The techinical writting

Artificial Intelligence cannot replace three human abilities, in which human beings present an insurmountable advantage today, and they are empathy, leadership, and creativity. AI can quickly take over essential verbal and visual communication services, such as digital assistant-based customer service. However, our ability to empathize with the client and to carry out non-verbal communication based on emotions gives us an advantage that Artificial Intelligence can never replace. These qualities can make the difference between a misunderstood and dissatisfied customer versus an understood and loyal customer. Comment by Daniel Creider: Spacing between lines must be 1 not 1.5

Gajane & Pechenizkiy (2017) stated that it is undeniable that AI will replace workers in essential economic-financial management, logistics, materials, human resources, and projects. Still, people have more advanced management capabilities that AI cannot return. The following two skills play a crucial role:

First is the ability to manage the growth of human groups. This is the ability to help members of the organization develop their skills and grow professionally through our innate leadership ability to set goals, motivate, lead by example, evaluate, delegate, and transmit experience.

Secondly, there is the ability to carry out the organization members' recovery management when they suffer problems derived from interpersonal relationships or other emotional reasons. It is based on the skills of understanding, counseling, care, and protection.

Yampolskiy (2019) found that AI can never replace the vision, invention, and original proposal of innovative and disruptive designs, not only applied to the individual as a genius but also the ability to carry out collective intelligence management focused on innovation, facilitating the appearance of new knowledge and wisdom. Besides, even more, difficult it will be able to replace the ability to implement new ideas in the organization, communicating attractively, persuading, and making the organization move smoothly to implement innovative ideas.

Keywords

Artificial Intelligence, Marketing, Human Resource Management, Medical Sciences, Nursing,

The possibility of thought in machines is a concern that has been raised for a long time; science fiction, as well as engineering and philosophy, have sought to provide an answer to the question "Can machines think?" Famous exponents of both affirmative answers, given by Turing or Kurzweil, and negative responses can be found in the bibliography, Searle or Penrose being notable examples. Although each of these represents a different position, there is a common characteristic to the other proposals. In all of them, "thinking" is defined by the purely human experience of doing it. By presenting the Game of Imitation as a criterion for determining whether machines think, one is declaring that they will only do so when they can carry out behavior’s characteristic of the human way of thinking.

Montani & Striani (2019) understand that by denying that a machine can think because, if we were in its position, there would be characteristics of our intelligence -such as semantic content and demonstration of mathematical truths so we would not have access, we are requesting that for a machine to "think," it can carry out all the functions of human thought. The proceeding is natural since our experience as thinking subjects is the one, we can most easily access. However, this seems to bring with it an unfair judgment for other possible mental forms.

Suppose that our first-person experience was equally important in establishing what it means, for example, "to breathe." In that case, we would have a fundamental component of the definition of the possession of lungs and a structure similar to the human one. Under this definition, fish or cells would never be considered exponents of subjects that breathe, leaving aside cellular or gill respiration phenomena. In the same way that we would leave out many other breathing subjects by exploring how the phenomenon presents itself exclusively in humans, I also believe that it is possible to leave out other minds by exploring the concept of thinking solely from our perspective.

Suppose it is considered that it is essential to understand forms of thought that go beyond the human. In that case, it will be necessary to build a tool that allows the configuration of the concept so that there is more information that configures it and our experience.

Montani & Striani (2019) understand that what is sought when using refractive equilibrium to define what it means to think is to change the weight that human experience has for definition. The Studies in artificial intelligence indicate that we take our expertise from subjects considering as the only criterion to define the characteristics that anything must meet to be classified as thinking. By producing a definition through the refractive equilibrium of According to the monograph's body's proposals, it will seek that now this experience is just one more tool for characterization. Our experience human will guide the report, but the way we extract information from it be careful to give other minds a chance to be taken into account despite their material realization. This is not to say that the refractive equilibrium process has to grant due to the existence of minds other than human; in fact, a result of the process may be that, according to the information available at the time, only we have examples of thought in us. However, they would find the difference in why we exclude other minds; currently seems to be that the main reason is of perspective. Through balance, it will find representative characteristics (and investigable in more subjects than ourselves) provide a definition. In this way, we would go from asking ourselves, "Can other objects think as humans do?” a "How to do the other processes of thought?" without assuming that they think, since the equilibrium process will allow that the investigation of the how modifies what the concept means Comment by Daniel Creider: This text looks like you have just copied the words from the reference rather than paraphrasing what the article was about. This is plagiarism. Any text that is a direct quote must be quoted but you cannot have too many quotes in the paper.

