ElegantJ BI

OCTOBER 16, 2018

Let’s look at two examples: Based on the historical data related to credit card payments, loan payments, delinquency rate, outstanding balance we want to classify/divide the customers into those who default and those who do not default. a bank needs to classify customers into those that will default and those that will not default.

Banking 162

Banking Retail Data Discovery Business Intelligence 162

ElegantJ BI

OCTOBER 17, 2018

Use Case – 1 Business Problem: A bank loans officer wants to predict if loan applicants will be a bank defaulter or non defaulter based on attributes such as loan amount, monthly installments, employment tenure, how many times has the applicant been delinquent, annual income, debt to income ratio etc.

Logistics 130

Logistics Banking Data Discovery Business Intelligence 130

ElegantJ BI

OCTOBER 17, 2018

Use Case – 1 Business Problem: A bank loans officer wants to predict if loan applicants will be a bank defaulter or non defaulter based on attributes such as loan amount, monthly installments, employment tenure, how many times has the applicant been delinquent, annual income, debt to income ratio etc.

Logistics 130

Logistics Banking Data Discovery Business Intelligence 130

ElegantJ BI

OCTOBER 17, 2018

Business Problem: A bank loans officer wants to predict if loan applicants will be a bank defaulter or non defaulter based on attributes such as loan amount, monthly installments, employment tenure, how many times has the applicant been delinquent, annual income, debt to income ratio etc. Use Case – 1.

Logistics 130

Logistics Banking Data Discovery Business Intelligence 130

ElegantJ BI

OCTOBER 16, 2018

Use Case – 1 Business Problem: A bank wants to group loan applicants into high/medium/low risk based on attributes such as loan amount, monthly installments, employment tenure, the number of times the applicant has been delinquent in other payments, annual income, debt to income ratio etc.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 16, 2018

Use Case – 1 Business Problem: A bank wants to group loan applicants into high/medium/low risk based on attributes such as loan amount, monthly installments, employment tenure, the number of times the applicant has been delinquent in other payments, annual income, debt to income ratio etc.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 16, 2018

Business Problem: A bank wants to group loan applicants into high/medium/low risk based on attributes such as loan amount, monthly installments, employment tenure, the number of times the applicant has been delinquent in other payments, annual income, debt to income ratio etc. Use Case – 2.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 18, 2018

KNN Classification analysis can be useful in evaluating many types of data. Credit/Loan Approval Analysis – Given a list of client transactional attributes, the business can predict whether a client will default on a bank loan. Weather Prediction – Based on temperature, humidity, pressure etc.,

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 18, 2018

KNN Classification analysis can be useful in evaluating many types of data. Credit/Loan Approval Analysis – Given a list of client transactional attributes, the business can predict whether a client will default on a bank loan. Weather Prediction – Based on temperature, humidity, pressure etc.,

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 18, 2018

KNN Classification analysis can be useful in evaluating many types of data. Credit/Loan Approval Analysis – Given a list of client transactional attributes, the business can predict whether a client will default on a bank loan. Weather Prediction – Based on temperature, humidity, pressure etc., Use Case – 1.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

AUGUST 14, 2018

Business Problem: A bank marketing manager wishes to analyze which products are frequently and sequentially bought together. Business Benefit: Based on the rules generated, the organization can determine which banking products can be cross sold to each existing or prospective customer to drive sales and bank revenue.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 16, 2018

Let’s look at two examples: Based on the historical data related to credit card payments, loan payments, delinquency rate, outstanding balance we want to classify/divide the customers into those who default and those who do not default. a bank needs to classify customers into those that will default and those that will not default.

Banking 130

Banking Retail Data Discovery Business Intelligence 130

ElegantJ BI

OCTOBER 16, 2018

Let’s look at two examples: Based on the historical data related to credit card payments, loan payments, delinquency rate, outstanding balance we want to classify/divide the customers into those who default and those who do not default. a bank needs to classify customers into those that will default and those that will not default.

