Digital dashboards provide the decision makers with a quick overview

2 CHAPTER

Decisions and Processes: Value Driven Business

CHAPTER OUTLINE

SECTION 2.1 Decision Support Systems

SECTION 2.2 Business Processes

Making Organizational Business Decisions

Measuring Organizational Business Decisions

Using MIS to Make Business Decisions

Using AI to Make Business Decisions

Managing Business Processes

Using MIS to Improve Business Processes

What’s in IT for me?

Working faster and smarter has become a necessity for companies. A firm’s value chain is directly affected by how well it designs and coordinates its business processes. Business processes offer competitive advantages if they enable a firm to lower operating costs, differentiate, or compete in a niche market. They can also be huge burdens if they are outdated, which impedes operations, efficiency, and effectiveness. Thus, the ability of management information systems to improve business processes is a key advantage.

The goal of Chapter 2 is to provide an overview of specific MIS tools managers can use to support the strategies discussed in Chapter 1 . After reading this chapter, you, the business student, should have detailed knowledge of the types of information systems that exist to support decision making and business process reengineering, which in turn can improve organization efficiency and effectiveness and help an organization create and maintain competitive advantages.

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opening case study

Business Is Booming for Wearable Technologies

What will be the next big thing in the world of technology? In the early 1990s, the laptop computer was the big thing, giving users mobility and productivity. Apple gave the world the smart phone in 2007, a device that could fit in the palm of your hand and offered more power than the equipment that NASA used to put men on the moon in the 1960s. Industry analysts are predicting the next big thing will be even smaller and more versatile than the iPhone and similar devices: wearable technology.

Wearable technologies are here, and the applications for both personal and business use are truly inspiring. You can expect them to have a major impact on your everyday lives over the next decade. Wearable technology is a device that you wear physically on your body that has tracking technologies to help manage your life. Wearable technologies include smart watches, intelligent glasses, and fitness tracking bands. To be considered a piece of wearable technology, the device must merge with clothing, accessories, or other essentials that are worn on the body rather than carried.

The current focus of wearable technologies is largely consumer driven, but expect this to change because there are even bigger opportunities in the business world, resulting in improved productivity, reduced job-related injuries, and billions of dollars in savings. Because wearable technologies allow users to go hands-free, there are many ways they will be useful in business, such as wearable devices that connect a customer’s data tracking his or her real-time journey through a store or hotel to eliminate the need for credit cards or hotel keys. Emergency personnel and search and rescue teams will receive hightech mobility and tracking features, ensuring both theirs and the victim’s safety. Sales representatives, auto mechanics, and remote service technicians will gain access to real time data, allowing them to view plans and schematics all hands-free and in real time. Any person requiring real-time access to data—sales representatives, lawyers, doctors, nurses, policemen, fire fighters, and military personnel—will benefit from using wearables in the workplace. (See Figure 2.1 for examples of wearable technologies.) 1

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FIGURE 2.1

Wearable Technology Examples

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section 2.1

Decision Support Systems

LEARNING OUTCOMES

2.1Explain the importance of decision making for managers at each of the three primary organization levels along with the associated decision characteristics.

2.2Define critical success factors (CSFs) and key performance indicators (KPIs) and explain how managers use them to measure the success of MIS projects.

2.3Classify the different operational support systems, managerial support systems, and strategic support systems and explain how managers can use these systems to make decisions and gain competitive advantages.

2.4Describe artificial intelligence and identify its five main types.

MAKING ORGANIZATIONAL BUSINESS DECISIONS

LO. 2.1: Explain the importance of decision making for managers at each of the three primary organization levels along with the associated decision characteristics.

Porter’s strategies outlined in Chapter 1 suggest entering markets with a competitive advantage in overall cost leadership, differentiation, or focus. To achieve these results, managers must be able to make decisions and forecast future business needs and requirements. The most important and most challenging question confronting managers today is how to lay the foundation for tomorrow’s success while competing to win in today’s business environment. A company will not have a future if it is not cultivating strategies for tomorrow. The goal of this section is to expand on Porter’s Five Forces Model, three generic strategies, and value chain analysis to demonstrate how managers can learn the concepts and practices of business decision making to add value. It will also highlight how companies heading into the 21st century are taking advantage of advanced MIS capable of generating significant competitive advantages across the value chain.

