Customer data plus alternative data equals statistical success
As banks consolidate—a fact of life since the financial crisis—they also feel pressure from non-bank competitors to get leaner, more efficient and bolster customer experience. This stands as more important than ever as banking becomes increasingly digital; today’s banks invest in big data technologies, customer analytics and mobile banking platforms.
The banking sector stands as one of the fastest growing areas in big data analytics: It accounts for almost 14 percent ($22 billion) of worldwide revenue this year, according to the IDC’s 2018 Worldwide Semiannual Big Data and Analytics Guide. Banks already collect massive amounts of data daily about their customers—including banking transactions, loan payments, investment portfolios and even ATM language preferences. Additionally, a wide variety of data is increasingly available from third-party sources. Such data includes consumer spending patterns, insurance claims data, utility payments, social media, GPS location data from mobile phones, and census data. All this data amounts to a treasure trove for banks, which can provide a great many insights to provide better informed operational, risk and management decision making.
While banks commonly use customer data for service execution, customer management and marketing, endless opportunities still exist to leverage the huge volume and variety of data now available. The challenge for banks—especially smaller community banks and credit unions—is to identify and prioritize the data-driven objectives to most effectively use their data and maximize the return on investment. With a rapidly maturing fintech market (and customers who increasingly demand more tailored and quick-response services) banks must make strategic decisions. These can be based on customer and third-party data to improve services, cut costs and manage risks accordingly. Banks that fail to effectively incorporate these capabilities risk losing market share and could concede competitive advantage in the coming years.
To be sure, the main process of lending money remains the same. But market dynamics and technology are changing. With further consolidation expected, data analytics that incorporate big data, visualization and machine learning algorithms will play an even bigger role in due diligence and synergy assessment throughout this lifecycle.
Potential borrowers, anticipated needs
Teaming customer data with alternative data allows banks to gain deeper insight about their customers and anticipate their needs. For example, banks can track their customers’ life events, such as a marriage, child birth or job transfer. This allows banks to create targeted promotions and personalized product offerings. A bank may offer a customer who moves to another city a credit card with perks and discounts to local businesses there. Offering mortgage loans to newly married customers, or designing loyalty cards based on customer spending habits, will increase customer satisfaction, reduce churn and optimize profits.
The underwriting overview
While banks traditionally rely on credit scores and credit history, online lending platforms that offer unsecured personal loans increasingly tap alternative data sources—such as spending patterns, education level and other demographic profiles. Analyzed via machine learning algorithms, this data helps banks to underwrite quicker and accurately as they extend more credit to underserved populations.
Online lenders that focus on improved customer experience, aided by big data analytics and micro-targeted product strategy, have also streamlined their processes. That makes it faster for customers to apply for a loan and get approved online. By integrating customer and alternative data, banks can enhance their risk assessment to identify new customers eligible for credit, predict customers at risk of default and improve quality of loan products.
Borrowing and big data
In recent years, banks have shifted their focus from products and sales to improving customer experience. Ease of use, accessibility and faster services represent critical factors these. Because fintechs have led big data innovations, more financial companies now partner with them to improve customer experience and collection rates. Banks are also enhancing their collections department through data analytics to identify watch-list loans and potential work-out solutions in a timelier manner.
Cross-selling and customer services
Big banks now offer premier and private banking services to their top tier customers. Premier and private banking services often include the services of a personal banker, who will periodically review all the data collected about the customer. While these services offer customers a personalized banking experience, they also allow banks to offer customized financial products based on a customer’s portfolio, such as special rates on mortgages and investments, and personalized credit card perks. Smaller banks can also utilize similar customer segmentation to tailor services and products that allow them to compete with big banks.
M&A meets mining and assessment
Banks also apply data analytics to mergers and acquisitions (M&A) as the industry further consolidates. However, data is only useful to the extent it becomes available and participants can effectively mine it. Technological advances have enhanced banks’ abilities to track and mine relevant information that buyers can leverage during diligence to more effectively assess credit risk and synergies.
Limits on resources—for example, the number of loans sellers can manually pull and buyers can assess—have historically limited credit diligence. Through deployment of data analytics, buyers can algorithmically assess a larger portion of loans. This will help extrapolate expected loss experience based on internally derived modeling and the use of data visualization software.
Soft synergies also benefit from predictive analytics. Buyers can utilize the seller’s customer data and third-party market demographic information to estimate revenue synergies due to cross-selling or pricing arbitrage—especially in fee-based ancillary offerings such as insurance or wealth management.
Putting it all together: Data’s day is here
Abundant data and the importance of analytics in the banking sector are indisputable. Banks now harness data to maximize the return on marketing spend, optimize product strategies, improve customer experience and reduce risks. To compete in this data-driven world, banks must deploy advanced data analytics capabilities and maximize the value of information. More insight means better decisions—as well as better service to customers and a better bottom line for banks.
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