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Category: Analytics

Machine learning in Finance – Present and Future Applications

Posted on by Imarticus Learning

If you think seeing, hearing and learning is a trait of human beings only, then probably you have not been introduced to the concept of machine learning yet. Computers can also see, hear and learn just like their human counterparts. Machine learning in the simplest terms means that a computer is able to learn automatically from its experiences without being programmed again and again or any kind of human intervention. It has automated jobs that were earlier thought could be done by humans only.
It is wise to work smarter than to work harder. Forward-looking financial service companies have started relying more on machine learning leading to its enhanced use in the finance sector. It has many advantages which have attracted financial institutions to embrace machine learning technology in huge numbers. Mention can be made of:

  1. Improvement in decision making capability
  2. Improvement in customer experience.
  3. Enhanced efficiency in utilization of resources
  4. Automation of tasks

Also Read: Everything You Need To Know About Machine Learning and Deep Learning
PRESENT APPLICATIONS OF MACHINE LEARNING IN FINANCE

Prevention of frauds

Protecting clients from fraudulent activities is one of the major challenges which financial institutions often face. Machine learning goes one step further and provides data security by out-living the brains of criminals. It keeps a sharp eye on every transaction of account, large cash withdrawals, continuous attempts to make a transaction and prompts alerts immediately. Moreover, it can also increase the number of steps to perform an operation in order to delay transaction until a human brain makes a decision. In doubtful cases, the transaction is often declined.
machine-learning-in-finance
As criminal minds are becoming smarter, computer systems are also being designed in the same manner to tackle them.

Providing Aids in Trading Services

Machine learning has aided investors by making accurate investment predictions. It allows them to place an order when the stock reaches a particular price and sell when drops below a certain limit. Thus, it has eliminated the role of brokers up to some extent.

Managing The Risk

Since machine learning technology can analyze huge chunks of data, it helps in preventing fraud investors from securing loans. It checks the financial status of the applicant and whether he/she holds multiple accounts. These operations simplify the task of investment managers and bankers for whom it was not possible to check such minute details and only covered the static portion of the application.

Assistance in Customer Service

Based on the reviews received from the customer side, machine learning can be used to pre-determine whether a customer is going to continue the services or abandon them. With these valuable insights, it becomes easier to improve the services and satiate the customer as well.
machine-learning-in-finance

FUTURE APPLICATIONS OF MACHINE LEARNING IN FINANCE

Portfolio Management

Robo- advisors have eradicated the need for a human helper who would give the best-suited advice to the consumer. Users enter their desired goals such as having savings of Rs 6,00,000 when they retire at the age of 60 and robot-advisor presents the whole range of investments the user can make. In the upcoming future, related applications would be considered more reliable.

Enhanced Security

In addition to passwords as the security key, facial recognition and speech recognition could be used to do away with loopholes in security.
Machine learning allows one to analyze huge chunks of data in a few seconds or minutes. On the one hand, it provides faster and accurate results but on the other hand, it also requires additional time and resources to train it effectively. Therefore, the need of the hour is to combine machine learning with artificial intelligence to deliver fruitful results.
Related Article:

  1. 7 Key Skills Required For Machine Learning Jobs
  2. What is The Easiest Way To Learn Machine Learning?

The Major Industries that Artificial Intelligence will Transform by the Year 2020!

Posted on by Imarticus Learning

In simple terms, Artificial Intelligence (or AI) is defined by the capability of a computer or a computer-controlled robot to perform tasks, which normally require human intelligence and skills such as visual perception and decision making.

Conceptualized initially as the means to impart intelligence to inanimate objects, AI is impacting multiple industries today including manufacturing, healthcare, and education. According to a Gartner report, AI will create 2.3 million new jobs by the year 2020, while eliminating 1.8 million at the same time. By the year 2022, 1 in every five workers will rely on AI to perform their non-routine task.

