business presentations and inspiring speeches follow a similar template. Here are two examples.

Steve Jobs, Macintosh: January 24, 1984

Act 1: Set-up (The hero’s world before the adventure starts)

It is 1958. IBM passes up the chance to buy a young, fledgling company that has invented a new technology called xerography. Two years later Xerox is born. IBM has been kicking themselves ever since. It is ten years later. IBM dismisses the mini-computer as too small to do serious mini computing and unimportant to their business…

Act II: Confrontation (The hero’s world is turned upside down)

It is now 1984. It appears IBM wants it all. Apple is perceived to be the only hope to offer IBM a run for its money. Dealers fear an IBM dominated and controlled future. They are increasingly turning back to Apple as the only force that can ensure their future freedom. IBM wants it all and is aiming its guns to its last obstacle to industry control. Apple.

Act III: Resolution (Hero conquers villain, but it’s not enough for the hero to survive. The hero or the world must be transformed).

Steve walks to the center of the stage and unveils the ‘hero,’ the first Macintosh. He pulls a floppy disk from his pocket, inserts it into the computer and let’s Macintosh ‘speak for itself.’ With the introduction of Macintosh the world will see why 1984 won’t be like ‘1984.’

The Macintosh story played itself out on stage just like a hit movie complete with heroes, villains, props, and surprises. Jobs even tapped Martian director Ridley Scott to create the first Macintosh television ad. Jobs even had a movie score, the theme song from Chariots of Fire playing in the background. Steve Jobs was a great marketer because he was great storyteller and he relied on great filmmakers to help tell the story.

Malala Yousafzai, Nobel Prize Speech Lecture: December 10, 2014

Act I: Set-up

In my paradise home, Swat, I always loved learning and discovering new things. We had a thirst for education. We would sit and learn and read together. We loved to wear neat and tidy school uniforms and we would sit there with big dreams in our eyes.

Act II: Confrontation

But things did not remain the same. When I was in Swat, which was a place of tourism and beauty, suddenly changed into a place of terrorism. I was just ten that more than 400 schools were destroyed. Women were flogged. People were killed. And our beautiful dreams turned into nightmares. Education went from being a right to being a crime.

Act III: Resolution

The terrorists tried to stop us and attacked me and my friends who are here today, on our school bus in 2012, but neither their ideas nor their bullets could win. We survived. And since that day, our voices have grown louder and louder. I tell my story, not because it is unique, but because it is not. It is the story of many girls.

Also on Forbes:

Tue, 02 Aug 2022 05:15:00 -0500 Carmine Gallo en text/html https://www.forbes.com/sites/carminegallo/2016/01/22/copy-hollywoods-3-act-structure-to-make-your-pitch-irresistible/
Killexams : IBM 'continuing to hire' ahead of recession

Read more: Safeguard Global CTO: Tech talent remains highly sought after

But IBM has always carved its own path. For example, the Armonk, NY-based company doesn’t use the term “Great Resignation,” at least internally. Of course, that doesn’t mean the tech giant isn’t aware of the nationwide talent shortage and the highly competitive labor market that’s resulted.

“This is a time to ensure we re-engage our population,” Louissaint says. “By nature of my title, my goal is to continue to transform and pivot our company toward being more growth-minded, transforming it directly through leadership: leadership development, getting people in the right jobs and ensuring we have the right succession plans.”

Like many companies since the COVID-19 pandemic, IBM has relied upon its business resource groups – its label for employee resource groups (ERGs) – to maintain and even boost retention. Traditionally, ERGs consist of employees who volunteer their time and effort to foster an inclusive workplace. Due to their motivations, needs and the general nature of ERG work, employees who lead these groups are more likely to be Black, Indigenous and People of Color (BIPOC) and oftentimes women. ERGs are a way for underrepresented groups to band together to recruit more talent like them into their companies and make sure that talent feels supported and gets promoted.

“It’s a lot easier to leave a company where you’ve only interacted with colleagues through a screen,” Louissaint says. “Our diversity groups and communities have gotten a lot stronger, which builds commitment to the company and community to each other. We’ve found that through our communities, business resource groups, open conversations and by democratizing leadership by using virtual technologies like Slack, the company has become smaller and the interactions are a lot more personal.”

A major contributor to the Great Resignation has been the push for workers to return to the office. While Apple and Google have ruffled feathers with requesting employees back for at least a couple days a week, Tesla went one step further by demanding employees head to the office five days a week, as if the COVID-19 pandemic never happened.

Ahead of the game, IBM was one of the first major tech firms to embrace remote work, with as much as 40% of its workforce at home during the 2000s. A shift came in 2017, but since the pandemic, only 20% of the company’s U.S. employees are in the office for three days a week or more, according to IBM CEO Arvind Krishna. In June, Krishna added that he doesn’t think the balance will ever get back to more than 60% of workers in the office.

“We’ve always been defined by flexibility, even prior to the pandemic that’s what we were known for and what differentiated us,” Louissaint says. “Continuing to double down on flexibility has been a value to us and to our people.”

IBM has also been defined by its eye toward the future, particularly when it comes to workforce development. Over the past decade, the tech giant has partnered with educational institutions, non-governmental organizations and other companies to discover and nurture talent from untapped pools and alternative channels. Last year, the company vowed to train 30 million individuals on technical skills by 2030.

“Our people crave learning and are highly curious,” Louissaint says, adding that the average IBM employee consumes about 88 hours of learning through its platform each year. Nearly all (95%) employees are on the platform in any given quarter.

“We’ve been building a strong learning environment where employees can build new skills and drive toward new jobs and experiences,” he says. “We also find that the individuals who consume the most learning are more likely to get promoted. It’s 30% more likely for a super learner to be promoted or switch jobs, so the incentive is continued growth and opportunity for advancement.”

Wed, 03 Aug 2022 16:00:00 -0500 en text/html https://www.hcamag.com/us/specialization/learning-development/ibm-continuing-to-hire-ahead-of-recession/415538
Killexams : 4 Reasons Your B2B Startup Needs Content Marketing

Opinions expressed by Entrepreneur contributors are their own.

You've probably heard this phrase ad nauseum: "No one ever got fired for buying from ." It speaks to one of the greatest challenges for most startups: Winning the trust that comes with having an established, recognized .