As we discussed in Chapter 1 , decision making is one of the most important and challenging aspects of management. Decisions range from routine choices, such as how many items to order or how many people to hire, to unexpected ones such as what to do if a key employee suddenly quits or needed materials do not arrive. Today, with massive volumes of information available, managers are challenged to make highly complex decisions—some involving far more information than the human brain can comprehend—in increasingly short time frames. Figure 2.2 displays the three primary challenges managers face when making decisions.

FIGURE 2.2

Managerial Decision-Making Challenges

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FIGURE 2.3

The Six-Step Decision-Making Process

The Decision-Making Process

The process of making decisions plays a crucial role in communication and leadership for operational, managerial, and strategic projects. Analytics is the science of fact-based decision making. There are numerous academic decision-making models; Figure 2.3 presents just one example. 2

Decision-Making Essentials

A few key concepts about organizational structure will help our discussion of MIS decision-making tools. The structure of a typical organization is similar to a pyramid, and the different levels require different types of information to assist in decision making, problem solving, and opportunity capturing (see Figure 2.4 ).

Operational At the operational level , employees develop, control, and maintain core business activities required to run the day-to-day operations. Operational decisions are considered structured decisions , which arise when established processes offer potential solutions. Structured decisions are made frequently and are almost repetitive in nature; they affect short-term business strategies. Reordering inventory and creating the employee staffing and weekly production schedules are examples of routine structured decisions. Figure 2.5 highlights the essential elements required for operational decision making. All the elements in the figure should be familiar except metrics, which are discussed in detail below.

Managerial At the managerial level , employees are continuously evaluating company operations to hone the firm’s abilities to identify, adapt to, and leverage change. A company that has a competitive advantage needs to adjust and revise its strategy constantly to remain ahead of fast-following competitors. Managerial decisions cover short- and medium-range plans, schedules, and budgets along with policies, procedures, and business objectives for the firm. They also allocate resources and monitor the performance of organizational subunits, including departments, divisions, process teams, project teams, and other work groups. These types of decisions are considered semistructured decisions; they occur in situations in which a few established processes help to evaluate potential solutions, but not enough to lead to a definite recommended decision. For example, decisions about producing new products or changing employee benefits range from unstructured to semistructured. Figure 2.5 highlights the essential elements required for managerial decision making.

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FIGURE 2.4

Common Company Structure

FIGURE 2.5

Overview of Decision Making

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Strategic At the strategic level , managers develop overall business strategies, goals, and objectives as part of the company’s strategic plan. They also monitor the strategic performance of the organization and its overall direction in the political, economic, and competitive business environment. Strategic decisions are highly unstructured decisions , occurring in situations in which no procedures or rules exist to guide decision makers toward the correct choice. They are infrequent, extremely important, and typically related to long-term business strategy. Examples include the decision to enter a new market or even a new industry over, say, the next three years. In these types of decisions, managers rely on many sources of information, along with personal knowledge, to find solutions. Figure 2.5 highlights the essential elements required for strategic decision making.

MEASURING ORGANIZATIONAL BUSINESS DECISIONS

LO 2.2: Define critical success factors (CSFs) and key performance indicators (KPIs) and explain how managers use them to measure the success of MIS projects.

A project is a temporary activity a company undertakes to create a unique product, service, or result. For example, the construction of a new subway station is a project, as is a movie theater chain’s adoption of a software program to allow online ticketing. Peter Drucker, a famous management writer, once said that if you cannot measure something, you cannot manage it. How do managers measure the progress of a complex business project?

Metrics are measurements that evaluate results to determine whether a project is meeting its goals. Two core metrics are critical success factors and key performance indicators. Critical success factors (CSFs) are the crucial steps companies perform to achieve their goals and objectives and implement their strategies (see Figure 2.6 ). Key performance indicators (KPIs) are the quantifiable metrics a company uses to evaluate progress toward critical success factors. KPIs are far more specific than CSFs.

It is important to understand the relationship between critical success factors and key performance indicators. CSFs are elements crucial for a business strategy’s success. KPIs measure the progress of CSFs with quantifiable measurements, and one CSF can have several KPIs. Of course, both categories will vary by company and industry. Imagine improve graduation rates as a CSF for a college. The KPIs to measure this CSF can include:

Average grades by course and gender.

Student dropout rates by gender and major.

Average graduation rate by gender and major.

Time spent in tutoring by gender and major.

KPIs can focus on external and internal measurements. A common external KPI is market share , or the proportion of the market that a firm captures. We calculate it by dividing the firm’s sales by the total market sales for the entire industry. Market share measures a firm’s external performance relative to that of its competitors. For example, if a firm’s total sales (revenues) are $2 million and sales for the entire industry are $10 million, the firm has captured 20 percent of the total market (2/10 = 20%) or a 20 percent market share.