Before resolving any doubts about the capabilities of a machine, it would seem natural first to determine the limits of what you are referring to by "machine." In the traditional problem of artificial intelligence, research is found focused on the study of digital computers presented by this definition proposed by Turing is given in an open enough way to include within them a great variety of later developments in computational engineering, this perspective will carry some problems that it will expose later. The historical moment in which CMI is written is one in which computer personnel did not yet have a significant presence in the world. Most machines computational systems belonged to research institutes and were out of the reach of the public generally.

Bogachov, et al, (2020) stated that to imply what a digital computer, a digital computer, understands, Turing defines them by an analogy with a human-machine (the processes that he could carry out a person under a closed system of instructions); from which the capabilities and limits the machine will encounter. This is an application of those strict definition presented in his famous "Computable Numbers with an Application in which devices are presented as a tool to solve a problem of mathematics. Digital computers will be composed of three main parts (i) storage, (ii) execution unit, and (iii) control, which make up a functional object. The storage represents the space in which the data is presented. The information to be manipulated and the results are there; the execution unit is in charge of carrying out the processes, and the control is the fixed rules, presented as tables that the system must follow.

The first proposition or premise states that all law students are shortsighted. Therefore, all law students are included in the universe of people who have myopia. The second proposition informs us that some myopic people are intolerant to contact lenses. That is, within the universe of myopic people, a group does not tolerate contact lenses. If these are our two premises, the conclusion is not correct because those two statements are compatible with a situation in which the group of myopic people who do not tolerate contact lenses does not include any of the law students - all of them myopic, as we know -. If true, the two premises are compatible with states of the world where all law students are intolerant of contact lenses, only some law students are intolerant of contact lenses, or even no law student is intolerant of contact lenses.

Bogachov, et al, (2020) stated that the alleged conclusion does not necessarily follow from the premises. It is a false syllogism or "paralogous." Something that looks like a syllogism but is not. A subtle trap is committed when the second premise - the one that contains the subject of the conclusion - is placed first, while the first premise - the one that includes the predicate of the decision - is presented in second place. In this way, when we find ourselves in the first place with a universal type statement - all law students are ... -the reasoning looks more like a syllogism. If we rearrange the two premises (some nearsighted people do not tolerate contact lenses; all law students are nearsighted; then…), the jump or logical inconsistency is perhaps more easily detected.

Heer (2019) thought that to see it more clearly, we can try a graphic representation of the matter. We draw a huge circle that represents all myopic people in the world. With a smaller process, we mean all law students in the world. We must necessarily draw this smaller circle in its entirety within the myopic circle (because our reasoning starts from the premise that all law students are myopic). Finally, we must draw a third circle - which is the one that interests us the most - that represents all subjects intolerant to contact lenses. Where and how do we draw this circle? Well, there are a few possibilities. It must necessarily be drying to some extent with respect to myopic circle because some myopic have the property of intolerance to contact lenses. However, fulfilling that requirement, we have, as I say, several possibilities. I invite the reader to play set logic for himself, using paper and pencil and draw all the possible combinations of circles compatible with the two premises. Comment by Daniel Creider: Starting most paragraphs with an author’s name makes the paper less interesting to read.

Bonacina (2017) mentioned that there is still another way to deal with the problem: simplify or "formalize" our language. Thus, we replace the expression "all law students are myopic" by the following: "all A is B"; the phrase "some myopic are intolerant to contact lenses," for "some B are C"; and our conclusion would become "then some A is C". We could use a more sophisticated notation like "for all x if x is A then x is B" (∀x A (x) → B (x)), But the important thing is that we have replaced some terms that had a specific meaning. Therefore, it referred to certain sets or classes of entities or realities existing in the world (law students, myopic, intolerant of contact lenses) by symbols (A, B, C) with which you can refer to any set of entities or realities. We could say that we have eliminated all the "semantics" from our reasoning. We are left only with the "syntax": with the position of subject or predicate that each of the terms (A, B, C) occupy in each of the propositions and with some "quantifiers" that model the scope of these terms (all, some). Reduced reasoning to its syntax - or in other words, to its formal structure - a purely ceremonial "calculation" is possible and comfortable, allowing us to verify the correctness of the reasoning.