If this describes you, can help. In fact, businesses that prioritize it generate three times as many leads and see 30% higher growth than those that don't.

If you're not convinced content marketing is for you, here are four reasons it's important for businesses of all sizes  —  especially startups.

Related: Here's How to Improve Your Business's Content Marketing

B2B buying habits have changed

B2B buyers now demand a purchasing experience that minimizes their direct interaction with brands and maximizes their reliance on digital channels for information.

A recent study by Gartner found that B2B customers spend approximately 5% of their total purchasing time interacting with a supplier, with the majority devoted to independent research.

New buying behaviors favor established brands by virtue of their name-brand recognition and perceived authority. Yet startups can compete by producing high-quality, . Studies show that 47% of B2B buyers say thought leadership made them discover and purchase from a company not among the established leaders of a specific niche.

To ensure your content reflects the needs and journey of your buyers:

  • Survey existing customers. Try to understand their unique journey and the dynamics of their buying team.
  • Download sales team insights. Regularly gather and document sales-team insights from interactions with prospects.
  • Perform an audit. Audit your existing content, research, and knowledge bases.
  • Get third-party validation. Use credible third-party research specific to your niche.
  • Tailor content to audiences. Develop detailed buyer personas and map your content to their journey.

Content only works if it speaks to your customer. To help guide you, always start with voice-of-customer data to drive content strategy and messaging. Their feedback should drive everything, with industry research and empirical evidence providing support.

Related: Content Marketing Quick-Start Guide: 3 Things Your B2B Startup Should Publish First

Analytics make it possible to better understand your target customer

Today, analytics tools enable businesses to learn more about their target customer than ever before. Still, data analytics need content to drive value.

The more than 100 data points that Google Analytics tracks offer nothing if you can't attract audiences to your website. All those social media metrics? They're meaningless too without posts that drive impressions and engagement.

Content facilitates a learning process that enables your startup to survive and win. The more you produce, the more you find out about your target customer and what motivates them to take profitable action. This is important for any business, but especially startups.

To get the best insights from your content:

  • Set a regular publishing schedule. Establish a consistent cadence for publishing your various content offers (e.g. weekly blog posts on Tuesday and Thursday).
  • Make content cohesive. Cross-reference and cross-promote your content offers.
  • Learn and apply. Update your content strategy regularly with gained insights.

This last bullet matters most and surprisingly receives the least attention. If you capture analytics but never apply them, what's the point? So, set weekly team meetings that evaluate content performance and focus on improvement.

Related: Why Your Startup Content-Marketing Strategy Isn't Working

B2B buyers use large and diverse teams

As -based products and services become more common, B2B buyers are increasingly using large and diverse teams to make a decision.

Buying groups tend to:

While these attributes favor established brands, startups can get more consideration. How? By developing thought leadership that attracts attention, demonstrates authority, and makes buying teams smarter.

To Improve the chances your content resonates with diverse groups:

  • Develop different functional decision-makers. Develop content that targets the specific interests of different decision-makers, such as content focused on business value for executives and technical design for software engineers.
  • Develop full-funnel content. Produce a balanced mix of content that focuses on each stage of the buying journey (i.e. awareness, consideration, decision).
  • Target different learning styles. Diversify the format of your content offering to suit your buying team's learning styles and preferences (e.g. audio, video, graphic, written).
  • Enhance your visibility. Make your content available where your buying teams go for information.

The above activities depend on factors specific to your business and niche, including your buyer's product awareness and sophistication levels. Your buyer, and what drives them to convert, should drive everything you do.

Related: 5 Tips to Launch a Content Marketing Program Faster (and With Fewer Resources)

Startups are building recognized brands  —  fast

Brand recognition is an inherent weakness for most startups. Still, that doesn't mean they can't become a recognized name for their niche fast  —  with content marketing as their rocket fuel.

The fintech company Mint famously adopted a content marketing strategy that contributed to its rapid success. Before ever launching a product, it started a successful blog catering to its target customer and built an email list of 20,000 subscribers. It took them only three years to get acquired by for $170 million.

Building is most important in the startup phase, and content marketing is the most cost-effective tool to accelerate the process.

To accelerate your brand building, leverage content marketing that can scale:

  • Leverage third-party outlets. Tap into channels that maximize your reach and authority (e.g. byline articles in credible publications, speaking engagements, guest podcast appearances).
  • Engage influencers. Leverage influencers to help market your brand and content (e.g. guest blog post, guest podcast appearances).
  • Collaborate with partners. Partner with existing customers and/or partners to develop and cross-promote content (e.g. case studies, co-branded whitepapers).
  • Gate content intentionally. Balance ungated and gated content to drive brand awareness and leads.

The exact content strategy you pick depends (again) on your buyer. But you should also take into consideration your internal strengths. If you're a charismatic founder who naturally shines on stage, a keynote speech or video interview might offer the highest ROI.

Bottom line: Invest in a content marketing strategy

All signs point to one big takeaway.

Content marketing is a reliable (and much-needed) vehicle to convince customers to take a chance on you.

So take a chance on it. Show the world why you're better than all the job-saving IBMs out there.

Sat, 30 Jul 2022 00:38:00 -0500 Todd Stansfield en text/html https://www.entrepreneur.com/article/430822
Killexams : Deep Learning in Security Market Overview, Analysis, Outlook and Forecast to 2027 : Graphcore, Mythic, Adapteva

Deep Learning in Security Market Overview, Analysis, Outlook and Forecast to 2027

This press release was orginally distributed by SBWire

New Jersey, NJ — (SBWIRE) — 07/30/2022 — The Deep Learning in Security Market study describes how the technology industry is evolving and how major and emerging players in the industry are responding to long term opportunities and short-term challenges they face. One major attraction about Deep Learning in Security Industry is its growth rate. Many major technology players – including NVIDIA (US), Intel (US), Xilinx (US), Samsung Electronics (South Korea), Micron Technology (US), Qualcomm (US), IBM (US), Google (US), Microsoft (US), AWS (US), Graphcore (UK), Mythic (US), Adapteva (US) & Koniku (US) etc have been looking into Deep Learning in Security as a way to increase their market share and reach towards consumers.

