As we move into a more networked economy with more informed and impatient customers, speed is the key. The new generation of customers wants a modern retail experience while shopping for financial services, and therefore banks need to offer a rich bouquet of offerings, which can be mass-customized and delivered quickly. However, in the banking industry we need to balance speed with accuracy in our daily decisions, as the cost of making a mistake is high. This fundamental need to balance speed and accuracy is the key driver of using analytics in all-important business decisions on customer service, product features and pricing throughout the customer lifecycle.
On the supply side, an explosion in data availability, massive leap in computing power and substantial reduction in data-storage costs have enabled a much wider adoption of analytics solutions by making them more affordable for even the small and mid-sized banks. This article highlights some of the key reasons why banks cannot afford to ignore analytics any longer:
Customer centricity—Analytics is the key enabler to a shift from a “product-centric” approach to a “customer-centric” one that takes a 360-degree view of all relationships across products to determine the potential value as well as risk that a customer brings to the bank. Analytics can also help banks to take more proactive measures in retaining and developing their best customers by giving them accurate insights on customers’ needs, propensities to buy new products and potential attritions.
Digital channels—Rapid technological advances in mobile applications and platforms have ensured the dramatic increase in the potential consumer universe and have enabled a new cost-effective channel that is available “anytime, anywhere”. Currently about 25 percent of Internet banking traffic comes from mobile devices, and in the next five years, these should account for around 25 percent of total retail-banking transactions. Social media is also evolving as a mainstream form of communication, where people provide detailed information about themselves, their preferences, particulars about their daily activities and other important aspects about their personalities. This information can help banks understand consumer needs and customize their product and service offerings accordingly to enhance customer experience and loyalty. However, to truly leverage on these alternate customer touchpoints, banks need to strengthen their analytical capabilities.
Risk management—Risk-and-fraud management has been one of the most common usages of analytical models traditionally as it enables a bank to assess customer risk profiles more accurately to move towards risk-based pricing from average pricing, while allowing “good” customers to build reputational collaterals and seek better credit terms. More sophisticated models using transactional patterns as well as external data will further improve the accuracy of these decisions.
Operational efficiency—Today, limited growth in credit expansion and stricter regulatory norms on liquidity are putting the banks under margin pressure and forcing them to reduce operational costs by driving higher efficiencies and greater automation in customer decisions. To stay competitive, banks need to replace traditional labor-intensive structures with analytics-driven decision engines for faster turnaround, consistent outcomes and reduced costs per transaction. Real-time integration between operational systems and analytical models will also enable the models to evolve on a dynamic basis to become self-correcting, thereby providing more accurate results.
Regulatory compliance—Post-financial crisis, there has been an overwhelming pressure on banks to meet multiple regulatory requirements in a wide range of areas, such as ALM (asset liability management), AML (anti-money laundering), Basel Accords, FATCA (Foreign Account Tax Compliance Act), stress testing, etc., across multiple business lines. Dynamically evolving requirements make this task even more difficult, and without robust analytics capabilities, these requirements can put a substantial drain on a bank’s resources.
To summarize, today analytics is an essential capability for a bank to have to compete in an industry characterized by economic uncertainties and increased competition. Some of the key areas in which analytics can make a significant impact are: efficient capital deployment, improved speed and accuracy of customer decisions, development of differentiated products based on customers’ needs and risk profiles, and reduction in operational costs through enhanced automation in routine decisions; thereby allowing banks to become more consistent, competitive and customer-centric.
However, the adoption of analytical solutions is still not as far-spread as it should be, especially in the emerging markets of South Asia, the Middle East and Africa. In a survey conducted last year by dun & bradstreet, it was found that only one-third of the banks had invested in analytics. Availability of sufficient data of acceptable quality and lack of conviction that analytics could deliver superior results were the top two reasons quoted by banks for not using analytics in making business decisions. We believe that as investments made in transaction automation and digitalization over the last decade improve banks’ abilities to capture customer-behavior data, they will be more prepared to exploit this data through analytics to improve their business performance. Senior management commitment is another key prerequisite for building an “analytics culture” in the bank. We find that a fundamental belief that analytics can produce the right answers, even if such answers are counterintuitive, is sometimes absent in higher levels of management. This may be because a generation ago, most banks lived in a data-starved environment, which resulted in most successful managers of the time relying on experience and intuition to make decisions. The mindset change, though significant, has yet to permeate to the board rooms completely. In the next few years, as more C-suite managers become comfortable with the current data-overload environment, they are more likely to begin experimenting with analytics, and soon the benefits will start accruing to justify additional investments.
The greatest push, however, will come from the new generation of customers, as they will expect and demand the same experience from their banks as they receive from other service providers. After all, as Victor Hugo said: “No one can resist an idea, whose time has come”.
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