Today, analytics is becoming a major game-changer in the Finance sectors. Following the tradition, the Banking, Financial Services, and Insurance (BFSI) organizations are putting their full potential to widen their business opportunities and enhance the services that they provide their customers. According to an article recently published by Forbes, “over 2.5 quintillion bytes of data created each day.” This is a massive number and therefore the significant increase in the customer volume is placing a humongous pressure on the levels of the services offered by these organizations.

Stakeholders are now looking for new ways to not just organize their data but to also use this data to track their customer’s behaviors. In this way, these organizations will be able to use their data to provide their customers with the exact type of resources needed at a given time.

Big Data resources have become a key player for the BFSI organizations helping them in a number of areas that provide these organizations with improved customer experience and boosted performance and profitability.

Why BFSI Organizations Need Big Data?

Big Data can help BFSI organizations in not just organizing their data but it also helps to improve customer experience. The answer to the question (Why BFSI organizations need Big Data?) could be summed up in three points. They are:

1. Customer Experience
2. Operation Optimization
3. Employee Engagement

Customer Experience

Customers place high expectations in the way they want their banks to interact with them. With the increase in customer volume it has become difficult for banks and other organizations to fulfill the demands of every customer. Big Data helps the organizations to acquire a 360-degree view of their customer. It provides them with basic personal information, transaction history, browsing history and other relevant information helping the organizations in creating customer segmentation. Big Data also helps in making marketing decisions that can bring a remarkable increase in Marketing productivity.

Operation Optimization

Big Data can help the BFSI organizations to improve the predictive power of their risk models, which is the main reason it is gaining popularity in the Banking Sector. Big Data provides more extensive risk coverage and significant cost savings. Following are some of the areas of risk management where Big Data can be used to attain risk intelligence:

• Fraud Management
• Credit Management
• Market and Commercial Loans
• Operational Risks
• Integrated Risk Management

Big Data helps to detect fraud signals which are analyzed utilizing Machine Learning and ultimately detecting legitimate users or transactions.

Employee Engagement

Employee Engagement is a hidden potential of Big Data that is yet to be unlocked. Big Data is not only concerned with customers but it also helps in detecting employee performance. With the help of Big Data, organizations could identify their top performers and if utilized in the right way, it can help organizations to boost up their performance ratio.

Using the right tool, such as analytics, a company can track, analyze and share:

• Individual Performance
• Team Spirit
• Interaction among Departments
• Overall Company Culture

Big Data helps companies to analyze their performances on a daily basis rather than just annual based reviews.

In a Nutshell

Big Data combined with other global trends can help the BFSI organizations to get a better hold of their needs both internally as well as with their customers and clients. It helps in providing improved services in real-time with less operational costs.

The post Will Big Data be a Game Changer for the Banking and Finance Sector in 2020? appeared first on Artificial Intelligence.

Bankify, a Finnish fintech company, and Nets, a leader in the payments industry, have entered into a strategic partnership that enables Bankify’s customers to consume Bankify’s services through Nets’ Open Banking platform. Under the terms of the partnership, Nets customers will also have easy access to Bankify’s portfolio of microservices via the platform. 

Bankify enriches the user experience of existing financial applications to align it with the needs of digital native customers. The services are developed by focusing on customer-centric use-cases using gamification, education, personalisation and social interaction.

Georg Olav Ramstad, Head of Open Banking Sales and Business Development at Nets says: “Bankify is an expert in keeping up with the demands of digital customers. Together, our combined offerings will accelerate the digital transformation of financial institutions. We are really looking forward to an exciting partnership with Bankify.”

The Open Banking Access from Nets provides PSD2-enabled account access to banks with a single-point-of-integration, instead of several integrations to multiple banks. With the solution from Nets, third parties can connect once and then refocus on delivering business value. Nets powers customers’ businesses by delivering an efficient access infrastructure for open API-based services.

Building blocks for future financial applications

Bankify has already launched eight microservices, each of which utilises a simple open API connection to improve the user experience of existing financial applications, and enable a variety of new use-cases, including social finances, gamified savings and group payments combined with a virtual card solution.

Antti Tarakkamäki, Co-Founder at Bankify, says: “With the Open Banking Access from Nets we can connect our offerings via a single integration, providing our customers access to multiple banks in the Nordics. This makes our microservice offering even more relevant, easier to implement, faster and more cost-efficient for our customers.”

