Introduction to Business Analytics
Content
Industry Introduction(Banking):
Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. The banking industry is data-intensive with typically massive graveyards of unused and unappreciated ATM and credit processing data. As banks face increasing pressure to stay profitable, understanding customer needs and preferences becomes a critical success factor. New models of proactive risk management are being increasingly adopted by major banks and financial institutions, especially in the wake of Basel II accord. Through Data mining and advanced analytics techniques, banks are better equipped to manage market uncertainty, minimize fraud, and control exposure risk. Banking technology, few transactions actually use cash. In fact, hard currency represents only 11% of the money supply in the U.S. The rest of our “money” flows digitally from a salary to a bank to a retailer, and then through the retailer’s supply chain, to be deposited in another business’ account. to start the journey over again. That means our money has been transformed into zeros and ones.
According to IBM’s 2010 Global Chief Executive Officer Study, 89 percent of banking and financial markets CEOs say their top priority is to better understand, predict and give customers what they want. Financial metrics and KPIs provide effective measures for summarizing your overall bank performance. But in order to discover the set of critical success factors that will help banks reach their strategic goals, they need to move beyond standard business reporting and sales forecasting. By applying data mining and predictive analytics to extract actionable intelligent insights and quantifiable predictions, banks can gain insights that encompass all types of customer behavior, including channel transactions, account opening and closing, default, fraud and customer departure.
Introduction to Business Analytics:
Analytics is the use of modern data mining , pattern matching , data visualization and predictive modelling tools for analyses and algorithm to make better business decisions. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling and fact-based management to drive decision making. Analytics may be used as input for human decisions or may drive fully automated decisions. Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (that is, predict), what is the best that can happen (that is, optimize . Generally Industry uses three types of analytics as per requirement to find insights from a large volume of data:
1.Descriptive (business intelligence and data mining) 2.Predictive (Prediction or likelihood /forecasting) 3.Prescriptive (optimization and simulation) I am giving you some brief knowledge about these analytics step by step:
1.Descriptive analytics looks at data and analyzes past events for insight as to how to
approach the future. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Almost all management reporting such as sales, marketing, operations, and finance, uses this type of post-mortem analysis. Descriptive models can be used, for example, to categorize customers by their product preferences and life stage. Descriptive modeling tools can be utilized to develop further models that can simulate large number of individualized agents and make predictions. For example, descriptive analytics examines historical electricity usage data to help plan power needs and allow electric companies to set optimal prices.
2.Predictive analytics turns data into valuable, actionable information. Predictive
analytics uses data to determine the probable future outcome of an event or a likelihood of a situation occurring. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Three basic cornerstones of predictive analytics are: Predictive modeling Decision Analysis and Optimization Transaction Profiling. Simple example is for an organization that offers multiple products, predictive analytics can help analyze customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships. Another example is When a client applies for a loan the bank would like to be sure that the client will pay back the full amount of the loan. So predictive analytics uses regression technique to past data and use this to produce a probability that the borrower will repay the loan. This probability, along with the lenders experience is then used to decide if the bank should lend to a particular client.
3.Prescriptive analytics is generally used for optimization of or business mode to make
predictions and then suggests decision options to take advantage of the predictions. Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve prediction accuracy and provide better decision options. An example is energy and utilities. Natural gas prices fluctuate dramatically depending upon supply, demand, econometrics, geo-politics, and weather conditions. Gas producers, transmission (pipeline) companies and utility firms have a keen interest in more accurately predicting gas prices so that they can lock in favorable terms while hedging downside risk. Prescriptive analytics can accurately predict prices by modeling internal and external variables simultaneously and also provide decision options and show the impact of each decision option. In model optimzation we use prescriptive analytics to find out best optimal solution that bank can use this for allow us to predict if a client will pay back the loan(80 % accuracy).
For example, prescriptive analytics can benefit healthcare strategic planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demographic trends and population health trends, to more accurately plan for future capital investments such as new facilities and equipment utilization as well as understand the trade-offs between adding additional beds and expanding an existing facility versus building a new one.
Role of Analytics:
While analytics aren’t exactly new to the world of banking, plenty of banks are gearing up for their next big analytics push, propelled by a load of data and new, sophisticated tools and technologies. Why has business analytics jumped to the top of the priority list for banks? Pick a reason. Regulatory reform, managing risk, changing business models, expansion into new markets, a renewed focus on customer profitability – any one of these is reason enough for many banks to reconsider what today’s analytics capabilities can offer.