Industries and key technological segments are evolving; navigate these changes with latest insights released on Deep Learning in Security Market Study

Check Free demo Copy @: https://www.htfmarketreport.com/sample-report/3407852-deep-learning-in-security-market

Major Highlights of Deep Learning in Security Market Report

1) Why this market research study would be beneficial?
– The study guides Deep Learning in Security companies with strategic planning to ensure they realize and drive business value from their plans for growth strategy.

2) How scope of study is defined?
– The Deep Learning in Security market is composed of different product/ service offering type, each with its own business models and technology. They include:

Type: Hardware, Software & Service;

Application: Identity and Access Management, Risk and Compliance Management, Encryption, Data Loss Prevention, Unified Threat Management, Antivirus/Antimalware, Intrusion Detection/Prevention Systems & Others (Firewall, Distributed Denial-of-Service (DDoS), Disaster Recovery);

**Further breakdown / Market segmentation can be provided; subject to availability and feasibility of data.

3) Why Deep Learning in Security Market would define new growth cycle ?
– Analysis says that Deep Learning in Security Companies that have continues to invest in new products and services including via acquisitions have seen sustainable growth, whereas one with slower R&D investment growth have become stagnant. Technology companies with annual R&D growth over 20% have outperformed their peer group in revenue growth.

View Complete Table of Content @ https://www.htfmarketreport.com/reports/3407852-deep-learning-in-security-market

Research shows that Deep Learning in Security companies have increased R&D spend and accelerated merger & acquisitions. The industry has one of the fastest innovation cycles studied across industry/applications such as Identity and Access Management, Risk and Compliance Management, Encryption, Data Loss Prevention, Unified Threat Management, Antivirus/Antimalware, Intrusion Detection/Prevention Systems & Others (Firewall, Distributed Denial-of-Service (DDoS), Disaster Recovery). To realize value they intend, companies like NVIDIA (US), Intel (US), Xilinx (US), Samsung Electronics (South Korea), Micron Technology (US), Qualcomm (US), IBM (US), Google (US), Microsoft (US), AWS (US), Graphcore (UK), Mythic (US), Adapteva (US) & Koniku (US) etc need to continuously evaluate their governance, risks and control, infrastructure, and talent to aligned planned growth strategies with their operating business models.

To comprehend Deep Learning in Security market dynamics, the market study is analysed across major geographical regions/country

– North America: United States, Canada, and Mexico
– South & Central America: Argentina, Chile, Brazil and Others
– Middle East & Africa: Saudi Arabia, UAE, Israel, Turkey, Egypt, South Africa & Rest of MEA.
– Europe: UK, France, Italy, Germany, Spain, BeNeLux, Russia, NORDIC Nations and Rest of Europe.
– Asia-Pacific: India, China, Japan, South Korea, Indonesia, Thailand, Singapore, Australia and Rest of APAC.

Important Years in Deep Learning in Security Market Study Major trends of Deep Learning in Security Market using final data for 2019 and previous years, as well as quarterly or annual reports for 2021. In general, Years considered in the study i.e., base year as 2020, Historical data considered as 2015-2020 and Forecast time frame is 2021-2027.

Get full access to Deep Learning in Security Market Report; Buy Latest Edition Now @: https://www.htfmarketreport.com/buy-now?format=1&report=3407852

The Deep Learning in Security study is a perfectly designed with mix of both statistically relevant quantitative data from industry, coupled with insightful qualitative comment and analysis from Industry experts and consultants. To ascertain a deeper view; Deep Learning in Security Market Size by key business segments and applications for each of above listed region/country is provided along with competitive landscape that includes Comparative Market Share Analysis by Players (M USD) (2021-2027) and market concentration rate of Deep Learning in Security Industry in 2021.

In-depth company profiles for 15+ Deep Learning in Security leading and emerging players that covers 3-years financial history, swot analysis and other vital information like legal name, website, headquarter, % market share and position, distribution and marketing channels and latest developments.

Driving and maintaining growth continues to be a top-of mind issue for Boards, CXOs, and investors in the Technology industry. Deep Learning in Security companies and the chain of services supporting them are facing profound business challenges majorly from three factors:

1. The explosive rate at which competitors and Deep Learning in Security industry is growing.
2. The amount of growth that is driven by innovation in technologies, value propositions, products and services.
3. The speed at which innovations needs to be furnished in order to drive growth in Deep Learning in Security Market.

Something not matching; Go with Customized Report @ https://www.htfmarketreport.com/enquiry-before-buy/3407852-deep-learning-in-security-market

Thanks for studying Deep Learning in Security Industry research publication; get customized report or need to have regional report like North America, Europe, USA, China, Asia Pacific, India etc then connect with us @ [email protected]

For more information on this press release visit: http://www.sbwire.com/press-releases/deep-learning-in-security-market-overview-analysis-outlook-and-forecast-to-2027-graphcore-mythic-adapteva-1361500.htm

Fri, 29 Jul 2022 12:00:00 -0500 ReleaseWire en-US text/html https://www.digitaljournal.com/pr/deep-learning-in-security-market-overview-analysis-outlook-and-forecast-to-2027-graphcore-mythic-adapteva
Killexams : BGF invests £2.3m in data firm Optima
Alan Crawley
Alan Crawley: expansion plans

Optima Partners, the Edinburgh-based data science consultancy, has received a £2.3m investment from BGF.

The funding will help the nine-year-old firm scale its software and take its 44-strong payroll to 100.

It has achieved 55% year-on-year growth, delivering £5.75m in full year revenue at the end of June.

Karen Thomas-Bland has been appointed non-executive chairman. Her former roles include partner of strategy and transformation at IBM Global; executive director of strategy & transformation at KPMG; and providing buy side investment assessment for Accenture and EY.

Optima has additional offices in London and Bristol and offers design-led customer and digital transformation services to clients across the customer, carbon and health sectors.

Using machine learning, the company identifies opportunities to Improve the efficiency and effectiveness of customer engagement, marketing and servicing to maximise client return on investment.

Alan Crawley, chief executive, said: “BGF’s investment will help us scale our business, allowing us to serve more customers. Enhancing the application of advanced data sciences, alongside the practical application of AI and machine learning, are other key pillars in our growth and expansion plans.