Bankify has conducted extensive user research during the last two years with the Millennial and Gen-Z users, globally. These segments require more personalised and engaging features, which represents Bankify’s core area of specialism.

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Every part of our business world is being changed by AI — even the way in which we communicate. Here’s how this is occurring today.

In today’s business world, face-to-face communication and interactions don’t happen as frequently as they did in the past. We rely more on conference calls, email, and social media to make communication faster, easier, and more feasible.

However, these same tools have also made it more challenging for us to form relationships with those we’re doing business with — something that’s especially true for the mature workforce, but not so much so for millennials. Nevertheless, technology has now become the norm both in our communication and in other parts of our work environment as well.

The Fast Got Faster

Artificial Intelligence communication can help you reduce the amount of time you spend in conversations, which results in better experiences for customers. This was first seen when email hastened communication. Being able to send virtually signed (a.k.a. e-signatures) documents as attachments made emails even more productive.

Tools like Skype followed this making conference calls both feasible and logical since you could talk to anyone, anywhere, at any time. Now we’re seeing other new tools arise — things like Slack, Trello, Atlassian, and Facebook’s Workplace. These are beneficial because of they:

Increase our ability to work remotely since we can complete projects without being in the same room.

Boost efficiency since they help us communicate, share information and knowledge, collaborate, and work on various tasks and projects simultaneously.

Increase cross-functional engagement since they let us have important discussions.

Challenges in Communication

Nothing in life is perfect and that’s true here as well. So, while workplace communication tools have greatly enhanced our collaboration and communication, they’ve brought some drawback with them as well. Now there’s no longer enough time to even pause and reread your email before sending it.

As such, you may be susceptible to various mistakes as communication has now become almost reflexive and impulsive. As such, it’s difficult not to involve your emotions since you don’t have time to pause and rethink what you’re saying. The pressure of responding immediately also increases the likelihood of responding emotionally without thinking everything through first — something that could get you in trouble.

There’s also an issue with engagement as more discussions are occurring in virtual environments instead of face-to-face today. While this saves money on things like travel expenses and has regular meetings with team members geographically anywhere. All that’s great because now we’re closer to our homes and spending less of the company’s resources.

However, it also eliminated the physical bonding that occurred through these meetings — even if it was just a handshake. No longer are we even engaged and conducting face-to-face meetings with those in our own office. Instead of walking across the office to your co-worker’s cubicle to have a discussion, you simply use your communication tool to quickly drop them a message.

This is bound to continue over the course of time, meaning we’ll be ‘seeing’ even less of our colleagues as we spend even more time communicating through tools, apps, and systems. Nevertheless, we still need to stay engaged and connected as a team.

We need those synergies that naturally happen within cross-functional and departmental teams. We can’t afford to have a highly disengaged work environment. As humans, we have an inherent need to have interactions, engagements, and communications that stimulate our senses — something that will continually decrease as we use more communication tools.

Use of Advanced Technology

Today’s businesses have truly evolved. This has rapidly occurred on a global level. Things like virtual teams, agile talent and remote offices are no longer trends — they’re global realities allowing us to conduct business with partners, suppliers, and customers who are located in other places throughout the world.

This is because time zones and office hours no longer matter as much today. We can communicate, make decisions, seek information, and learn new things at any time of the day or night now thanks to advancements in mobile technology.

The traditional 9–5 hours are quickly becoming history. Today’s employees have the flexibility to work later and leave earlier because enhanced voice AI communication lets us stay in touch regardless of where we are at the time. We can send emails, have conference calls, and send documents from almost any location throughout the world.

Since our global village is so connected and still shrinking, being on a call with your business partner in the middle of the night is no longer unthought of. This type of flexibility is now required, meaning we must all be much more accountable too.

Another thing that’s simultaneously happening is that people can now strike a great work/life balance. This isn’t because their priorities have changed. It’s simply because their possibilities have increased, making it so you can now attend your child’s school play while taking care of business at the same time.

No longer is it impossible to arrive home on time for a family dinner either. The tradeoff here is that you may need to answer your mobile phone or send an email while there, but many people think this is fair.

Improving the Future of Communication

Unfortunately, disengaged offices aren’t very productive or effective. Nevertheless, we can’t avoid this revolution in communication either. As such, we should consider making a few tweaks like:

Hosting engaging activities is essential

Setting boundaries play a critical role in successfully embracing today’s technology. This is similar to email etiquette. The only difference is that you must now apply these boundaries and standards to how your communication flows through these tools that you’re using. This will define your company’s culture and help everyone maintain healthy working relationships.