 

AI in 2020

How AI is transforming every industry that it touches

The use of AI-based technologies is transforming every industry that it touches by generating a business value projected to value $1.2 trillion globally in the year 2018, marking a 70% increase from 2017. Furthermore, this figure is predicted to reach $3.9 billion globally by the year 2022. According to Gartner, AI is generating business value through the following three sources:

  • Enhanced customer experience
  • New revenue generation through the increase in sales of existing products (or services) or through new products (or services)
  • Reduction in the cost of production and delivery of existing products (or services)

According to Svetlana Sicular of the Gartner research team, “AI will improve the productivity of many job roles while creating millions of skilled management positions including entry-level jobs.” AI will positively impact the technology job market by creating 2 million new jobs globally by the year 2025.
AI in 2020
Svetlana Sicular also adds that most predictions about job losses due to AI technologies is associated with job automation, but ignores the benefits of AI augmentation that complements both human and machine intelligence. For instance, AI and robotics can be leveraged to identify and automate labor-intensive tasks performed by retail workers, thus reducing labor and distribution costs.

Despite the immense potential of AI, the mass-scale adoption of this technology still faces numerous hurdles (including the following), which needs to be immediately tackled:

  • The supervised or structured form of learning by AI systems, which does not imitate the way humans learn naturally from our environment.
  • Lack of creativity and abstract level thinking on the part of AI machines that can process raw data and convert them into intuitive and easy-to-grasp concepts.
  • Being a relatively new concept, AI does not enjoy full public support and trust, thus halting its increased adoption.

AI-based virtual agents (including chatbots) are taking over the handling of simple customer requests from a call center or customer support executives, thus improving business revenue and freeing up employee time for more complex activities and decision making. While virtual agents are accounting for 46% of the AI-based business value in 2018, it will account for only 26% by the year 2022.

Right from our smartphones to self-driving vehicles, Artificial Intelligence is enabling the faster execution of tasks with more accuracy and increased knowledge. This article summarises how AI technology will transform many industries along with the many hurdles that it needs to overcome.

What is the difference between Machine Learning and Deep Learning?

Posted on by Imarticus Learning

With the recent advancements in technology, the concept of Artificial Intelligence has been upgraded with the introduction of different algorithms and learning mechanisms. Two such learning mechanisms are Machine Learning and Deep Learning. Machine Learning and Deep Learning are practices that are more similar than not.

What is Machine Learning?

Machine Learning belongs to the aggregated set of techniques related to Artificial Intelligence (AI). It uses algorithms that build models by parsing data and eventually use the obtained parameters to make predictions. Strategies involved include clustering, Bayesian approaches, decision trees and regression.

Though Machine Learning started off as a fairly small and insignificant part of Artificial Intelligence, its importance in the field of research has grown leaps and bounds in the last couple of years. Machine Learning, coupled with hardware support has made it widely adopted as a technological advancement.

Also Read: What is The Easiest Way To Learn Machine Learning?

Machine learning and Deep Learning
Today, Machine Learning is used for a variety of applications.
Some of them include:

  • Pattern Recognition and Computer Vision
  • Machine Learning Algorithms to determine patterns by Social Media websites such as Facebook.
  • Easy and optimal filtering out of data through search engines such as Google.

What is Deep Learning?

One of the approaches to Machine Learning was the introduction of artificial neural networks to determine algorithms. However, this was not given as much importance in the past until its capabilities were brought to the forefront. Neural Networks in the brain helps stimulate the activity of the neurons present in a very systematic and layered fashion. Thus, Deep Learning uses a similar approach for data propagation and thus enables machines to make predictions that are a lot more accurate and detailed. A major advantage of using Deep Learning is that larger volumes of data can be handled with greater precision. Also, the probability of negative or false models is completely eliminated.

Major applications of Deep Learning include:

  • Large-scale business applications.
  • Autonomous recommendation systems.
  • Image recognition applications that rely on Computer Vision principles.

 

Comparison between Machine Learning and Deep Learning:

Both Machine Learning and Deep Learning are similar in the sense that they are both subsets of Artificial Intelligence. Thus, the basic principle of both these learning practices is the collection of information and data for making informed decisions as and when required. However, some of the stark differences between the two are:

  • On the basis of applications: Machine Learning works best for systems and applications where the priority is to learn from the data collected and use that learning for a particular task. Thus, Big Data analysis and data mining are best suited for Machine Learning.
  • On the other hand, those applications that are said to belong to more of a niche-category which simply means that the data is a large corpus of image or texts uses Deep Learning algorithms. This includes using Graphics Processing Units to create models for specialized video and image recognition tasks such as in the case of navigation and autonomous driving.

