artificial intelligence, machine learning

Artificial Intelligence gains tremendous investment

Artificial intelligence is transforming multiple industries, as we know them: financial sector and business development, healthcare and real estate. Many businesses already started applying AI, and many-many more are expected to follow this trend in the nearest future. Artificial intelligence helps companies solve multiple problems, it brings financial benefits and improves efficiency. Let’s see what are the most common current uses of artificial intelligence in the real world.

1. Artificial Intelligence in Finance

Innovation has always kept pace with the financial technology. So the speedy development of the digital world has also significantly changed the financial sector. And for sure artificial intelligence is the latest trend in fintech. Both startups, SMBs and large institutions apply AI in order to provide faster and safer services for their clients. Let’s check the biggest benefits of artificial intelligence for the finance sphere.

— Safer Trading

Last century trading relied mostly on price fluctuations and guessings, and financial analysts relied more on their personal expertise and good luck. Now smart Machine Learning algorithms can make automated decisions based on a huge amount of data very quickly; and this is particularly useful in high-frequency trading.

— Fraud Prevention

E-commerce has become so popular recently, and online fraud has become extraordinarily fluid and sophisticated. And AI can be very helpful here. Machine learning algorithms analyze various data points and detect fraudulent transactions that could go unnoticed by human analysts; they could also improve the accuracy of real-time approvals and reduce false declines.

Here are some of the fastest eCommerce Fraud Prevention Tools: Riskified / Simility/ Forter / FraudLabs / Trustev /DupZapper / Kount

— Banking chatbots

Banking chatbots for “conversational commerce” or “Voice-First Banking” become more and more popular in the banking sphere. They allow brief interaction between banks and customers via messaging and digital platforms. Chatbots are not only helping banks with customer service, but also can impact the bottom line via upselling or identifying ideas for new products.

Here are the top 5 banks that have adopted chatbots:

1.   Bank of America whose assistant Erica can send notifications to customers, update them on their FICO score, identifying and acknowledging the areas where they can save money, and pay their bills.

2.  JPMorgan Chase: The bank avails a personal assistant to its customers to add ease to its back office operations. By integrating this, they have managed to save more than 360,000 hours of their workforce.

3. Capital One: This bank has introduced a text-based chatbot assistant named Eno. The artificial assistant helps a customer to save their money.

4.  Master Card: Mastercard took a step ahead by introducing a chatbot on Facebook Messenger to better their digital services. Customers can reap the benefits of the bots in reviewing their purchase history, spending habits, and account balance.

5.  American Express: The bank uses technology like Master Card. It provides the customers with real-time sale notifications, contextual recommendations, and also it reminds them about credit card benefits.

These were only a few out of the many banks that are offering benefits to their customers with the help of chatbots. According to a survey report, 73% Millennials look ahead to have new financial services from Amazon, Google, PayPal, Apple or Square.

— Personal financial management

Personal financial management is currently going through a sea change, with PSD2 opening up new possibilities for banks in terms of what they can do for customers once they have access to all of their financial information. One of the best ways AI can help to improve personal financial management is spend forecasting. It uses customer data going back weeks, months and years to offer customers realistic predictions of how much they will spend in coming months, which helps them to determine where they can cut down and make savings. Some platforms, such as Yolt and Pariti, already offer this type of facility, but it will take time until we are sure this information can be considered reliable enough to entirely depend on.

2. Artificial Intelligence in Medicine

No one can deny that AI in medicine is changing healthcare. Booming applications of ML in pharma and medicine are glimmers of a potential future in which synchronicity of data, analysis, and innovation are an everyday reality. We provide a breakdown of several of the pioneering applications, and provide insight into areas for continued innovation.

— Clinical Trial Research

Machine learning has a number of useful potential applications that can shape and direct clinical trial research. Applying advanced predictive analytics in identifying candidates for clinical trials could draw on a much wider range of data than at present, including social media and doctor visits, for example, as well as genetic information when looking to target specific populations; this would result in smaller, quicker, and less expensive trials overall.

ML can also be used for remote monitoring and real-time data access for increased safety; for example, monitoring biological and other signals for any sign of harm or death to participants.

— Smart Electronic Health Records

Document classification (sorting patient queries via email, for example) using support vector machines, and optical character recognition (transforming cursive or other sketched handwriting into digitized characters), are both essential ML-based technologies in helping advance the collection and digitization of electronic health information. MATLAB’s ML handwriting recognition technologies and Google’s Cloud Vision API for optical character recognition are just two examples of innovations in this area.

— Personalized Treatment

Personalized medicine, or more effective treatment based on individual health data paired with predictive analytics, is also a hot research area and closely related to better disease assessment. The domain is presently ruled by supervised learning, which allows physicians to select from more limited sets of diagnoses, for example, or estimate patient risk based on symptoms and genetic information.