The ability of a system to correctly interpret external data, learn from such data, and use that knowledge to achieve more specific tasks and goals through flexible adaptation." One of its uses for decades was the application of game theory to defeat the best human players.

Kaplan & Haenlein (2019) stated that it is structured from different areas of knowledge such as computer science, logic, mathematics, philosophy, and experience to develop computational models capable of carrying out human activities, based on two fundamental characteristics: reasoning and behavior. In teaching, it is summarized from a pedagogical solution to the problem. It is presented as a discipline that is responsible for studying and building "intelligent agents," that is, systems capable of perceiving their environment and acting on it to achieve the proposed objectives, where each “agent” is implemented through a function, which establishes a correspondence between his or her perceptions and their actions.

Kaplan & Haenlein (2019) stated that Artificial intelligence gives machines the ability to "reason and learn." Two capabilities are very useful in clinical diagnosis. For example, a computer program can analyze the photo of a spot on the skin and comparing it with its database, establish the probabilities that it is a melanoma. Similar applications are being developed for many other diseases, although for now, AI complements and strengthens the diagnosis of doctors.

A robotic nurse? It seems that it will be one of the keys to assisting the elderly and dependent patients in the future. So far, robotic pets have been developed for therapeutic purposes to help Alzheimer's patients. Robotic pets stimulate brain functions in patients by delaying cognitive problems that improve quality of life and reduce dependency on social services.

Ease the burden on doctors Comment by Daniel Creider: Wong font and does not need to be italicized.

Wang (2019) found that analysis tests, X-rays, CT scans, data entry, and other mundane tasks can be performed faster and more accurately if carried out by robots. Cardiology and radiology are two examples of disciplines where the amount of data to analyze can be overwhelming.

Perhaps in the future, simple cases will be left exclusively in the hands of AI, and human doctors will only deal with the most complicated ones. Comment by Daniel Creider: Why do you have a one sentence paragraph?

Drug development

Wang (2019) found that getting effective new drugs over clinical trials can take more than a period and cost too much. Thus, streamlining the process by using AI could transform the globe. In the modern Ebola calamity, they used an AI-powered software to examine current drugs that it could reform to fight the ailment or disease. The program found two medications that can lower Ebola contagion in one day when such study mostly takes months or years - a change that maybe saved many lives or more.

They are the virtual assistant's AI, Chabot that respond to requests of different difficulty. A Chabot is a software tool designed to help users carry out other tasks by implementing machine learning and artificial intelligence (AI). As their name indicates, they can assist us in carrying out internal processes related to the administrative part of Human Resources; they can provide information on policies and procedures or filter curriculum vitae, as we said. These Chabot’s are capable of learning, so we are working on communicating with them because we do not know how far their understanding could go, both intellectual and sensory. We know that they can read emotions on people's faces, which would bring their level of understanding closer to ours, and that they can process levels of information in such a way that they can transform it into knowledge.

Kolski & Vanderdonckt (2020) stated that the question is at what level of trust you would place in a "machine" and if you would leave such human issues as talent management and professional development in their hands. Can a robot evaluate, and plan areas related to our performance? It is predicted that after developing the appropriate algorithms, an algorithm and therefore a robot could learn from human behavior and its needs. It could plan solutions to needs related to the human essence and that is so much needed in organizations, such as motivation and learning. Many things are said about the future that is to come. Still, it is difficult, at least for me, to believe that a robot will be able to intervene in such subtle matters without the intervention of a person who supervises the process.

Let us look at the paradigm shift that new technologies are generating and, from there, in the latest models of the relationship between people that are being established. We can discover new models of Human Resource Management that are more effective and empowering. Suppose we start from the initial consensus on the concept that "organizations achieve results through the behaviors manifested by the people who compose them". In that case, an effective people management model will be one that maximizes their performance and productivity, guaranteeing the best results that it can expect from them.

