Regardless of how much people earn, the jobs they have or whether they’re part of the workforce, banking is a necessary routine.
It may not come to mind as one of the most technologically advanced industries, but has still undergone some significant advancements that make the industry at large ready to serve an increasingly digital society.
Some Banks Have No Physical Branches
Not so long ago, one of the first things people verified before switching to new banks is that those financial institutions had locations near their homes. Now, that’s no longer as necessary as it was, especially since numerous banks only operate online and don’t have facilities to visit.
In addition to the convenience of not having to find the appropriate banking brand when traveling, these online-only banks typically provide excellent customer service.
They’re also known for other perks, such as low fees and the option to choose familiar names such as Capital One, which has an online-based business arm.
The Rise of 24/7 Mobile Banking
Typical business hours don’t always suit people who work night jobs and sleep during the day or are sole caregivers for family members who can’t leave home or stay somewhere unattended. However, thanks to the increased prevalence of banking apps, it’s possible for people to deal with finances whenever their schedules suit.
In addition to the banks above that exist in the digital world — and usually have their own apps — most of today’s leading banks know that to compete in the industry, they must cater to emerging customer needs.
That means designing feature-rich apps that help people use their time productively. Customers can transfer money, check their accounts, and talk to customer service agents with just a few taps on their smartphones or tablet screens.
Also a few years ago, people often raised their eyebrows at the thought of banking online, especially if they had security concerns. However, things have changed. The Mobile Ecosystem Forum conducted its Mobile Money Report study and found 61% of respondents engage in banking via their smartphones, and nearly half of respondents (48%) use mobile banking apps.
However, as mobile banking continues to gain strength and recognition in the marketplace, it’ll be perpetually necessary for providers to keep their platforms secure. Apps could become tempting targets for hackers, and if they become compromised, people won’t be so willing to use them.
A Diversification of Accepted Currencies
Silver and gold are two of the longest-standing forms of currency, with the latter considered by some as the only metal that universally symbolizes wealth. Also, silver mining has occurred since as far back as 500 B.C. People can find references to both these valuable metals in Greek mythology, books of the Bible and other ancient texts. Back then, banking often occurred in temples.
Today, there are still various accepted forms of currency, with certain kinds being more popular than others depending on trends, geographic locations and so on. Paying with a debit or credit card offers a widely established way to buy things without carrying cash.
People are also exploring cryptocurrencies, such as Bitcoin, an entirely digital form of payment. They appreciate the advantages it offers, such as sending money anywhere and at any time.
Bitcoin does not have a single governing body determining usage. That fact appeals to people who prefer being in full control of their money.
Although Bitcoins are one of the most well-known cryptocurrencies, there are hundreds of others to consider as well. Litecoin, Ripple and Ethereum are a few of them.
Traditional ATMs Are Losing Popularity
There was a time when the concept of getting money dispensed from a hole in the wall seemed like something straight out of a science-fiction novel. But now, that’s commonplace as people use ATMs.
However, in some places, such as India, digital money transfers are so popular that banking institutions are removing ATMs from the country. In the future, people may see them as fixtures as relevant to the modern world as dinosaurs.
The machines are often located in urban areas where people can easily rely on other ways to get money. That makes sense, especially because digital means are more private in the right circumstances and do not include the safety risk of a thief snatching the money immediately before a person reaches to retrieve it.
Banks have also looked at more diverse uses for ATMs. Returning to the example of India, there are some banks there evaluating ways of presenting ATMs as platforms for money transfers. In Ireland, people regularly use ATMs to purchase credit for mobile phones.
Also, Wells Fargo recently unveiled cardless ATM technology, which is ideal for people who realize they forgot their wallets during evenings out. Individuals use a corresponding app that distributes numerical passwords authorizing access to the ATM. Other banking entities use QR codes to verify identities.
As the banking sector keeps pace with people’s needs and desires associated with managing their finances, it’ll be interesting to see how opportunities continue to evolve.
The industry has already come a long way and obviously understands the need to continually deliver up-to-date capabilities for stakeholders.
