Top 7 Scenarios for Using Data Science in Finance
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In recent years, data science and machine learning have become much better able to cope with basic financial tasks. This shows that companies want to know more about technological advantages and how they change business strategies.
To tell you about it, we have prepared a list of data science use casecases that have the greatest impact on the financial sector. They cover a wide range of businesses: from data management to trading strategies. They all share the enormous potential to improve financial decisions.
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Risk management automation
Risk management is an extremely important environment for a financial institution that is responsible for the security, reliability, and strategic decisions of a company. The approach to risk management has changed significantly in recent years, which has changed the financial sector itself. Today, machine learning models determine the vector of business development like never before.
There are many sources of risk: competitors, investors, regulators, or customers. The risks themselves differ in significance and potential damage. Therefore, the main steps — identifying, prioritizing, and monitoring risks — are excellent tasks for machine learning. Training on large amounts of customer data, financial loans, and insurance results helps algorithms not only improve risk assessment models, but also increase cost-effectiveness and sustainability.
One of the most important applications of data science and artificial intelligence in risk management is determining the creditworthiness of potential customers. Companies use machine learning algorithms that analyze past spending patterns and patterns to establish a suitable loan amount for a particular customer. This approach is very useful when working with both new clients and clients with a short credit history.
Digitalization and automation of risk management processes are still in the early stages, but the potential is unlimited. Financial institutions still need to prepare for such changes by automating key financial processes, improving the analytical skills of the financial team and strategically investing in technology. It is enough to start moving in this direction so that the profit does not take long to wait.
Customer data management
Data is the most important resource for financial companies. Therefore, effective data management is the key to business success. Today there is a huge amount of data that is diverse in its structure and volume: from activity data on social networks and mobile interactions to market data and transaction details. Often, financial professionals have to work with data that is structured poorly or not structured at all. Handle them very difficult.
Therefore, for most companies, it is obvious that the integration of machine learning methods into the management process is the basic need for extracting real information from data. AI-tools, in particular, natural language processing, data mining and text analysis, help to convert data into information, which contributes to their more rational management and improvement of business decisions. And this as a result increases profitability. Thus, machine learning algorithms analyze the influence of specific financial trends and market changes, studying the financial data of clients for a certain period. These methods can also be used to generate automatic reports.
Predictive analytics
Today, analytics is the basis of financial services. Of particular note is the predictive analytics, which reveals patterns in the data to predict future events, which can be guided now.
Thanks to the analysis of social networks, news trends and other sources of information, this skilful analytics has found several applications: the prediction of prices and LTV buyers, future events, the expected outflow, changes in the stock market. And, most importantly, these methods help to find answers to the complex question: “How best to interfere?”
Real Time Analytics
Real-time analytics fundamentally changes financial processes by analyzing a large amount of data from different sources, quickly identifying any changes, and finding the best response to them. There are 3 main areas of application of real-time analytics in finance:
Fraud Detection
Financial services are required to guarantee the highest level of security to their customers. Criminals are constantly looking for new ways to set traps, so an important task for the company is to find a good system to detect fraud. Only a qualified data scientist will be able to create ideal algorithms to detect and prevent abnormalities in user behavior or current workflows amid such a variety of fraud. So notifications about strange financial purchases or large cash withdrawals of certain users will lead to blocking of these actions until the client confirms. In the stock market, machine learning tools identify patterns in trade data that may indicate manipulation and warn of the need to investigate. However, the most important thing in these algorithms is the ability to self-learn, to become smarter and more effective over time.
Buyer analysis
Real-time analytics help you better understand customers and apply effective personalization. Complex machine learning algorithms and methods for analyzing the tonality of clients help to understand customer behavior, interaction in social networks, feedback and opinions that improves personalization and increases profits. Since the amount of data is huge, only an experienced data scientist will be able to make an accurate analysis.