“Karen’s appointment as our Non-Executive chair will be of great value given her considerable consulting and data science expertise. We are grateful for BGF facilitating the appointment through its Talent Network

Euan Baxter, investor at BGF, said: “Optima Partners uses data and machine learning in a way that can revolutionise marketing and customer experience, and this investment from BGF is designed to help the business achieve further scale.

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“Rooted in Edinburgh as a centre of data excellence, the company has built on strong academic foundations to prove its credentials through innovative work with some of the biggest names in financial services, energy and pharmaceuticals.”

Optima Partners has long-standing ties with academia with an office at the University of Edinburgh and strong links in the business school and medical genetics.

Ms Thomas-Bland added: “Optima Partners’ approach combines key elements of my previous consulting and data science expertise, so as soon as I was introduced via BGF’s Talent Network, it felt like a natural fit.”

It leads a consortium including academic colleagues and a leading pharmaceutical business driving innovation through advanced mathematics and software development in early phase drug discovery.

BGF’s investment in Optima Partners follows a record year in Scotland, having invested close to £60 million in 2021 in a diverse range of sectors, from technology to healthcare to consumer goods.

Wed, 03 Aug 2022 19:11:00 -0500 Terry Murden, Editor en-GB text/html https://dailybusinessgroup.co.uk/2022/08/data-firm-optima-receives-2-3m-from-bgf/
Killexams : Learning with the Internet of Things and Artificial Intelligence: harnessing their potential

Vinay K Mayer

Artificial Intelligence (AI) and the Internet of Things (IoT) are working together to transform the education industry.  With students being given the chance to receive personalized guidance from teachers or other knowledgeable sources boasting years of experience in their fields, AI and IoT are taking education into bold new territory. 

Technologies not only secure independence for students, but also facilitate interaction between teachers and students through the use of new platforms and applications designed to promote learning based on what’s appealing to today’s youth seeking assurance they can succeed in their journeys into the future. 

Research shows there’s significant potential for growth in the global AI in the Education sector and here’s some background detail on it. Companies that are leveraging AI in innovative ways include: 

– Great Minds, Henry Harvin, EduThinker, BrainPop, TutorMe, Unacademy, upGrad are just a few of the EdTech platforms while Microsoft AI School, Apple Education, and IBM Watson Education/ Google AI School are examples of tech giants who offer similar programs.

-It is estimated that AI in education will generate a revenue of 25 billion USD by 2030. 

-According to the e-learning industry, over 47% of learning management tools will incorporate artificial intelligence within the next three years. 

-Higher education and research institutes play an essential role in bridging the skills gap for AI.

How will AI and IoT be used to make the education industry more efficient?

There has been significant growth in the digital simulation of education, mostly due to recent compliance issues around current curriculum policies and the pandemic that crippled the educator industry. Students should be able to learn both locally and globally, so the government has proposed to build a more resilient system of higher education.

AI can help the education industry by:

  • AI is not intended to replace teachers, but to assist them in understanding each student’s potential and limitations.
  • AI is also helping teachers see what is happening in a classroom in real-time. It helps teachers identify children who are struggling and allows them to intervene and provide support before it’s too late.  
  • Using AI in education can help automate activities like multiple choice questions, fill in the blanks, etc.
  • Automating administrative tasks allows teachers to spend more time with students, thus improving student learning. Like Report cards are a difficult and tedious task for teachers to prepare. However, AI can simplify the process.
  • Learning new courses has not only become easier, but it has also provided students with more time to pursue other interests, such as working with classmates to teach them skills they do not yet fully understand during their free study time.
  • Students who are unable to attend school due to illness or injury or live in remote areas can reap the benefits of AI.

Teachers are further beginning to incorporate AI into their lessons. A great example of this is the use of voice assistants like Alexa, Siri, and Cortana – which can be used with learning materials in a classroom or educational environment for students to converse within a place of their teachers. Also, interactive AI has been shown to be effective in engaging students!

Educating the next generation – prospects for the future

AI and ML across India’s entire educational system will be a significant challenge because various policy decisions will need to be made to ensure that teachers are properly trained and equipped to integrate new technological possibilities into their classrooms, etc. 

While most countries are struggling to implement innovations in their infrastructure, education is no exception. The fact is, if more efforts are not invested in modernizing educational systems, especially in the area of technology, we cannot expect transformation easily.

The author is director, market research and consulting at Asia Research Partners.