A common internal KPI is return on investment (ROI) , which indicates the earning power of a project. We measure it by dividing the profitability of a project by the costs. This sounds easy, and for many departments where the projects are tangible and self-contained it is; however, for projects that are intangible and cross departmental lines (such as MIS projects), ROI is challenging to measure. Imagine attempting to calculate the ROI of a fire extinguisher. If the fire extinguisher is never used, its ROI is low. If the fire extinguisher puts out a fire that could have destroyed the entire building, its ROI is astronomically high.

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FIGURE 2.6

CSF and KPI Metrics

Creating KPIs to measure the success of an MIS project offers similar challenges. Think about a firm’s email system. How could managers track departmental costs and profits associated with company email? Measuring by volume does not account for profitability because one sales email could land a million-dollar deal while 300 others might not generate any revenue. Non revenue-generating departments such as human resources and legal require email but will not be using it to generate profits. For this reason, many managers turn to higher-level metrics, such as efficiency and effectiveness, to measure MIS projects. Best practices are the most successful solutions or problem-solving methods that have been developed by a specific organization or industry. Measuring MIS projects helps determine the best practices for an industry.

Efficiency and Effectiveness Metrics

Efficiency MIS metrics measure the performance of MIS itself, such as throughput, transaction speed, and system availability. Effectiveness MIS metrics measure the impact MIS has on business processes and activities, including customer satisfaction and customer conversion rates. Efficiency focuses on the extent to which a firm is using its resources in an optimal way, whereas effectiveness focuses on how well a firm is achieving its goals and objectives. Peter Drucker offers a helpful distinction between efficiency and effectiveness: Doing things right addresses efficiency—getting the most from each resource. Doing the right things addresses effectiveness—setting the right goals and objectives and ensuring they are accomplished. Figure 2.7 describes a few of the common types of efficiency and effectiveness MIS metrics. KPIs that measure MIS projects include both efficiency and effectiveness metrics. Of course, these metrics are not as concrete as market share or ROI, but they do offer valuable insight into project performance. 3

Large increases in productivity typically result from increases in effectiveness, which focus on CSFs. Efficiency MIS metrics are far easier to measure, however, so most managers tend to focus on them, often incorrectly, to measure the success of MIS projects. Consider measuring the success of automated teller machines (ATMs). Thinking in terms of MIS efficiency metrics, a manager would measure the number of daily transactions, the average amount per transaction, and the average speed per transaction to determine the success of the ATM. Although these offer solid metrics on how well the system is performing, they miss many of the intangible or value-added benefits associated with ATM effectiveness. Effectiveness MIS metrics might measure how many new customers joined the bank due to its ATM locations or the ATMs’ ease of use. They can also measure increases in customer satisfaction due to reduced ATM fees or additional ATM services such as the sale of stamps and movie tickets, significant time savers and value-added features for customers. Being a great manager means using the added viewpoint offered by effectiveness MIS metrics to analyze all benefits associated with an MIS project.

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FIGURE 2.7

Common Types of Efficiency and Effectiveness Metrics

Efficiency and effectiveness are definitely related. However, success in one area does not necessarily imply success in the other. Efficiency MIS metrics focus on the technology itself. Although these efficiency MIS metrics are important to monitor, they do not always guarantee effectiveness. Effectiveness MIS metrics are determined according to an organization’s goals, strategies, and objectives. Here, it becomes important to consider a company’s CSFs, such as a broad cost leadership strategy (Walmart, for example), as well as KPIs such as increasing new customers by 10 percent or reducing new-product development cycle times to six months. In the private sector, eBay continuously benchmarks its MIS projects for efficiency and effectiveness. Maintaining constant website availability and optimal throughput performance are CSFs for eBay.

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APPLY YOUR KNOWLEDGE

BUSINESS DRIVEN DISCUSSION

IS IT EFFECTIVE OR IS IT EFFICIENT?

Making business decisions is a key skill for all managers. Review the following list and, in a group, determine whether the question is focusing on efficiency, effectiveness, or both.

Business Decision

Efficiency

Effectiveness

What is the best route for dropping off products?

Should we change suppliers?

Should we reduce costs by buying lower-quality materials?

Should we sell products to a younger market?

Did we make our sales targets?

What was the turnover rate of employees?

What is the average customer spending?

How many new customers purchased products?

Did the amount of daily transactions increase?