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I am a big Google fan. Before I start pouring in praises for the search giant, it’s quite amusing to note that their well-loved search routine begins with the modest process of web crawling performed by crawlers (aka spiders or bots) commonly referred to as Google bots.
How they tested, tried and played with the crawled data has turned them into a massive unparalleled search sensation.
So what set them apart?
The Google Journal
About 2 decades back, Google – currently the most valuable company in the world, started out with a rather simple looking mission statement – to organize the world’s information and make it universally accessible and useful.
Formulated by Stanford students Larry Page and Sergey Brin, the search engine was powered in 1998 from a garage in suburban Menlo Park, California. The building blocks of their search engine, as covered by Google engineer Matt Cutts’ video on the fundamentals of search include -
No wonder you get most of your answers from Google.
The Google “WOW” factor
Search engines like Archie, WebCrawler, and Yahoo existed prior to Google but fizzled out with Google dominating the Internet in the years that followed. A few key areas where Google stood out remarkably were -
1. Passion and innovation – Founder Larry Page had a perfect vision for his search engine – to understand correctly what a user wanted and give back exactly what they wanted. Google was not satisfied with giving just a bunch of websites as results but had a passion to serve the right answer. They worked hard in fine-tuning their algorithms to bring about better search answers making about 1600 improvements in 2016 alone.
Google was also futuristic in terms of trying out various innovative features like interpreting spelling corrections in search queries, finishing off people’s thoughts with Google autocomplete and returning answers in different languages having relevant images with universal search. It didn’t stop there.
Getting about a trillion search queries a day, Google took an extra mile to reason out why a user was typing in a particular search query. Was it for directions to a particular place or to find a bakery nearby or to check out local clothing trends? With these questions, they went on to build more search-based products like Google maps, Google news, Google trends, Google alerts, and Google flights, growing to be more than just another search engine.
2. Faster results – In a short video presented by Google on the Evolution of Search, Ben Gnomes, a Google Fellow outlines the goal of the Google team to get answers to users faster and faster. Amit Singhal, another Google Fellow, describes how Google failed to give relevant search results during the Twin Towers attack in 2001 as the Google index was crawled a month earlier. To address this they initiated crawling the news quickly and improved the frequency of their web crawling process in the years to follow.
3. Staying Relevant always – Though Google made a lot of strides in their search and software products, they stayed 100% true to their initial mission of making as much relevant information as possible and available not just to a chosen few but to everyone. Rather than crowding their homepage with irrelevant content, they stuck to a simple box for typing in the search query. Serving the most relevant answers to all users mattered the most to them.
The Google lesson in web crawling
Google kicked off with the humble web crawling process of systematically looking at web pages across the Internet. But understanding user intent, Google knew the nature of questions that they had to work with presenting the right answers from their crawled data.
There is a lot of information out there on the World Wide Web that holds answers to your business questions. Web crawling, a can gather information according to the nature of your business query. It could be anything like – What is the best price I can sell my commodity at? Who are the competitors in my area of expertise? What is lacking in them that I possess? What companies are Venture Capitalists interested in? Is there a relevant business technology I can invest in? Can my solutions be of help to potential client profiles whom my solutions I can reach out?
Ask right, innovative questions like Google and look for answers from the information gathered in the web crawling process. If you are new to the web crawling activity, do get in touch with us!
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Artificial intelligence has changed the way we do everything. Order takers have been replaced with automated menu screens that allow you to customize your meal. The digital assistant on your smartphone allows you to get information, make calls, or send text messages hands-free. In the world of recruiting, bots can take over numerous routine tasks you need to perform in order to turn a candidate into a new hire.
Automation is changing the way that so many of us work, allowing us to get twice as much done in a single day. You have a lot of important things to do that need the time and attention that only a human can provide. There’s no need to waste time clicking through pages and pages of irrelevant information anymore – some things are best left to the robots.
1. Asking Questions and Compiling Data
That big long list of screening questions you need answers to takes up a large portion of your time with a candidate. You can get the same answers from a recruiting bot. The bot can take that information and compile it into an overview of the candidate, telling you where your hits and misses are. Educational background and qualifications aren’t the most exciting things to cover – the great thing about recruiting bots is that they don’t get bored.