As per Deloitte research, three business drivers increase the importance of analytics within the banking industry
•
Regulatory reform – Major legislation such as Dodd-Frank, the CARD Act, FATCA (Foreign Account Tax Compliance Act) and Basel III have changed the business environment for banks. Given the focus on systemic risk, regulators are pushing banks to demonstrate better understanding of data they possess, turn data into information that supports business decisions and manage risk more effectively. Each request has major ramifications on data collection, governance and reporting. Over the next several years, regulators will finalize details in the recently passed legislation. However, banks should start transforming their business models today to comply with a radically different regulatory environment.
•
Customer profitability – Personalized offerings are expected to play a big role in attracting and retaining the most profitable customers, but studies show that a small percentage of banks have strong capabilities in this area. The CARD Act and Durbin Amendment make it even more important to understand the behavioural economics of each customer and find ways to gain wallet share in the most profitable segments.
•
Operational efficiency – while banks have trimmed a lot of fat over the past few years, there is still plenty of room for improvement, including reducing duplicative systems, manual reconciliation tasks and information technology costs.
Credit Scoring Modelling for Retail Banking Sector: • • • • Our problem is concerned with who a bank should loan its money to. When a client applies for a loan, the bank would like to be sure that the client will pay back the full amount of the loan. We need effective models that allow us to predict if a client will pay back the loan. What we have is historical data for several variables.
•
We are trying to fit a model to this historical data so we can estimate a probability of default. So analytics helps to determine the answers to all these questions and build a strategy for effective decision making in banks so that banks can reduce the risk for granting loan to which customers according to defaulting probability for respective customers in a bank.
With banking analytics your organization gains a complete and consistent view of all key profitability drivers so you can: • Manage risk effectively • Track and monitor sales, margins and operational performance • Analyze results and identify and predict trends in channels, regions, products, demographics and customer behaviour. • Dynamically adjust plans to achieve profitable growth. • Help meet regulatory demands.
How analytics can help you increase customer profitability and satisfaction, manage risk and be more operationally efficient.? Fraud detection in banking is one of the major complicated task that discover by the use of analytics. Fraud detection in banking is a critical activity that can span a series of fraud schemes and fraudulent activity from bank employees and customers alike. Since banking is a relatively highly regulated industry, there are also a number of external compliance requirements that banks must adhere to in the combat against fraudulent and criminal activity. Banks Need To Improve Use of Customer Analytics. Banks also can use analytics to enhance their strategic, back-office activities by: ● Identifying the profitability of customers across lines of business ● Reducing risk by better forecasting defaults and late payments ● Using predictive analytics technology to determine the amount of money each unique customer is allowed to withdraw from an ATM
● Segmenting customers based on demographics, relationships and transaction behaviours to increase wallet share and individual profitability ● Enhancing online merchandizing by analyzing customers’ purchasing ● Supporting mobile phone transactions and analysis of those transaction
Conclusion: In banks, analytics can help immensely in a variety of areas ranging from fraud detection and prevention to risk management. At last we can say that the world is dynamic , time is less , data is large so for taking smarter decisions we have to address most impacting factors that influence core issues regarding any domain knowledge and companies can answer fundamental questions such as What is happening?, Why is it happening?, What is likely to happen in the future?, and How should we plan for that future? and helps your organization rise to the challenge with better business insight, planning and performance.
CASE STUDY : We have a banking data and based on this data we can analyse certain questions that bank manager would likely to ask: 1. Create a dataset with only those Sales officers who have highest number of accounts within every branch. 2. Which branch officer has highest number of accounts? 3. Calculate the total number of accounts from every zone? 4. Which zone has highest accounts? Also find the zone with maximum branches? 5. Create a dataset with only those Sales officers who are from central 1 zone and main branch. 6. Categorize the account officers into the following 4 categories: a. Top performer – greater than 65% active accounts b. Average performer– between 45% to 65% active accounts c. Below average performer – less than 45% of active accounts 7. Which is the worst performing zone? 8. Create a new variable by combining Sales officer’s name with their respective branch code. 9. Create a pdf report by listing only those Sales officers whose dormant (inactive) accounts is 0.
10. Give the list of Sales officers who has neither dormant (inactive) accounts nor active accounts in a HTML file. 11. Create a new variable in the dataset, wherever number of dormant (inactive) is greater than active accounts then give it as bad else give it as Good. 12. Create a new dataset consisting of account officers with 100% dormancy (inactive) percentage. Create the following: a. A report consisting of total number of accounts and balance under different categories for “Main Branch” b. A branch level report of total number of accounts and balance, listed in the order of the best performing branch to the least. c. List out performing and non-performing account officers in 2 reports, consisting of the details of their zone, branch, accounts and balance. Also, detail the criteria used for this differentiation.
Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. The banking industry is data-intensive with typically massive graveyards of unused and unappreciated ATM and credit processing data. As banks face increasing pressure to stay profitable, understanding customer needs and preferences becomes a critical success factor. New models of proactive risk management are being increasingly adopted by major banks and financial institutions, especially in the wake of Basel II accord. Through Data mining and advanced analytics techniques, banks are better equipped to manage market uncertainty, minimize fraud, and control exposure risk. Banking technology, few transactions actually use cash. In fact, hard currency represents only 11% of the money supply in the U.S. The rest of our “money” flows digitally from a salary to a bank to a retailer, and then through the retailer’s supply chain, to be deposited in another business’ account. to start the journey over again. That means our money has been transformed into zeros and ones.
According to IBM’s 2010 Global Chief Executive Officer Study, 89 percent of banking and financial markets CEOs say their top priority is to better understand, predict and give customers what they want. Financial metrics and KPIs provide effective measures for summarizing your overall bank performance. But in order to discover the set of critical success factors that will help banks reach their strategic goals, they need to move beyond standard business reporting and sales forecasting. By applying data mining and predictive analytics to extract actionable intelligent insights and quantifiable predictions, banks can gain insights that encompass all types of customer behavior, including channel transactions, account opening and closing, default, fraud and customer departure.
Introduction to Business Analytics:
Analytics is the use of modern data mining , pattern matching , data visualization and predictive modelling tools for analyses and algorithm to make better business decisions. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling and fact-based management to drive decision making. Analytics may be used as input for human decisions or may drive fully automated decisions. Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (that is, predict), what is the best that can happen (that is, optimize . Generally Industry uses three types of analytics as per requirement to find insights from a large volume of data:
1.Descriptive (business intelligence and data mining) 2.Predictive (Prediction or likelihood /forecasting) 3.Prescriptive (optimization and simulation) I am giving you some brief knowledge about these analytics step by step:
1.Descriptive analytics looks at data and analyzes past events for insight as to how to
approach the future. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Almost all management reporting such as sales, marketing, operations, and finance, uses this type of post-mortem analysis. Descriptive models can be used, for example, to categorize customers by their product preferences and life stage. Descriptive modeling tools can be utilized to develop further models that can simulate large number of individualized agents and make predictions. For example, descriptive analytics examines historical electricity usage data to help plan power needs and allow electric companies to set optimal prices.
2.Predictive analytics turns data into valuable, actionable information. Predictive
analytics uses data to determine the probable future outcome of an event or a likelihood of a situation occurring. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Three basic cornerstones of predictive analytics are: Predictive modeling Decision Analysis and Optimization Transaction Profiling. Simple example is for an organization that offers multiple products, predictive analytics can help analyze customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships. Another example is When a client applies for a loan the bank would like to be sure that the client will pay back the full amount of the loan. So predictive analytics uses regression technique to past data and use this to produce a probability that the borrower will repay the loan. This probability, along with the lenders experience is then used to decide if the bank should lend to a particular client.
3.Prescriptive analytics is generally used for optimization of or business mode to make
predictions and then suggests decision options to take advantage of the predictions. Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve prediction accuracy and provide better decision options. An example is energy and utilities. Natural gas prices fluctuate dramatically depending upon supply, demand, econometrics, geo-politics, and weather conditions. Gas producers, transmission (pipeline) companies and utility firms have a keen interest in more accurately predicting gas prices so that they can lock in favorable terms while hedging downside risk. Prescriptive analytics can accurately predict prices by modeling internal and external variables simultaneously and also provide decision options and show the impact of each decision option. In model optimzation we use prescriptive analytics to find out best optimal solution that bank can use this for allow us to predict if a client will pay back the loan(80 % accuracy).
For example, prescriptive analytics can benefit healthcare strategic planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demographic trends and population health trends, to more accurately plan for future capital investments such as new facilities and equipment utilization as well as understand the trade-offs between adding additional beds and expanding an existing facility versus building a new one.
Role of Analytics:
While analytics aren’t exactly new to the world of banking, plenty of banks are gearing up for their next big analytics push, propelled by a load of data and new, sophisticated tools and technologies. Why has business analytics jumped to the top of the priority list for banks? Pick a reason. Regulatory reform, managing risk, changing business models, expansion into new markets, a renewed focus on customer profitability – any one of these is reason enough for many banks to reconsider what today’s analytics capabilities can offer.