Is there a better way to restructure a store to increase sales?

Figure 2.8 depicts the interrelationships between efficiency and effectiveness. Ideally, a firm wants to operate in the upper right-hand corner of the graph, realizing significant increases in both efficiency and effectiveness. However, operating in the upper left-hand corner (minimal effectiveness with increased efficiency) or the lower right-hand corner (significant effectiveness with minimal efficiency) may be in line with an organization’s particular strategies. In general, operating in the lower left-hand corner (minimal efficiency and minimal effectiveness) is not ideal for the operation of any organization.

Regardless of what process is measured, how it is measured, and whether it is performed for the sake of efficiency or effectiveness, managers must set benchmarks , or baseline values the system seeks to attain. Benchmarking is a process of continuously measuring system results, comparing those results to optimal system performance (benchmark values), and identifying steps and procedures to improve system performance. Benchmarks help assess how an MIS project performs over time. For instance, if a system held a benchmark for response time of 15 seconds, the manager would want to ensure response time continued to decrease until it reached that point. If response time suddenly increased to 1 minute, the manager would know the system was not functioning correctly and could start looking into potential problems. Continuously measuring MIS projects against benchmarks provides feedback so managers can control the system.

USING MIS TO MAKE BUSINESS DECISIONS

LO 2.3: Classify the different operational support systems, managerial support systems, and strategic support systems and explain how managers can use these systems to make decisions and gain competitive advantages.

Now that we’ve reviewed the essentials of decision making, we are ready to understand the powerful benefits associated with using MIS to support managers making decisions.

A model is a simplified representation or abstraction of reality. Models help managers calculate risks, understand uncertainty, change variables, and manipulate time to make decisions. MIS support systems rely on models for computational and analytical routines that mathematically express relationships among variables. For example, a spreadsheet program, such as Microsoft Excel, might contain models that calculate market share or ROI. MIS has the capability and functionality to express far more complex modeling relationships that provide information, business intelligence, and knowledge. Figure 2.9 highlights the three primary types of management information systems available to support decision making across the company levels.

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APPLY YOUR KNOWLEDGE

BUSINESS DRIVEN GLOBALIZATION

Get the Cow Out of the Ditch

Fortune magazine asked Anne Mulcahy, former Chairman and CEO of Xerox, what the best advice she had ever received in business was. She said it occurred at a breakfast meeting in Dallas, to which she had invited a group of business leaders. One of them, a plainspoken, self-made, streetwise guy, came up to Mulcahy and said:

When everything gets really complicated and you feel overwhelmed, think about it this way. You gotta do three things. First, get the cow out of the ditch. Second, find out how the cow got into the ditch. Third, make sure you do whatever it takes so the cow doesn’t go into the ditch again. 4

You are working for an international app developer that produces games. For months, you have been collecting metrics on usage by players from all over the world. You notice the metrics on the Asian and European players are falling sharply and sales are dropping. The United States and Canada metrics are still growing strongly, and sales are increasing. What can you do to get this cow out of the ditch?

Operational Support Systems

Transactional information encompasses all the information contained within a single business process or unit of work, and its primary purpose is to support the performance of daily operational or structured decisions. Transactional information is created, for example, when customers are purchasing stocks, making an airline reservation, or withdrawing cash from an ATM. Managers use transactional information when making structured decisions at the operational level, such as when analyzing daily sales reports to determine how much inventory to carry.

FIGURE 2.8

The Interrelationships between Efficiency and Effectiveness

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FIGURE 2.9

Primary Types of MIS Systems for Decision Making

Online transaction processing (OLTP) is the capture of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, and (3) update existing information to reflect the new information. During OLTP, the organization must capture every detail of transactions and events. A transaction processing system (TPS) is the basic business system that serves the operational level (analysts) and assists in making structured decisions. The most common example of a TPS is an operational accounting system such as a payroll system or an order-entry system.

Using systems thinking, we can see that the inputs for a TPS are source documents , the original transaction record. Source documents for a payroll system can include time sheets, wage rates, and employee benefit reports. Transformation includes common procedures such as creating, reading, updating, and deleting (commonly referred to as CRUD) employee records along with calculating the payroll and summarizing benefits. The output includes cutting the paychecks and generating payroll reports. Figure 2.10 demonstrates the systems thinking view of a TPS. 5

Managerial Support Systems

Analytical information encompasses all organizational information, and its primary purpose is to support the performance of managerial analysis or semistructured decisions. Analytical information includes transactional information along with other information such as market and industry information. Examples of analytical information are trends, sales, product statistics, and future growth projections. Managers use analytical information when making important semistructured decisions such as whether the organization should build a new manufacturing plant or hire additional sales reps.