IBM Watson Oncology is a leading institution at the forefront of driving change in treatment decisions, using patient medical information and history to optimize the selection of treatment option

Over the next decade, increased use of micro biosensors and devices, as well as mobile apps with more sophisticated health-measurement and remote monitoring capabilities, will provide another deluge of data that can be used to help facilitate R&D and treatment efficacy. This type of personalized treatment has important implications for the individual in terms of health optimization, but also for reducing overall healthcare costs. If more patients adhere to following prescribed medicine or treatment plans, for example, the decrease in health-care costs will trickle up and (hopefully) back down.

— Robotic Surgery

Surgeons are trained to accurately operate on you when you need it, but robotic assistants could help them get to hard-to-reach areas and boost their accuracy even more. For example, Senhance, the robotic surgical assistant that has just earned the FDA’s approval, was designed to accomplish both of those. The machine can help surgeons carry out minimally invasive surgery — in fact, the FDA has approved its use because after a pilot test involving 150 patients, the agency has concluded that Senhance is as accurate as the da Vinci robot when it came to gynecological and colorectal procedures.

— Epidemic Outbreak Prediction

ML and AI technologies are also being applied to monitoring and predicting epidemic outbreaks around the world, based on data collected from satellites, historical information on the web, real-time social media updates, and other sources. Support vector machines and artificial neural networks have been used, for example, to predict malaria outbreaks, taking into account data such as temperature, average monthly rainfall, total number of positive cases, and other data points.

Predicting outbreak severity is particularly pressing in third-world countries, which often lack medical infrastructure, educational avenues, and access to treatments. For example, ProMED-mail is an internet-based reporting program for monitoring emerging diseases and providing outbreak reports in real-time.

And these are only a few of the advantages that AI can bring to the healthcare sector, not mentioning collecting scaled up / crowdsourced medical data from various mobile devices in order to aggregate and make sense of more live health data, or preliminary (early-stage) drug discovery that has the potential for various uses, from initial screening of drug compounds to predicted success rate based on biological factors, or using machine learning algorithms capable of detecting differences in healthy and cancerous tissues to help improve radiation treatments.

So as you may see, the potential of artificial intelligence for making healthcare better is indisputable. The question is how to integrate it successfully into our healthcare systems. For doing so, we have to overcome technical, medical limitations, as well as regulatory obstacles, soothe ethical concerns and mitigate the tendency to oversell the technology. But there’s “no pain, no gain”, isn’t it?

3. Artificial Intelligence in Marketing

There are plenty of ways that machine learning algorithms can help marketers boost sales and launch profit-generating campaigns. This requires making decisions based on huge amounts of business data and analytics, which is just where AI comes in handy!

Let’s briefly go over the most important:

— Automating Repetitive Tasks

Modern machine learning algorithms allow marketers to automate email and social media marketing as well as other similar tasks. Also AI provides marketers with predictive analytics.

There are a number of companies offering marketing automation tools. For example, Marketo helps to build campaigns, attract and retain customers, and perform thorough performance analyses. The company’s platform uses machine learning for sales forecasting and predicting user behavior.

Another example of artificial intelligence is Lucy, a smart marketing platform by Equals 3. Lucy is a cloud-based tool that performs in-depth research by analyzing enormous amounts of data, draws complex portraits of target markets, and builds adjustable marketing strategies.

— Increasing Sales through Images and Videos

Image recognition is absolutely great for marketers in order to optimize all of their marketing strategies for boosting sales. By implementing visual listening, they can gain much clearer brand insights, data, and metrics that they wouldn’t have if they weren’t using image recognition technology. The customers can strengthen their CRM and better their lead nurturing strategy in order to improve customer engagement throughout all stages of the consumer journey.

— Faster Content Generation

Though images, videos, and animations are driving modern content marketing strategies, textual content is still important. However, writing good, unique, and easy-to-read texts isn’t simple. The major problem is that authors need time to research topics and generate cohesive texts. But now it is no longer a problem. Modern AI algorithms can generate unique and relevant content on various topics. Thus, artificial intelligence saves time and money, as unique articles can be generated within minutes.

Articoolo’s content creator is an example of this type of AI-enabled technology. It can not only write an article, but even analyze a topic and research the best resources to take information from. The company’s AI algorithm never produces plagiarized text, which means there will be no problems with uniqueness.

4. Artificial Intelligence in Customer Support

Businesses should never underestimate the importance of customer support. Forbes has counted that companies lose $62 billion per year due to poor customer experience. This figure is really frightening for marketers and financial analysts. A positive customer experience builds trust and creates brand awareness. Here artificial intelligence can be the best solution.

— Handling Multiple Customers at a Time

Very often, companies fail to provide helpful client support due to a lack of employees. And AI in the form of chatbots can be the best solution as well.

Unlike people, a single chatbot can answer the questions of thousands of clients at a time. Needless to say, using a chatbot is far cheaper than hiring a team of human support specialists. Chatbots are used at the call centers and customer support services of many companies. Sometimes, people don’t even know they’re chatting with bots!