Algorithmic Trading
Analytics in real time affects this area the most, because every second counts. Based on the latest information obtained from the analysis of traditional and non-traditional data, financial institutions can make appropriate decisions in real time. And since such data has value only for a short time, competitiveness in this area means the fastest methods for analysis.
Another promising direction opens up if we combine real-time and predictive analytics in this area. Previously, mathematicians were often hired to build a statistical model and use historical data to create trading algorithms that predict market opportunities. However, today artificial intelligence offers methods to accelerate this process and, most importantly, is constantly being improved.
Thus, data science and artificial intelligence have revolutionized the trading sector by running algorithmic trading strategies. Most exchanges use computers to make decisions based on algorithms and adjust strategies based on new data. AI endlessly processes tons of information, including tweets, financials, data from news, books, and even television programs. As a result, he understands current global trends and continues to improve predictions about financial markets.
In general, real-time analytics and predictive analytics have significantly changed the situation in various financial areas. Technologies such as Hadoop, NoSQL and Storm, traditional and non-traditional data sets and the most accurate algorithms help data engineers change finances.
Deep personalization and customization
Organizations understand that in order to compete in today's market, you need to increase engagement through high-quality, personalized relationships with customers. The idea is to analyze the client's digital experience and change it according to their interests and preferences. AI already understands human language and emotions much better, which brings personalization to a whole new level. In addition, data engineers can build models that study customer behavior to identify situations where they need financial assistance. The combination of predictive analysis tools and digital delivery capabilities will help to cope with such a complex task, guiding the client to the best financial solution at the most appropriate time, recommending personalized offers based on consumer preferences, socio-demographic trends and other parameters.
Conclusion
Using data science methods provides financial institutions with a tremendous opportunity to stand out among competitors and upgrade business. The large amount of constantly changing financial data creates the need to involve machine learning and AI tools in different aspects of the business.
We focused on 7 scenarios for using data science in the financial sector, which we think are the main ones, but there are other noteworthy ones. If you have ideas on this, share them in the comments!
Traditionally, we are waiting for your comments. And we remind you that less than a week is left before the course starts!
In recent years, data science and machine learning have become much better able to cope with basic financial tasks. This shows that companies want to know more about technological advantages and how they change business strategies.
To tell you about it, we have prepared a list of data science use casecases that have the greatest impact on the financial sector. They cover a wide range of businesses: from data management to trading strategies. They all share the enormous potential to improve financial decisions.
')
Risk management automation
Risk management is an extremely important environment for a financial institution that is responsible for the security, reliability, and strategic decisions of a company. The approach to risk management has changed significantly in recent years, which has changed the financial sector itself. Today, machine learning models determine the vector of business development like never before.
There are many sources of risk: competitors, investors, regulators, or customers. The risks themselves differ in significance and potential damage. Therefore, the main steps — identifying, prioritizing, and monitoring risks — are excellent tasks for machine learning. Training on large amounts of customer data, financial loans, and insurance results helps algorithms not only improve risk assessment models, but also increase cost-effectiveness and sustainability.
One of the most important applications of data science and artificial intelligence in risk management is determining the creditworthiness of potential customers. Companies use machine learning algorithms that analyze past spending patterns and patterns to establish a suitable loan amount for a particular customer. This approach is very useful when working with both new clients and clients with a short credit history.
Digitalization and automation of risk management processes are still in the early stages, but the potential is unlimited. Financial institutions still need to prepare for such changes by automating key financial processes, improving the analytical skills of the financial team and strategically investing in technology. It is enough to start moving in this direction so that the profit does not take long to wait.
Customer data management
Data is the most important resource for financial companies. Therefore, effective data management is the key to business success. Today there is a huge amount of data that is diverse in its structure and volume: from activity data on social networks and mobile interactions to market data and transaction details. Often, financial professionals have to work with data that is structured poorly or not structured at all. Handle them very difficult.
