ALL banks use some type of business intelligence (BI) platform to aid in their goals and strategies. The largest banks expend significant effort in getting the best data possible, then compiling that data into actionable decisions. This effort has become even more important in recent years, as banks strive to remain profitable in an ever more challenging business environment. Banking organizations use BI to identify the most critical points for operational improvement. Many of these organizations have identified three key areas — risk management, cost control, and customer relations — as their critical action points. Here are some considerations for using business intelligence for banking to improve profitability in each of these areas.
Risk Management
The key to successful banking has always been risk management. The basic rule of risk is that return must be linearly proportional to risk. Therefore, a low-risk investment such as a Treasury bill will have the lowest interest rate, while a moderate risk investment such as a house loan will have a somewhat higher rate, and a high-risk investment such as an unsecured credit card will have the highest rate. Although the government fixes the T-bill rate, the Beta of the risk curve (the cost of incremental risk) is always a challenge to determine. During certain periods, the Beta was determined in true market fashion by supply and demand. Banks had to lower their interest rates to compete against other banks desiring to lend money. However, many surmise that the recent financial failures may be traced back to the market rate of interest falling below supportable levels. In other words, banks did not charge enough interest for the associated risk.
Regardless of the market value of interest for various types of banking instruments, an individual bank can use big data from business intelligence to calculate if they should enter a particular market at the market rate. For instance, a bank can analyze the used car loan instrument by compiling quality data for used car loans over as many transactions as possible, then use the data to determine the default rate. This will determine the return on investment at a particular loan interest rate. This rate may be higher than the current market rate, in which case, the bank would know to avoid that market segment.
Cost Control
Customers often view a bank as a place to keep their money as an alternative to storing cash or valuables at home. Generally speaking, customers sacrifice access for security. However, they still want as much access as possible. This can mean that they would ideally like availability for 24-hour access. Banks can satisfy this need by providing ATM’s, but must still satisfy the customers who want to make face-to-face transactions with a live teller. As tellers and ATMs can be a redundant resource, the bank must decide how many tellers and how many ATMs they will maintain in each location. What is the optimal number for a given customer base? Big data will reveal the answer through statistical analysis.
Customer Relations
How can a banking entity improve customer relations? This is a classic marketing question, which banks answer through traditional methods such as focus groups and customer surveys. However, big data can yield excellent actionable directions. For instance, a bank could classify the types of problems customers have when they call the support desk, and find which classifications get the most hits. For instance, banks can see that there a large number of calls come from customers who need new debit cards because their magnetic strip wore out. They can correlate these customers against the length of time they had their cards, and determine, for instance, that they should automatically issue new debit cards every three years instead of four.
Summary
Because of modern technology, there is more big data available than ever before. However, the growth of data has made it more complex to draw straightforward conclusions. Business intelligence for banking can compile large amounts of disparate data, and help improve and streamline operations to improve banking profitability.
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