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Fintech is a buzzword in the modern world, which essentially means financial technology. It uses technology to offer improved financial services and solutions.

How AI and machine learning are making ways across industries, including fintech? Its an important question in the business world globally.

The use of artificial intelligence (AI) and machine learning (ML) is evolving in the finance market, owing to their exceptional benefits like more efficient processes, better financial analysis and customer engagement.

According to the prediction of Autonomous Research, AI technologies will allow financial institutions to reduce their operational costs by 22%, by 2030.AI and ML are truly efficient tools in the financial sector. In this blog, we are going to discuss how they actually help fintech? What benefits do these technologies can bring to the industry?

The implementation of AI and ML in the financial landscape has been transforming the industry. As fintech is a developing market, it requires industry-specific solutions to meet its goals. AI tools and machine learning can offer something great here.

Are you eager to know the impact of AI and ML on fintech? These disruptive technologies are not only effective in improving the accuracy level but also speeds up the entire financial process by applying various proven methodologies.

AI-based financial solutions are focused on the crucial needs of the modern financial sector such as better customer experience, cost-effectiveness, real-time data integration, and enhanced security. Adoption of AI and allied its applications enables the industry to create a better, engaging financial environment for its customers.

Use of AI and ML has facilitated financial and banking operations. With the help of such smart developments, fintech companies are delivering tailored products and services as per the needs of the evolving market.

According to a study by research group Forrester, around 50% of financial services and insurance companies already use AI globally. And the number is expected to grow with newer technology advancements.

You will be thinking why AI and ML are becoming more important in fintech? In this section, we explain how these technologies are infiltrating the industry.

The need for better, safer, and customized solutions is rising with expectations of customers. Automation has helped the fintech industry to provide better customer service and experience.

Customer-facing systems such as AI interfaces and Chatbots can offer useful advice while reducing the cost of staffing. Moreover, AI can automate the back office process and make it seamless.

Automation can greatly help Fintech firms to save time as well as money. Using AI and ML, the industry has ample opportunities for reducing human errors and improving customer support.

Finance, insurance and banking firms can leverage AI tools to make better decisions. Here management decisions are data-driven, which creates a unique way for management.

Machine learning effectively analyzes the data and brings required outcomes that help officials to cut costs. Also, it empowers organizations to solve specific problems effectively.

Technologies are meant to deliver convenience and improved speed. But, along with these benefits, there is also an increase in online fraud. Keeping this in mind, Fintech companies and financial institutions are investing in AI and machine learning to defeat fraudulent transactions.

AI and machine learning solutions are strong enough to react in real-time and can analyze more data quickly. The organizations can efficiently find patterns and recognize fraudulent process using different models of machine learning. The fintech software development company can help build secured financial software and apps using these technologies.

With AI and ML, a huge amount of data can be analyzed and optimized for better applications. Hence fintech is the right industry where there is a great future of AI and machine learning innovations.

Owing to their potential benefits, automation and machine learning are increasingly used in the Fintech industry. In the case of smart wallets, they learn and monitor users behaviour and activities, so that appropriate information can be provided for their expenses.

Fintech firms are working with development and technology leaders to bring new concepts that are effective and personalized. Artificial intelligence, machine learning, and allied technologies are playing a vital role in financial organizations to improve skills, customer satisfaction, and reduce costs.

In the developing world, it is crucial for fintech companies to categorize clients by data analyzing, and allied patterns. AI tools show excellent capabilities in it to automate the process of profiling clients, based on their risk profile. This profiling work helps experts give product recommendations to customers in an appropriate and automated way.

Predictive analytics is another competitive advantage of using AI tools in the financial sector. It is helpful to improve sales, optimize resource use, and enhance operational efficiency.

With machine learning algorithms, businesses can effectively gather and analyze huge data sets to make faster and more accurate predictions of future trends in the financial market. Accordingly, they can offer specific solutions for customers.

As the market continues to demand easier and faster transactions, emerging technologies, such as artificial intelligence and machine learning, will remain crucial for the Fintech sector.

Innovations based on AI and ML are empowering the Fintech industry significantly. As a result, financial institutions are now offering better financial services to customers with excellence.

