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1. The digital revolution and finance I

Chapter 1

THE DIGITAL REVOLUTION AND FINANCE I

 

by: josavere

The digital revolution, an excellent opportunity for those who have enough skills to take advantage of it; that confronts us with a very complex change, forcing traditional companies to reinvent themselves to adapt to the new or disappear.

The most transcendental step concerns the human resource to teach them the ability to adapt, to face disruptive changes, thanks to the possibility of exploiting large volumes of information at high speed, a capacity that has developed rapidly in recent years, in terms of learning, decision making and execution tools.

Organizations must carry out comprehensive planning that covers all areas to be developed in a coordinated manner: identification and access to information sources, methodologies, systematization and control of correlative risks.

The digital disruption produces a reordering of the world and helps to redistribute wealth, especially with regard to social inclusion, improvement in health, knowledge available to all and takes advantage of clean sources to lower energy prices, respecting the Sustainability, attacking the socialism/capitalism dichotomy, promoting transcendental mutations in politics and economics; In addition, it facilitates social mobility, improves the quality of life and facilitates the increase in productivity that the world cries out for in order to face the inflation caused by Covid-19. It is required to integrate digital technology in all business areas, changing the way it operates and adds value, complemented by a cultural change to better interact.

It involves getting to know oneself to enhance the creative side, because the ability to generate experiences, provide new points of view; looking for different forms of expression and everything that is not likely to be automated to be executed with a robot, is and will continue to be highly appreciated. Cultivate empathy, think about contributing to the quality of life, not accumulate in excess, seek wisdom and live with a collaborative spirit, working in a Holacracy (an organization in which the category and decision-making are distributed horizontally, without there being a management hierarchy (also known as Holacracy). THE FINANCIAL SECTOR has been transformed a lot in recent years and with prospects of continuing to advance as required by the Digital Revolution. The highlight, El Nubank is an appreciable example to learn from the Colombian David Vélez, the banker who does not have small print in contracts; he presents the conditions head-on with a one hundred percent human criterion; digital and no handling fee. It says that its users do not buy products but rather its culture and that the value of the company is a consequence of seeking the solution to a large-scale problem because it does not have clients but rather users whom they listen to carefully because they are depositors of money, as a basis for creating products. That respond to your needs. He seeks to democratize finances by placing the user of his services at the center of importance, proposing distribution of wealth and services, making banking procedures easier for the popular classes and providing credits to those who are not owners. The clear trend is digital banking, using all the technological developments such as: voice-enabled payments, use of disintermediation platforms, Artificial Intelligence and Big Data on the rise, the approach to 5G, increasing data transmission speed and the Blockchain, technology based on a chain of blocks with a public and distributed database in which the transactions carried out on the network are securely recorded. Inflation, to a greater or lesser degree, is a global phenomenon that has increased as a result of the Covid-19, for which the need for businessmen to learn to live with it is imposed, in the same way that coffee growers in some towns learned to produce in the midst of rust. Due to the increase in prices, cash management became a topic of special importance in our time. It is an action characterized by the search for mechanisms that allow increasing the speed of circulation of money; we have to think like people in countries with runaway inflation: banknotes burn hands, damage safes and rot mattresses.

The faster the currency circulates, the less it loses its purchasing power. The bank, which has traditionally taken advantage of people's carelessness in this regard, has entered this market with vigor respecting the right of its client to increase the mileage of the cash. Electronic banking represents an excellent advance, which is now within the reach of any ordinary person.
Now with the Digital Revolution, using Artificial Intelligence (AI) that is designed to identify contexts and scenarios, work with some predictive models and make autonomous decisions and Big Data with the capacity to process huge volumes of information, the design of models to define credit quotas objectively following the guidelines developed so far.

In special circumstances, such as the one presented to plan the recovery of the world economy from the 2020 crisis caused by the COVID-19 pandemic, as far as possible, look for exponential alternatives (capable of generating disproportionately higher results to the traditional ones, having technology as an ally in the structure and development of the business) to increase productivity, the most powerful tool to counteract global inflation as has been seen with the emergence of companies with quite notorious results at a global level. According to press figures, the world's largest hotel company, Airbnb, offers more than 7 million accommodation options in more than 100,000 cities in 210 countries, without owning a single hotel and with just under 6,000 employees. The world's largest carrier, Uber manages more than 3.5 million drivers in 10,000 cities in 71 countries, without owning a single car. Nubank, one of the most valuable financial institutions in Latin America, has more than 48 million customers without having a single bank branch or safe with physical money. Tesla, founded just 20 years ago, is today the most valuable automaker in the world, with a market value of almost $1 trillion dollars, more than the following nine automotive companies combined, many of them with brands such as Mercedes Benz, Ford or Toyota with decades of existence.

Breakeven analysis is compatible with direct costing. Its great advantage, in addition to its simplicity, lies in the graphical representation and the practicality that it is made using excell; In addition, it facilitates the mathematical calculations to prepare the value generation plan at different levels of production and sales.

When companies have too many fixed charges, they can introduce special revenue-earning programs by covering variable costs plus a contribution to fixed costs and profit. This is the case of airlines, theaters, hotels and entertainment venues. Highly recommended for professional sports clubs in setting the value of the ticket office to enter the stages.

