By: Alan Mandelbaum, Senior Manager
Data analytics is the practice of determining the most effective ways to mine data in order to enhance customer relationships and mitigate fraud. As the scope and importance of advanced data analytics continue to increase, banks are well-advised to consider reviewing and upgrading their analytics capabilities.
Changing environment drives increased pressure to focus on data analytics
According to the Tower Group, the volume of data housed by mid-tier banks has multiplied by as much as 150 times over the past seven to eight years. Using data analytics and modeling tools, banks are able to uncover key information about customer preferences and how customer relationships are evolving.
Analytics and business intelligence tools are not new to banks, but their scope and importance have increased recently due to the evolving economic and regulatory environment and the massive amounts of data available. For those institutions that have a data mining/data analytics program in place, it may be time to review current data analytic methodologies. These reviews ensure that data are being used most effectively to improve decision-making, mitigate risks, drive retention and acquisition of high-value customers, and improve compliance mandates. And for institutions that have yet to make any significant investment in data analytics, the range of options can be daunting.
New regulatory requirements will compel banks to deploy analytical tools on data aggregated from various business units. These regulations will continue to push many banks from their traditional “siloed” approach toward enterprise-wide data architectures and a more integrated model. In addition, customer behavior, payment preferences, and channel usage continues to evolve at a greater pace.
Banks that have built data around a product-oriented focus should look at shifting to a more customer-centric orientation. A single customer view, however, can be challenging to create in today’s business environment, as most organizations have different databases and different channels through which they do business. Payments analysis can provide a customer view across products and channels in order to gain valuable insights.
Utilize advanced analytics to enhance cross-sell, reduce attrition and mitigate fraud
In the payments arena, forward-thinking banks understand that customer payment preferences can provide the opportunity to model behaviors – and enhance potential credit, savings, and wealth management cross-sell success. By utilizing payment analytics, a bank also can identify and target opportunities more precisely for promoting and migrating customers to more profitable payment and delivery channel alternatives, with the potential to reduce attrition and increase fee income.
Banks can further deploy payment analytics to create dynamic marketing campaigns that target customers based on products matching their current preferences. In a highly competitive market, this ability to communicate with customers at the right time with the right offer using the right channel can provide a significant advantage.
Another area that stands to gain heavily from advanced analytics is fraud detection. Fraud remains a looming threat for banks, and advanced analytics can help counter this threat. Analytics can help identify fraud patterns to uncover actual and potential fraudulent activities and suspicious accounts. Banks can perform point-in-time analysis for one-off investigations or repetitive analysis for areas where fraud tends to occur.
Is it time to review your approach?
Acknowledging that many institutions have already begun this journey, a typical analytics methodology review or implementation begins by establishing (or re-establishing) a vision and end-state, and confirming that a proposed approach fits in with broader organizational strategy. Tactically, a successful implementation requires an audit of data integrity, strong executive leadership, significant subject matter expertise, and the development of a compelling business case and associated metrics.
Data structures will require standardization, given that the quality and quantity of customer data often varies across business lines. Such an effort requires the backing of the organization’s leaders and a cultural shift that increases the comfort level with data-driven decision-making.
The next important requirement for successful analytics implementation is expertise. Banks may have all the data they need, but it takes subject matter expertise to determine vendor selection and ultimately identify the relevant data points. Hence, banks need to either invest in the right analytical talent pool or source these capabilities from a trusted partner.
Vendors have developed sophisticated analysis engines to assist banks in sorting through voluminous amounts of customer data. Each vendor tends to have slight variations in approach, but all examine previous transactions to spot patterns. Increasingly they are applying layers of analysis to attempt to predict consumer behavior, such as the likelihood consumers will actually buy something and how much they are likely to pay. Typically these engines can produce output based on 15 months of data, but that is improving. Some are becoming more adept at making judgments with as little as three months of data and as few as a few hundred transactions.
Lastly, a bank must develop metrics that demonstrate the return on investment. Altogether, this requires an ability to understand the business environment in its full complexity, in addition to understanding how to deploy the required technology effectively.
As the scope and importance of advanced data analytics continue to increase, banks are well-advised to consider a strategic implementation/review /upgrade. The effort requires decisive thinking, management leadership, and solid subject-matter expertise to ensure best-in-class deployment and provide flexibility for future needs.