The promise of business intelligence and Big Data/Analytics has been around for years. Companies have been making claims that data-driven decision-making will revolutionize organizations but have failed to fully deliver. It is true that descriptive analytics (reporting) is necessary and valuable but in order to create real value (Return on Investment) for data analytics, organizations must think about the future. In order to achieve real value from Big Data/Analytics, organizations must execute predictive analytics.
Big Data/Analytics’ Past: Business Intelligence
Organizations have been using business intelligence to analyze historical data for years. This was the promise many received when they were first sold business intelligence (BI) solutions. While BI is incredibly valuable, it is only just a fraction of value when you start to consider analytics. Most of the past Big Data/Analytics (Business Intelligence) solutions were focused primarily on descriptive analytics. Descriptive analytics is the most simplistic form of analytics a credit union (or any organization) can utilize.
Descriptive analytics takes large data sets, commonly referred to as big data, and looks at what has already happened. Rather than trying to learn from the data and make predictions about how strategy can be altered, it aims to summarize the data. For example, a credit union can look at the average yield of their loan portfolio. Descriptive analytics can be also referred to as reporting, a practice already carried out by most credit unions today. The real value of descriptive analytics is the ability to, according to management guru Peter Drucker, “measure what you manage.” As humans we are conditioned to work towards goals and descriptive analytics does an excellent job of telling us what progress we are making against those goals and prompts us to look for ways to improve.
The Future of Analytics (Predictive Analytics)
Predictive analytics harnesses patterns found in historical and transactional data to identify risks and opportunities. Through utilization of sophisticated statistical modeling techniques, machine learning, and data mining, predictive analytics looks at past and present facts to make predictions about future events. Predictive analytics allows financial institutions to look at loan portfolios and apply statistical models to affect the outcome of their future yield.
“One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.” – Nyce, Charles (2007), Predictive Analytics White Paper, American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America, p. 1
The Challenge of Predictive Analytics (The Requirements)
Predictive analytics empowers credit unions to gain valuable insights into areas like, new product opportunities, new markets via risk based pricing and completely re-defined credit scoring models. Unfortunately, predictive insights are not easily obtainable. Predictive analytics require deep analysis of transactional data which is extremely difficult for credit unions with many disparate data sources. In a recent whitepaper from Filene Research Institute, author Philipp Kallerhoff states:
“A prerequisite for developing these (predictive) and other models is a well-maintained database with as much transactional detail as possible. The credit unions that can capture transaction types and locations will come out ahead, because transaction origin correlates highly with credit scores and helps to predict future financial products.”
The Opportunity for Credit Unions
While predictive analytics is difficult to obtain, it is not farfetched for credit unions. Unlike new competitors in the financial services industries (e.g. Apple Pay, Lending Clubs, ect.), credit unions sit on an astronomical amount of transactional data. Credit unions, through their years of building brand loyalty, have racked up countless transactions from their members. Unfortunately credit unions have not been able to build well-maintained databases with as much transactional data as possible, until now.
As an industry, credit unions have developed incredible vehicles called Credit Union Service Organizations (CUSOs). CUSOs enable the credit union industry to leverage their inherit collaboratively to access technologies, such as Big Data/Analytics, that are typically out of their financial means. Without credit union collaboration, achieving a well-maintained data warehouse with as much transactional data as possible would be difficult to achieve. As a result, predictive analytics would be out of reach. Fortunately, the future of analytics is here. Many credit unions are beginning to realize the importance of Big Data/Analytics and are starting to embrace collaboration as the key to keeping the industry alive.