Leading financial and banking firms globally are using the convenient features of artificial intelligence to make business more stable and streamlined.

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How AI & Machine Learning is Infiltrating the Fintech Industry? - Customer Think

An era of virtual learning, when interviews, education, etc. are being conducted from home through laptops and the internet. The clarity of the camera for video calls, maybe work or class calls are the primary need of the hour. But laptop webcam still has 720p or 1080 resolutions with low color accuracy and light performance. Understanding the vast market for this NeuralCam introduces an app that converts an apple iPhone into smart webcam. The best part of the deal is its free.

Neuralcam live platform uses machine learning to generate a high-quality computer video stream using the iPhones front camera. Prerequisites are installing the IOS app and MAC driver. iPhone sends a live stream to your computer with features such as video enhancement. Video processing will be handled in the device rather than on the computer. The company is also building an IOS SDK for third-party video calling and streaming apps to control the enhancement features.

The main attractions of the NeuralCam live are

Few shortcomings at present are

A roadmap has been planned by NeuralCam to overcome these drawbacks. They also plan to release windows support soon and serve industries like education, health care, and entertainment.

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NeuralCam Launches NeuralCam Live App Using Machine Learning to Turn iPhones into Smart Webcams - MarkTechPost

As hard as it is for data scientists to tag data and develop accurate machine learning models, managing models in production can be even more daunting.

Recognising model drift, retraining models with updating data sets, improving performance, and maintaining the underlying technology platforms are all important data science practices. Without these disciplines, models can produce erroneous results that significantly impact business.

Developing production-ready models is no easy feat. According to one machine learning study, 55 per cent of companies had not deployed models into production, and 40 per cent or more require more than 30 days to deploy one model. Success brings new challenges, and 41 per cent of respondents acknowledge the difficulty of versioning machine learning models and reproducibility.

The lesson here is that new obstacles emerge once machine learning models are deployed to production and used in business processes.

Model management and operations were once challenges for the more advanced data science teams. Now tasks include monitoring production machine learning models for drift, automating the retraining of models, alerting when the drift is significant, and recognising when models require upgrades.

As more organisations invest in machine learning, there is a greater need to build awareness around model management and operations.

The good news is platforms and libraries such as open source MLFlow and DVC, and commercial tools from Alteryx, Databricks, Dataiku, SAS, DataRobot, ModelOp, and others are making model management and operations easier for data science teams. The public cloud providers are also sharing practices such as implementing MLops with Azure Machine Learning.

There are several similarities between model management and devops. Many refer to model management and operations as MLops and define it as the culture, practices, and technologies required to develop and maintain machine learning models.

Understanding model management and operations

To better understand model management and operations, consider the union of software development practices with scientific methods.

As a software developer, you know that completing the version of an application and deploying it to production isnt trivial. But an even greater challenge begins once the application reaches production. End-users expect regular enhancements, and the underlying infrastructure, platforms, and libraries require patching and maintenance.

Now lets shift to the scientific world where questions lead to multiple hypotheses and repetitive experimentation. You learned in science class to maintain a log of these experiments and track the journey of tweaking different variables from one experiment to the next. Experimentation leads to improved results, and documenting the journey helps convince peers that youve explored all the variables and that results are reproducible.

Data scientists experimenting with machine learning models must incorporate disciplines from both software development and scientific research.

Machine learning models are software code developed in languages such as Python and R, constructed with TensorFlow, PyTorch, or other machine learning libraries, run on platforms such as Apache Spark, and deployed to cloud infrastructure. The development and support of machine learning models require significant experimentation and optimisation, and data scientists must prove the accuracy of their models.

Like software development, machine learning models need ongoing maintenance and enhancements. Some of that comes from maintaining the code, libraries, platforms, and infrastructure, but data scientists must also be concerned about model drift. In simple terms, model drift occurs as new data becomes available, and the predictions, clusters, segmentations, and recommendations provided by machine learning models deviate from expected outcomes.

Successful model management starts with developing optimal models

I spoke with Alan Jacobson, chief data and analytics officer at Alteryx, about how organisations succeed and scale machine learning model development.