2. Interview Planning
When a candidate meets interview criteria, your recruiting bot can set up that interview. If you keep a current digital calendar that lists your availability, your recruiting bot can share your availability with candidates who meet interview criteria, allowing them to schedule a timeslot that works for you. You will then get a notification that the recruitment bot has scheduled an interview. Combine that with the information the bot has extracted from the candidate, and you have everything you need to make a well-informed confirmation call.
3. Touching Base with Candidates
The follow up is important. Busy recruiters don’t always get to follow up in a timely manner. There are so many balls up in the air that it can be hard to catch them all before they fall. Leaving the first follow up to the bot makes things a lot easier. Sending reminders or generalized information out through the bot can provide candidates with what they need to set up another meeting with you, or to call you and provide you with additional details you may need.
4. Getting a Candidate Work Ready
After you’ve decided that a candidate would be a great fit, robotic recruitment tools can start the onboarding process. What do you need the candidate to bring? Are there virtual resources they can use to research their job responsibilities and the specifics of their position before they begin working? What about training materials and information about policies and company culture? Your recruitment bot can provide a fully inclusive welcome package for employees who are ready to start.
5. Freeing Up Your Time
Above all else, the main advantage of outsourcing tedious work to a recruitment bot is that you’re no longer personally responsible for that tedious work. Bots don’t need a break to sleep or eat – they can constantly screen candidates and help the cream rise to the top. All you need to do is review the information the bot has provided for you and take its suggestions, which will all come from your set specifications.
This frees you up to do anything and everything else. Focus on the equally important tasks which require creativity, such as writing great job descriptions for your next board post, or developing better interview questions. Since your end of the deal only involves phone calls and interviews, you’re able to give your full attention to other aspects of your job. The automation will help you become more productive, and even give you extra time to spend with a candidate who seems promising. In the end, you’ll be making more informed decisions because you’ve had more time to make them.
It’s safe to say that automation is the next best thing in recruiting. If you aren’t already using bots to do a lot of the heavy lifting for you, you’re missing out. Recruiters are already busy enough – leave the burdens to the machine and focus on the things that matter most.
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Artificial intelligence, machine learning and automation are poised to change not just the modern enterprise, but the world as we know it. They are completely changing the way consumers engage with companies. They are altering the way enterprise thinks and operates regarding business intelligence.
In fact, the influence of these technologies and platforms has entered nearly every industry from construction and engineering to medical and health.
Forrester Research predicted an increase of about 300% in AI investment, for 2017 alone, compared to the year prior.
Looking at the performance of these technologies throughout the year, we’d say Forrester was pretty darn close, if not spot on.
But it’s not as if this doesn’t make sense. The AI and machine learning tools of today are not just improving themselves, but also improving in the way they are deployed and implemented.
Neural networks, for example, have evolved business analytics to offer more accurate and precise predictive data. It wasn’t long ago that human teams were tasked with sorting, organizing and reviewing troves of data for actionable intel and insights. Now, that tedious process can be automated.
With all the growth we’re seeing, it does beg the question: How will the AI and machine learning landscape change over the course of the next year? What new technologies, deployments and devices will crop up or take hold of the market?
1. Deep Learning and Neural Networks
Through a combination of learned processes, algorithms, analytics and automation, deep learning and neural networks can and will completely change the way businesses handle information in the modern world.
Some of the potential applications include automatic speech recognition for communication channels, image and optical character recognition, advanced prediction and deployment. The idea is that analytics will become more than just collected, processed and static or dynamic data. It will become a living, thinking system that influences how your business and team operate.
A neural network might, for example, be able to predict exactly how your holiday sales and performance will play out, based on current annual trends, past performance and other data. This would, in turn, allow you to make more informed decisions when preparing for the holiday rush, helping you to maximize your performance and revenue.
2. Biometrics and Authentication
Passwords, passcodes, and patterns work, sure, but they’re wholly ineffective when it comes to security. You lose your pass, and you lose access to your accounts and services. It also happens when you lose access to linked channels such as email and mobile.