Tue, 02 Aug 2022 10:19:00 -0500 en text/html https://www.financialexpress.com/education-2/learning-with-the-internet-of-things-and-artificial-intelligence-harnessing-their-potential/2611503/
Killexams : International Business Machines Corp. 01/04/22 Gary D. CohnVice Chairman 5112 Derivative/Non-derivative trans. at $0.00 per share 0 01/04/22 Gary D. CohnVice Chairman 2220 Derivative/Non-derivative trans. at $137.93 per share 306,205 02/01/22 Arvind KrishnaChairman and CEO; Director 88602 Award at $0.00 per share 0 02/01/22 Arvind KrishnaChairman and CEO; Director 18516 Award at $0.00 per share 0 02/01/22 James J. KavanaughSr. VP and CFO 17899 Award at $0.00 per share 0 02/01/22 Michelle H. BrowdySenior Vice President 13270 Award at $0.00 per share 0 02/01/22 Thomas W. RosamiliaSenior Vice President 17899 Award at $0.00 per share 0 02/01/22 Gary D. CohnVice Chairman 4004 Award at $0.00 per share 0 02/01/22 Robert F. Del BeneVP, Controller 3561 Award at $0.00 per share 0 02/01/22 Nickle J. LaMoreauxSenior Vice President 1187 Award at $0.00 per share 0 02/01/22 Arvind KrishnaChairman and CEO; Director 42657 Derivative/Non-derivative trans. at $134.20 per share 5,724,569 02/01/22 Arvind KrishnaChairman and CEO; Director 9147 Derivative/Non-derivative trans. at $134.20 per share 1,227,527 02/01/22 James J. KavanaughSr. VP and CFO 7875 Derivative/Non-derivative trans. at $134.20 per share 1,056,825 02/01/22 Michelle H. BrowdySenior Vice President 5682 Derivative/Non-derivative trans. at $134.20 per share 762,524 02/01/22 Thomas W. RosamiliaSenior Vice President 7626 Derivative/Non-derivative trans. at $134.20 per share 1,023,409 02/01/22 Gary D. CohnVice Chairman 1885 Derivative/Non-derivative trans. at $134.20 per share 252,967 02/01/22 Robert F. Del BeneVP, Controller 1469 Derivative/Non-derivative trans. at $134.20 per share 197,140 02/01/22 Nickle J. LaMoreauxSenior Vice President 460 Derivative/Non-derivative trans. at $134.20 per share 61,732 06/08/22 Arvind KrishnaChairman and CEO; Director 8604 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Arvind KrishnaChairman and CEO; Director 10091 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Arvind KrishnaChairman and CEO; Director 3108 Derivative/Non-derivative trans. at $0.00 per share 0 06/07/22 Arvind KrishnaChairman and CEO; Director 4020 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 James J. KavanaughSr. VP and CFO 5162 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 James J. KavanaughSr. VP and CFO 5687 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 James J. KavanaughSr. VP and CFO 2968 Derivative/Non-derivative trans. at $0.00 per share 0 06/07/22 James J. KavanaughSr. VP and CFO 3885 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Michelle H. BrowdySenior Vice President 2972 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Michelle H. BrowdySenior Vice President 3228 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Michelle H. BrowdySenior Vice President 2148 Derivative/Non-derivative trans. at $0.00 per share 0 06/07/22 Michelle H. BrowdySenior Vice President 2880 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Thomas W. RosamiliaSenior Vice President 4067 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Thomas W. RosamiliaSenior Vice President 4403 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Thomas W. RosamiliaSenior Vice President 3108 Derivative/Non-derivative trans. at $0.00 per share 0 06/07/22 Thomas W. RosamiliaSenior Vice President 3885 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Robert F. Del BeneVP, Controller 1429 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Robert F. Del BeneVP, Controller 1676 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Robert F. Del BeneVP, Controller 971 Derivative/Non-derivative trans. at $0.00 per share 0 06/07/22 Robert F. Del BeneVP, Controller 1435 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Nickle J. LaMoreauxSenior Vice President 1376 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Nickle J. LaMoreauxSenior Vice President 523 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Nickle J. LaMoreauxSenior Vice President 284 Derivative/Non-derivative trans. at $0.00 per share 0 06/07/22 Nickle J. LaMoreauxSenior Vice President 478 Derivative/Non-derivative trans. at $0.00 per share 0 06/01/22 Nickle J. LaMoreauxSenior Vice President 1615 Derivative/Non-derivative trans. at $0.00 per share 0 06/08/22 Arvind KrishnaChairman and CEO; Director 4251 Derivative/Non-derivative trans. at $141.28 per share 600,581 06/08/22 Arvind KrishnaChairman and CEO; Director 4985 Derivative/Non-derivative trans. at $141.28 per share 704,281 06/08/22 Arvind KrishnaChairman and CEO; Director 1536 Derivative/Non-derivative trans. at $141.28 per share 217,006 06/07/22 Arvind KrishnaChairman and CEO; Director 1986 Derivative/Non-derivative trans. at $141.98 per share 281,972 06/08/22 James J. KavanaughSr. VP and CFO 2587 Derivative/Non-derivative trans. at $141.28 per share 365,491 06/08/22 James J. KavanaughSr. VP and CFO 2851 Derivative/Non-derivative trans. at $141.28 per share 402,789 06/08/22 James J. KavanaughSr. VP and CFO 1488 Derivative/Non-derivative trans. at $141.28 per share 210,225 06/07/22 James J. KavanaughSr. VP and CFO 1948 Derivative/Non-derivative trans. at $141.98 per share 276,577 06/08/22 Michelle H. BrowdySenior Vice President 1518 Derivative/Non-derivative trans. at $141.28 per share 214,463 06/08/22 Michelle H. BrowdySenior Vice President 1649 Derivative/Non-derivative trans. at $141.28 per share 232,971 06/08/22 Michelle H. BrowdySenior Vice President 1098 Derivative/Non-derivative trans. at $141.28 per share 155,125 06/07/22 Michelle H. BrowdySenior Vice President 1471 Derivative/Non-derivative trans. at $141.98 per share 208,853 06/08/22 Thomas W. RosamiliaSenior Vice President 1999 Derivative/Non-derivative trans. at $141.28 per share 282,419 06/08/22 Thomas W. RosamiliaSenior Vice President 2154 Derivative/Non-derivative trans. at $141.28 per share 304,317 06/08/22 Thomas W. RosamiliaSenior Vice President 1517 Derivative/Non-derivative trans. at $141.28 per share 214,322 06/07/22 Thomas W. RosamiliaSenior Vice President 1898 Derivative/Non-derivative trans. at $141.98 per share 269,478 06/08/22 Robert F. Del BeneVP, Controller 791 Derivative/Non-derivative trans. at $141.28 per share 111,752 06/08/22 Robert F. Del BeneVP, Controller 928 Derivative/Non-derivative trans. at $141.28 per share 131,108 06/08/22 Robert F. Del BeneVP, Controller 538 Derivative/Non-derivative trans. at $141.28 per share 76,009 06/07/22 Robert F. Del BeneVP, Controller 795 Derivative/Non-derivative trans. at $141.98 per share 112,874 06/08/22 Nickle J. LaMoreauxSenior Vice President 703 Derivative/Non-derivative trans. at $141.28 per share 99,320 06/08/22 Nickle J. LaMoreauxSenior Vice President 268 Derivative/Non-derivative trans. at $141.28 per share 37,863 06/08/22 Nickle J. LaMoreauxSenior Vice President 146 Derivative/Non-derivative trans. at $141.28 per share 20,627 06/07/22 Nickle J. LaMoreauxSenior Vice President 245 Derivative/Non-derivative trans. at $141.98 per share 34,785 06/01/22 Nickle J. LaMoreauxSenior Vice President 752 Derivative/Non-derivative trans. at $139.48 per share 104,889 06/02/22 Robert F. Del BeneVP, Controller 1600 Disposition at $139.04 per share 222,464 08/18/21 Nickle J. LaMoreauxSenior Vice President 4033 Derivative/Non-derivative trans. at $0.00 per share 0 08/18/21 Nickle J. LaMoreauxSenior Vice President 1968 Derivative/Non-derivative trans. at $140.64 per share 276,780 12/28/21 Gary D. CohnVice Chairman 33672 Derivative/Non-derivative trans. at $0.00 per share 0 12/11/21 Arvind KrishnaChairman and CEO; Director 802 Derivative/Non-derivative trans. at $0.00 per share 0 12/11/21 Arvind KrishnaChairman and CEO; Director 24194 Derivative/Non-derivative trans. at $0.00 per share 0 12/13/21 Michelle J. HowardDirector 80 Acquisition at $123.76 per share 9,901 12/28/21 Gary D. CohnVice Chairman 19322 Derivative/Non-derivative trans. at $132.40 per share 2,558,233 12/11/21 Arvind KrishnaChairman and CEO; Director 408 Derivative/Non-derivative trans. at $124.34 per share 50,731 12/11/21 Arvind KrishnaChairman and CEO; Director 12281 Derivative/Non-derivative trans. at $124.34 per share 1,527,020 Thu, 28 Jul 2022 12:00:00 -0500 text/html https://www.wsj.com/market-data/quotes/US/IBM/company-people Killexams : AI Regulation: Where do China, the EU, and the U.S. Stand Today?