To simplify model development, the first challenge for most data scientists is ensuring strong problem formulation. Many complex business problems can be solved with very simple analytics, but this first requires structuring the problem in a way that data and analytics can help answer the question. Even when complex models are leveraged, the most difficult part of the process is typically structuring the data and ensuring the right inputs are being used are at the right quality levels.

I agree with Jacobson. Too many data and technology implementations start with poor or no problem statements and with inadequate time, tools, and subject matter expertise to ensure adequate data quality. Organisations must first start with asking smart questions about big data, investing in dataops, and then using agile methodologies in data science to iterate toward solutions.

Monitoring machine learning models for model drift

Getting a precise problem definition is critical for ongoing management and monitoring of models in production.

Jacobson went on to explain, monitoring models is an important process, but doing it right takes a strong understanding of the goals and potential adverse effects that warrant watching. While most discuss monitoring model performance and change over time, whats more important and challenging in this space is the analysis of unintended consequences.

One easy way to understand model drift and unintended consequences is to consider the impact of Covid-19 on machine learning models developed with training data from before the pandemic.

Machine learning models based on human behaviours, natural language processing, consumer demand models, or fraud patterns have all been affected by changing behaviours during the pandemic that are messing with AI models.

Technology providers are releasing new MLops capabilities as more organisations are getting value and maturing their data science programs. For example, SAS introduced a feature contribution index that helps data scientists evaluate models without a target variable. Cloudera recently announced an ML Monitoring Service that captures technical performance metrics and tracking model predictions.

MLops also addresses automation and collaboration

In between developing a machine learning model and monitoring it in production are additional tools, processes, collaborations, and capabilities that enable data science practices to scale. Some of the automation and infrastructure practices are analogous to devops and include infrastructure as code and CI/CD (continuous integration/continuous deployment) for machine learning models.

Others include developer capabilities such as versioning models with their underlying training data and searching the model repository.

The more interesting aspects of MLops bring scientific methodology and collaboration to data science teams. For example, DataRobot enables a champion-challenger model that can run multiple experimental models in parallel to challenge the production versions accuracy.

SAS wants to help data scientists improve speed to markets and data quality. Alteryx recently introduced Analytics Hub to help collaboration and sharing between data science teams.

All this shows that managing and scaling machine learning requires a lot more discipline and practice than simply asking a data scientist to code and test a random forest, k-means, or convolutional neural network in Python.

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MLops: The rise of machine learning operations - Reseller News

Allegro AI offers the first true end-to-end ML / DL product life-cycle management solution with a focus on deep learning applied to unstructured data.

Machine learning projects involve iterative and recursive R&D process of data gathering, data annotation, research, QA, deployment, additional data gathering from deployed units and back again. The effectiveness of a machine learning product depends on how intact the synergies are between data, model and various teams across the organisation.

In this informative session at CVDC 2020, a 2 day event organised by ADaSci, Dan Malowany of Allegro.AI presented the attendees with the best practices to imbibe during the lifecycle of an ML productfrom inception to production.

Dan Malowany is currently the head of deep learning research at allegro.ai. His Ph.D. research at the Laboratory of Autonomous Robotics (LAR) was focused on integrating mechanisms of the human visual system with deep convolutional neural networks. His research interests include computer vision, convolutional neural networks, reinforcement learning, the visual cortex and robotics.

Dan spoke about the features required to boost productivity in the different R&D stages. This talk specifically focused on the following:

Dan, who has worked for 15 years at the Directorate for Defense Research & Development and led various R&D programs, briefed the attendees about various complexities involved in developing deep learning applications. He shed light on the unattractive and often overlooked aspects of research. He explained the trade offs between effort and accuracy through concepts of diminishing returns in the case of increased inputs.

When your model is as good as your data then the role of data management becomes crucial. Organisations are often in the pursuit of achieving better results with less data. Practices such as mixing and matching data sets with detailed control and creating optimised synthetic data come in handy.

Underlining the importance of data and experiment management, Dan advised the attendees to track the various versions of data and treat it as a hyperparameter. Dan also highlighted the various risk factors involved in improper data management. He took the example of developing a deep learning solution for diagnosis of diabetic retinopathy. He followed this up with an overview of the benefits of resource management.