Online analytical processing (OLAP) is the manipulation of information to create business intelligence in support of strategic decision making. Decision support systems (DSSs) model information using OLAP, which provides assistance in evaluating and choosing among different courses of action. DSSs enable high-level managers to examine and manipulate large amounts of detailed data from different internal and external sources. Analyzing complex relationships among thousands or even millions of data items to discover patterns, trends, and exception conditions is one of the key uses associated with a DSS. For example, doctors may enter symptoms into a decision support system so it can help diagnose and treat patients. Insurance companies also use a DSS to gauge the risk of providing insurance to drivers who have imperfect driving records. One company found that married women who are homeowners with one speeding ticket are rarely cited for speeding again. Armed with this business intelligence, the company achieved a cost advantage by lowering insurance rates to this specific group of customers. Figure 2.11 displays the common DSS analysis techniques.

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APPLY YOUR KNOWLEDGE

BUSINESS DRIVEN ETHICS AND SECURITY

The Criminal in the Cube Next Door

What if the person sitting in the cubicle next to you was running a scam that cost your company $7 billion? An employee at a French bank allegedly used his inside knowledge of business processes to bypass the system and place roughly $73 billion in bogus trades that cost the bank more than $7 billion to unwind.

Findings from the U.S. Secret Service’s examination of 23 incidents conducted by 26 insiders determined that 70 percent of the time, insiders took advantage of failures in business process rules and authorization mechanisms to steal from their company. These insiders were authorized and active computer users 78 percent of the time, and a surprising 43 percent used their own user name and password to commit their crimes. 6

This is a daunting reminder that every employee has the potential to become a knowledgeable insider and, if started on a criminal path, to do tremendous damage to your company. Many DSSs and EISs contain the business intelligence your company needs to operate effectively, and you need to protect these assets. What types of sensitive information are housed in a company’s TPS, DSS, and EIS? What problems could you encounter if one of your employees decided to steal the information stored in your DSS? How could you protect your EIS from unethical users? What would you do if you thought the person sharing your cube was a rogue insider?

FIGURE 2.10

Systems Thinking Example of a TPS

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FIGURE 2.11

Common DSS Analysis Techniques

Figure 2.12 shows the common systems view of a DSS. Figure 2.13 shows how TPSs supply transactional data to a DSS. The DSS then summarizes and aggregates the information from the different TPSs, which assist managers in making semistructured decisions.

Strategic Support Systems

Decision making at the strategic level requires both business intelligence and knowledge to support the uncertainty and complexity associated with business strategies. An executive information system (EIS) is a specialized DSS that supports senior-level executives and unstructured, long-term, nonroutine decisions requiring judgment, evaluation, and insight. These decisions do not have a right or wrong answer, only efficient and effective answers. Moving up through the organizational pyramid, managers deal less with the details (finer information) and more with meaningful aggregations of information (coarser information). Granularity refers to the level of detail in the model or the decision-making process. The greater the granularity, the deeper the level of detail or fineness of data (see Figure 2.14 ). A DSS differs from an EIS in that an EIS requires data from external sources to support unstructured decisions (see Figure 2.15 ). This is not to say that DSSs never use data from external sources, but typically DSS semistructured decisions rely on internal data only.

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FIGURE 2.12

Systems Thinking Example of a DSS

Visualization produces graphical displays of patterns and complex relationships in large amounts of data. Executive information systems use visualization to deliver specific key information to top managers at a glance, with little or no interaction with the system. A common tool that supports visualization is a digital dashboard , which tracks KPIs and CSFs by compiling information from multiple sources and tailoring it to meet user needs. Following is a list of potential features included in a dashboard designed for a manufacturing team:

A hot list of key performance indicators, refreshed every 15 minutes.

A running line graph of planned versus actual production for the past 24 hours.

A table showing actual versus forecasted product prices and inventories.

FIGURE 2.13

Interaction Between TPS and DSS to Support Semistructured Decisions

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APPLY YOUR KNOWLEDGE

BUSINESS DRIVEN START-UP

TRACK YOUR LIFE

With wearable technology, you can track your entire life. Nike’s Fuelband and Jawbone’s Up tracks all of your physical activity, caloric burn, and sleep patterns. You can track your driving patterns, tooth-brushing habits, and even laundry status. The question now becomes how to track all of your trackers.

