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Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. Typically, this approach is essential, especially for the banking and finance sector in today’s world. Banking institutions actively use the data within their reach in a bid to keep their customers happy. The Role of Big Data.
In this article, we will discuss how Python runs data preprocessing with its exhaustive machine learning libraries and influences business decision-making. Banking & Digital Payment Solutions. Banking and digital financial transactions require high-end security to stop breaching and safety of the transactions.
More notably, they have an intelligence data scanning facility that doesn’t break the bank, making it a great option for businesses trying to save on their cloud usage bill. Finally, JupiterOne provides visualization tools and reports to assess security posture and track compliance with regulatory standards like PCI, HIPAA, and SOC2.
Here are some financial analytics tools that are worth exploring: TrendingView is a financial analytics tool that helps you create useful financial visualizations. This article will list five fundamental focus areas for a business’ financial performance. This can help save on expenses, including rent, utility bills, and transport.
The truth behind customer onboarding processes in many industries such as banks is relatively poor at managing and collecting consumer data. Create visualizations and reports. To further improve your onboarding process flow, you need to visualize it. Visualizations are easier to process for our brains. Wrapping it up.
An article on Towards Data Science for a paper for a course on Computing and Society at Bucknell University showed that there are a number of case studies on artificial intelligence. When you have found the ones you want, you can then buy visually similar items with the click of a button. It’s also becoming more efficient too.
In this article, I am going to explain descriptive analytics in-depth with a real-life use case. Any form of analytics starts with the collection of data and developing a model to summarize and create visual patterns for better understanding. Predictive and prescriptive are the other two types of analytics.
In this article, I would like to share some ideas and try to give answers to those questions. For example, the desired business goal of some bank might be to develop more long-term relationships with the clients, through contracting more retail lending products. All these visual models are the source of stories for your backlog.
In this article, we will discuss the Decision Tree analysis method. To access the characteristics of a customer such as his or her purchase frequency, income, age, type of bank account, occupation etc. that leads to purchase of a particular banking product such as installment loan, personal loan, checking account etc.
Visual graphs are the core of descriptive statistics. The customer care of the bank may call or send a message to the user to verify the transaction. Publish Articles. Published articles are a fine addition to the resume. Basic knowledge of statistics is essential for data science.
In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default. What is Binary Logistic Regression Classification?
In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default. What is Binary Logistic Regression Classification?
In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default. What is Binary Logistic Regression Classification?
In this article, we will discuss the KNN Classification method of analysis. Credit/Loan Approval Analysis – Given a list of client transactional attributes, the business can predict whether a client will default on a bank loan. What is the KNN Classification Algorithm? It is useful for recognizing patterns and for estimating.
In this article, we will discuss the KNN Classification method of analysis. Credit/Loan Approval Analysis – Given a list of client transactional attributes, the business can predict whether a client will default on a bank loan. What is the KNN Classification Algorithm? It is useful for recognizing patterns and for estimating.
In this article, we will discuss the KNN Classification method of analysis. Credit/Loan Approval Analysis – Given a list of client transactional attributes, the business can predict whether a client will default on a bank loan. What is the KNN Classification Algorithm? It is useful for recognizing patterns and for estimating.
This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes. Business Benefit: Once the segments are identified, the bank will have a loan applicants’ dataset with each applicant labeled as high/medium/low risk. What is Hierarchical Clustering?
This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes. Business Benefit: Once the segments are identified, the bank will have a loan applicants’ dataset with each applicant labeled as high/medium/low risk. What is Hierarchical Clustering?
This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes. Business Benefit: Once the segments are identified, the bank will have a loan applicants’ dataset with each applicant labeled as high/medium/low risk. What is Hierarchical Clustering?
Do a Google images search for “vaults” and you’ll get a quick visual tour of architectural history, a display of the varied designs of bank safes created to secure cash and other valuables, and even an introduction to gymnastic vaults. The post The Architecture of Cyber Recovery: Cyber Recovery Vaults appeared first on DATAVERSITY.
In this article, we will discuss the Decision Tree analysis method. To access the characteristics of a customer such as his or her purchase frequency, income, age, type of bank account, occupation etc. that leads to purchase of a particular banking product such as installment loan, personal loan, checking account etc.