As per Deloitte research, three business drivers increase the importance of analytics within the banking industry
•
Regulatory reform – Major legislation such as Dodd-Frank, the CARD Act, FATCA (Foreign Account Tax Compliance Act) and Basel III have changed the business environment for banks. Given the focus on systemic risk, regulators are pushing banks to demonstrate better understanding of data they possess, turn data into information that supports business decisions and manage risk more effectively. Each request has major ramifications on data collection, governance and reporting. Over the next several years, regulators will finalize details in the recently passed legislation. However, banks should start transforming their business models today to comply with a radically different regulatory environment.
•
Customer profitability – Personalized offerings are expected to play a big role in attracting and retaining the most profitable customers, but studies show that a small percentage of banks have strong capabilities in this area. The CARD Act and Durbin Amendment make it even more important to understand the behavioural economics of each customer and find ways to gain wallet share in the most profitable segments.
•
Operational efficiency – while banks have trimmed a lot of fat over the past few years, there is still plenty of room for improvement, including reducing duplicative systems, manual reconciliation tasks and information technology costs.
Credit Scoring Modelling for Retail Banking Sector: • • • • Our problem is concerned with who a bank should loan its money to. When a client applies for a loan, the bank would like to be sure that the client will pay back the full amount of the loan. We need effective models that allow us to predict if a client will pay back the loan. What we have is historical data for several variables.
•
We are trying to fit a model to this historical data so we can estimate a probability of default. So analytics helps to determine the answers to all these questions and build a strategy for effective decision making in banks so that banks can reduce the risk for granting loan to which customers according to defaulting probability for respective customers in a bank.
With banking analytics your organization gains a complete and consistent view of all key profitability drivers so you can: • Manage risk effectively • Track and monitor sales, margins and operational performance • Analyze results and identify and predict trends in channels, regions, products, demographics and customer behaviour. • Dynamically adjust plans to achieve profitable growth. • Help meet regulatory demands.
How analytics can help you increase customer profitability and satisfaction, manage risk and be more operationally efficient.? Fraud detection in banking is one of the major complicated task that discover by the use of analytics. Fraud detection in banking is a critical activity that can span a series of fraud schemes and fraudulent activity from bank employees and customers alike. Since banking is a relatively highly regulated industry, there are also a number of external compliance requirements that banks must adhere to in the combat against fraudulent and criminal activity. Banks Need To Improve Use of Customer Analytics. Banks also can use analytics to enhance their strategic, back-office activities by: ● Identifying the profitability of customers across lines of business ● Reducing risk by better forecasting defaults and late payments ● Using predictive analytics technology to determine the amount of money each unique customer is allowed to withdraw from an ATM
● Segmenting customers based on demographics, relationships and transaction behaviours to increase wallet share and individual profitability ● Enhancing online merchandizing by analyzing customers’ purchasing ● Supporting mobile phone transactions and analysis of those transaction
Conclusion: In banks, analytics can help immensely in a variety of areas ranging from fraud detection and prevention to risk management. At last we can say that the world is dynamic , time is less , data is large so for taking smarter decisions we have to address most impacting factors that influence core issues regarding any domain knowledge and companies can answer fundamental questions such as What is happening?, Why is it happening?, What is likely to happen in the future?, and How should we plan for that future? and helps your organization rise to the challenge with better business insight, planning and performance.
CASE STUDY : We have a banking data and based on this data we can analyse certain questions that bank manager would likely to ask: 1. Create a dataset with only those Sales officers who have highest number of accounts within every branch. 2. Which branch officer has highest number of accounts? 3. Calculate the total number of accounts from every zone? 4. Which zone has highest accounts? Also find the zone with maximum branches? 5. Create a dataset with only those Sales officers who are from central 1 zone and main branch. 6. Categorize the account officers into the following 4 categories: a. Top performer – greater than 65% active accounts b. Average performer– between 45% to 65% active accounts c. Below average performer – less than 45% of active accounts 7. Which is the worst performing zone? 8. Create a new variable by combining Sales officer’s name with their respective branch code. 9. Create a pdf report by listing only those Sales officers whose dormant (inactive) accounts is 0.
10. Give the list of Sales officers who has neither dormant (inactive) accounts nor active accounts in a HTML file. 11. Create a new variable in the dataset, wherever number of dormant (inactive) is greater than active accounts then give it as bad else give it as Good. 12. Create a new dataset consisting of account officers with 100% dormancy (inactive) percentage. Create the following: a. A report consisting of total number of accounts and balance under different categories for “Main Branch” b. A branch level report of total number of accounts and balance, listed in the order of the best performing branch to the least. c. List out performing and non-performing account officers in 2 reports, consisting of the details of their zone, branch, accounts and balance. Also, detail the criteria used for this differentiation.