Understanding that it’s easy to get burnt out when you’re expected to reply and always remain in touch regardless of what time of the day or night is also essential. You must set parameters that let you enjoy some personal time too.

While workplace communication is a vital part of conducting business today, you need to find ways to enhance business performance through technology. Doing so will improve your business and your employees’ well-being too.

The post Trends in Artificial Intelligence that are Changing the Way We Talk appeared first on Artificial Intelligence.

Though many jobs are well suited for today’s artificial intelligence applications, dealing with human emotions may not, at first, seem like one of them. But what about the areas in which human emotion may get in the way?

Conservative estimates put the percentage of workplace harassment or discrimination that goes unreported at around 75 percent (pdf) — and the researchers who made those estimates found that the least common way people deal with the experience of harassment is to report it. The reasons for this are myriad, and include the fear of judgment or reprisals and the pain and difficulty of recalling emotionally charged situations. “People are shy and they don’t [necessarily] want to talk to a human being, so tech can help solve that,” says Julia Shaw, a London-based memory scientist and author of The Memory Illusion.

In February 2018, Shaw, along with two partners who are software engineers, launched Spot, a Web-based chatbot that uses artificial intelligence (AI) to help people report distressing incidents. The app is based on an interview technique developed by psychologists and used by police departments to ensure that a recorded narrative is as sound and accurate as possible; it also gives the person reporting the incident the option of remaining anonymous.

The chatbot learns from the user’s initial description and responses to its prompts, asking specific, but not leading, questions about what happened. Together, the user and the bot generate a time-stamped PDF report of the incident, which the user can then send to his or her organization or choose to keep. So far, more than 50,000 people have accessed Spot (the site does not keep records of who then goes on to make a report). And it’s available 24 hours a day, so you don’t need to make an appointment with HR.

“Evidence really matters in these cases, precision really matters in these cases,” Shaw says. What makes Spot useful is that it removes a layer of human interaction that is not only often fraught, but can also introduce inconsistencies in memory.

Spot is just one tool in an emerging market for machine learning–assisted apps that are tackling the juggernaut of human emotions. The National Bureau of Economic Research reports that the number of U.S. patent filings mentioning machine learning rose from 145 in 2010 to 594 in 2016; near the end of 2018, there are already 14 times the number of active AI startups that there were in 2000. And among them is a growing host of companies that are designing AI specifically around human emotion. Spot is intended to help end workplace discrimination, but according to Shaw, it’s also a “memory scientist for your pocket” that bolsters one of our weaknesses: emotional memory recall.

It might seem ironic, maybe even wrong, to employ machine learning and artificial intelligence to understand and game human emotion, but machines can “think” clearly in some specific areas where humans find it difficult.

The messaging app Ixy, for example, is marketed as a “personal AI mediator” that helps facilitate text chats; it previews texts to tell a user how he or she comes across to others, aiming to remove a layer of anxiety in human-to-human communication. And Israel-based Beyond Verbal already licenses its “emotions analytics” software, patented technology that gauges the emotional content of an individual voice, based on intonation. The tech is being used to help call centers fine-tune their employees’ interactions with customers and enable companies to monitor employee morale, and could be deployed to help AI virtual assistants better understand their users’ emotional state.

Yoram Levanon, chief science officer of Beyond Verbal and inventor of its technology, envisions even more ambitious applications, including virtual assistants that monitor people’s physical and emotional state by analyzing their vocal biomarkers. The app is able to recognize that how we say something may be more important than what we’re saying.

“AI is going to help us. Not replace us,” Levanon says. “I see AI as a complementary aid for humans. Making AI empathic and [capable of] understanding the emotions of humans is crucial for being complementary.”

Some organizations are already adopting similar AI, such as the audio deep learning developed by Australia-based company Sherlok. The Brisbane City Council uses the tech to scan emails, letters, forum discussions, and voice calls to uncover callers’ “pain points” and improve its own staff’s skills. Another emotion-led AI technology is being used to identify best practices for sales, and, in one example, a leading company used it to detect discrepancies in emotion and enthusiasm between C-suite executives on the quarterly analyst calls to gain better insight on performance.

At this point, you might be starting to hear dystopian alarm bells. Concerns that AI could be used to judge our personalities and not our performance — and incorrectly at that — are valid. AI has a problem with biases of all kinds, largely because the humans who build it do. Developers have a saying — “garbage in, garbage out” — meaning that the technology may only be as good or as fair as its data sets and algorithms.