Banking 130

Banking Retail Data Discovery Business Intelligence 130

ElegantJ BI

OCTOBER 16, 2018

Use Case – 1 Business Problem: A bank loan officer wants to predict if the loan applicant will default on a loan, based attributes such as Loan amount, monthly payment installments, employment tenure, number of times delinquent, annual income, debt to income ratio etc. How Can SVM Classification Analysis Benefit Business Analytics?

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 16, 2018

Use Case – 1 Business Problem: A bank loan officer wants to predict if the loan applicant will default on a loan, based attributes such as Loan amount, monthly payment installments, employment tenure, number of times delinquent, annual income, debt to income ratio etc. How Can SVM Classification Analysis Benefit Business Analytics?

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 16, 2018

Business Problem: A bank loan officer wants to predict if the loan applicant will default on a loan, based attributes such as Loan amount, monthly payment installments, employment tenure, number of times delinquent, annual income, debt to income ratio etc. How Can SVM Classification Analysis Benefit Business Analytics? Use Case – 1.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

AUGUST 14, 2018

Use Case – 2 Business Problem: A bank marketing manager wishes to analyze which products are frequently and sequentially bought together. Business Benefit: Based on the rules generated, the organization can determine which banking products can be cross sold to each existing or prospective customer to drive sales and bank revenue.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

AUGUST 14, 2018

Use Case – 2 Business Problem: A bank marketing manager wishes to analyze which products are frequently and sequentially bought together. Business Benefit: Based on the rules generated, the organization can determine which banking products can be cross sold to each existing or prospective customer to drive sales and bank revenue.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

AUGUST 11, 2017

Its customers include well-renowned entities in banking, life and general insurance and non-banking finance companies in India. ElegantJ BI provides deep dive analytics and dazzling visualization without the need for user-technical skills and offers a driving force in analytics around the world.

Data Discovery 130

Data Discovery Banking Business Intelligence Business Intelligence 130

ElegantJ BI

AUGUST 11, 2017

Its customers include well-renowned entities in banking, life and general insurance and non-banking finance companies in India. ElegantJ BI provides deep dive analytics and dazzling visualization without the need for user-technical skills and offers a driving force in analytics around the world.

Data Discovery 130

Data Discovery Banking Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 17, 2018

Business Problem: A bank wants to find the correlation between income and credit card delinquency rate of credit card holders. Input Data: The delinquency rate of each credit card customer and the monthly income of each credit card customer. How Can the Karl Pearson Correlation Method Be Used to Target Enterprise Analytical Needs?

Data Discovery 130

Data Discovery Banking Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 17, 2018

Business Problem: A bank wants to find the correlation between income and credit card delinquency rate of credit card holders. Input Data: The delinquency rate of each credit card customer and the monthly income of each credit card customer. How Can the Karl Pearson Correlation Method Be Used to Target Enterprise Analytical Needs?

Data Discovery 130

Data Discovery Banking Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 17, 2018

Business Problem: A bank wants to find the correlation between income and credit card delinquency rate of credit card holders. Input Data: The delinquency rate of each credit card customer and the monthly income of each credit card customer. How Can the Karl Pearson Correlation Method Be Used to Target Enterprise Analytical Needs?

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Banking 130

ElegantJ BI

OCTOBER 18, 2018

Use Case – 2 Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together. Business Benefit: Based on the rules generated, banking products can be cross-sold to each existing or prospective customer to drive sales and bank revenue.

Business Analysis 130

Business Analysis Business Analysis Banking Data Discovery 130

ElegantJ BI

OCTOBER 18, 2018

Use Case – 2 Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together. Business Benefit: Based on the rules generated, banking products can be cross-sold to each existing or prospective customer to drive sales and bank revenue.