Unstructured data management is only a part of the solution. There are other challenges, which Allegro AI claims to solve. In this talk Dan introduced the audience to their customised solutions.

Towards the end of the talk, Dan gave a glimpse about the various tools integrated with allegro.ais services. Allegro AIs products are market proven and have partnered with leading global brands, such as Intel, NVIDIA, NetApp, IBM and Microsoft. Allegro AI is backed by world-class firms including household name strategic investors: Samsung, Bosch and Hyundai.

Allegro AI helps companies develop, deploy and manage machine & deep learning solutions. The companys products are based on the Allegro Trains open source ML & DL experiment manager and ML-Ops package. Here are a few features:

Unstructured Data Management

Resource Management & ML-Ops

Know more here.

Stay tuned to AIM for more updates on CVDC2020.

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I have a master's degree in Robotics and I write about machine learning advancements.email:ram.sagar@analyticsindiamag.com

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Machine Learning Practices And The Art of Research Management - Analytics India Magazine

About the position

TheDepartment of Electronic Systemshas a vacancy for 2 PhD Research Fellows in machine-learning/signal-processing for Industry 4.0. The successful candidate will be offered a 3-year position (or 4-year with 25% work assignments for the Department).

The position reports to the Head of the Department of Electronic Systems. The work place will be Trondheim.

Duties of the position

The positions are linked to the project SIGNIFY which is funded by the Research Council of Norway. Big data, Internet of Things (IoT) and artificial intelligence (AI) represent key enablers of the digital transformation and the development of digital twins. The main objective the SIGNIFY is the development and the integration of signal processing and machine learning methodologies into novel hybrid-analytics solutions aiming at sensor validation for digital twins of safety-critical systems. Building upon ground-breaking concepts from graph signal processing, deep learning and transfer learning, SIGNIFY focuses on designing tailored strategies from a Bayesian perspective and testing them on two use cases related to Carbon Capture and Storage (CCS) in collaboration with SINTEF Energy.

The research plans of the two positions focus on safety issues and lie within the general area of anomaly detection. The PhD candidates will have the opportunity to be affiliated with theIoT@NTNUand with theNorwegian Open AI lab, and to collaborate with research scientists from international partner institutions.

PhD Position N.1 Model-Based Sensor Validation

The activities related to this position include exploring and defining advanced solutions by integrating domain knowledge related to the considered use cases with linear/nonlinear estimation/detection and data fusion techniques according to a Bayesian framework. A peculiar aspect is represented by considering dynamic risk analysis into the algorithm design to be used with real-time data availability.

PhD Position N.2 Data-Driven Sensor Validation

The activities related to this position include time-series modeling and monitoring based on shallow and/or deep networks with focus on soft decisions and related confidence in order to be compatible with a Bayesian framework. A peculiar aspect is represented by considering dynamic risk analysis into the algorithm design to be used with real-time data availability.

Required selection criteria

The PhD-position's main objective is to qualify for work in research. The qualification requirement is competition of a masters degree or second degree (equivalent to 120 credits) with a strong academic background in Machine Learning and or Signal Processing (or equivalent education) with a grade of B or better in terms ofNTNUs grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you are unable to meet these criteria you may be considered only if you can document that you are particularly suitable for education leading to a PhD degree.

We seek two highly-motivated individuals having

Publication activity in the aforementioned disciplines will be considered an advantage, but is not a requirement.

Applicants must be qualified for admission to aPhD study program at NTNU.

Applicants who do not master a Scandinavian language should provide evidence of good written and spoken English language skills. The following tests can be used as documentation: TOEFL, IELTS, Cambridge Certificate in Advanced English (CAE), or Cambridge Certificate of Proficiency in English (CPE). Minimum scores are:

The appointment is to be made in accordance with the regulations in force concerningState Employees and Civil Servants and national guidelines for appointment as PhD, post doctor and research assistant.

Personal characteristics

The successful candidates should be

We offer

Salary and conditions

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 479 600 per annum before tax, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is 3 years (or 4 with academic duties).

Appointment to a PhD position requires that you are admitted to the PhD programme inElectronics and Telecommunicationwithin three months of employment, and that you participate in an organized PhD programme during the employment period.

