Artificial Intelligence Impacts


Power Your Business with the Capabilities of Artificial Intelligence

Artificial intelligence (AI) is marching its way into every domain of life and business. Driven by machine learning technologies and expert systems, AI revolutionizes the approach to transforming unstructured data into valuable insights or actions. With a need to survive in today’s competitive world, enterprises are exploring the ways to transform their business processes by automating workflows and decision-making. Relying on AI, companies deliver end-to-end solutions to improve customer service, operational efficiency, etc.

Artificial Intelligence Will Reinvent Investment Advisory

Machine Learning, Big Data, Artificial Intelligence (AI). Three buzzwords you have likely seen in the media lately. It is challenging to surf a news website and not read about the impact of AI on the economy and the applications it will enable. Is all the fuss well founded? The answer is yes and this new digital frontier will affect your business, as an investment advisor, more than you think.

Applications in Investment Management

AI solves many problems which traditionally relied on complex analysis and years of industry experience. Trading desks at large investment banks, which tend to invest early on in cutting edge technology, have been relying on AI for years to generate insights which allow them to predict retail investors behavior, stock movements based on trading pattern and company sentiment using mentions on social media. Within the scope of investment management, the applications are now maturing and we are observing trends developing in three fields: customer service, compliance and investment research.

Artificial Intelligence and Value Investing

What are the Research Questions?

Can machines allocate capital in the classic style of a value investor  like Benjamin Graham or Warren Buffett?

What are the Academic Insights?

By connecting two broad and disparate areas(the innovative AI domain and the more old-fashioned value investing)three specific areas of AI that are relevant in the domain of value investing:

  • SEARCH- For value investing (purchasing stocks at a price below their intrinsic value), the search problem has both quantitative ( accounting ratios for instance) and qualitative aspects (guidance during conference calls). It is with the second ones that AI can shine. For example, it could be that managers who discuss their firm in conservative language are more likely to represent a value company. Capturing this is possible with textual mining.
  • LEARNING- There are two categories of learning: “supervised” and “unsupervised.” In the first category, there is human supervision where a human classifies a set of input-output pairs according to one or multiple hypotheses, which is the so-called training set that is fed to the machine. The main issue here is to have a sufficient training set ( value investors typically have small concentrated portfolios of stocks, which will require supplementing the set with intermediate data). In the second category, the computer learns on its own. This is where the cutting edge is. It is called reinforcement learning. For value investing, it means that the computer needs to simulate the selection of the attributes that lead to the selection of securities.
  • REASONING-This is a goal of computer science, that has not been reached yet but could very realistically happen in the near future. In value investing, it could mean the possibility for a computer to reason about managerial attributes and company quality based on what is and what is not disclosed in its financials.

Why does it matter?

Value investing is about rationality in the face of irrational sentiment and mass investor psychology (Brandes, 1998). The irrationality of human investors drives prices away from their fundamental value. Computers, rational machine, should be at an advantage compared to the discretionary  investment  manager. The author suggests that the value investing community should embrace, rather than fight, the AI secular trend. After all, computation can assist human reasoning.

A Brief Overview

Artificial Intelligence is an umbrella term to refer to any process, computerized or other, that replicates human intelligence.

Machine Learning refers to software algorithms (an example is deep learning) which allow computers to implement artificial intelligence.

Big Data is an umbrella term which refers to large amounts of data, usually cloud-hosted, which because of their sheer quantity are statistically significant enough to allow you to generate valuable insights.

Let’s consider a simple example, your bank uses predictive analytics tools (“Machine Learning”) to determine whether or not to approve your car loan (“Artificial Intelligence”, usually performed by a human analyst) using massive amounts of loan default data they gathered over the years (“Big Data”).

Research in Machine Learning has been going on for several decades. Only over the past two years have the software algorithms gone mainstream and companies outside the tech industry started to realize the business values of using these tools to generate actionable insights. Low estimates place revenues generated from AI at 2T USD by 2020, high estimates at more than 10T USD. That pretty much explains why AI is such a popular topic today.

24/7 Customer Support

Chat bots rely on AI technology to mimic the services provided by a support desk. Several banks have already implemented these tools on their retail investing platform to improve customer service. Chat bots are implemented in online chat applications. They engage clients to answer frequently asked questions before any of the queries reach a human support analyst. The conversation flow sounds so natural that the client does not realize he is chatting with a computer. The technology allows you to better service your clients outside working hours and reduce response time thus improving customer satisfaction.