Kormalev, et al, (2018) realized that for this, technology will allow us to put integral tools at people's service that generate a collaborative and interrelated context that connects processes and people, adding value to the business chain and maximizing results. Let us take an example; a phone has no value on its own if it has no other phone to communicate with. In this way, two telephones will double in value once they can contact each other. However, this valued relationship will be reduced to exchanging information between the two terminals unless a third or other terminals can connect simultaneously to the same conversation. In this way, each telephone that joins the discussion will add exponential value to it in terms of time and communication efficiency.

The marketing world discovered it a long time ago, and for that reason, it makes the client/consumer the protagonist of the experience with the company. The question would be if we were prepared to create a people management model based on empowering employees to become the real protagonists of business activity. The times of control ended since we discovered that the power exercised over others is inversely proportional to the trust that is generated in the other.

Acay, Sonenberg & Tidhar (2019) found that the times have come to empower, to listen to others, to enhance autonomy and risk, to assume responsibilities and decisions, to favor the establishment of links and networks, to share knowledge, to learn from others, to awaken interest in the search for information, to filter and select the relevant information, to maximize performance and productivity.

In short, we are in times of transparency and information available to anyone, in times of collaboration and handing over the power of action to the real protagonists, people, and talent. If this is the new paradigm for managing people and human resources in the future companies, how can technology help us manage this model?

Perez, et al, (2018) stated that Artificial Intelligence systems differ, among other things, from traditional Data Warehouse systems in that the latter generate indicators calculated from the analysis of data from the source systems (transactional systems). The former can establish predictive hypotheses based on the same data, follow up the selected predictions, confirm or correct these hypotheses with the temporal monitoring of the evolution of the data and the results. In this way, as time passes and the system handles a larger and more complete sample of data, it learns to be more and more efficient in its prediction models.

The result of the predictions will be more reliable and accurate. The more inputs are collected within the system and the longer it takes to establish and follow (to confirm or readjust) its predictive hypotheses. To do this, technological systems allow us to collect and integrate all kinds of day-to-day data from each employee, their social interaction with other people and their results through their manifest behaviors in the workplace.

Hassabis, et al, (2017) told that companies need dynamic structures aimed at effective project management. Knowledge is quickly transferred from one person to another, where productivity is maximized, talent is enhanced, and unlimited collaboration between people in different positions is favored. The relevant information flows through fast and accessible channels sharing acceptable practices, experiences, or concerns, where emotional bonds are generated capable of retaining the best and supporting and empowering those who need it most, where communication between the employee and their company is transparent, efficient, and reliable and find multiple channels of channeling.

The rise of AI in sectors such as hospitality or medicine, to name two examples, is worth studying, but there are still challenges to be met soon. Most of them are related to the main handicaps that this technological innovation has:

Offer care with greater empathy. The idea is that it is the corresponding system that responds or attends to a potential client without the participation of a worker. The chatbots can hold a conversation, but her coldness can cause alterations in engagement with the company. One of the challenges is, therefore, to get more empathetic applications that are not limited to offering answers, but also to interact with each client.

Townsend & Hunt (2019) stated that facilitate access to this technology. The more production and innovation, the easier it will be to find options at an affordable price. The improvement of the applications in the market has the final objective of generalizing their use.

Manage to avoid rejection of potential customers. It is undoubtedly the most important objective, since in not a few cases it is quite common to find a frontal rejection of this alternative. Meeting the challenges of the two previous sections will surely contribute to normalizing the use of AI in any marketing campaign.

Now, as we anticipated at the beginning, you are surely wondering how to take advantage of this resource in your company. Marketing that incorporates Artificial Intelligence is based on the premise of using the latest technologies for the benefit of consumers.

Chan & Yuan (2019) found that it is also one of the main ways that advertisers can ensure the Return on Investment (ROI) of ad campaigns. With these tools, you can use customer data and machine learning to get the most out of your advertising investments.

Varlamov, et al (2019) noted that the main objective that AI will allow you to achieve is the creation of appropriate content for target audience, in addition to process optimization. Considering AI is essential if you want to take your business to the next level and create more effective and far-reaching digital marketing strategies.

Benefits of integrating Artificial Intelligence in Marketing Strategy

So far, we have a brief overview of what the incorporation of Artificial Intelligence can do in your business. However, here we show you in detail some of the specific benefits that it will offer you.