The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.

The positions are subject to external funding from the Research Council of Norway.

It is a prerequisite you can be present at and accessible to the institution on a daily basis.

About the application

The application and supporting documentation to be used as the basis for the assessment must be in English.

Publications and other scientific work must follow the application. Please note that applications are only evaluated based on the information available on the application deadline. You should ensure that your application shows clearly how your skills and experience meet the criteria which are set out above.

The application must include:

Joint works will be considered. If it is difficult to identify your contribution to joint works, you must attach a brief description of your participation.

In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal suitability.

NTNU is committed to following evaluation criteria for research quality according toThe San Francisco Declaration on Research Assessment - DORA.

General information

Working at NTNU

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background.

The city of Trondheimis a modern European city with a rich cultural scene. Trondheim is the innovation capital of Norway with a population of 200,000. The Norwegian welfare state, including healthcare, schools, kindergartens and overall equality, is probably the best of its kind in the world. Professional subsidized day-care for children is easily available. Furthermore, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life and has low crime rates and clean air quality.

As an employeeatNTNU, you must at all times adhere to the changes that the development in the subject entails and the organizational changes that are adopted.

Information Act (Offentleglova), your name, age, position and municipality may be made public even if you have requested not to have your name entered on the list of applicants.

If you have any questions about the position, please contact Professor Pierluigi Salvo Rossi (email:pierluigi.salvorossi@ntnu.no).

Please submit your application electronically via jobbnorge.no with your CV, diplomas and certificates. Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of Master Diploma fromChina Credentials Verification (CHSI).

If you are invited for interview you must include certified copies of transcripts and reference letters. Please refer to the application number 2020/23734 when applying.

Application deadline: 10.10.2020

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

Department of Electronic Systems

The digitalization of Norway is impossible withoutelectronic systems.We are Norways leading academic environment in this field, and contribute with our expertise in areas ranging from nanoelectronics, phototonics, signal processing, radio technology and acoustics to satellite technology and autonomous systems. Knowledge of electronic systems is also vital for addressing important challenges in transport, energy, the environment, and health.The Department of Electronic Systemsis one of seven departments in theFaculty of Information Technology and Electrical Engineering .

Deadline10th October 2020EmployerNTNU - Norwegian University of Science and TechnologyMunicipalityTrondheimScopeFulltimeDurationTemporaryPlace of serviceCampus Glshaugen

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PhD Research Fellow in Machine-learning/signal Processing for Industry 4.0 job with NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU | 221424 -...

Market Scenario of the Machine Learning as a Service (MLaaS) Market:

The most recent Machine Learning as a Service (MLaaS) Market Research study includes some significant activities of the current market size for the worldwide Machine Learning as a Service (MLaaS) market. It presents a point by point analysis dependent on the exhaustive research of the market elements like market size, development situation, potential opportunities, and operation landscape and trend analysis. This report centers around the Machine Learning as a Service (MLaaS)-business status, presents volume and worth, key market, product type, consumers, regions, and key players.

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The prominent players covered in this report: Microsoft, IBM Corporation, International Business Machine, Amazon Web Services, Google, Bigml, Fico, Hewlett-Packard Enterprise Development, At&T, Fuzzy.ai, Yottamine Analytics, Ersatz Labs, Inc., and Sift Science Inc.

The market is segmented into By Type (Special Services and Management Services), By Organization Size (SMEs and Large Enterprises), By Application (Marketing & Advertising, Fraud Detection & Risk Analytics, Predictive Maintenance, Augmented Reality, Network Analytics, and Automated Traffic Management), By End User (BFSI, IT & Telecom, Automobile, Healthcare, Defense, Retail, Media & Entertainment, and Communication)

Geographical segments are North America, Europe, Asia Pacific, Middle East & Africa, and South America.

A 360 degree outline of the competitive scenario of the Global Machine Learning as a Service (MLaaS) Market is presented by Quince Market Insights. It has a massive data allied to the recent product and technological developments in the markets.

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This is anticipated to drive the Global Machine Learning as a Service (MLaaS) Market over the forecast period. This research report covers the market landscape and its progress prospects in the near future. After studying key companies, the report focuses on the new entrants contributing to the growth of the market. Most companies in the Global Machine Learning as a Service (MLaaS) Market are currently adopting new technological trends in the market.