Business Analysis 130

Business Analysis Business Analysis Banking Data Discovery 130

ElegantJ BI

OCTOBER 18, 2018

Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together. Business Benefit: Based on the rules generated, banking products can be cross-sold to each existing or prospective customer to drive sales and bank revenue. Use Case – 2.

Business Analysis 130

Business Analysis Business Analysis Banking Data Discovery 130

ElegantJ BI

JULY 7, 2016

Graph Engine: A new graph engine for superior performance and visualization effects. This enhancement further enables crisp, clear visualization and supports the concept of Smart Visualization. GeoMaps: The GeoMaps component supports worldwide GeoMaps visualization. Here are just some of the highlights of version 4.3

Business Intelligence 130

Business Intelligence Business Intelligence Data Discovery Logistics 130

ElegantJ BI

JULY 7, 2016

Graph Engine: A new graph engine for superior performance and visualization effects. This enhancement further enables crisp, clear visualization and supports the concept of Smart Visualization. GeoMaps: The GeoMaps component supports worldwide GeoMaps visualization. Here are just some of the highlights of version 4.3

Business Intelligence 130

Business Intelligence Business Intelligence Data Discovery Logistics 130

ElegantJ BI

JULY 7, 2016

Graph Engine: A new graph engine for superior performance and visualization effects. This enhancement further enables crisp, clear visualization and supports the concept of Smart Visualization. GeoMaps: The GeoMaps component supports worldwide GeoMaps visualization. Here are just some of the highlights of version 4.3

Business Intelligence 130

Business Intelligence Business Intelligence Data Discovery Logistics 130

ElegantJ BI

AUGUST 10, 2018

How Does an Enterprise Use the KMeans Clustering Algorithm to Analyze Data? 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.

Data Discovery 130

Data Discovery Banking Business Intelligence Business Intelligence 130

ElegantJ BI

AUGUST 10, 2018

How Does an Enterprise Use the KMeans Clustering Algorithm to Analyze Data? 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.

Data Discovery 130

Data Discovery Banking Business Intelligence Business Intelligence 130

ElegantJ BI

AUGUST 10, 2018

How Does an Enterprise Use the KMeans Clustering Algorithm to Analyze Data? 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. Use Case – 2.

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Banking 130

ElegantJ BI

OCTOBER 18, 2018

Use Case – 1 Business Problem: A bank loans officer wants to predict if a loan applicant will be a bank defaulter or non defaulter based on attributes such as loan amount, monthly installment, employment tenure, the number of times delinquent, annual income, debt to income ratio etc.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 18, 2018

Use Case – 1 Business Problem: A bank loans officer wants to predict if a loan applicant will be a bank defaulter or non defaulter based on attributes such as loan amount, monthly installment, employment tenure, the number of times delinquent, annual income, debt to income ratio etc.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

OCTOBER 18, 2018

Business Problem: A bank loans officer wants to predict if a loan applicant will be a bank defaulter or non defaulter based on attributes such as loan amount, monthly installment, employment tenure, the number of times delinquent, annual income, debt to income ratio etc. a business can predict the likelihood of fraud.

Banking 130

Banking Data Discovery Business Intelligence Business Intelligence 130

ElegantJ BI

MAY 20, 2016

ElegantJ BI has created a clear roadmap toward ‘Smart Data Discovery’ that promotes self-serve data preparation, smart visualization, and Plug n’ Play predictive analytics , to put the power of BI tools in the hands of business users to transform them into citizen data scientists. .”

Business Intelligence 130

Business Intelligence Business Intelligence Data Discovery Logistics 130

ElegantJ BI

MAY 20, 2016

ElegantJ BI has created a clear roadmap toward ‘Smart Data Discovery’ that promotes self-serve data preparation, smart visualization, and Plug n’ Play predictive analytics , to put the power of BI tools in the hands of business users to transform them into citizen data scientists. .”

Business Intelligence 130

Business Intelligence Business Intelligence Data Discovery Logistics 130