Sales Forecasts

Varlamov, et al (2019) noted that using Artificial Intelligence in your marketing strategy will allow you to combine and cross records of all the operations you carry out. AI can collect data from multiple channels like emails, phone calls, and even face-to-face meetings. With all this information, this type of technology can predict the performance of current campaigns and forecast future sales. Data analysis is a key strategy to learn more about the performance of your business and, in this way, prepare projections from now on.

The reports that the AI ​​obtains on the behavior of users and your interactions with them will also allow you to carry out much more precise segmentations and even personalize the messages you send to your customers. This will help you offer products or services that they really need or that are interesting to them. Similarly, it greatly facilitates the sales process and automates routine processes such as receiving data.

Gajane & Pechenizkiy (2017) stated that by having more information about your audience, you will be able to understand their behaviors and their particular characteristics to interact with them in the most effective way. This way you can evaluate if your strategies are suitable for users, if they are working optimally or if they need some adjustment.

All these procedures, when performed by a machine, are done faster and at a lower cost, which will leave more human resources free to be allocated to the most important part: the direct and face-to-face relationship with customers.

Yampolskiy (2019) found that Artificial Intelligence will also allow you a deep analysis of the activity and actions of your competition. Through this technology, you will be able to have a detailed approach to their methods and their audiences, to compare them with your work and propose new positioning tactics.

The artificial intelligence has allowed something as simple as what we now are much habituated, like housing search by attributes and the fact is that the development of internet listings in the real estate sector has allowed buyers to search for homes by location, price, square meters or number of rooms. However, even after narrowing your search based on these attributes, it can return results for hundreds or thousands of properties. However, thanks to artificial intelligence, search engines learn what behavior patterns of each customer and restrict even more the answer, in addition to focusing it more precisely.

Montani & Striani (2019) understand that several companies have developed artificial intelligence applications that serve as conversational interfaces with customers to answer customer questions.

AI technology also offers a powerful tool to help agents discover their potential customers, screening, for example, those who are only looking for a home for entertainment, but for whom buying a home is a distant reality, from those who truly they are prepared to acquire a property.

De Oliveira, Sanin & Szczerbicki (2019) found that these systems also use natural language processing (NLP) to analyze the conversations of the potential client with the real estate agent and assess the level of commitment of the potential buyer. In the near future, an agent will be able to use a robot to schedule customer appointments by phone, in any language, by using a CRM capable of managing customers in a multilingual format.

Artificial intelligence can also be used in a real estate agency to select the work team more efficiently, for example, eliminating personal bias when interviewing candidates or providing information, based on an in-depth market study, on where it is necessary to hire more personal because the market has untapped potential.  Obviously, an artificial intelligence system does not replace the work of an expert in the real estate sector, but it does provide you with in-depth information to make more precise and accurate decisions.  

Bogachov, et al, (2020) stated that by combining the use of a CRM and analysis of market data, artificial intelligence can help real estate agents better predict the future value of a home in a specific market, as the system can synthesize information from a very wide variety from sources, including about transportation, security, services, and about market activity and, since most buyers view a new home as an investment, having a more reliable forecast of its value can help increase the interest of potential buyers. In addition, artificial intelligence applied in the real estate sector will continue to evolve to facilitate numerous processes, such as offering faster closing times, smarter mobile applications, more detailed sector reports, virtual visits to clients, among many others.

Shook, et al, (2019) mentioned that artificial intelligence to determine, depending on the actual preferences of buyers if the visit or purchase generates no. Thus eliminating a high number of unnecessary meetings or in which there is no real purchase interest. This type of predictive analysis is carried out thanks to artificial intelligence, allowing a forecast of the homes that can best be adjusted to the profile of the interested buyer.

Heer (2019) thought that with block chain systems, it will be possible to streamline and provide greater security to all financial operations carried out digitally. Perhaps in the not too distant future it will also be possible to use this technology to provide greater security for buying and selling operations or mortgages. Customer service is essential for a correct relationship with the customer and for the brand image. More and more, companies are betting on improving customer service and promoting a more personalized service. Real estate companies have already begun to introduce some questions to clients in their systems that seek to identify what that client is looking for in order to direct the call to the specific agent who can help them. With artificial intelligence, the computer is being given the ability to think and process information in such a way that it gives meaning to the data and seeks a solution.