Wednesday, August 3, 2022

Artificial Intelligence (AI) systems are poised to drastically alter the way businesses and governments operate on a global scale, with significant changes already under way. This technology has manifested itself in multiple forms including natural language processing, machine learning, and autonomous systems, but with the proper inputs can be leveraged to make predictions, recommendations, and even decisions.

Accordingly,enterprises are increasingly embracing this dynamic technology. A 2022 global study by IBM found that 77% of companies are either currently using AI or exploring AI for future use, creating value by increasing productivity through automation, improved decision-making, and enhanced customer experience. Further, according to a 2021 PwC study the COVID-19 pandemic increased the pace of AI adoption for 52% of companies as they sought to mitigate the crises’ impact on workforce planning, supply chain resilience, and demand projection.  

Challenges of Global Regulation

For these many businesses investing significant resources into AI, it is critical to understand the current and proposed legal frameworks regulating this novel technology. Specifically for businesses operating globally, the task of ensuring that their AI technology complies with applicable regulations will be complicated by the differing standards that are emerging from China, the European Union (EU), and the U.S.

China

China has taken the lead in moving AI regulations past the proposal stage. In March 2022, China passed a regulation governing companies’ use of algorithms in online recommendation systems, requiring that such services are moral, ethical, accountable, transparent, and “disseminate positive energy.” The regulation mandates companies notify users when an AI algorithm is playing a role in determining which information to display to them and give users the option to opt out of being targeted. Additionally, the regulation prohibits algorithms that use personal data to offer different prices to consumers. We expect these themes to manifest themselves in AI regulations throughout the world as they develop.

European Union

Meanwhile in the EU, the European Commission has published an overarching regulatory framework proposal titled the Artificial Intelligence Act which would have a much broader scope than China’s enacted regulation. The proposal focuses on the risks created by AI, with applications sorted into categories of minimal risk, limited risk, high risk, or unacceptable risk. Depending on an application’s designated risk level, there will be corresponding government action or obligations. So far, the proposed obligations focus on enhancing the security, transparency, and accountability of AI applications through human oversight and ongoing monitoring. Specifically, companies will be required to register stand-alone high-risk AI systems, such as remote biometric identification systems, in an EU database. If the proposed regulation is passed, the earliest date for compliance would be the second half of 2024 with potential fines for noncompliance ranging from 2-6% of a company’s annual revenue.

Additionally, the previously enacted EU General Data Protection Regulation (GDPR) already carries implications for AI technology. Article 22 prohibits decisions based on solely automated processes that produce legal consequences or similar effects for individuals unless the program gains the user’s explicit consent or meets other requirements. 

United States

In the United States there has been a fragmented approach to AI regulation thus far, with states enacting their own patchwork AI laws. Many of the enacted regulations focus on establishing various commissions to determine how state agencies can utilize AI technology and to study AI’s potential impacts on the workforce and consumers. Common pending state initiatives go a step further and would regulate AI systems’ accountability and transparency when they process and make decisions based on consumer data. 

On a national level, the U.S. Congress enacted the National AI Initiative Act in January 2021, creating the National AI Initiative that provides “an overarching framework to strengthen and coordinate AI research, development, demonstration, and education activities across all U.S. Departments and Agencies . . . .” The Act created new offices and task forces aimed at implementing a national AI strategy, implicating a multitude of U.S. administrative agencies including the Federal Trade Commission (FTC), Department of Defense, Department of Agriculture, Department of Education, and the Department of Health and Human Services.

Pending national legislation includes the Algorithmic Accountability Act of 2022, which was introduced in both houses of Congress in February 2022. In response to reports that AI systems can lead to biased and discriminatory outcomes, the proposed Act would direct the FTC to create regulations that mandate “covered entities”, including businesses meeting certain criteria, to perform impact assessments when using automated decision-making processes. This would specifically include those derived from AI or machine learning. 

The Federal Trade Commission is Proactive

While the FTC has not promulgated AI-specific regulations, this technology is on the agency’s radar. In April 2021 the FTC issued a memo which apprised companies that using AI that produces discriminatory outcomes equates to a violation of Section 5 of the FTC Act, which prohibits unfair or deceptive practices. And the FTC may soon take this warning a step farther—in June 2022 the agency indicated that it will submit an Advanced Notice of Preliminary Rulemaking to “ensure that algorithmic decision-making does not result in harmful discrimination” with the public comment period ending in August 2022. The FTC also recently issued a report to Congress discussing how AI may be used to combat online harms, ranging from scams, deep fakes, and opioid sales, but advised against over-reliance on these tools, citing the technology’s susceptibility to producing inaccurate, biased, and discriminatory outcomes.

Potential Liability for Businesses in the U.S.

Companies should carefully discern whether other non-AI specific regulations could subject them to potential liability for their use of AI technology. For example, the U.S. Equal Employment Opportunity Commission (EEOC) put forth guidance in May 2022 warning companies that their use of algorithmic decision-making tools to assess job applicants and employees could violate the Americans with Disabilities Act by, in part, intentionally or unintentionally screening out individuals with disabilities. Further analysis of the EEOC’s guidance can be found here.    

Broader Impact on U.S. Businesses

Many other U.S. agencies and offices are beginning to delve into the fray of AI. In November 2021, the White House Office of Science and Technology Policy solicited engagement from stakeholders across industries in an effort to develop a “Bill of Rights for an Automated Society.” Such a Bill of Rights could cover courses like AI’s role in the criminal justice system, equal opportunities, consumer rights, and the healthcare system. Additionally, the National Institute of Standards and Technology (NIST), which falls under the U.S. Department of Commerce, is engaging with stakeholders to develop “a voluntary risk management framework for trustworthy AI systems.” The output of this project may be analogous to the EU’s proposed regulatory framework, but in a voluntary format.

What’s Next?

The overall theme of enacted and pending AI regulations globally is maintaining the accountability, transparency, and fairness of AI. For companies leveraging AI technology, ensuring that their systems remain compliant with the various regulations intended to achieve these goals could be difficult and costly. Two aspects of AI’s decision-making process make oversight particularly demanding:

  • Opaqueness where users can control data inputs and view outputs, but are often unable to explain how and with which data points the system made a decision.

  • Frequent adaptation where processes evolve over time as the system learns.

Therefore, it is important for regulators to avoid overburdening businesses to ensure that stakeholders may still leverage AI technologies’ great benefits in a cost-effective manner. The U.S. has the opportunity to observe the outcomes of the current regulatory action from China and the EU to determine whether their approaches strike a favorable balance. However, the U.S. should potentially accelerate its promulgation of similar laws so that it can play a role in setting the global tone for AI regulatory standards.  

 

Thank you to co-author Lara Coole, a summer associate in Foley & Lardner’s Jacksonville office, for her contributions to this post.

Wed, 03 Aug 2022 09:20:00 -0500 en text/html https://www.natlawreview.com/article/ai-regulation-where-do-china-eu-and-us-stand-today
Killexams : Digital Transformation Market worth $1247.5 billion by 2026 - Exclusive Report by MarketsandMarkets™

CHICAGO  , July 29, 2022 /PRNewswire/ -- Digital Transformation Market size is expected to grow from USD 521.5 billion in 2021 to USD 1247.5 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 19.1% during the forecast period, according to a new report by MarketsandMarkets™. Various factors such as increasing spending on marketing and advertising activities by enterprises, changing landscape of customer intelligence to drive the market, and proliferation of customer channels are expected to drive the adoption of digital transformation technologies and services.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=43010479

Browse in-depth TOC on "Digital Transformation Market"
208 – Tables   
55 – Figures  
283 – Pages

Scope of the Report:

Report Metrics

Details

Market size available for years

2015–2026

Base year considered

2020

Forecast period

2021–2026

Forecast units

USD Million

Segments covered

Technology, Deployment Mode, Organization Size, Industry Vertical, and Region

Geographies covered

North America, Europe, APAC, MEA, and Latin America

Highest Market

North America

Digital Transformation Market Size in 2026

USD 1247.5 billion

Companies covered

Microsoft(US), IBM(US), SAP(Germany), Oracle(US), Google(US), Cognizant(US), HPE(US), Adobe(US), Accenture(Ireland), HCL Technologies(India), Broadcom(US), Equinix(US), Dell(US), Tibco(US), Marlabs(US), Alcor Solutions(US), Smart Stream(UK), Yash Technologies(US), Interfacing(Canada), Kissflow(India), Emudhra(India), Process Maker(US), Process Street(US), Happiest Minds(India), Scoro(UK), Brillio(US), and Aexonic Technologies(US).

Digital transformation is the outcome of changes that occur with the application of digital technologies. The use of digital transformation across business and organizational activities, processes, competencies, and business models leverages the changes and opportunities of a mix of digital technologies and their impact on society. Digital transformation helps enterprises Improve the customer experience, optimize the workforce, enhance the operational activities, and transform the products and services of the organization. The evolution of digital technologies, such as cloud computing, big data and analytics, mobility/social media, blockchain, Artificial Intelligence (AI), Internet of Things (IoT), robotics, and cybersecurity, has created the need for digitalization across several industries. These technologies are used by enterprises to Improve or add more features to their traditional business processes while also helping enhance customer relationships.

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By deployment mode, the Digital Transformation Market has been segmented into on-premises and cloud. The CAGR of the on-premises deployment mode is estimated to be the largest during the forecast period. On-premises solutions are deployed with a one-time license fee and an annual service agreement, which includes free upgrades after a specified time. On-premises software solutions are depreciable assets and are affordable for companies that have the budget to make the initial investment.

The Digital Transformation Market has been segmented by organization size into large enterprises and SMEs. The market for SMEs is expected to register a higher CAGR during the forecast period. These enterprises are early adopters of digital transformation solutions. They are faced with the troublesome task of effectively managing security because of the diverse nature of IT infrastructure, which is complex in nature.

The Digital Transformation Market by vertical has been categorized into banking, financial services, and insurance, retail and eCommerce, media and entertainment, IT and telecom, healthcare and life sciences, government and defense, manufacturing, and education. The healthcare and life sciences vertical is expected to witness the highest growth rate, while the BFSI vertical is expected to have the largest market size during the forecast period. The larger market size of the BFSI vertical can be attributed to the increasing usage of mobile devices to access banking services is driving the adoption of digital transformation solutions in the BFSI vertical.

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The Digital Transformation Market has been segmented into five major regions: North America, Europe, APAC, Latin America, and MEA. APAC is expected to grow at a good pace during the forecast period. The region will be booming, as it is experiencing a lot of new entrepreneur setups, which would be looking forward to acquiring new customers and gaining customer trust by involving new paradigms of digital technologies to have a competitive advantage over the established players. Digital transformation vendors in this region focus on innovations related to their product line. China, Japan, and India have displayed ample growth opportunities in the Digital Transformation Market.

The major vendors in the global digital transformation market include Microsoft (US), IBM (US), SAP (Germany), Oracle (US), Google (US), Cognizant (US), HPE (US), Adobe (US), Accenture (Ireland), HCL Technologies (India), Broadcom (US), Equinix (US), Dell (US), Tibco (US) and Marlabs (US).