 

  • On the basis of Hardware: Hardware and high-end machines are needed to incorporate Deep Learning as opposed to Machine Learning. This is because Graphics Processing Units do complex matrix multiplications and other algorithm interpretations which is not the case with simple Machine Learning.
  • On the basis of execution time: Deep algorithms, due to their complexity and number of parameters that are processed takes a longer time to train. On the other hand, Machine Learning takes a lot lesser time, ranging from a few seconds to a couple of hours.

Related Article:

The Importance of Big Data Analytics in The Banking and Financial Services Industry

Posted on by Imarticus Learning

In this data-driven world, Data Analytics has become vital in the decision making processes in the Banking and Financial Services Industry. In Investment banking, volume, as well as the velocity of data, has become very important factors. Big Data Analytics comes into the picture in cases like this when the sheer volume and size of the data is beyond the capability of traditional databases to collect.
Today, data analytics practices have made the monitoring and evaluation of vast amounts of client data including personal and security informant data-driven and other financial organizations much simpler.
There are several use cases in which Big Data Analytics has contributed significantly to ensure the effective use of data. This data opens up new and exciting opportunities for customer service that can help defend battlegrounds like payments and open up new service and revenue opportunities.
For example, in October 2106, Lloyds Banking Group had become the first European bank to implement Pindrop’s PhoneprintingTM technology for detecting fraud. Their technology used AI to create an ‘audio fingerprint’ of every call by analyzing over 1300 unique call features – such as location, background noise, number history, and call type – the o highlight unusual activity, and identify potential fraud.
It cracks down on tactics like caller ID spoofing, voice distortion, and social engineering without any need for customers to provide additional information. Subsequently, Lloyds Banking Group went on to win the Gold Award for ‘best risk and fraud management program’ at the European Contact Centre & Customer Service Awards 2017.
Danske Bank uses its in-house start-up, advanced analytics to evaluate customer behavior and determine preferences, as well as to better identify fraud while reducing false positives.
JPMorgan Chase also developed a proprietary Machine Learning algorithm called Contract Intelligence or COiN for analyzing various documentations and extracting important information from them.
Big Data is also used for personalized marketing, which targets customers based on the analysis of their individual buying habits. Here, financial services firms can collect data from customers’ social media profiles to figure out their needs through sentiment analysis and then create a credit risk assessment. This can also help establish an automated, accurate and highly personalized customer support service. Big Data also helps in Human Resources management by implementing incentive optimization, attrition modeling, and salary optimization.
The list of use cases implemented in the workflows of the Banking and Financial sector is growing day by day. The huge increase in the amount of data to be analyzed and acted upon in the Banking and Financial Sector has made it essential to incorporate increase the implementation of Big Data Analytics.
Knowing the importance of data science is crucial in these sectors and should be integrated into all decision-making processes based on actionable insights from customer data. Big Data is the next step in ensuring highly personalized and secure banking and financial services to improve customer satisfaction.

What is Difference Between Blockchain and Machine Learning?

Posted on by Imarticus Learning

In 2010, Indian director, Shankar’s hit movie with Rajinikanth in the lead role (namely Robot) made us question the existence of robots and their capabilities. It was also a period when we easily waived away any such speculations claiming to have Shankar’s visual effect imaginations. Come 2018; this could already be possible! Now I am not sure about machines catching feelings, but Deepblue’s victory against chess champion Kasparov in 1997 should have been a heads up to us!
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Machine learning is exactly this. It is when a machine learns either by programs or by its experience, it is called as machine learning just the way the name suggests. Just like the process of human learning, machine learning also constitutes of identifying patterns in given data and designing a more efficient system to outperform the previous versions. One might ask as to what’s the point in making a machine learn.

Wasn’t last year’s slogan “Educate Women/Children”? However, the mere answer to this is that a machine has to learn to adapt itself in an intelligent manner when exposed to varying data. Thinking about it, you would understand that this would also avoid human interventions keeping the costs and human errors at bay.

A simple day to day example of this is when we mark an email as spam and the mailbox assigns the future emails from that email id to the spam folder without human intervention. Machine learning can be very useful in the financial sectors or consulting sector where new patterns can be unearthed.