Bonacina (2017) mentioned that technology has evolved in an incredible way and many of these processes have been automated. Visits are reduced, paperwork is minimized and results improve because we increase productivity thanks to the new machines and processes that we are integrating.

The volume of data that we handle now is exponentially greater than a few years ago, Big Data is already in charge of it, but it is precisely this enormous volume of information that forces us to trust Artificial Intelligence tools.

Kaplan & Haenlein (2019) stated that the Knowledge is power, but only the AI can allow us to select the information necessary and proper for us to take those decisions. The key is no longer to access the data that the Internet offers us, but to have the tools that analyze it for us in the way that best suits us. It is no longer a question of spending hours in front of a computer looking at properties but rather of telling an application to search for us all over the Internet for what we need.

Dignum (2018) realized that the future of the real estate sector goes through AI because Big Data is already available in the network of networks. Now it is essential to have tools that find the specific properties that we are looking for among all that tangle of webs and that select them based on some guidelines that we tell you, such as the city, neighborhood, price, square meters, public transport, etc. With AI, the real estate sector will be able to respond to all the needs of its clients in a faster, more efficient and effective way and will reduce the weight that the old commercials had in the sector.

Customer Relationship Management or better known as CRM is the software that has been helping companies for years to improve their relationship with their customers. According to the results obtained by a study by Forrester and published by the digital Merca2.0, these solutions increase the productivity of the teams by 50%, increase sales by 5% and the cost of customer service is reduced by 40%.

Dignum (2018) realized that the information extracted by these tools can be further benefited if we integrate Artificial Intelligence in them, resulting in three great advantages of applying AI to CRM. Reduces the time of the most mechanical tasks. Thanks to the incorporation of Artificial Intelligence in CRM systems, workers are freed from the routine efforts such as entering data in databases, answering emails, managing invoices or scheduling meetings, among others. According to Salesforce estimates published in the CRM Today portal, up to four hours a week can be saved in repetitive tasks.

Kokina & Davenport (2017) realized that AI is the best virtual assistant. In line with routine tasks that take time and effort from workers, Artificial Intelligence is much faster and more efficient when it comes to generating immediate and automatic responses, emails, functions related to data collection and monitoring thanks to its ability to obtain customer information such as behavior on the site or demographic data. In addition to the speed in obtaining customer data, the AI ​​applied to CRM collects quality and very specific information regarding gender, geographic location, and purchase history, behavior on the web or other sections that allow companies to develop content very specific to develop personalized messages that have a better impact. Which translates into a higher percentage of success of the sales strategy and a closer loyalty of the customers.

Kokina & Davenport (2017) realized that with a unified customer success platform, Salesforce is the heart of key customer data for most businesses. Bringing Salesforce Einstein, an intelligence layer, to the platform that can also easily integrate additional data from third-party systems avoids the problem of customer data silos. To eliminate the need to build artificial intelligence models from scratch, Salesforce Einstein brings sophisticated data analytics and pre-built predictive models to business process and application creators, without complexities or cumbersome processes. The Salesforce CRM and its AI capabilities foster the ideal environment for creating diverse applications and experiences, not just for customers, but also for everyone in the organization. Using the same data model, business logic, and experience layer that powers CRM, combined with the simple point-and-click way to build applications, both IT and business users can leverage an intelligence boost to provide experiences with artificial intelligence and a 360-degree view of the customer to anyone.

Kormalev, et al, (2018) realized that using a consumer shopping experience as an example, the AI ​​models embedded in the Salesforce CRM system's personalization engine take into account the catalog that a given buyer sees and the context of how the merchant interacts with that buyer, and then classifies each product for that buyer in terms of relevance to search results, achieving the most specific and personalized results of all time. This is done at scale, constantly refining consumer recommendations based on learning.

Acay, Sonenberg & Tidhar (2019) found that the data for such recommendations includes real-time and historical data on clicks from various sources. By selecting a challenging champion model algorithm, each experience becomes more personalized and intelligent because the algorithm "learns" from past data and can produce results with greater precision. The bottom line of accurate recommendations is that customer conversions skyrocket and your inventory value increase.