DigitalGenius is an example of such AI solutions for businesses. This software combines human and artificial intelligence to provide effective customer support. If the DigitalGenius chatbot is 100% confident about the answer to an enquiry, it will send a response automatically. For more complicated tasks, a human assistant will take the reins.

— More Effective Phone Support

Though chatbots are becoming more and more popular, phone conversations are still the best way to provide informative support. However, not all callers remain satisfied with the assistance they get. Artificial intelligence can be applied here to make a live conversation more engaging by analyzing the way both the caller and the support specialist are speaking and by giving guidance to the specialist. That’s just the way the AI solution developed by Cogito works. Thanks to this technology, business owners can know that their clients’ problems will be solved on time.

5. Artificial Intelligence in Real Estate

Real estate has always been a challenging industry. Buyers want better locations, more amenities, cleaner areas, better safety, and – in most cases – lower prices. Agents and brokers have a hard time finding properties that meet their clients’ requirements.

Artificial intelligence can help real estate agents and property owners do their jobs better, faster, and easier, and consumers can get what they want without year-long searches. Here are the most common applications of machine learning in the real estate industry.

— Better And Faster Communication

Chatbots are used in many industries, and real estate isn’t an exception. An AI-enabled system can process the information it receives, analyze it, and even offer relevant options. Based on machine learning algorithms, chatbots can analyze the characteristics of every property. Moreover, bots store these analyses, so agents can quickly find the information they need.

Apartment Ocean is a successful example of how AI can be used in real estate services to automate the initial contact between a client and an agent. The Apartment Ocean chatbot asks several questions to find out what a client wants and provides agents with leads.

— Automating Property Valuation

Developers, agents, and homeowners can leverage artificial intelligence to automate property valuation. Modern AI algorithms can collect and analyze relevant data about properties like location, neighborhood, zoning, demographics, competitors, and more, sparing real estate agents and investors from unnecessary and inefficient work.
Property valuation AI solutions are indeed being developed. For example, CityBldr uses machine learning to provide owners, developers, and investors with information about their properties. Apart from valuation, this AI technology promotes properties to potential investors.

— Promoting Rental Booking

Today, traveling is popular as never before. It’s probably difficult to find someone who doesn’t like traveling. The vacation rental industry is booming! Thanks to AI, online rental services can provide hosts with price tips, increasing the probability of attracting new clients. For example, Aerosolve (a service used by Airbnb) uses machine learning to provide dynamic pricing. This smart algorithm analyzes data about each property (local events, neighborhood, number of reviews) and shows hosts a recommended price. This way, hosts can choose the right business strategy and make more money.

6. Artificial Intelligence in Retail

Sales are the backbone of the modern free market. And with the growing number of internet users, retail ecommerce sales are likely to expand to $4 trillion by 2020. For sure, it’s a large slice of pie for marketers, who are racking their brains trying to boost online sales and attract more customers. What does this have to do with artificial intelligence? AI offers a lot of advantages to both consumers and marketers, so let’s go over the biggest of them.

— Relevant Product Recommendations

In a conventional store, a sales assistant can recommend items that a buyer might want. But it doesn’t work this way in an online store. Many internet users don’t wish to spend hours looking through dozens of products. Instead, they want shopping to be simple and intuitive.

Artificial intelligence can easily provide customers with relevant recommendations that increase the likelihood of extra purchases. AI algorithms can analyze user behavior and lots of other data to provide personalized recommendations. As a result, different customers receive totally different product suggestions.

Brilliance is an example of an AI-based product recommendation solution which is self-learning, so suggestions get better and more relevant over time. Moreover, this artificial intelligence solution is helpful for marketers, since it can automatically send personalized emails with product recommendations.

Needless to say, similar AI algorithms are extensively used by large companies such as Amazon.

— Customer Engagement

In the past, digital retailers, unlike conventional retailers, lacked one important thing: a personalized customer experience. Instead of browsing through menus on a website or in an application, customers would simply quit without buying anything. Having a team of human assistants was, of course, too costly, so there seemed to be no way out.

Now many websites and apps now offer robotic assistants, or chatbots which are smart enough to hold meaningful conversations. So instead of finding whether a website (or an application) offers some service, customers simply ask questions using natural language and a chatbot gives answers in the blink of an eye. Moreover, the latest-generation chatbots even support payments.

AI-enabled programs such as Mr.Chatbot help ecommerce companies interact with their clients. These solutions provide customer support and boost sales.

Conversica offers a chatbot for email conversations that analyzes emails from clients and helps salespeople prioritize to reach better sales productivity.

7. Artificial Intelligence: Technology of the Future

Even though making 100% accurate predictions about future uses of artificial intelligence is impossible, there’s no doubt that AI will find more applications in industry, business, and everyday life. The pros of artificial intelligence are obvious, so businesses should realize it’s the right time to opt for AI. If you have ideas or questions how to apply AI in your businesses, don’t hesitate to share your thoughts in the comments below.

Julia Govor

Julia Govor

Business Development Manager

Skype: julia__govor
LI Profile: Julia Govor

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