The Blockchain is entirely different from what Machine learning is. Blockchain refers to blocks of information that are connected via a unique number peculiar to each block called hash thus giving the name “chain” to it. As of now, it is the most secure database in the world with a decentralised approach that prevents malicious monopolistic activities. By eliminating human third parties and substituting them to a virtual third party is what adds the niche to blockchain and gains it the trust it holds.

The Blockchain is something that can find a place in many sectors. For example, in the logistics industry, Blockchain can help to make the process more quality effective and contribute to root down the cause of any defects. This is very helpful when it comes to saving vast amounts of money for the companies. The Blockchain is generally a medium which can support a variety of servers or users.

Machine learning with Blockchain can create a lethal combination if utilized properly. Both are more or less like the two sides of a coin if used together. While blockchain helps to store correct data that is unaltered and permanent, Machine learning can utilize this data to notice patterns and give accurate predictions. This is more helpful in research related fields where there is a need for accurate data to predict plausible results.

Blockchain can ensure security and ownership of the collected data and contracts can be programmed to send the data from the user to the data researcher avoiding middlemen. Working together even new and more efficient tools can be designed with this ground-breaking technology. For example, on using Blockchain, a new data set can be unleashed which is a better dataset and a new model can be formed from which new insights can be gained.

This might also be the platform to attain new cutting-edge technologies. Additionally, this could be more beneficial to businesses to establish their niche and be ahead of the competition.

Probably one day the story of Arnold Schwarzenegger starrer “Terminator” might come true but as of now, we can say for sure that Machine Learning or Blockchain technology are in for the long run.

Artificial Intelligence – The Big Game Changer For Business!

Posted on by Imarticus Learning

Artificial Intelligence as a concept seemed very distant and tucked away till firms like Google, Amazon, and Facebook brought it into our daily lives and we started seeing its impact in our day to day activities and interactions. Today realizingly or unknowingly we are surrounded by AI in almost every aspect of life from Health to fitness, Finances, Entertainment, education, business and selling, marketing and market research media, and lots more.

Let us try and understand the impact of AI on the key aspects of economic inequality & business cycles and business productivity.

Economic inequality is one of the biggest changes that artificial intelligence is bringing to our doorstep. Artificial intelligence poses the greatest threat to people employed in low skilled or unskilled repetitive jobs, and as more and more companies incorporate AI into their business model, the disparity between low skilled and highly skilled workers is set to increase.

If we consider three major sectors – agriculture, industrial and service, we will observe that the manpower employed in agriculture has reduced the most over the years (although output hasn’t reduced that sharply), and the services sector has seen the steepest rise in a number of people employed.

Every threat and weakness also brings in an opportunity wrapped in strength, and AI is no different. Although a lot of jobs are at risk due to artificial intelligence, AI is laying the field open for several other alternate professions. Programming and testing professionals, coding heroes, data scientists, they all are going to be in great demand in the coming years. Upskilling and learning a new skill is something the workforce would need to embrace if they are to keep their careers on the burn instead of fizzling out.

Artificial Intelligence has impacted almost every facet of business already and looks set to forcefully influence many other areas too. One of the ways in which artificial intelligence is in the way business cycles occur and repeat. Business cycles are the periodic changes from great prosperity and increasing revenue to economic downfall and losses. The business cycles are impacted by AI because AI pushes these cycles closer together and makes them shorter.

We have the growth phase and the consolidation phase during the upswing, but these are now happening more swiftly because AI helps to improve by course correction in a much shorter time. Thousands of data points are analyzed with the help of AI, which then trains itself to come closer to the required output. Compared to that, humans would go through the cycle at a much slower rate.

Let us take an easy example to understand how business cycles are affected by artificial intelligence. In the banking sector, the first quarter of the financial year is usually the most popular for customers to open new accounts to align with the start of a new financial year. From the point of view of the banks themselves, the last quarter is a big quarter to push for new accounts and deposits, primarily because they are rushing to fulfill their annual targets. The remaining two quarters are comparatively less rushed.

What happens to this scenario when the bank applies artificial intelligence to its systems? For the first and last quarters, the system could throw up the details of existing customers who would be likely to need new accounts, so that the bank officials could target them. This data-crunching could begin in the last quarter (for first-quarter acquisitions) and in the third quarter (for last quarter acquisitions).