Machine Learning as a Service (MLaaS)

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Machine Learning as a Service (MLaaS) Market Size: Opportunities, Current Trends And Industry Analysis by 2028 | Microsoft, IBM Corporation,...

Most organizations recognize the transformational benefits of machine learning (ML) and have already taken steps to implement it.

However, they still face several challenges when it comes to deploying ML models in production and operating them at scale.

These challenges stem from the fact that most enterprise ML workflows lack the standardized processes typically associated with software engineering. The answer is a set of standard practices collectively known as MLOps (machine learning operations). MLOps brings standardization to the ML lifecycle, helping enterprises move beyond experimentation to large-scale deployments of ML.

In a recent study, Forrester found that 98% of IT leaders believe that MLOps will give their company a competitive edge and increased profitability. But only 6% feel that their MLOps capabilities are mature or very mature.

So, why the disparity?

Very few firms have a robust, operationalized process around ML model development and deployment. Its not necessarily through lack of trying or recognitionits not an easy undertaking.

Organizations looking to continually use ML to improve their business processes or deliver new customer experiences face consistent, significant challenges:

How do enterprises overcome these challenges and reap the benefits of artificial intelligence (AI) and machine learning? What are the key action steps to operationalize ML and deploy more ML use cases at enterprise scale?

Based on the findings from the HPE/Forrester paper, operationalization is a four-step process.

HPE has the solutions to help enterprises succeed with ML. HPE Ezmeral ML Ops is a software solution that brings DevOps-like speed and agility to ML workflows with support for every stage of the machine learning lifecycle.

HPE Ezmeral ML Ops leverages containers and Kubernetes to support the entire ML lifecycle. It offers containerized data science environments with the ability to use any open-source or third-party data science tool for model development and the ease of one-click model deployment to scalable containerized endpoints on-premise, in the cloudor hybrid. Data scientists benefit from a single pane of glass to monitor and deploy all of their data science applications across any infrastructure platform. More importantly, enterprises can rapidly operationalize ML models and speed the time to value of their ML initiatives to gain a competitive advantage.

To learn more about how to operationalize machine learning by leveraging MLOps at scale, read the whitepaper Operationalize Machine Learning.

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Leveraging MLOps to operationalize ML at Scale Sponsored Content by HPE - EnterpriseAI

So far insurers have seen healthcare use plummet since the onset of the COVID-19 pandemic.

But experts are concerned about a wave of deferred care that could hit as patients start to return to patients and hospitals putting insurers on the hook for an unexpected surge of healthcare spending.

Artificial intelligence and machine learning could lend insurers a hand.

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We are using the AI approaches to try to protect future cost bubbles, said Colt Courtright, chief data and analytics officer at Premera Blue Cross, during a session with Fierce AI Week on Wednesday.

WATCH THE ON-DEMAND PLAYBACK:What Payers Should Know About How AI Can Change Their Business

He noted that people are not going in and getting even routine cancer screenings.

If people have delay in diagnostics and delay in medical care how is that going to play out in the future when we think about those individuals and the need for clinical programs and the cost and how do we manage that? he said.

Insurers have started in some ways to incorporate AI and machine learning in several different facets such as claims management and customer service, but insurers are also starting to explore how AI can be used to predict healthcare costs and outcomes.

In some ways, the pandemic has accelerated the use of AI and digital technologies in general.

If we can predict, forecast and personalize care virtually, then why not do that, said Rajeev Ronanki, senior vice president and chief digital officer for Anthem, during the session.

The pandemic has led to a boom in virtual telemedicine as the Trump administration has increased flexibility for getting Medicare payments for telehealth and patients have been scared to go to hospitals and physician offices.

But Ronanki said that AI cant just help with predicting healthcare costs, but also on fixing supply chains wracked by the pandemic.

He noted that the manufacturing global supply chain is extremely optimized, especially with just-in-time ordering that doesnt require businesses to have a large amount of inventory.

But that method doesnt really work during a pandemic when there is a vast imbalance in supply and demand with personal protective equipment, said Ronanki.