In this article, we will discuss the Decision Tree analysis method. To access the characteristics of a customer such as his or her purchase frequency, income, age, type of bank account, occupation etc. that leads to purchase of a particular banking product such as installment loan, personal loan, checking account etc.
This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining. Business Problem: A bank marketing manager wishes to analyze which products are frequently and sequentially bought together. What is the FP Growth Algorithm? Use Case – 2.
This article provides a brief overview of isotonic regression technique. What is Isotonic Regression? Isotonic Regression is a variant of linear regression and allows us to build models in piecewise linear manner i.e., breaking up the problem into few or many linear segments and performing linear interpolation of each function.
This article provides a brief overview of isotonic regression technique. What is Isotonic Regression? Isotonic Regression is a variant of linear regression and allows us to build models in piecewise linear manner i.e., breaking up the problem into few or many linear segments and performing linear interpolation of each function. Use Case – 2.
This article provides a brief overview of isotonic regression technique. What is Isotonic Regression? Isotonic Regression is a variant of linear regression and allows us to build models in piecewise linear manner i.e., breaking up the problem into few or many linear segments and performing linear interpolation of each function. Use Case – 2.
This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining. Use Case – 2 Business Problem: A bank marketing manager wishes to analyze which products are frequently and sequentially bought together. What is the FP Growth Algorithm?
This article provides a brief explanation of the SVM Classification method of analytics. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. What is SVM Classification Analysis?
This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining. Use Case – 2 Business Problem: A bank marketing manager wishes to analyze which products are frequently and sequentially bought together. What is the FP Growth Algorithm?
This article provides a brief explanation of the SVM Classification method of analytics. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. What is SVM Classification Analysis?
This article provides a brief explanation of the SVM Classification method of analytics. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. What is SVM Classification Analysis? Use Case – 1.
How I aced the CBAP exam – In this article, Satheesh S is sharing his experience. Disclaimer: This article no way suggests or claims that Satheesh S did any course with Techcanvass. This article is for sharing the successful professional’s experiences so that you can use that for your benefit.
In this article, we discuss the analytical method known as frequent pattern mining, previously known as ‘association’ What is Frequent Pattern Mining? Use Case – 2 Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together.
In this article, we discuss the analytical method known as frequent pattern mining, previously known as ‘association’ What is Frequent Pattern Mining? Use Case – 2 Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together.
In this article, we discuss the analytical method known as frequent pattern mining, previously known as ‘association’ What is Frequent Pattern Mining? Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together. Use Case – 2.
In this article, I have listed these roles, listed down their responsibilities and their core skills. Data Visualization Specialist/Designer These experts convey trends and insights through visual data. Data Visualization Specialist/Designer These experts convey trends and insights through visual data.
This article provides a brief overview of random forest classification technique. What is Random Forest Classification? The Random Forest Classification model constructs many decision trees wherein each tree votes and outputs the most popular class as the prediction result. Business Use Case 2 Business Problem: Predict quality of Red Wine.
This article provides a brief overview of random forest classification technique. Business Benefit: The predictive model will help us identify whether a customer fails to repay the loan depending on certain factors, which would lead to easier identification of risky customers and help the bank avert the risk delinquencies.
This article provides a brief overview of random forest classification technique. Business Benefit: The predictive model will help us identify whether a customer fails to repay the loan depending on certain factors, which would lead to easier identification of risky customers and help the bank avert the risk delinquencies.
This article provides a brief explanation of the KMeans Clustering algorithm. Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc. What is the KMeans Clustering algorithm?
This article provides a brief explanation of the KMeans Clustering algorithm. Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc. What is the KMeans Clustering algorithm?
This article provides a brief explanation of the KMeans Clustering algorithm. Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc. What is the KMeans Clustering algorithm?
This article explains the Karl Pearson Correlation method of analysis, and how it can be applied in business. Business Problem: A bank wants to find the correlation between income and credit card delinquency rate of credit card holders. What is the Karl Pearson Correlation Analytical Technique?
In this article, we discuss four critical features to consider when integrating analytics with Tally ERP. Designed for Business Users The tools you provide for your business users should include Augmented Analytics, Business Intelligence and Reporting Integrated with the Tally ERP solution for easy access and intuitive use.
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