Data-driven Compliance

Compliance is another area affected by AI in particular within the scope of Anti-Money Laundering (AML) and Know Your Customer (KYC). The basic idea is that two clients who happen to share similar traits (an example would be income level, marital status, job industry and sport interests) are likely to share the same risk profile. How do you figure out what these traits are? AI tools will not only help you answer that question but also determine the weight each trait carries in determining a client’s risk profile. The technology is not faultless but it provides you solid data-driven insights to better understand who your clients are.

Relevant Investment Products

Investment recommendation is a third area where we have observed large bets placed by banks on Wall Street. How do you determine which investments your clients are interested in? If you succeed in recommending a product which is aligned with both your client’s risk profile and portfolio objectives, you have already fulfilled a large part of the advisory function. This trend could potentially shift your role from pushing investment products to further cultivating the relationship with your clients.

AI For Your Portfolio

Investment Advisors are leveraging AI tools to help find options fortheir clients investments which are relevant for targeted bespoke portfolio selections. 

The Future of the Work Space

There is good news to share about the future. Despite what you may have heard elsewhere, the future of work in a world with Artificial Intelligence  (AI) is not all doom and gloom. According to a study “What to Do When Machines Do Everything”, we have data to prove it.

Thanks to new educational approaches, we are better equipped to prepare students and misplaced workers for a future with AI. Actually, we’re experiencing the fourth industrial revolution. The percentage of job loss from AI will correspond with job loss rates during other periods of automation throughout history, including automation through looms, steam engines and assembly lines.

Workforce Changes From AI

Fundamentally, workforce changes from AI will be like those during the industrial revolution and the introduction of the assembly line. About 12 percent of jobs will be lost. Around 75 percent of jobs will be augmented. And there will be new jobs created.

Artificial Intelligence will create more jobs than it destroys.  

We take an interest in the entire impact AI  will make on society. In areas where AI can augment work, the economy will develop new technology offerings. In areas where AI will displace work, we will see a commitment to providing education and training programs that can help retrain workers.

Consider these two recent examples:

A partnership with Syracuse University and the Institute for Veterans and Military Families, provides free skills training for US military veterans, including statistics, analytics and programming skills.

A collaboration with Cisco, Cloudera and Nanyang Polytechnic to launch a Digital Engineering Innovation Centre (DEIC) in Singapore. The innovation center will provide students advanced analytics solutions so they can innovate and develop skills needed for the new economy.

Educators to Train Workers for the New Economy

Workers are not alone. Today’s educators are creating programs to train workers for the new economy. We don’t mean two- and four-year degree curricula only. Instead, educators are embracing new, innovative programs to train workers around the world with new skill sets, including:

Micro degrees, certificates or other credentials that can be obtained with a few courses in less than a year. These types of courses can teach programming skills, robotics, electronics and other trade skills that are in demand. Digital learning opportunities, including open online courses and new virtual classrooms where students can select learning paths and engage with professors despite distance and schedule conflicts.

Two factors are driving the need for autonomous logistic solutions right now:

  • The growth of e-commerce — Product distribution has totally flipped on its head over the last decade. Products are now packaged individually and sent direct to customers, rather than in bulk to suppliers as they were in the past. There is a huge variety of different packaging requirements, the weight of shipments is increasing as heavier items are being bought online, and the speed of distribution has increased.
  • The lack of available workforce — Skills shortages are affecting many industries, not just logistics. There are just not enough skilled workers to fill needed posts

There is also a technological reason that logistics is the new “hot industry” for robotics. Several technologies have matured recently allowing them to be viable in real-world logistic applications such as Autonomous Guided Vehicle. 

Artificial Intelligence (AI) applications are popping up all over the place.

Collaborative Robots are allowing robots to work alongside human workers.Robots are already being used in logistics, sometimes extensively. Therefore, the logistics of the future will look similar to how it looks today. Over time, robotics will just creep into more and more applications.

The impact of artificial intelligence from a societal perspective.
  • The explosion in AI revolves around three things:
    • The use of more sensors: smart watches, heart rate and mobile devices–they must all be meshed together in order to manage all the information obtained by sensors
    • More collection and storage of data occurring
    • Powerful algorithms that can extract insights from the data collected
  • The effect of AI on job growth
  • AI is here and already solving major challenges affecting society

In 2017, Chinese internet giant Baidu began investing in artificial intelligence (AI) technology and machine learning to capitalize on a potential customer base of 731 million internet users–this represents double the U.S. population.

