From better targeting and risk assessment to streamlining operations and optimizing business decisions in all areas, predictive analytics is the next big step toward gaining and maintaining a competitive edge. So what is predictive analytics?
Nuts and bolts of predictive analytics
Predictive analytics is a blend of tools and techniques that enable organizations to identify patterns in data that can be used to make predictions of future outcomes. In business, predictive analytics typically take the form of predictive models that are used to drive better decision-making. They unveil and measure patterns to identify risks and opportunities using transactional, demographic, web-based, historical, text, sensor, economic, and unstructured data. These powerful models are able to consider multiple factors and predict outcomes with a high level of accuracy.
Why use predictive analytics?
Advances in technology over the past two decades have led to a highly volatile, global economy that enables instant communication and connection worldwide. The result is increased competition through broader access to global markets, shrinking business cycles, and changing rules. In other words, there is no more “business as usual.”
The biggest shift has been in the power of consumers to influence their buying experience. With access to global products and services, they can demand better quality, lower prices and faster delivery. For example, given the competitive nature of interest rates in the credit card industry one bank decided to examine its portfolio each quarter to determine the effect of changes in terms on customer profitability. The bank defined customer profitability as a combination of expected balances, payment behavior and risk of default over the next three years.
Predictive models were built to estimate the change in balance and payment behavior if either the interest rate or the credit limit was increased or decreased. A standard risk model was used. Based on quarterly scoring, the models estimated three-year profitability for each scenario based on the potential change in balances, payment behavior and likelihood of default for each customer. These quarterly adjustments in terms for each customer allowed the bank to maximize rolling three-year profitability.
Applications in risk
A well-known application of predictive modeling is credit scoring. Based on credit history financial profile, demographics and other information, a lending institution or other business can determine the likelihood of an individual or business meeting a loan obligation. Banks also use aggregations of expected loss to meet regulatory requirements for loan reserves.
Insurance companies use predictive model scores to determine approval and optimize pricing for life, health, auto and homeowners insurance based on demographics, claim history and other risk factors. Risk pricing plays a very important role in auto insurance, where the rates are highly regulated. Warranty companies often use product information and safety records to model the likelihood of a claim.
Customers who fall behind in their payments and are sent to collections can be segmented much like those being considered for a marketing program. Combining the likelihood of default with the amount of the balance allows the collection department to determine the severity of the potential loss. In one example, customers with the highest expected loss were called immediately while those with moderate risk were sent a letter and those with the lowest risk were sent an email reminder.
With the increase in electronic and online transactions, fraud is increasing at an alarming rate. According to fraud research by the Association of Certified Fraud Examiners, fraud cost the global economy more than $2.9 trillion in 2009. From stolen credit card purchases, insurance claims, cellular usage, tax returns, online tracking, account/ACH fraud and money laundering, fraud is increasing costs for businesses and consumers alike.
Powerful models are currently in use and in development to thwart these costly actions.
- Credit card banks use predictive models to identify the types of purchases that are typically made with a stolen card.
- Today, predictive models estimate the likelihood that an insurance claim is fraudulent based on characteristics of past fraudulent claims, thus allowing insurers to automate the approval of low-risk claims and place a priority on claims with a high likelihood of fraud.
- Government institutions, including the IRS and social service agencies such as Medicare and the Social Security Administration, use predictive models to identify false tax returns or fraudulent requests for benefits.
Models using social network data are being used by government agencies and private firms to detect security risk. Predictive models are being developed to predict the timing and location of breaches in security.
The role of leadership and culture
The only way to survive in this new economy is to embrace and leverage the power of information. Knowledge gleaned from accurate, accessible, actionable information is essential for survival. Companies must now have up-to-the-minute information about their customers, operations, suppliers, competitors, and markets.
Fortunately, the same advances in technology that have empowered consumers have also enabled companies to obtain, organize, analyze, store, and retrieve huge amounts of information about their markets and customers and automate many marketing and operational tasks. Once these systems are in place, companies can leverage predictive analytics to gain a competitive edge. The first step is to understand how to connect the tools and techniques with your business goals.
Success in today’s high-tech, fast-paced global economy will be enjoyed by those organizations that drive their business with predictive analytics with the agility of empowered leadership.
For more advice on how you can apply predictive analytics to your business processes, download the white paper, Drive Your Business with Predictive Analytics.