In one of the more spectacular examples of AI gone wrong, Microsoft was forced to shut down its “social chatbot,” Tay, within 24 hours of its launch in March 2016. Tay was designed to learn from its conversations with real people on social media and personalize its responses based on previous interactions. But no one had counted on the trolls, who gorged Tay on a diet of racist, misogynist, homophobic, and grossly offensive chat. The chatbot soon began spouting its own contemptible comments.

Tay’s turn wasn’t an outlier. “If [AI tech] is not harnessed responsibly, there’s a risk that it leads to poor outcomes, increased bias, and increased discriminatory outcomes,” says Rob McCargow, director of artificial intelligence at PwC UK and an advisory board member of the All-Party Parliamentary Group on Artificial Intelligence in the United Kingdom. “There’s very much a double-edged sword to the technology.”

The consequences of irresponsible AI technology are potentially devastating. For example, in May 2016, investigative journalism organization ProPublica found that an algorithm used by the U.S. court system for determining whether a defendant was likely to pose a risk was wrongly labeling black people as potential reoffenders at twice the rate of whites. In this instance, AI isn’t so much a better angel of our nature as it is the devil in the mirror.

At the heart of the problem is the fact that engineers are overwhelmingly white and male, and tech is designed in a kind of vacuum, without the benefit of other perspectives. Unconscious bias is baked in, and it can have problematic consequences for tools such as recruitment software, which in some cases has been found to disadvantage certain groups of job applicants. And then there’s the other question — do we even want this? Earlier this year, Amazon won two patents for wristbands that could monitor their workers’ movements, prompting a flurry of headlines with Big Brother references.

“We’re in the foothills of the mountains in this. There are increasingly large numbers of businesses experimenting with and piloting this technology, but not necessarily applying it across the breadth of their enterprises,” says McCargow. “I think that the point is [that although] it might not be having a substantial impact on the workforce yet, it’s important we…think about this now, for the future.”

This future is already here. The Spot app offers a way for people to bolster and clarify their reports of harassment. Ixy can give real-time feedback on mobile chat to help users navigate difficult conversations. Beyond Verbal wants to help humans hear between the lines of conversations. These are just a few examples among a growing number of new technologies designed to help users navigate the eddies and whirlpools of human emotion. There will be more; we can count on it.

With most technology throughout history — the printing press, the textile mills, the telephone — the tectonic shifts for workers were both huge and subtle, shaping a landscape that was changed but not completely unrecognizable. AI, for all its transformative implications, is similar. As McCargow points out, there’s been a lot of concern over machines eroding our humanity, chipping away at what it means to be human. But that doesn’t have to be the case. Done right, machines could help us learn to be better humans.

The post How Artificial Intelligence Can Make Us Better at Being Human appeared first on Artificial Intelligence.

Today, Artificial Intelligence (AI) and Machine Learning (ML) are two popular terms that tech companies cannot stop talking about. Everyone from Google and Microsoft to Apple, Samsung and Amazon are going big on AI. Besides smartphones, smart speakers, voice assistants, apps, connected cars, security surveillance, healthcare and customer support are other areas where AI is used.

Machine Learning (and deep learning) has been going on for years, and now with the data that exists, tech companies are putting it to the best use. On device machine learning combined with artificial intelligence can help in anticipating things in advance. But while you may not know, AI and ML can be very helpful in customer service too. In fact, according to a recent research by Gartner, 85 percent of client relationship with a business will be managed without human interaction by 2020.

Now, in a conversation with BGR India, Olivier Klein, Head of Emerging Technologies, Asia-Pacific at Amazon Web Services, offered some insights on how the company and its clients are using AI and ML. He also mentioned that Amazon has been doing machine learning for the past 20 years, and now, the company has made it available for end users through the platform. We spoke about how AI is evolving to be the core, and at the forefront of improving customer experience, and Olivier offers a number of examples to help understand the benefits.

24-hour customer support, Chatbots

There are times when you order some product from an e-commerce website, and you want to know the status of your package, or raise a complaint for refund or replacement in case of a faulty product. These are the times when we end up calling the customer support holding the phone for at least 5 minutes to get to the representative, just so you can explain your issue and ask for refund. But this process can be completely automated, and a lot of companies are using this to their advantage. And with several advances in natural language processing (NLP), these chatbots are getting better all the time.

