Financial Data Mining: How Big Data Tools Account for Customer Behavior and Risk

Because the gathering and analysis of big data has become such an integral aspect of improving business relations and growth models, it should be no surprise that the benefits of financial data mining are nearly infinite.

Although credit reports, career inquiries, references, and other tools have long been utilized by financial institutions as a way of determining the risk factors associated with individuals seeking loans, the mortgage crisis of 2008 and the global financial crisis of 2009 has proven that these screening techniques are not enough.

In order to truly evaluate customer behavior and make educated assumptions regarding their fiscal responsibility, the process of data mining for finance must be employed. Organizations will also need the right data mining resources to get started. In this post we will explore the reasons for which financial data mining is necessary, and how it can be accomplished.

What can we achieve with financial data mining?

One of the biggest problems within the financial industry is associated with attempting to find a sure-fire way to lock down customer behavior patterns. The variable nature of consumers makes it difficult to make accurate assumptions regarding the level of risk that each individual may present.

From the standpoint of financial institutions, insufficient analysis of customer data can cause uncertainty and discomfort when extending loans and lines of credit. At the same time, consumers may experience frustration as a lack of data could lead to a loan refusal or higher interest rate than he or she believes to be fair.

Through a structured and logical data mining model, these problems can be resolved.

Data mining for finance can reveal hidden patterns and relationships in big data which provide “big picture” customer profiles. Ultimately, the mining of data for finance will enable financial institutions to make well-informed decisions based upon factual evidence as opposed to guesswork.

Which data mining tools are right for finance?

In order to get the most out of financial data, there are several data mining tools which can be utilized to collect, organize, analyze, and report useful information. Two of the most common and practical applications employed in data mining for finance include:

• Association Rules - One of the more widely utilized data mining techniques, association rules are primarily used to find useful patterns and relationships within data sets. For some, it may be easiest to think of association rules in the same way that one would think of “If-Then” rules. Both rules employ a condition clause wherein “if” triggers a “then” result (ie: “IF a customer has a checking account THEN they are 75% more likely to also have a savings account”).

• Clustering Analysis - In most cases, cluster analysis is used to identify interesting distributions and patterns within data sets. Data mining software can be programmed to automate the process of grouping relevant data into similar “clusters” in order to simplify the processes of deep analysis, pattern recognition, and making sense of information. In some cases, association rules will be used in conjunction with clustering in order to discover further hidden patterns within a specific cluster (ie: a “cluster” of insufficient fund issues including overdraft fees, late loan payments and bounced checks could be further analyzed to discover that “IF a customer has spending habits of <$1500/month THEN they are more likely to have problems with insufficient funds”). Ultimately, the pairing of association rules and clustering can yield faster and more insightful information that human analysis might overlook.

The end result of data mining for finance is a clearer, fuller picture of customer behavior and risk. In this way, banks can make better-informed decisions in a way that is accurate and fair.

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