With the Digital Revolution, predictive analytics looks for future results using data from the past; the models handle different methodologies and mathematics with a very similar general objective; some techniques are specific to classification (the results of the model are binary; a yes or a no, in the form of 0 and 1) and others are regression techniques that allow a value to be predicted. It can also be applied to any type of unknown event in the past, present or future. The Digital Revolution brings BIG DATA with an abundance of structured variables, such as data tables, and unstructured ones, such as texts, images or videos that offer new possibilities for prediction. Flexible and heterogeneous prediction rules with proven ability to predict logical outcomes are now built by combining different models, procedures, and data types. Decision Trees, Neural Networks, Support Vector Machines, Bayesian Analysis, Logistic Regression, Linear Regression, Time Series and Data Mining, K-Nearest Neighbors, Ensemble Models, Gradient Boosting, Incremental Response Models, Replace, Introducing Multiple parameters extracted from Big Data, with many advantages over the models traditionally used by statistics. Big Data Analytics is the technology used to analyze a huge amount of structured and unstructured data that is gathered, organized and interpreted by software, transforming it into useful information for decision-making and to generate ideas about market trends. In addition, it contributes to the generation of ideas for new products and services, customer attraction, audience understanding, security and more benefits to make strategic decisions.

To prepare a value generation plan, it is practical to use the Holistic model of financial analysis to make a first estimate of the projected financial statements, especially the flow of funds. It is something like talking about a pre-feasibility analysis to see if positive results are expected to properly start a process that requires the participation of duly trained interdisciplinary teams for an exercise of such scope.

The Digital Revolution brought a very useful development to project, Big Data. Predictive analytics is a statistical study to obtain new or historical information that is used to predict patterns of behavior that can be applied to any type of unknown event in the past, present or future.

Predictive models are statistical tools that use machine learning supported by Big Data extraction to predict and forecast likely outcomes with the help of historical data, introducing multiple statistical parameters that facilitate obtaining new or historical information useful for predicting behavior patterns.

Through techniques such as exploration, description, comparison and analysis, it is possible to anticipate the possibilities of success for an organization, foreseeing contingencies and challenges based on certain circumstances.
Big Data Analytics is the technology used to analyze a huge amount of structured and unstructured data that is collected, organized and interpreted by software, transforming it into useful information for decision making and to generate ideas about market trends and behavior; Big Data Analytics has a positive impact on businesses in any sector because it contributes to the generation of new products and services, customer attraction, audience understanding, security and more benefits; it is an approach that involves analyzing data to draw conclusions. By using it, companies can be better equipped to make strategic decisions and increase their turnover, to the extent that they know how to take advantage of the information.

A DMP (Data Management Platform) is a platform dedicated to processing data that can be organized into profiles for decision making. It analyzes how a given behaves digitally, while a CDP creates customer profiles based on personal identifiers. Through techniques or tools such as exploration, description, comparison and analysis, the possibilities of future success for an organization can be anticipated; anticipate contingencies and challenges based on certain circumstances.

Predictive analytics techniques look for future results using data from the past with a very similar overall goal. Those developed so far are:

1. Decision Trees, are statistical algorithms or machine learning techniques that allow us to build predictive data analytics models for Big Data based on a classification according to certain characteristics or properties, or on regression through the relationship between different variables to predict the value of another.

2. Neural Networks, Artificial Intelligence and Deep Learning, a pattern recognition technique that mimics the neurons of the human brain, capable of modeling extremely complex relationships and using them when the exact nature of the relationship between input and output values is not known. the exit ones. Deep learning processes data to detect objects, recognize conversations, translate languages, and make decisions.

3. Support Vector Machines (SVM), are supervised machine learning algorithms in order to recognize patterns, status related to classification or regression problems.

4. Bayesian analysis, a statistical inference in which evidence or observations are used to update or infer the probability that a hypothesis might be true.

5. Logistic Regression, logistic regressions are used to predict the result of a categorical variable (a variable that can adopt a limited number of categories) based on the independent or predictive variables. It is useful for modeling the probability of an event occurring as a function of other factors. For example, it can be used to predict credit risk.

6. Linear Regression, consists of a straight line that shows the “best fit” of all the points of the numerical values. It is also called the method of least squares because it calculates the sum of the squared distances between the points that represent the data and the points on the line that the model generates. Thus, the best estimate will be the one that minimizes these distances.

7. Time Series and Data Mining, consists of using large databases to obtain perspectives on behaviors that are repeated consistently. This is achieved by developing algorithms that manage to identify patterns in the data and establish correlations between them.

8. K-Nearest Neighbors is a clustering algorithm. It consists of recognizing patterns to calculate the probability that an element belongs to a class according to its proximity in space to the elements of that classification. It is a method of classifying cases based on their resemblance to others; it was developed as a way to recognize patterns in data without the need for an exact match to stored patterns or cases.

9. Ensemble Models is famous for its accuracy due to the availability of boosting and bagging algorithms, which are general procedures for reducing the variance of a statistical learning method. The basic idea is to combine simple (weak) prediction methods, to obtain a very powerful (robust) prediction method. Create a new model by training several similar models and combining the results to improve accuracy, reduce variance and bias, and identify the best model to use with new data.

10. Gradient Enhancement, performs a resampling (resampling method) of a data set to generate results that form a weighted average of the data set. It can also be used to build hypothesis tests

11. Incremental Response Models, used to reduce Churn or check the effectiveness of different Marketing actions. The probability change caused by an action is modelled.


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Josavere