Take an example of Amazon, when you click on ‘contact us’ you get the chatbot interface where you select your problem from the list and go forward. Most times, you don’t have to talk to a customer care as things are fed in the system and you just need to choose the relevant option. I recently had to cancel a product that didn’t get delivered in the said time, and the process was completely fluid. I got the product cancelled and refund processed in under two minutes without having to talk to a representative.

Facial analysis – Travel without passport and ticket

AI is today used for facial analysis to recognize and authenticate you. It is most commonly used in a lot of smartphones today, in the form of Face Unlock feature. But, there are a wide range of applications where facial analysis can be used. A good example could be while boarding an aircraft. Generally, you have to show your passport and ticket, but facial analysis can offer a seamless experience.

Your data, such as facial structure and other characteristics are already present in the system. When you head towards the check-in counter, the computer just has to run your face through the database, find a match, and then look for bookings that you may have made. Once authenticated, it can check you in, and offer seamless experience. Olivier mentioned that the company has been testing this solution in Asia Pacific region, but it may take a few years before it is deployed for use by mass audience.

Movies and series recommendation using computer vision

If you have an account with Netflix or Amazon Prime Video, you may have noticed that these streaming services recommend you top shows to watch. Ever wondered how the list is populated? Well, it uses machine learning model to understand your likes and recommend a TV show or movie accordingly. The Amazon AWS machine learning model runs facial recognition on top of the video to understand the actors in the video content that you are watching.

Along with facial recognition, computer vision model is also used to understand the scenes – is it an action packed movie with lots of action, chasing cars and explosions, and more. Based on this analysis, the computer vision and machine learning model understands that kind of things you generally like, and will offer content suggestions and recommendations based on that.

Amazon X-Ray and image recognition

When watching a TV show or a movie, have you even come across a situation where you feel that you have seen that face or actor before? At times it drives you nuts when you can’t remember where you’ve seen the face. This is where X-Ray feature comes to rescue. If you have Amazon Prime Video subscription, chances are that you may be aware of the X-Ray feature.

X-Ray is an image recognition service where the platform uses Amazon Rekognition API for image an video analysis. It also uses a feature called Celebrity Rekognition that runs on top of video to detect and recognize faces in the video. Once the recognition is complete, it then ties it back to IMDB. As you can see in the above screenshot, you can exactly see the actors in the scene along with their name and face. Clicking on the link will take you to IMDB page of the actor, where you can check all of their details.

Automatic due date reminders

 Have you ever got an automated call from your insurance provider or your telecom operator to remind you about the payment due date? Well, these are automated calls and some of them use Amazon Polly services which allows companies to render a personalized and natural sounding voice response.

Policybazaar is one of the insurance service providers using the service. When the required details are feeded to the system, it will call the customer, greet with their name, read out the policy number and inform about the due date and the premium they need to pay. And the beauty is the voice is more natural sounding, than being robotic, offering a personalized experience.

Flight price tracking

Services like Google flights tracker, Skyscanner, Ixigo have deployed Artificial Intelligence that can help in a number of ways. Say you are looking for flight tickets from Mumbai to Bali, and the pricing shows as Rs 35,000, and you know anything above Rs 28,000 is a little on the expensive side. You can then set price alerts which will track the price changes on a daily basis and mail you about same. You can then see how the price is fluctuating, and book when you think it can’t go any lower than that.

Flight delays, train ticket confirmation prediction

AI also keeps a track of the flight timings, the delays in take-offs and early landings, and based on some permutations and combinations, it can predict if your flight will be delayed or will be on time. Same goes with train ticket bookings too. IRCTC’s new website, and its booking confirmation prediction system is one of the best examples.

The AI has years of data to track the waiting list tickets from source station to the destination for a given time. For instance, during peak seasons, Mumbai – Madgaon route is quite busy, and even a ticket with waiting list 15 seems difficult to get confirmed. Other times, even waiting list 80 gets confirmed. Based on the historical data, the system can predict the odds of your waitlisted ticket getting confirmed, thus helping you keep your option for other transport modes open at the same. 

Home security and sentiment analysis

Artificial Intelligence can also help in home security where you simply need a CCTV camera and Amazon DeepLens API. Of course, you have to first feed to the system with photos of your family members, closed ones and the regular people, like the watchman, milkman, and more.