Perez, et al, (2018) stated that these advanced artificial intelligence algorithms can also improve both employee and customer experiences using tools such as speech recognition, sentiment analysis, intent, content summarization through natural language processing, and data table-based responses.

The key to the intelligence layer built into Salesforce CRM is the point-and-click capabilities to enable AI for any application, without the need for a team of data scientists on standby. Everyone in the organization, from developers to business users, can collaborate to create the right customer experience using rapid, iterative prototyping.

Hassabis, et al, (2017) told that the low-cost code-free programming environment of the Salesforce platform brings traditionally non-tech teams on board, such as customer service and marketing teams that can be actively involved in creating a truly intelligent customer experience. This enables IT to take a coaching role to empower teams across the organization to create their own artificial intelligence solutions.

Without a doubt, artificial intelligence in the banking and finance sector is one of the most interested in the development of this technology, due to the enormous volume of data it holds. Not surprisingly, it is the world's leading investor in artificial intelligence.

Hassabis, et al, (2017) told that figures from the European Commission reflect a strong investment imbalance between Europe and its main competitors. Private investment in artificial intelligence in Asia and North America exceeds 6,500 and 12,000 million euros respectively. This contrasts with the 3,500 million euros that the European financial sector allocates to the development of this technology. The aforementioned Bank of Spain report indicates that Asian and American companies have the advantage of having the infrastructure and the volume of data necessary for the development of artificial intelligence in the banking and finance sector.

Chan & Yuan (2019) found that according to a report published by the World Economic Forum and Deloitte, artificial intelligence will have a disruptive nature in the financial sector “weakening the ties that have held together the components of existing financial institutions.”

Varlamov, et al (2019) noted that the new operating models will favor those institutions focused on the sophistication and scale of the data, rather than on capital. It is expected that the entities are going to turn to the relationship with their clients, advisory services and collaborative solutions.

On the other hand, a new battlefield is presented, in which the rules of the traditional financial and banking sector will be relegated by automated systems.

Wamba-Taguimdje, et al (2020) noted that these virtual assistants can respond to a multitude of requests from users. This allows us to provide an automated customer service available 24 hours a day. Likewise, it also makes it possible to collect in an automated way the interactions carried out by the users themselves, to later improve the quality of the service. This automation process will not only benefit the execution of customer relationships, but also the execution of internal processes. Consequently, it is expected that the sector will see its productivity increase significantly in the coming years.

Thanks to the information gathered about each client, it will also be possible to create more personalized experiences, as well as to adjust the conditions and characteristics of the products and services offered to each client.

Credit Rating, Fraud Detection and Risk Management

Gajane & Pechenizkiy (2017) stated that artificial intelligence applied to predictive analytics is going to make a considerable change in credit rating and risk management systems. On the one hand, risk analysis will gain in reliability, which allows the conditions for granting credit or making the investment to be adjusted more precisely.

In addition, the analysis of unstructured data opens the door to granting credit to previously ineligible clients.

Gajane & Pechenizkiy (2017) stated that artificial intelligence in the financial sector is extremely effective in preventing fraud in the use of credit cards. This problem has been increasing over the last few years; following the evolution of electronic commerce. Analytical systems can anticipate a change in user behavior patterns, and issue alerts or even carry out preventive measures, to avoid fraud.

On the other hand, these same systems can also be used to detect practices that may be involving money laundering.

Finally, artificial intelligence algorithms are already being used for the execution of investment strategies. This technology is especially valued in those strategies that require high refinement, together with a high speed of execution.

Montani & Striani (2019) understand that decision-making is thus automated, under the parameters included in the algorithm, which is in charge of executing the operations. Ultimately, artificial intelligence is completely changing the rules of the game within the banking and finance sector.

Present and Future of AI in Financial Sector

AI can drive efficiency of operations in many area of finance like trading or risk management. While some uses are pertinent to certain zones within monetary services or they can easily exploit by anyone.

Risk Management

AI has proven its worth when it comes for fraud detection and security. Common fraud detection ways like analyze the structured data with set of rules is one example. Let`s say a company set threshold for wire transfer of $20,0000, then any transaction that goes above or exceeds this threshold may flagged for investigation.