A new company called Exist incorporates tracking devices with weather data, music choices, Netflix favorites, and Twitter activity all in one digital dashboard. Exist wants to understand every area of your life and provide correlation information between such things as your personal productivity and mood. As the different types of data expand, so will the breadth of correlations Exist can point out. For instance, do you tweet more when you are working at home? If so, does this increase productivity? Exist wants to track all of your trackers and analyze the information to help you become more efficient and more effective. 7

Create a digital dashboard for tracking your life. Choose four areas you want to track and determine three ways you would measure each area. For example, if you track eating habits, you might want to measure calories and place unacceptable levels in red and acceptable levels in green. Once completed, determine whether you can find any correlations among the areas in your life.

A list of outstanding alerts and their resolution status.

A graph of stock market prices.

Digital dashboards, whether basic or comprehensive, deliver results quickly. As they become easier to use, more employees can perform their own analyses without inundating MIS staff with questions and requests for reports. Digital dashboards enable employees to move beyond reporting to using information to increase business performance directly. With them, employees can react to information as soon as it becomes available and make decisions, solve problems, and change strategies daily instead of monthly. Digital dashboards offer the analytical capabilities illustrated in Figure 2.16 .

FIGURE 2.14

Information Levels Throughout an Organization

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FIGURE 2.15

Interaction Between a TPS and EIS

One thing to remember when making decisions is the old saying, “Garbage in, garbage out.” If the transactional data used in the support system are wrong, then the managerial analysis will be wrong and the DSS will simply assist in making a wrong decision faster. Managers should also ask, “What is the DSS not telling me before I make my final decision?”

USING AI TO MAKE BUSINESS DECISIONS

LO 2.4: Describe artificial intelligence and identify its five main types.

Executive information systems are starting to take advantage of artificial intelligence to facilitate unstructured strategic decision making. Artificial intelligence (AI) simulates human thinking and behavior, such as the ability to reason and learn. Its ultimate goal is to build a system that can mimic human intelligence.

Intelligent systems are various commercial applications of artificial intelligence. They include sensors, software, and devices that emulate and enhance human capabilities, learn or understand from experience, make sense of ambiguous or contradictory information, and even use reasoning to solve problems and make decisions effectively. Intelligent systems perform such tasks as boosting productivity in factories by monitoring equipment and signaling when preventive maintenance is required.

AI systems increase the speed and consistency of decision making, solve problems with incomplete information, and resolve complicated issues that cannot be solved by conventional computing. There are many categories of AI systems; five of the most familiar are (1) expert systems, (2) neural networks, (3) genetic algorithms, (4) intelligent agents, and (5) virtual reality (see Figure 2.17 ).

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FIGURE 2.16

Digital Dashboard Analytical Capabilities

Expert Systems

Expert systems are computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. Typically, they include a knowledge base containing various accumulated experience and a set of rules for applying the knowledge base to each particular situation. Expert systems are the most common form of AI in the business arena because they fill the gap when human experts are difficult to find or retain or are too expensive. The best-known systems play chess and assist in medical diagnosis.

FIGURE 2.17

Examples of Artificial Intelligence

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Neural Networks

A neural network , also called an artificial neural network, is a category of AI that attempts to emulate the way the human brain works. Neural networks analyze large quantities of information to establish patterns and characteristics when the logic or rules are unknown. Neural networks’ many features include:

Learning and adjusting to new circumstances on their own.

Lending themselves to massive parallel processing.

Functioning without complete or well-structured information.

Coping with huge volumes of information with many dependent variables.

Analyzing nonlinear relationships in information. (They have been called fancy regression analysis systems.)

The finance industry is a veteran in the use of neural network technology and has been relying on various forms for over two decades. It uses neural networks to review loan applications and create patterns or profiles of applications that fall into two categories—approved or denied. Here are some examples of neural networks in finance:

Citibank uses neural networks to find opportunities in financial markets. By carefully examining historical stock market data with neural network software, Citibank financial managers learn of interesting coincidences or small anomalies (called market inefficiencies). For example, it could be that whenever IBM stock goes up, so does Unisys stock, or that a U.S. Treasury note is selling for 1 cent less in Japan than in the United States. These snippets of information can make a big difference to Citibank’s bottom line in a very competitive financial market.

Visa, MasterCard, and many other credit card companies use a neural network to spot peculiarities in individual accounts and follow up by checking for fraud. MasterCard estimates neural networks save it $50 million annually.