Therefore, for most companies, it is obvious that the integration of machine learning methods into the management process is the basic need for extracting real information from data. AI-tools, in particular, natural language processing, data mining and text analysis, help to convert data into information, which contributes to their more rational management and improvement of business decisions. And this as a result increases profitability. Thus, machine learning algorithms analyze the influence of specific financial trends and market changes, studying the financial data of clients for a certain period. These methods can also be used to generate automatic reports.
Predictive analytics
Today, analytics is the basis of financial services. Of particular note is the predictive analytics, which reveals patterns in the data to predict future events, which can be guided now.
Thanks to the analysis of social networks, news trends and other sources of information, this skilful analytics has found several applications: the prediction of prices and LTV buyers, future events, the expected outflow, changes in the stock market. And, most importantly, these methods help to find answers to the complex question: “How best to interfere?”
Real Time Analytics
Real-time analytics fundamentally changes financial processes by analyzing a large amount of data from different sources, quickly identifying any changes, and finding the best response to them. There are 3 main areas of application of real-time analytics in finance:
Fraud Detection
Financial services are required to guarantee the highest level of security to their customers. Criminals are constantly looking for new ways to set traps, so an important task for the company is to find a good system to detect fraud. Only a qualified data scientist will be able to create ideal algorithms to detect and prevent abnormalities in user behavior or current workflows amid such a variety of fraud. So notifications about strange financial purchases or large cash withdrawals of certain users will lead to blocking of these actions until the client confirms. In the stock market, machine learning tools identify patterns in trade data that may indicate manipulation and warn of the need to investigate. However, the most important thing in these algorithms is the ability to self-learn, to become smarter and more effective over time.
Buyer analysis
Real-time analytics help you better understand customers and apply effective personalization. Complex machine learning algorithms and methods for analyzing the tonality of clients help to understand customer behavior, interaction in social networks, feedback and opinions that improves personalization and increases profits. Since the amount of data is huge, only an experienced data scientist will be able to make an accurate analysis.
Algorithmic Trading
Analytics in real time affects this area the most, because every second counts. Based on the latest information obtained from the analysis of traditional and non-traditional data, financial institutions can make appropriate decisions in real time. And since such data has value only for a short time, competitiveness in this area means the fastest methods for analysis.
Another promising direction opens up if we combine real-time and predictive analytics in this area. Previously, mathematicians were often hired to build a statistical model and use historical data to create trading algorithms that predict market opportunities. However, today artificial intelligence offers methods to accelerate this process and, most importantly, is constantly being improved.
Thus, data science and artificial intelligence have revolutionized the trading sector by running algorithmic trading strategies. Most exchanges use computers to make decisions based on algorithms and adjust strategies based on new data. AI endlessly processes tons of information, including tweets, financials, data from news, books, and even television programs. As a result, he understands current global trends and continues to improve predictions about financial markets.
In general, real-time analytics and predictive analytics have significantly changed the situation in various financial areas. Technologies such as Hadoop, NoSQL and Storm, traditional and non-traditional data sets and the most accurate algorithms help data engineers change finances.
Deep personalization and customization
Organizations understand that in order to compete in today's market, you need to increase engagement through high-quality, personalized relationships with customers. The idea is to analyze the client's digital experience and change it according to their interests and preferences. AI already understands human language and emotions much better, which brings personalization to a whole new level. In addition, data engineers can build models that study customer behavior to identify situations where they need financial assistance. The combination of predictive analysis tools and digital delivery capabilities will help to cope with such a complex task, guiding the client to the best financial solution at the most appropriate time, recommending personalized offers based on consumer preferences, socio-demographic trends and other parameters.
Conclusion
Using data science methods provides financial institutions with a tremendous opportunity to stand out among competitors and upgrade business. The large amount of constantly changing financial data creates the need to involve machine learning and AI tools in different aspects of the business.
We focused on 7 scenarios for using data science in the financial sector, which we think are the main ones, but there are other noteworthy ones. If you have ideas on this, share them in the comments!
Traditionally, we are waiting for your comments. And we remind you that less than a week is left before the course starts!
Source: https://habr.com/ru/post/444802/
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