You will have to teach the Amazon Facial Rekognition API this is the face of my mother, sister, father and so on. It can also do facial analysis on the basis of whether you have a beard, moustache, etc. What’s more, the API can also read emotions and do sentiment analysis. It will use facial recognition tech to understand if it is the person you know, and if you don’t know the person, it can immediately text or call you.

Most of these solutions are already available out there and with Amazon AWS, you can either built them yourself or opt for professional help in for coding, testing and deployment. Services like  Amazon SageMaker, Amazon Kinesis and Amazon S3 allow you to build a platform customized to your needs.

The post 8 ways how AI and machine learning is improving customer experience appeared first on Artificial Intelligence.

As machine learning algorithms are increasingly embedded in today’s analytics applications, this offspring of artificial intelligence is grabbing headlines.

In reality, however, the technology isn’t as new as you might think. Its roots in academia date back to the 1950s. Machine learning algorithms have risen out of predictive analytics and use data to learn and improve behaviors and recognize patterns — without being explicitly programmed to do so. While the technology may not be new, its business application are.

To better understand the impact of machine learning, let’s look how it intersects with customer experience and where this combination may be headed.

How Machine Learning Works

Machine Learning relies on automation and analytical algorithms to detect patterns and derive insight from data to achieve specific goals.

Until recently, machine learning was used only by organizations that had large analytical teams. Now machine learning is becoming democratized and, as a result, is not only impacting every day analytical programs (e.g., analytical segmentation, modeling and optimization,) it is also being used to achieve specific analytical goals.

These goals, particularly when they support improving customer experience, translate into personalizing (or even individualizing) an offer for a product or service. Personalization involves increasing the speed, accuracy and context of interaction. For example, imagine a father who receives a stellar offer for a pair of noise-cancelling headphones on his mobile device shortly after purchasing an electric guitar and amplifier. That’s an offer that has the right speed (right after purchase), accuracy (mobile as his preferred channel) and context (knowing he may need the headphones to silence the noise coming from his teenage son’s room). This type of offer increases customer satisfaction as it is pertinent at the time of need. The end-result is a happier customer (this company understands me) and a happier brand (increased revenues.)

So, what are some practical applications we are seeing today? Below are a few examples of how machine learning is improving customer experience through next-best-offer personalization, customer behavior analytics with new data sources and analytical optimization. The examples are mainly from financial services organizations (banks and insurers), but similar use cases and results are being seen across other industries as well.

Next-Best-Offer Personalization

Machine learning, particularly predictive models, helps banks personalize offers with an extreme amount of certainty. The results:

• Customers interacting via branch and contact center channels do not have to negotiate nearly as much to receive an offer they want.
• Certain segments of banking customers are receiving tailored offers initially instead of irrelevant non-contextual offers that have a low degree of probability for conversion.
• Data and analytics allow regional banks to understand customer preferences — including preferred rates and terms — making the setup process more efficient for both bank and customer.

Customer Behavior Analytics with New Data Sources

Using machine learning in combination with new data sources, whether it is Internet of Things (IoT,) telematics, geographic or social data is leading to an augmentation of the customer profile and an increased understanding of how and why an insurance customer behaves in the manner that they do. The results:

• Property and casualty insurers can use vehicle data (pending opt-in) to better understand the best policies to offer drivers. No longer does the 16-to-25-year-old male automatically get the highest premium.
• Health and life insurers are collecting device data from fitness devices (pending opt-in) and combining it with machine learning algorithms to better segment and classify customers for certain fitness or wellness programs.

Analytical Optimization

Optimization of this nature is not priority or business-rule based, but is instead rooted in operations research and analytical algorithms, and helps financial services companies not only predict how a customer may behave or act, but also how the competition may move in the future. The results:

• Financial services companies are using machine learning algorithms (random forest and gradient boosted models) to predict the probability to be ranked in a certain place (i.e., top 3) in an aggregator portal for financial services products (policies, bank cards, loan rates and so on).
• Insurance companies are using random forest and gradient boosted trees to predict mid-term cancellation rates on policies.
• Banks are able to predict volumes for credit card lines and adjust rates and terms accordingly in order to attract the right type and volume of customers for a certain product.

These are the practical applications we are seeing today, mainly where machine learning operates behind the scenes — as an accuracy, speed, and efficiency enabler. But what may happen in both the near- and long-term with regard to the intersection of machine learning and customer experience? Three main trends will emerge:

1. The Use of Augmented Analytics

Augmented analytics combines data preparation, business intelligence, predictive analytics and machine learning capabilities into a single, more automatic process than we have seen to date. In the near future, augmented analytics capabilities will help organizations prepare and cleanse data, find key insights and hidden patterns, and report on findings in an automated fashion. Data volumes of today have created millions of variable combinations that are nearly impossible to process manually, thus automation via augmented analytics will be needed. The result will be quicker time to insight, which will affect the customer experience through continual improvement. Customer frustration levels will decrease and delight will be on the rise. Rita Sallam of Gartner has co-authored a great article on the topic here.

2. Collaborative Machine Learning Replaces Collaborative Filtering

Many open source machine learning libraries, algorithms and frameworks will join forces to become stronger and thus replace some of the lower-level personalization we see today, namely the approach of collaborative filtering. Collaborative filtering is the technique most commonly used in recommender systems, think Amazon and Netflix. The result will be an even higher degree of personalization and contextualization than what we have seen to date, resulting in better music, food, movie, travel, product and purchase recommendations than ever before.

3. Neural Networks and Decision Trees to the Forefront

The techniques that are beginning to be used with machine learning, neural networks and decision trees will be more prevalent as use cases, which will require these techniques be incorporated into business operations. Neural networks will support better classification and forecasting, while decision trees will support more complex rule and relationship-based customer experience programs.

Machine Learning: The Future Impact

Machine learning will increase automation in industries like manufacturing and medicine, but from a customer experience perspective, we will see a continual embedding of machine learning into customer experience programs. Business process automation of these programs will continue to increase — from both an operational and executional perspective. This will improve the organization’s ability to support complex decisions, forecasts and optimizations.

As the organizational understanding of the customer continues to improve, the delivery of offers and interactions will be further contextualized. The beneficiary — customer experience programs and the metrics that track their success — customer lifetime value, net promoter score and so on. Based on the progression over just the past few years, I can’t wait to see how organizations will delight customers in the near future.

The post How Machine Learning Improves Customer Experience (and Increases Revenue) appeared first on Artificial Intelligence.

As it relates to technology, the private aviation industry has been flagging lately. While some industries — health, retail, manufacturing, automotive — have adopted machine learning in recent years and used it to transform their businesses, our sector has fallen behind.

What the private aviation industry specifically lacks is machine learning technology to streamline workflows, business processes, customer procurement, and financial settlements. Midway through 2017, however, a corner is being turned, and aviation companies are increasingly starting to deploy consumer-facing technology to bolster customer service.

The private aviation industry has lagged behind many commercial airlines where technology is concerned. But delivering a service built upon automation and intuitive self-learning that fulfils every need of that highly demanding customer segment, the private flier, is a particular challenge.

Improving service via machine learning

To date, machine learning technology in aviation has mostly focused on the control of aircrafts or their internal systems. Most organizations are currently obsessed with the idea of automated piloting for self-flying planes. While that phenomenon is on the horizon, there are more immediate ways for us to use machine learning to enhance customer experience.

Whether you’re working in private or commercial aviation, there’s never been a more exciting time to be in the sector. We’re seeing airlines like Etihad Airways use machine learning to provide more personalized services to its passengers. Other airlines, such as EasyJet and Emirates, are using these advances to simplify the ticketing process and enhance internal operations.

Over time, the goal for the industry will be to leverage automation to personalize private charter for global travelers, as well as make the booking process more transparent, timely, and accessible. It’s all about smoothing and enhancing the flying experience for customers.

Additionally, private jet providers and airlines must leverage machine learning to identify how customers engage with the brand, detect their purchasing patterns, and segment them accordingly. By learning whether consumers prefer to make requests via a mobile app or the web, what time of the day they prefer to fly, and the amount they are willing to pay for flights, those companies will better understand and cater to consumer behavior.

It’s also imperative to know whether consumers are more influenced by prices, the various amenities that come with different jet options, or both. That kind of important information will allow private jet providers and airlines to adjust their offerings to best build genuine, long-term engagement.

Machine learning will ultimately help private and commercial aviation companies service their customers more effectively than ever. By helping customers make smarter, better-informed travel decisions, they will give customers greater control and instill loyalty. To reach that objective, however, they must operate on a healthy combination of intelligent automation and skillful human resources.

Increasing efficiency through AI

This is a ground-breaking period for society, not just aviation. Gartner estimates that six billion “connected things” will require AI support by 2018. Inevitably, the rise of automation will mean some human workers in our industry losing their jobs. But in the long run, I don’t think that is tremendous cause for concern.

The reality is that any tasks that take less 10 seconds to perform should be automated. Why? By automating more basic undertakings, aviation businesses can enable their employees to focus their efforts on improving the quality of the human interactions between their brand, the marketplace, and consumers. It’s about striking the perfect balance of hi-tech and high touch.

Although some employees will be phased out in favor of automation, it’s a net positive for any aviation company to perform more work for customers — and maintain a high level of performance — with a lower headcount.

Automation will also catalyze innovative new services that make private jets more available to the wider public, not just the elite. Within the next decade, I anticipate that the private aviation industry will have a mobile application that simplifies how customers can share jet charters with one another, like a carpool service for flights — assuming we can overcome regulatory hurdles. If it comes to fruition, this could stimulate new business and create human employment in other valuable ways.

Sharing a private aircraft will eventually prove as effortless as creating a WhatsApp group or sending images via text. As ecommerce platforms evolve, the concept of fluidly promoting and selling seats on booked charters will solidify. By leveraging the capabilities of social media networks, jet providers will not only help primary charterers defray their costs (attractive for business flights) but ensure that private air travel becomes ever more efficient and flexible. This kind of connected movement is the future of transportation for our busy world.

To gain new customers and keep them, private and commercial aviation companies must leverage machine-learning algorithms to truly understand their consumers’ preferences and deliver personalized, forward-thinking offerings accordingly. Otherwise, they’ll never shed the late bloomer label.

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Have you ever thought about what the future holds for us? Self-driven cars, automatically-curated Netflix queues, and an intelligent home that auto sets the temperature are some of the things that come to mind. It is no secret, then, that artificial intelligence (AI) will impact us in myriad ways as technology progresses.

A recent study by research firm Gartner found that over 55% of forward-looking organisations are either already investing in AI, or have plans to do so by 2020. This is a clear indication of the importance of this phenomenon, one that is bound to impact decision-making for businesses. By looking at current trends that are driving growth in customer experience, one can identify topical innovations and where they are bound to be powered by AI.

Look at popular consumer brands like Uber, Airbnb, Amazon, and Dropbox. They manage to support a growing list of customers that is in millions, every day, every hour and every minute. It is virtually impossible to staff enough people to handle each and every query, clarification or problem raised by such a massive cluster of customers. AI is the answer to the problem, for it delivers solutions using machine and deep learning.

While it is hard to predict how exactly AI will influence our lives, the changes it will bring about in customer experience can be predicted. This can be done by studying the emerging patterns.

Rise of the chatbot
Chatbots can deliver personalised communication by assessing patterns. As interactive platforms, they can manage many basic functions hitherto managed by humans. That will free up precious time of support agents, allowing them to apply their minds to more important functions. Automating most processes through bots also gives a company an image of being forward-looking and technology-oriented. Chatbots can also provide immense scope for companies to improvise and learn consistently about their own customers.

Multichannel to the rescue
With the rising number of communication platforms available today, how does a company choose one over others? AI presents potential for delivering personalised communication options for customers. Email, social media, chat, phone, etc. are some communication options today. The important point to note is, different customers have different preferences in the way they would like to interact with a company. Take, for instance, a millennial who is likely to prefer chat or social media as their preferred channel. But the older generation is likely to choose a telephone call or email. AI has the potential to learn these preferences and intuitively manage them for a company, thereby saving precious time and monies.

AI helps streamline processes better by directing a customer to a chatbot to respond to basic queries from a customer, and then escalate them to email or a phone call if required. Such engineered smart processes can help deliver better customer satisfaction and, not to mention, free up agents’ time in the process.

Agile responses
AI has the capability to quickly solve problems. Automated services can deliver quick turnaround time by providing easy-to-understand responses for most basic queries, thereby optimising agents’ time to manage more critical issues. Machine learning can also provide customers with assistance and advice on how to prioritise and handle an issue more effectively. This inherent quality of AI to streamline customer support by analysing patterns and delivering human-like responses to queries is advantageous both for customers as well as agents.

We live in a world that is constantly changing. A growing number of new-age companies demand new-age solutions to help keep pace with customer needs. The customer, on his part, is also transitioning and expects brands to relate to him and provide custom experiences. Customers across the world today have access to social media and on-demand TV, which is different from the offerings available a few years ago. To stay relevant, companies can rely on feedback and learning from their existing customer interaction and keep improving it through AI.

That said, one thing is for sure. AI, which was a speculation till five years ago, is now a reality and here to stay.

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