Big Data, Big Impact on Lending: Engaging the Right Customers With the Right Solutions at the Right Time


Lenders process enormous quantities of data every day while serving their customers – and have a growing need to effectively harness that data to efficiently manage their business and meet accelerated customer expectations. Big data – the vast amount of data organizations are mining to reveal insights, trends and patterns – is making a big impact on lending.

Lenders have always had data points to help them make decisions, but technology enables an enterprise-wide, 360-degree view of customers and the lending operation. Data is collected through multiple databases, channels, products and services across an organization, but the key is to bring all pertinent data together in a logical, intuitive way. Data can help lenders engage the right customers with the right solutions at the right time, ultimately driving increased loan activity, profitability and efficiency.

Data can help lenders engage the right customers with the right solutions at the right time, ultimately driving increased loan activity, profitability and efficiency.

Using data, lenders can understand what products to offer individual customers – and at what price point they will be effective, thereby maximizing profitability. In the same way, understanding what mix of products and delivery channels leads to better acceptance from consumers and impacts marketing and growth strategies. If a lender learns certain loan products lead to greater delinquencies or increased collection activity, that knowledge can impact risk management.

Lenders can use data to respond to market trends, competitive forces and mandated priorities with information gleaned from loan quality adjustments, customer population changes and product segment adoption. Data drives loan activity in four key ways.

  1. Demographics

    It's no secret millennials behave differently than baby boomers and seniors have different financial needs than members of Gen X. Those generational differences impact lending decisions. Using data gleaned from demographic information, an organization can fine tune how it structures and prices loans – and what kind of loan programs, incentives and fees are offered – to impact bottom-line results. For example, targeting the right 40-plus demographic with a right-priced home equity program will likely lead to increased originations, lower delinquencies and greater profitability than if the product had not been targeted to the right segment. Data enables lenders to identify and analyze demographic segments to make better business decisions, such as introducing a new loan product or going after a certain market segment.

  2. Loan Behavior

    When lenders take data from loan servicing systems and combine it with external information – credit score, recent loan activity, home ownership, length of residency, children – patterns and trends begin to emerge. A lender might learn that one collection letter is all that's needed to rectify past-due accounts for borrowers within a certain credit score range, while borrowers with scores in a different range might respond better to phone calls. Looking at data from a home equity product, a lender might learn 80 percent of applications originated through online channels, which would inform future marketing efforts.

  3. Loyalty

    The more products and services customers have with their financial institutions – home equity loan, credit card, debit card, checking account and more – the more apt they are to have long-term relationships with that bank or credit union. Tracking these customers is key. What's their total household balances and aggregated transaction volume? What kind of products do they come back for? How's their track record for repayment? Lenders can use information gleaned from data to customize incentives and cross-sell appropriate additional products and services.

  4. Seasonality

    Certain financial products are more popular at certain times of the year. Most auto lenders notice a bump in activity in the spring and fall, for example. But there are other, less obvious peaks and valleys that can impact price and staffing levels. By analyzing service levels, a lender might set a standard that no customer will wait more than 24 hours from filling out a credit application until closing, for example. But what happens if there's a seasonal surge in loan activity? Does that standard need to be adjusted?

Even moderately sized financial institutions have hundreds of thousands, possibly millions, of diverse lending accounts – home equity, overdraft protection, bank cards, auto loans and more – in diverse channels and with diverse pricing. Data analytics helps lenders sift through the wealth of information to focus on what's profitable, what's being done efficiently and what carries less risk. Using this information helps lenders not only better manage their business, but better align products and services to meet consumers' changing expectations for personalized, relevant lending experiences.

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