For the second and third quarters too, which are usually leaner, the prospective clients for new acquisitions could be highlighted. What this whole setup would do after repeating for a few cycles is that the usual cycle of quarters would be disrupted, and acquisitions of new clients would be more uniform throughout the year.

The future looks very exciting for industries who are using artificial intelligence, and the possibilities seem immense.

Customer Service Trends – 2018 Is Making Operations Become Faster?

Posted on by Imarticus Learning

A recent publication shows that a smart AI strategy ensures transformative customer service in times where the customer is spoilt for choices in every area, and how firms can use AI as a weapon to offer uniquely differentiated products on the back of its usage. Unlike common perception, the authors have demonstrated how usage of AI will make operations faster, more effective, and cheaper yet more human. It has various updates on chatbots and how they enhance the customer self-service experience at L1.

Usage of prescriptive AI for quelling headcount growth by taking over routine tasks and allowing agents to focus on deeper customer experiences.

There has been a  steady rise of virtual assistants like Siri & Alexa and they will become independent local hubs of customer experience. Visual engagement avenues such as co-browsing & screen sharing and the expected uptick in usage across age groups.

IOT will transform business models as It will allow production companies to provide proactive services for their high-end products, through preemptive on-site /user monitoring and reporting to a centralized center versus reactive service upon a product breakdown.

Usage of Robotic Process automation for improved delivery of repetitive tasks and end to end automation of basic processes allowing humans to take escalations. Machine learning along with this allows them to learn through interactions that they go through to become more cognitive and intelligent.

Enhanced field service by equipping agents with enough information and parts to ensure that they get the customer’s job done in a single visit. This also covers the usage of augmented reality along with digital interactions for deeper interactions in the physical world without physical presence.

The emergence of “superagents- equipped with AI tools” where companies will relook & redefine their workforce basis their skills and charge a premium for the usage of these services. There will also be seen a rise in customer service ecosystems where firms will use a combination of AI, their resources, and partners to see the customer through their entire journey and not just a portion of it.

With the above, we are looking at a new world order where Artificial intelligence will impact every aspect of our lives knowingly or unknowingly and transform the way we lead our lives including individual privacy and experiences.

Quit Playing Games With Artificial Intelligence – Its Serious Business Now!

Posted on by Imarticus Learning

The gaming industry is no longer a simple and cheap way to keep restless children occupied during their summer vacations. People of all ages actually spend hours together in front of their gaming consoles playing a variety of games. The quality of games has improved beyond recognition today, keeping serious gamers glued to their games for long periods of time, helping make these games economically feasible.

One big change in recent years that has turned the gaming industry around is the development of artificial intelligence and virtual reality. Let us see a few ways in which this has happened.

Gaming Realism

We have seen how virtual reality helps to generate 3D images or an overall environment that interacts with reality. We have all been amazed and impressed to see a real character in a VR environment wave his or her hands in the air and conjure up a screen on which different dials and buttons are available. Similar virtual reality environments inside games have added a touch of the realistic to these games.

Adaptive Environment

Unlike the static code of earlier games, where a certain action X by a character or the player would result in a fixed outcome Y only. But with the introduction of artificial intelligence, the environment and the responses to actions could be varied, with the game throwing up different responses in different scenarios.

The Move to Responsive

Most of the activities we do today are moving from the computer to our mobile phones, like watching sports or news or looking at weather forecasts, ordering takeaway, booking tickets, etc. The situation is no different for the gaming industries. They are having to adapt to this scenario and create games that are easy to view and easy to maneuver on mobile phones. Responsiveness is the new buzzword.

Heavy Computing Power

This is a phenomenon observed in all our gadgets like computers, mobile phones etc. There has been a surge in computing power. This has affected gaming as well, with super-fast responses from the characters. This is because the gaming consoles now carry unbelievable computing power.

Machine Learning

Artificial intelligence in gaming consoles is encouraging the gaming programs to learn from past experience and adjust its responses accordingly, making the gaming experience more difficult for the players. The games are getting smarter because of the use of these artificial intelligence tools, making them all the more challenging for the players.

Real-Time Reactions

Games earlier were one-dimensional collections of graphics and code which threw up situations for the gamers, to which they would provide certain reactions, to which the game would again provide a certain response. This was done with the help of a detailed algorithm which dictated the machine’s response. But now, with AI, the events happening within the game would influence the reaction of the computer, and these changes in reaction would also go into its knowledge bank and contribute to its machine learning.

Developer Skills

One more aspect of the industry changes in the gaming industry is that the developers writing the code for these games now have to contend with all the changes listed above, and therefore have to pick up adequate skills for incorporating elements of artificial intelligence and virtual reality in these games.

Industry Change

The gaming industry is seeing far-reaching changes as a result of the addition of virtual reality elements into games. The gaming experience becomes much more rich and intense for the player, therefore making them more willing to fork out much higher prices for the games they buy. Advertisements linked to different games have also become more visible, providing gaming companies to look at a rich stream of revenue.

Should You Fear Machine Learning?

Posted on by Imarticus Learning

Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The goal of machine learning is to get computers to learn in a similar manner to humans.

Machine learning is a type of artificial intelligence that helps computers learn without having to be programmed by a person. These computers are programmed in a way that focuses on data that they receive on a regular basis. This data can then help the machine “learn” what preferences are and adjust itself accordingly.

Nowadays, the development in Artificial Intelligence (AI) has brought us to the stage where organizations are using various algorithms, analysis, and experience to learn and program themselves without human intervention.

This type of procedure will create changes in too many industries. The use of machine learning has grown exponentially in the past few years, and you may not realize how widely it is used.
Following stats is just tip of the iceberg:

  • 85% of customer interactions will be managed without humans by 2020.
  • 38% of jobs could be replaced by AI/machine learning by the 2030s.
  • 20% of top executives rely on machine learning to run their businesses.
  • 48% projected growth in the Automotive Industry by 2025.

Source: jigtechnologies.com; elearninginfographics.com; pwc.co.uk; mckinsey.com

Why Should I Pursue A Career in Analytics?

Posted on by Imarticus Learning

Deciding the line in which you want to make your career is a crucial step. There are various fields in which you can excel but finding an area that lets you reap your efforts is not that easy in today’s date. An area which is increasingly gaining recognition is Data Analytics. But why should you try to make your future in this?
Also Read : What is the Scope of Analytics?

Becoming the Priority

There is no company that doesn’t require a data analyst. Be it a creative job or a desk one, and it is essential to analyze your data so that you can gain maximum revenue. You must go for a data analytics course as it can help you understand and master everything that you might require to become the best of your class. A data analyst is needed to figure out new marketing strategies and opportunities that are going to help the company make a profit in this stiff market competition. If you want to gain an edge over the rest, going for python certification is highly recommended. R Programming is also recommended if you’re going to carry out useful data analysis.
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Increasing Pay

The effort you put in when it comes to the data analytics course, python certification, as well as R programming, will pay off at this stage as the pay for data analysts in increasing day by day due to the need for these specialists in every company. As compared to other IT jobs, going for that data analytics course is going to get you an extra 50%.

Perfect Freelancing Opportunity

In today’s date, we are observing that majority of the population is not wanting to do a full-time desk job. People want to work from big companies from all over the world. Why should distance limit you? This is a job that you can do from anywhere in the world, and you can do it from your own home’s comfort! You can work for one of the biggest companies in the world without having to step out of your place. But for this you need to be the best at what you do, so make sure that you go for R programming.

Decision-making opportunities

Just like deciding on going for a data analytics course is essential, so will be every other decision you make during your job. The best part is that data is necessary for every major decision that a company makes. So, you will get a high seat amongst all others and will be a part of the core decision-making team. By going for R programming, you will learn how to install and configure necessary software for statistical programming efficiently.Big data analytics banner
Data is essential in every feed. There is hardly any company in today’s date which doesn’t have its social media handle. Social media has become one of the best ways to source business, but merely making a profile doesn’t suffice. You need an expert who can study and analyze the data and further help you devise strategies. A good data analyst can work wonders for a company, be it a budding one or a well-flourished one. Do not underestimate the value of R programming if you want to be better than the rest. And as you know, every company prefers to hire a certified person. This is where the value of python certification comes in as it is considered to be top notch. By going for python certification, you will end up having the edge over all the other data analysts on a global level. So, what are you waiting for? Go ahead!
Related Articles : Knowledge Series – Hottest skills for Analytics in 2018