Browse Adjacent Markets: Software & Services Research Reports & Consulting

Related Reports:

Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing), Deployment Mode, Organization Size, Business Function (Law, Security), Vertical, and Region - Global Forecast to 2026

Big Data Market by Component, Deployment Mode, Organization Size, Business Function (Operations, Finance, and Marketing and Sales), Industry Vertical (BFSI, Manufacturing, and Healthcare and Life Sciences), and Region - Global Forecast to 2025

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Killexams : Banking Credit Analytics Market to Witness Huge Growth by 2027: IBM, Moody’s, Provenir

This press release was orginally distributed by SBWire

New Jersey, USA — (SBWIRE) — 07/30/2022 — The latest study released on the Global Banking Credit Analytics Market by AMA Research evaluates market size, trend, and forecast to 2027. The Banking Credit Analytics market study covers significant research data and proofs to be a handy resource document for managers, analysts, industry experts and other key people to have ready-to-access and self-analyzed study to help understand market trends, growth drivers, opportunities and upcoming challenges and about the competitors.

Key Players in This Report Include:
IBM Corporation (United States),SAP (Germany),Oracle Corporation (United States),Moody's Corporation (United States),SAS Institute (United States),Verisk Analytics, Inc. (United States),Provenir (United States),AxiomSL, Inc. (United States),Recorded Future, Inc. (United States),Digital Fineprint (England)

Download demo Report PDF (Including Full TOC, Table & Figures) @ https://www.advancemarketanalytics.com/sample-report/168106-global-banking-credit-analytics-market

Definition:
Banking Credit Analytics are solutions which offer credit risk analytics, credit rating, ETL, EMI Calculations like Services. Emergence of Fintech Companies has made this process more digitised with use of artificial intelligence and machine learning. The Banking Credit Analysis enable firms to access deep insights into their credit management and identifies any potential risks associated with it. Although dynamic changes in Regulations by various national governments presents a challenge for the growth of the market of banking credit analytics. Geographically, North America is the biggest market although Asia Pacific is steadily rising behind it.

Market Trends:
– Adoption of Artificial Intelligence and Machine Learning by Banks for Credit Analytics
– Emergence of Fintech Companies

Market Drivers:
– Surge in Demand for Credit
– Growth of Banking Industry

Market Opportunities:
– Software Segment is expected to Boom over the coming years

The Global Banking Credit Analytics Market segments and Market Data Break Down are illuminated below:
by Application (Risk Calculation or Credit Rating, EMI Calculations, ETL, Visualisation), End User (BFSI, IT and Telecom, Retail, Healthcare, Energy and Utilities, Others), Organisation Size (SMEs, Large Enterprises), Offerings (Software, Services)

Global Banking Credit Analytics market report highlights information regarding the current and future industry trends, growth patterns, as well as it offers business strategies to helps the stakeholders in making sound decisions that may help to ensure the profit trajectory over the forecast years.

Have a query? Market an enquiry before purchase @ https://www.advancemarketanalytics.com/enquiry-before-buy/168106-global-banking-credit-analytics-market

Geographically, the detailed analysis of consumption, revenue, market share, and growth rate of the following regions:
– The Middle East and Africa (South Africa, Saudi Arabia, UAE, Israel, Egypt, etc.)
– North America (United States, Mexico & Canada)
– South America (Brazil, Venezuela, Argentina, Ecuador, Peru, Colombia, etc.)
– Europe (Turkey, Spain, Turkey, Netherlands Denmark, Belgium, Switzerland, Germany, Russia UK, Italy, France, etc.)
– Asia-Pacific (Taiwan, Hong Kong, Singapore, Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia).

Objectives of the Report
– -To carefully analyze and forecast the size of the Banking Credit Analytics market by value and volume.
– -To estimate the market shares of major segments of the Banking Credit Analytics market.
– -To showcase the development of the Banking Credit Analytics market in different parts of the world.
– -To analyze and study micro-markets in terms of their contributions to the Banking Credit Analytics market, their prospects, and individual growth trends.
– -To offer precise and useful details about factors affecting the growth of the Banking Credit Analytics market.
– -To provide a meticulous assessment of crucial business strategies used by leading companies operating in the Banking Credit Analytics market, which include research and development, collaborations, agreements, partnerships, acquisitions, mergers, new developments, and product launches.

Buy Complete Assessment of Banking Credit Analytics market Now @ https://www.advancemarketanalytics.com/buy-now?format=1&report=168106

Major highlights from Table of Contents:
Banking Credit Analytics Market Study Coverage:
– It includes major manufacturers, emerging player's growth story, and major business segments of Banking Credit Analytics market, years considered, and research objectives. Additionally, segmentation on the basis of the type of product, application, and technology.
– Banking Credit Analytics Market Executive Summary: It gives a summary of overall studies, growth rate, available market, competitive landscape, market drivers, trends, and issues, and macroscopic indicators.
– Banking Credit Analytics Market Production by Region Banking Credit Analytics Market Profile of Manufacturers-players are studied on the basis of SWOT, their products, production, value, financials, and other vital factors.
– Key Points Covered in Banking Credit Analytics Market Report:
– Banking Credit Analytics Overview, Definition and Classification Market drivers and barriers
– Banking Credit Analytics Market Competition by Manufacturers
– Impact Analysis of COVID-19 on Banking Credit Analytics Market
– Banking Credit Analytics Capacity, Production, Revenue (Value) by Region (2022-2027)
– Banking Credit Analytics Supply (Production), Consumption, Export, Import by Region (2022-2027)
– Banking Credit Analytics Market Analysis by Application {Risk Calculation or Credit Rating ,EMI Calculations ,ETL,Visualisation}
– Banking Credit Analytics Manufacturers Profiles/Analysis Banking Credit Analytics Manufacturing Cost Analysis, Industrial/Supply Chain Analysis, Sourcing Strategy and Downstream Buyers, Marketing
– Strategy by Key Manufacturers/Players, Connected Distributors/Traders Standardization, Regulatory and collaborative initiatives, Industry road map and value chain Market Effect Factors Analysis.

Browse Complete Summary and Table of Content @ https://www.advancemarketanalytics.com/reports/168106-global-banking-credit-analytics-market

Key questions answered
– How feasible is Banking Credit Analytics market for long-term investment?
– What are influencing factors driving the demand for Banking Credit Analytics near future?
– What is the impact analysis of various factors in the Global Banking Credit Analytics market growth?
– What are the recent trends in the regional market and how successful they are?

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