When you connect all those dots, AI can then be used to configure supply and demand better in anticipation of issues like this, he said.

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How AI can help payers navigate a coming wave of delayed and deferred care - FierceHealthcare

Global Machine Learning and Artificial Intelligence in Healthcare Market: Trends Estimates High Demand by 2026

Machine Learning and Artificial Intelligence in Healthcare Market report 2020, discusses various factors driving or restraining the market, which will help the future market to grow with promising CAGR. The Machine Learning and Artificial Intelligence in Healthcare Market research Reports offers an extensive collection of reports on different markets covering crucial details. The report studies the competitive environment of the Machine Learning and Artificial Intelligence in Healthcare Market is based on company profiles and their efforts on increasing product value and production.

The Global Machine Learning and Artificial Intelligence in Healthcare market 2020 research provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Global Machine Learning and Artificial Intelligence in Healthcare market report is provided for the international markets as well as development trends, competitive landscape analysis, and key regions development status. Development policies and plans are discussed as well as manufacturing processes and cost structures are also analysed. This report additionally states import/export consumption, supply and demand Figures, cost, price, revenue and gross margins.

The final report will add the analysis of the Impact of Covid-19 in this report Machine Learning and Artificial Intelligence in Healthcare industry.

Key players in global Machine Learning and Artificial Intelligence in Healthcare market include: Intel Corporation, IBM Corporation, Nvidia Corporation, Microsoft Corporation, Alphabet Inc (Google Inc.), General Electric (GE) Company, Enlitic, Inc., Verint Systems, General Vision, Inc., Welltok, Inc., iCarbonX.

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Machine Learning and Artificial Intelligence in Healthcare Market: Region-wise Outlook

Depending on the geographic region, Machine Learning and Artificial Intelligence in Healthcare market is divided into seven key regions: North America, Eastern Europe, Latin America, Western Europe, Japan, Asia-Pacific, and the Middle East & Africa. North America dominates the Machine Learning and Artificial Intelligence in Healthcare market followed by Europe, and Japan owing to high internet penetration, the establishment of key players in the field of the internet such as Google, and Facebook. Asia Pacific, Middle East, and Africa hold huge potential and shows substantial growth in terms of expanding the use of electronic devices, rising innovative technologies, consumer awareness, and expanding telecommunication sector are some of the factors which strengthen the growth of Machine Learning and Artificial Intelligence in Healthcare market throughout the forecast period.

Questions answered in the report with respect to the regional expanse of the Machine Learning and Artificial Intelligence in Healthcare market:

The scope of the Report:

The report segments the global Machine Learning and Artificial Intelligence in Healthcare market on the basis of application, type, service, technology, and region. Each chapter under this segmentation allows readers to grasp the nitty-gritties of the market. A magnified look at the segment-based analysis is aimed at giving the readers a closer look at the opportunities and threats in the market. It also address political scenarios that are expected to impact the market in both small and big ways.The report on the global Machine Learning and Artificial Intelligence in Healthcare market examines changing regulatory scenario to make accurate projections about potential investments. It also evaluates the risk for new entrants and the intensity of the competitive rivalry.

Reasons to read this Report:

TABLE OF CONTENT:

Chapter 1:Machine Learning and Artificial Intelligence in Healthcare Market Overview

Chapter 2: Global Economic Impact on Industry

Chapter 3:Machine Learning and Artificial Intelligence in Healthcare Market Competition by Manufacturers

Chapter 4: Global Production, Revenue (Value) by Region

Chapter 5: Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6: Global Production, Revenue (Value), Price Trend by Type

Chapter 7: Global Market Analysis by Application

Chapter 8: Manufacturing Cost Analysis

Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10: Marketing Strategy Analysis, Distributors/Traders

Chapter 11: Machine Learning and Artificial Intelligence in Healthcare Market Effect Factors Analysis

Chapter 12: GlobalMachine Learning and Artificial Intelligence in Healthcare Market Forecast to 2026

Complete Report @ https://www.reportsandmarkets.com/sample-request/global-machine-learning-and-artificial-intelligence-in-healthcare-market-analysis-and-forecast-2019-2025?utm_source=thedailychronicle&utm_medium=33

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