Baidu is exploring ways to utilize artificial intelligence and machine learning, poaching talent from Microsoft, such as AI pioneer Qi Lu who would eventually become Baidu’s COO.

This is just a short list of the AI developments Baidu has in store:

  • DuerOS voice assistant
  • AI-powered mobile devices
  • Self-driving cars
  • Deep learning-based algorithms for medical applications
AI and Robotic Logistics.

Logistics is the management of the flow of things. These can either be physical things (product, inventory, materials, etc) or even the flow of information.

The logistics industry is huge. I think it’s fair to say that it holds together our modern world. Without logistics, we would all still be living on self-sufficient farms — we would have to, it’s the only way we would survive. Logistics keeps us alive.

Because logistics is so far reaching, it includes a huge amount of different processes. For example: warehousing, ordering, transportation, picking, packing, disposal, delivery, inventory, merge in transit, routing, recycling, expediting, and that’s just to name a few!

Robotic logistics simply means applying robotics to one or more of these processes.There are obviously a huge number of potential robotic applications. But, here are a few common ones:

  • Robotic palletizing — Robots are used to load or unload products and materials from pallets.
  • Robotic packaging — Robots are used in both primary processes — to package raw materials — and secondary processes — to package pre-packaged goods into larger boxes, crates, etc.
  • Robotic picking — Robots are starting to be used in warehousing and sorting to pick products from shelves.

The future of logistics is now! And it’s going to be robotic and AI.

The current boom of logistics robots has been a long time coming — at least 10 years, in fact. We have been fortunate enough to witness the whole journey.

Back in 2008, we saw the arrival of the innovative Kiva Systems Robotic Warehouse.

Four years later, in 2012, Amazon acquired Kiva Systems. This acquisition could reasonably be said to be the starting point for a new type of automated logistics.

But, why is this year, 2018, the moment to pay attention to robotic logistics?

Well, it seems we are reaching a tipping point. A 2016 trend report from logistics-giant DHL explained that two factors are driving the need for autonomous logistic solutions right now:

  • The growth of e-commerce — Product distribution has totally flipped on its head over the last decade. Products are now packaged individually and sent direct to customers, rather than in bulk to suppliers as they were in the past. There is a huge variety of different packaging requirements, the weight of shipments is increasing as heavier items are being bought online, and the speed of distribution has increased too.
  • The lack of available workforce — Skills shortages are affecting many industries, not just logistics. There are just not enough skilled workers to fill needed posts. I’ve written before about how robotics can help to tackle skills shortages in manufacturing, and robots are also a solution for logistics.
Robot Technology and AI Comes of Age

There is also a technological reason that logistics is the new “hot industry” for robotics. Several technologies have matured recently allowing them to be viable in real-world logistic applications.

Artificial Intelligence (AI) applications are popping up all over the place.

Collaborative Robots are allowing robots to work alongside human workers.

How will Robotic Logistics Look in the Near Future?

Robots are already being used in logistics, sometimes extensively. Therefore, the logistics of the future will look similar to how it looks today. Over time, robotics will just creep into more and more applications.

Artificial Intelligence in conjunction with robotics is being used for everything from entire versions are actually doing things and that is it makes it makes the day go into user.

  • The global impact of AI through 2030 indicates that this will be a $15.7 Trillion dollar industry with the consumption contribution at 60 percent and productivity contribution at 40 percent.
  • According to the Gartner hype cycle of 2016, machine learning is at its peak of inflated expectations. Effective machine learning is difficult because finding patterns is hard and often not enough training data is available; as a result, machine-learning programs often fail to deliver.
  • AI implementation is not difficult. Big data and AI have become extant where implantation is the key. Implementation requires linkage to existing business systems. Proving Systems is extremely important and treacherous and always making sure a ‘human’ is in the loop.
  • To fully automate processes, we need to use both an expanded spectrum of automation and better use of people
  • Enablesoft partners with 500 banks and credit unions. 1/3 of the top 1,000 banks and ½ of the top financial performers, along with healthcare and 12 other industries

Most experts agree that there will nearly always be the “human in the loop” and AI will augment human effectiveness.

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