Montani & Striani (2019) understand that this type of tests often makes false positive results and needs much effort. Perchance, it needs more enhanced techniques that cybercrime scammers change frequently. In this way, systems must made smarter.

Bogachov, et al, (2020) stated that with new learning methods for machine to understand algorithms such as deep learning or reinforcement learning, new features can help systems to become dynamic and self-aware in some way. With help of cognitive analytics, fraud detection models can become smart, accurate and robust.

Shook, et al, (2019) mentioned that if an intelligent system removes somewhat that it fixes as possible scam, and a human being controls that it is not scam due to X, Y, and Z, the computer acquires from perception of humans, and it will not send any same detection.

Gajane, P., & Pechenizkiy, M. (2017). On formalizing fairness in prediction with machine learning. arXiv preprint arXiv:1710.03184. Comment by Daniel Creider: The references are not in alphabetical order. *************************************** The references are not acceptable without a URL which is a hyperlink. ****************************************

Yampolskiy, R. V. (2019). Unexplainability and incomprehensibility of artificial intelligence. arXiv preprint arXiv:1907.03869.

Montani, S., & Striani, M. (2019). Artificial intelligence in clinical decision support: a focused literature survey. Yearbook of medical informatics, 28(1), 120.

de Oliveira, C. S., Sanin, C., & Szczerbicki, E. (2019, April). Towards knowledge formalization and sharing in a cognitive vision platform for hazard control (CVP-HC). In Asian Conference on Intelligent Information and Database Systems (pp. 53-61). Springer, Cham.

Bogachov, S., Kwilinski, A., Miethlich, B., Bartosova, V., & Gurnak, A. (2020). Artificial intelligence components and fuzzy regulators in entrepreneurship development. Entrepreneurship and Sustainability Issues, 8(2), 487.

Shook, E., Bowlick, F. J., Kemp, K. K., Ahlqvist, O., Carbajeles-Dale, P., DiBiase, D., ... & Wang, S. (2019). Cyber literacy for GIScience: Toward formalizing geospatial computing education. The professional geographer, 71(2), 221-238.

Heer, J. (2019). Agency plus automation: Designing artificial intelligence into interactive systems. Proceedings of the National Academy of Sciences, 116(6), 1844-1850.

Bonacina, M. P. (2017, November). Automated Reasoning for Explainable Artificial Intelligence. In ARCADE@ CADE (pp. 24-28).

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.

Dignum, F. (2018). Interactions as social practices: towards a formalization. arXiv preprint arXiv:1809.08751.

Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1-37.

Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of emerging technologies in accounting, 14(1), 115-122.

Kolski, C., Boy, G. A., Melançon, G., Ochs, M., & Vanderdonckt, J. (2020). Cross-fertilisation between human-computer interaction and artificial intelligence. In A guided tour of artificial intelligence research (pp. 365-388). Springer, Cham.

Kormalev, D. A., Kurshev, E. P., Vinogradov, A. N., Belov, S. A., & Paramonov, S. V. (2018). Interactive visualization of non-formalized data extracted from news feeds: Approaches and Perspectives. In Proceedings of the Scientific-Practical Conference" Research and Development-2016" (pp. 93-100). Springer, Cham.

Acay, D. L., Sonenberg, L., & Tidhar, G. (2019). Formalizing tool use in intelligent environments. Journal of Ambient Intelligence and Humanized Computing, 10(4), 1597-1610.

Perez, J. A., Deligianni, F., Ravi, D., & Yang, G. Z. (2018). Artificial intelligence and robotics. arXiv preprint arXiv:1803.10813.

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.

Townsend, D. M., & Hunt, R. A. (2019). Entrepreneurial action, creativity, & judgment in the age of artificial intelligence. Journal of Business Venturing Insights, 11, e00126.

Chan, H. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019). Advancing drug discovery via artificial intelligence. Trends in pharmacological sciences, 40(8), 592-604.

Varlamov, O. O., Chuvikov, D. A., Aladin, D. V., Adamova, L. E., & Osipov, V. G. (2019, May). Logical artificial intelligence Mivar technologies for autonomous road vehicles. In IOP Conference Series: Materials Science and Engineering (Vol. 534, No. 1, p. 012015). IOP Publishing.

Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal.