Question: Why is BI necessary, in any industry? Answer: BI helps in finding answers to questions about the business and hence is necessary.
But what is often ignored is the fact that it is more important to ask the right questions than to find accurate answers to the wrong questions.
Let us dig deeper into the idea of asking the right questions taking illustrations from banking:
a) Bank performance
First, the usual questions:
What is the profitability of my bank, broken down by geographies and branches? (x)
What is the operating efficiency of my bank across geographies? (y)
How is the quality of my asset portfolio? Has it been improving? (z)
Now, traditional Transaction Processing Systems used in banks have the available data to answer these questions. In fact, there are Core Banking Systems with bundled BI that go one step further and answer these questions for the bank.
However, if we were to link the above questions with each other, we can relate the profitability of a bank with its operating efficiency and credit losses. Ignoring other factors to keep it simple, we can put this relationship down as:
Profitability(x) = f(y,z).
So, the correct question here is:
How should I improve my operating efficiency and asset quality in order to improve my profitability?
This was as a simplistic illustration of a meaningful question that can be answered using Business Intelligence. Business Intelligence and Analytics not only help answer complex questions; they also help decision makers ask the right questions.
b) RM performance
Relationship Banking, apart from improving customer service levels and increasing business from customers, also helps attribute customer business to individuals or teams within banks who can then be rewarded or incentivized to bring in more business to the bank.
Understandably, the single most often asked question in this area is,
How are my RMs performing?
The conventional approach of answering this question circles around the volume and value of new products sold across assets, liabilities and investments.
However, structuring an incentive scheme around such an approach could prove ineffective.
For instance, some RMs could have brought in new checking accounts / current accounts which a quarter down the line proves to be of little or no value to the bank.
Or, some assets could have gone bad in the very first or second quarter.
An effective incentive scheme should then ask questions such as:
What is the true value of the new business brought in by my relationship managers?
Depending on the liquidity, risk capital position and goals of the bank, a BI solution could help the bank come up with a scoring model that factors in credit quality, customer loyalty and business value to reward the right people and teams.
c) How do I increase business through Cross-sell and Up-sell?
Ordinarily, in the case of individual customers, this question seeks to identify those customers who can be cross-sold or up-sold some products based on the “disposable” income of such individual customers and possibly the household.
However, the “Share of Wallet” approach used in BI and Analytics goes one step further from cross-sell since it helps understand and fight the competition to “steal” market share.
Although it is difficult to find data regarding other banks where my customers could possibly have accounts, there is some mining that could be done by answering the questions below:
Is my customer using his account with my bank as a “link-in-the-chain” and then using an account with another bank as his “primary account”? What can I do to change this?
Can I onboard those parties with whom my customer(s) often or significantly transacts with?
It is common knowledge that individuals as well as businesses have accounts with more than one bank for reasons ranging from convenience to cost and compliance. In such a scenario, it is important to analyze customer transactions – both payments and receipts to identify competition and possibly onboard new customers.
For instance, an importer frequently asks his bank to issue letters of credit favoring an exporter. If the exporter is not a customer of the bank, it is meaningful to target the latter. A typical individual customer makes several payments such as school fees, utility payments, investments etc., which can be analyzed to identify prospective customers.
The above examples are only indicative of the power of BI and Analytics in banking. While traditional transaction processing systems and data warehousing systems help answer traditional questions about bank, customer, product and people performance, BI and Analytics raise the bar by helping ask the right questions. Finding answers has never been a problem given the availability of data. However, if the right questions are not asked, the right answers will not be as useful or important as they seem.