Author: Daniel Medina
The generation of value in a for-profit company can be associated, among others, with profitability indicators, EBITDA and business value. Undoubtedly, the managers of organizations continually seek to establish strategies, objectives and projects that lead to growth, which is why research on frameworks for the development of strategies to increase business value based on data science is challenging and promising. On the other hand, the financial sector is a transversal enabler in the economy for the circulation of credit resources and correct administration of surpluses, for which reason delving into monetization scenarios taking advantage of data science techniques is considered relevant and notorious. Given the diversity of initiatives and investment in the Fintech sector, it is proposed that the financial sector and its supply chain have as a strategic priority the use of data as a differential in the market.
The MIT (Massachusetts Institute of Technology) specifically in the Research Center for Information Systems MIT CISR for its acronym in English, is one of the most active actors in the investigation of frameworks and business success stories related to the use of data in business. This institution proposes the five capacities that must be developed in an organization that wishes to effectively develop a data-based monetization strategy: Data assets, Platforms for data exploitation, Data Science, Legal capacity to use data and understanding of the needs of the client. These five capabilities in turn have three levels of maturity that allow establishing the status of an organization to develop effective data monetization initiatives.
On the other hand, the MIT CISR has also developed a model that allows data-based monetization initiatives to be classified into three categories according to their business approach. This model is known as (Improving, Wrapping, Selling) and describes the business characteristics that are addressed in data science projects in companies, giving it a classification for industry studies. The main elements of the categories are summarized below:
In this category are the projects that increase the productivity of organizations, reducing costs or increasing income. “Improving core business processes using data: making money by doing things better, cheaper, and faster.”
They are initiatives that generate additional income to a product by improving its reports, reports, dashboards, among others. It is proposed to generate revenue growth in current customers. “Analysis of packaging around offers: making money by distinguishing offers with features and experiences.”
Initiatives intended to generate information sales services are considered for this category. This category requires having a high level of maturity in the five organizational capabilities.” Sell Information Solutions: Earn money by implementing new information offerings.
Since 2005, with the beginning of the internet revolution of applications and social content, an urgent need arose for the digital transformation of processes and the value chain, seeking to improve income, mitigate risks and reduce costs. In order to argue the thesis of strategic approach to data science, we are going to list the main and diverse fields of action of data science initiatives in the financial sector.
Each time a credit request is generated, the bank proceeds to evaluate whether the client is in a position to pay that loan with its corresponding interest in a determined period of time based on multiple variables such as profitability, leverage or liquidity to calculate the risk. Below are some of the problems to be solved in the financial sector in relation to the optimization of credit placement:
Reduce the provision of portfolio in the Banks: The accounting of the financial sector is regulated by the Financial Superintendence of Colombia, that is to say, that the accounting policies of Provision of Portfolio of the financial entities that originate credit cards specify conditions that in case of default of installments of a customer’s credit card, the financial entity must make a provision for accounting purposes that responds to a possible non-payment by the customer. Consequently, a customer who does not pay a fee generates a decrease in the Bank’s profits.
Limited credit placement capacity (solvency): The credit placement capacity of a financial institution can be associated with commercial, technological and commercial coverage factors, however, one of the main restrictions for a Bank, for example, to hand over a credit card to Colombians is that in the event of a massive default the Bank would have no way to respond to its savers. Therefore, the Financial Superintendency establishes indicators that allow monitoring the level of solvency of an entity and thus knowing the risk that it is generating in the financial market.
Data science techniques facilitate these complex tasks, calculating critical variables and with great precision evaluating credit risk models based on financial data, credit behavior and customer consumption. In this way, it is possible to identify when to increase or reduce a client’s credit line based on the bank’s aversion to risk.
The study and analysis of data is the best way to know the behavior of customers, their level of affinity, relationship and what their behavior patterns are in certain segments, which allows designing strategies or recommending specific products through marketing campaigns. personalized advertising.
Through machine learning, possible fraud can be detected in real time. The correlation of transactional events allows extracting data and behavior patterns to identify anomalous behavior or suspicious actions, in which case there is the possibility of requesting a second authentication factor or additional verification of transactions.
Most of the technological innovations in the financial sector are aimed at reducing the friction that may exist in processes both with the client and internally. The automation of automatic and recurring processes allows employees who previously performed those tasks to focus on other tasks or functions of greater value or urgency.
We can conclude that value creation initiatives in for-profit organizations, based on the use of data, can be classified into projects of Improvement, Packaging or Sales of information services. In the financial sector, the objective of developing differentiated capabilities for understanding customer needs and data science is identified.
At the center of the innovation of these companies are managers with a high degree of conviction and data scientists with high technical and business skills, making the financial sector one of the sectors with the greatest participation in the evolution of data science.