Biometrics and biometric data are unique to your body and include things like your fingerprints, retinal pattern, voice and even facial structure. The related technology deals with identification, measurement and authentication of various body segments or behaviors. This can be leveraged to, say, unlock a personal device or account.
Not only will this technology become more reliable and prominent, but it’s also not a stretch to claim the technology will soon be integrated with AI and automation. Imagine walking up to a work computer, where an AI system automatically scans your appearance, measures your facial features, unlocks hardware and opens the necessary software and documents.
3. Natural Language Processing
Voice assistants and automation systems have been in use for years, and you’ve encountered them nearly every time you’ve called a public hotline or corporate number. Sometimes they work. Other times, not so much.
Thanks to natural language processing, however, these tools can become much more advanced, capable and precise. This applies not just to voice-based but to text and messaging tools as well. Through a combination of machine learning, big data processing, and statistical methods, these systems could effectively communicate with human beings with little to no conversational hurdles.
4. Chatbots and Virtual Agents
Natural language processing will also give way to modern chatbots and automated, virtual agents. These systems are already being deployed on a broad scale, especially in retail and e-commerce. The bots or systems rely on language processing, data troves and reactive algorithms to interact with human audiences.
More importantly, they will become much more capable and convenient as they are programmed to do and achieve more. We’ll go from the simple tasks we have right now, like checking account balances, or reading back purchase history, to incredibly advanced support.
A bot could, for instance, reach out to the proper in-company reps when you message it for support. The system would acquire answers and take action to address a problem. Calling into a bank hotline, for instance, would result in the AI looking over recent purchases and refunding the improper charge(s) with little to no oversight.
It sounds a bit crazy, especially when you consider what could happen if this technology malfunctions. In the previous example, such a scenario could result in lots of lost revenue for the bank. That said, it’s not beyond the realm of possibility because of how advanced these systems now are.
5. Decision and Project Management
AI and machine learning tools will not only be able to predict future patterns and trends, but they’ll also be capable of deciding on a viable course of action for the future. This could be used in project or decision management to influence the outcome of an event. While success is never guaranteed, imagine being about 90% sure something is going to pan out the way you want it to. That could change the way you handle your business considerably.
6. Robotics, Cobots and Androids
We’re far off from human-like androids that are impossible to tell apart from human beings. That said, the robotic systems of today are capable of much more than just simple tasks and automation. Even in the consumer world, we have robotic vacuums, smart speakers, home automation tools and much more.
Corporate processes, manufacturing, shipping and fulfillment can and will benefit from deploying modern robotics. Amazon is already doing this, and other companies have followed suit.
7. Speech Recognition and Dictation
Assistants like Alexa, Siri and Cortana use speech recognition software to discern what you’re saying and then take action.
The same is true of tools or software that transcribes your actual words into text on the screen. Sometimes, the accuracy of these systems can suffer for a variety of reasons. Maybe your voice tone or accent is unrecognizable? Maybe the system isn’t developed well enough to find less common words and phrases? Maybe it’s the hardware involved, like a low-quality mic?
AI and machine learning are currently being integrated with speech recognition to enable a better understanding of human language. This differs from NLP and chatbots because of the different ways we use the recognition tools. You can’t have any of these components without the others.
8. Process Optimization
In business, it happens that sometimes you think you’re doing things in the most efficient way possible when that’s not actually the case. And when it comes to incredibly large organizations, sometimes it can be nearly impossible to coordinate all the necessary information and communications to improve a process or deploy a new one.
Generic machine learning, analytics and big data systems can change that completely. Imagine toolsets that can identify business problems and deliver an optimized business case or strategy to fix it.
9. Digital Twin or AI Modeling
In a variety of industries, including architecture and engineering, having a digital or virtual twin is beneficial. Most of the time, humans handle the information, data and designs used to create these models. AI and machine learning tools will soon be able to create these elements on their own with little to no external input.
Agent-based modeling or computational algorithms are just one example of this technology in use today.