Author: Daniel Medina
Author:
The generation of value in a for-profit company can be associated, among others, with profitability indicators, EBITDA and business value. Undoubtedly, the managers of organizations continually seek to establish strategies, objectives and projects that lead to growth, which is why research on frameworks for the development of strategies to increase business value based on data science is challenging and promising. On the other hand, the financial sector is a transversal enabler in the economy for the circulation of credit resources and correct administration of surpluses, for which reason delving into monetization scenarios taking advantage of data science techniques is considered relevant and notorious. Given the diversity of initiatives and investment in the Fintech sector, it is proposed that the financial sector and its supply chain have as a strategic priority the use of data as a differential in the market.
The MIT (Massachusetts Institute of Technology) specifically in the Research Center for Information Systems MIT CISR for its acronym in English, is one of the most active actors in the investigation of frameworks and business success stories related to the use of data in business. This institution proposes the five capacities that must be developed in an organization that wishes to effectively develop a data-based monetization strategy: Data assets, Platforms for data exploitation, Data Science, Legal capacity to use data and understanding of the needs of the client. These five capabilities in turn have three levels of maturity that allow establishing the status of an organization to develop effective data monetization initiatives.
On the other hand, the MIT CISR has also developed a model that allows data-based monetization initiatives to be classified into three categories according to their business approach. This model is known as (Improving, Wrapping, Selling) and describes the business characteristics that are addressed in data science projects in companies, giving it a classification for industry studies. The main elements of the categories are summarized below:
In this category are the projects that increase the productivity of organizations, reducing costs or increasing income. “Improving core business processes using data: making money by doing things better, cheaper, and faster.”
They are initiatives that generate additional income to a product by improving its reports, reports, dashboards, among others. It is proposed to generate revenue growth in current customers. “Analysis of packaging around offers: making money by distinguishing offers with features and experiences.”
Initiatives intended to generate information sales services are considered for this category. This category requires having a high level of maturity in the five organizational capabilities.” Sell Information Solutions: Earn money by implementing new information offerings.
Since 2005, with the beginning of the internet revolution of applications and social content, an urgent need arose for the digital transformation of processes and the value chain, seeking to improve income, mitigate risks and reduce costs. In order to argue the thesis of strategic approach to data science, we are going to list the main and diverse fields of action of data science initiatives in the financial sector.
Each time a credit request is generated, the bank proceeds to evaluate whether the client is in a position to pay that loan with its corresponding interest in a determined period of time based on multiple variables such as profitability, leverage or liquidity to calculate the risk. Below are some of the problems to be solved in the financial sector in relation to the optimization of credit placement:
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Reduce the provision of portfolio in the Banks: The accounting of the financial sector is regulated by the Financial Superintendence of Colombia, that is to say, that the accounting policies of Provision of Portfolio of the financial entities that originate credit cards specify conditions that in case of default of installments of a customer’s credit card, the financial entity must make a provision for accounting purposes that responds to a possible non-payment by the customer. Consequently, a customer who does not pay a fee generates a decrease in the Bank’s profits.
Limited credit placement capacity (solvency): The credit placement capacity of a financial institution can be associated with commercial, technological and commercial coverage factors, however, one of the main restrictions for a Bank, for example, to hand over a credit card to Colombians is that in the event of a massive default the Bank would have no way to respond to its savers. Therefore, the Financial Superintendency establishes indicators that allow monitoring the level of solvency of an entity and thus knowing the risk that it is generating in the financial market.
Data science techniques facilitate these complex tasks, calculating critical variables and with great precision evaluating credit risk models based on financial data, credit behavior and customer consumption. In this way, it is possible to identify when to increase or reduce a client’s credit line based on the bank’s aversion to risk.
The study and analysis of data is the best way to know the behavior of customers, their level of affinity, relationship and what their behavior patterns are in certain segments, which allows designing strategies or recommending specific products through marketing campaigns. personalized advertising.
Through machine learning, possible fraud can be detected in real time. The correlation of transactional events allows extracting data and behavior patterns to identify anomalous behavior or suspicious actions, in which case there is the possibility of requesting a second authentication factor or additional verification of transactions.
Most of the technological innovations in the financial sector are aimed at reducing the friction that may exist in processes both with the client and internally. The automation of automatic and recurring processes allows employees who previously performed those tasks to focus on other tasks or functions of greater value or urgency.
We can conclude that value creation initiatives in for-profit organizations, based on the use of data, can be classified into projects of Improvement, Packaging or Sales of information services. In the financial sector, the objective of developing differentiated capabilities for understanding customer needs and data science is identified.
At the center of the innovation of these companies are managers with a high degree of conviction and data scientists with high technical and business skills, making the financial sector one of the sectors with the greatest participation in the evolution of data science.