10. Explainable AI and No More Black Boxes
One of the issues with more recent analytics and machine learning tools is that they are considered “black boxes” with little to no informational data regarding how they approached an action or decision or why they made a particular choice. The problem with this is that you end up with accurate systems, but you cannot duplicate a lot of the processes and achievements because you don’t know why they occurred.
Explainable and better monitored AI will become more prominent in the business world over the next year. Those mysterious processes will no longer be so questionable, which means more replication and success for everyone.
It’s hard to say precisely how technology will impact the way we live our lives in the future, but one thing’s for sure — artificial intelligence is going to play a big part in the changes that occur.
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It is easy to forget in our busy lives how much we rely on institutions, specifically banks, financial institutions and businesses. The role of AI in these industries could facilitate improvements for employees and customers alike.
Big data has surfaced as a buzzword in recent months and can seem intimidating — big data is a set of analytical tools geared toward fast and meaningful processing of large data sets. You can see how this is useful to financial institutions. Artificial intelligence, often referred to as “weak AI,” are machines capable of performing specific tasks normally requiring human intelligence. These machines can complete the “busy work” piling up on your desk.
Here’s how big data and AI may play a role in your financial institutions next year.
Say Goodbye to Busy Work and Hello to AI
A widely cited study by Oxford University professors says robots will replace an estimated 47 percent of U.S. jobs in the next 10 to 20 years. What exactly would it look like for robots to take over jobs in the workforce? The goal is not to put American workers out of business — it is more about efficiency and the efficiency of having a robot doing the busy clinical work so that the skilled employee can focus on more important work.
By eliminating the repetitive tasks soaking up an employee’s day, companies can better utilize their employee’s skills for higher value tasks. Along with giving employees the opportunity to work on higher-value skills and functions, AI can also eliminate the amount of human error associated with these essential but repetitive tasks.
Increasing Cyber Security for Financial Institutions
Beginning in 2018, many financial institutions will begin investing in new systems such as AI and big data to maintain security.
Financial institutions often have handled information and investments of hundreds to thousands of people. Therefore, the security of these institutions is of the utmost importance. With cyber-attacks happening more often than ever, banks, financial institutions and other businesses, have put cybersecurity as top priority. Banking cybersecurity software will be crucial in 2018 to protect customers and financial intuitions from hackers.
AI Could Equal Better Customer Experience
AI offers another opportunity to increase business functionality in the realm of improving customer experience.
AI offers an advanced analysis of patterns and trends as far as customer satisfaction. AI might even help identify nonstandard behavior patterns. Companies can also use AI to model how customers may react to different scenarios and can test assumptions.
AI’s Help with Regularity Demands
Already retail banking organizations have started using AI to keep up with the growing regulatory demands. Citigroup says banks like J.P. Morgan and HSBC had to double the number of employees to help handle compliance and regulation. This additional hiring has cost the banking industry around $270 billion per year. The $270 billion accounts for almost 10 percent of its operating costs. To combat these extra financial burdens, companies have shifted from human labor to AI to keep up with the demands of regulators.
AI software solutions can help make sure the institution is compliant with all of the regulations. Some software even includes reporting tools used in case of a breach to ensure all notification requirements are fulfilled.
Big Data’s Role
Big data is big in the financial sector. High volumes of quotes, market data and trade data are regularly produced. This data all need processing as a high velocity to be efficient, otherwise who knows how long it would take for humans to go through and process this data. For financial markets, faster data processing equals faster management of trading.
As far as big data goes, it helps process a large volume of data at a high velocity and uses various formats and sources to process that data. For example, in corporate banking, institutions work with reference data, market and trade data and other sources, which means big data needs to work with a variety of data using a variety of methods to process it all.
Big Data and AI Necessary for Business
Yes, big data and AI are new and complicated. Reading about them may cause the eyes to glaze over. However, big data helps process a large amount of data, including our information, accurately, quickly and efficiently. AI allows you and fellow employers and employees to stop the busy work and enjoy meaningful tasks. And don’t forget, it can improve customer experience.
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Technology always takes a dominant position in economy and society. Millions of people, therefore, found their careers, and many others have even dived into a completely different field just for entering this industry. Even so, enterprises are still trying hard to seek for skilled programmers; when the right one shows, companies would even raise HR budgets. Technology is continuing to infiltrate into new platforms and industries, hence, to maximize one’s profit potentials, also for ensuring one’s place in the future of tech fields, choosing the right programming language is very important for a person. Now, let’s take a look at the 15 highest paying programming languages in 2017.
1. Go:
Go programmers get paid at an average of 110 thousand dollars per year, and it has been on the top for the recent years. Go is an open source programming language that makes it easy to build simple, reliable, and efficient software. Created at Google in 2009, after that it has been used in Uber, SoundCloud, Netflix, and Dropbox.
2. Scala:
Scala is a general-purpose programming language providing support for functional programming and a b static type system. Designed to be concise, many of Scala’s design decisions aimed to address criticisms of Java. Programmers equipped with Scala earn up to 110 thousand dollars a year.
3.Objective-C:
Objective-C is one of the longest existence programming languages, and it’s also one of the programming languages that programmers know best. It was the main programming language used by Apple for the OS X and iOS operating systems. It is one of the most profitable programming languages. Estimated salary is between 100k to 110k dollars per year.
4. CoffeScript:
CoffeeScript is a programming language that transcompiles to JavaScript. Specific additional features include list comprehension and pattern matching. CoffeeScript programmers earn averagely 105 thousand dollars a year.
5. R:
R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R programmers’ average salary is 100k dollars.
6. TypeScript:
TypeScript is a free and open-source programming language developed and maintained by Microsoft. It is a strict syntactical superset of JavaScript and adds optional static typing to the language. If you can master TypeScript, you can earn 100k dollars a year.
7. SQL:
SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). Companies like Google, Helix, IBM, Microsoft, Oracle, and Amazon are continuing in using SQL, and offer 70k-90k dollars a year to SQL programmers.
8. JAVA:
Java is one of the most popular and profitable programming languages. Particularly for client-server web applications, with a reported 9 million developers. The most qualified candidate can be offered a wage up to 117k dollars a year.
9. Python:
Python is a widely used high-level programming language for general-purpose programming. An interpreted language, Python has a design philosophy that emphasizes code readability, and a syntax that allows programmers to express concepts in fewer lines of code than might be used in languages such as C++ or Java. Python experts are likely to have 99k dollars a year.
10. JavaScript:
Alongside HTML and CSS, JavaScript is one of the three core technologies of World Wide Web content production. It is used to make web pages interactive and provide online programs, including video games. JavaScript programmers’ salaries can be reached up to 110k dollars a year.
11. C++:
C++ is a general-purpose programming language. It has imperative, object-oriented and generic programming features, while also providing facilities for low-level memory manipulation. C++ professionals can find jobs that offer 90k-100k a year.
12. C#:
C# was developed by Microsoft within its .NET initiative and later approved as a standard by Ecma (ECMA-334) and ISO (ISO/IEC 23270:2006). C# is one of the programming languages designed for the Common Language Infrastructure. C# programmers can earn 107k dollars a year.
13. Perl:
Perl is a family of high-level, general-purpose, interpreted, dynamic programming languages. Because of its power and reliability, it is aka Swiss Army Knife in programming languages. Perl programmers can earn about 110k dollars a year.
14. PHP:
PHP is a server-side scripting language designed primarily for web development but also used as a general-purpose programming language relied on C and C++ programming languages. Programmers that are expert in these three languages can easily find a high waged job, possibly can be reached up to 120k dollars a year.
15. IOS/Swift:
Swift is likely to be the most important language that has been released recent years. Swift is a general-purpose, multi-paradigm and compiled programming language developed by Apple Inc. for iOS, macOS, watchOS, tvOS, and Linux. Generally, programmers work with Swift R&D can earn 80k a year, but some high-end position’s salary can reach 120k dollars a year.
Since the market is lack of such elite programmers nowadays, and corporations wish to fill up the blanks in R&D projects, so as long as you master one of the above languages, you’ll get more advantages when talking about your salaries.
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