Predict Customer Behavior; No Crystal Ball Needed

by Guest Contributor 3 min read July 30, 2019

Consumer behavior is constantly evolving — from the channels they prefer to the economic trends spurring varying interest and activity. It’s no surprise that businesses find it challenging to know what their customers want today or tomorrow. But knowing and understanding this information is essential to growing your bottom line. Through years of working with businesses across every vertical, we’ve found that a solid approach to growing your business revolves around your customers. The better you know your customers, the better you can achieve your goals.

Seeing the future.

How well can you identify and rank your current customer population? Are you leveraging that insight to acquire new customers, manage current customers and prioritize collections efforts? If so, you’re probably using custom models in your business strategy. But if your organization is like many businesses, you may use a more traditional approach. In our highly competitive market, strategy and decisions must be based on the right data and insights. No excuses. The data is there, and we can help you turn it into actionable insights. Implementing a custom model can maximize your return on investment and help you make more profitable business decisions — now and in the future.

No palm reading required.

Without visiting your local fortuneteller, you still can predict the future. You need a model, but not the “runway” type. What constitutes a highly predictive and effective model? Many factors. A highly predictive custom model should incorporate robust data, advanced modeling methodologies, analytical expertise and attributes. Having these foundational components is essential to knowing your customers and making confident decisions.

Models aren’t one-size-fits-all. When you take an innovative approach to model development, the model is targeted to support your specific business goals while providing the documentation required for regulatory reviews. Consider these items as you develop your custom model:

  • Data — It all starts with the right data. Combining multiple data assets — your master-file data, our credit data and any additional data sources — is key to developing a robust model development sample. In other words, a model development sample should represent your future through-the-door population.
  • Model design — To ensure the custom model is designed to help you achieve your specific goals, you’ll want to incorporate the latest analytics and modeling methodologies. An experienced analytics team will be essential here.
  • Segmentation — With the right model development and segmentation strategies, you can identify optimal segments that will result in a more predictive custom model. This way, each consumer is scored on a scorecard developed using a credit profile similar to theirs.
  • Validation — To ensure the model’s predictive ability and longevity, validate each custom model on a holdout sample and compare it with other scores to ensure it accounts for the current and future (through-the-door) consumer populations, as well as policy rule and behavioral changes.
  • Regulatory review — Don’t forget about the documentation needed for compliance. While audits are unpleasant , fines and extensive scrutiny can significantly impact your business.

Take your fortunetelling to the next level.

Machine learning is all the rage. This cutting-edge technology can be embedded in your predictive models to help uncover patterns in data that may not be apparent otherwise. This can be done by comparing the performance of the machine learning model with your existing models. Once you know that machine learning can add the lift you’re looking for, you can apply that methodology to develop a custom model focused on stability, cost-efficiency, transparency and predictive performance.

Predicting behavior across the Customer Life Cycle.

How can a custom model benefit you? From improving baseline performance and increasing profitability by approving more good accounts to uncovering opportunities within your target market, custom models can provide the confidence needed to grow your business. Which one of these models can help you achieve your business goals?

When it comes to accurately predicting customer behavior, you don’t need a crystal ball. You need a well-built, highly predictive custom model. Use the data that’s available to gain insight into your customers and grow your bottom line. If you need help, we’re here. We have the data, analytics and expertise to help you get started.

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For decades, credit data has given lenders a trusted foundation for assessing risk. Now, consumer-permissioned cash flow data is adding another layer of insight into consumers’ financial lives, including income, spending, liquidity and changing financial obligations. The opportunity is significant. Nearly one in five consumers lacks access to or has damaged credit. New Experian research also shows that 60% of consumers who have previously been denied credit or received less favorable terms than expected believe the outcome would have been different if lenders had considered their recent income and banking activity alongside their credit history. 1,2 For lenders, cash flow insights can provide additional context about a consumer’s financial capacity and support more informed underwriting decisions. The next challenge is making those insights easier to use consistently and responsibly at scale. What are cash flow insights? Cash flow insights are derived from consumer-permissioned banking data and provide visibility into how money moves into and out of a consumer’s accounts. Depending on the information available, cash flow data can provide insight into financial behaviors such as: Income and income stability: Regular and irregular income streams and changes over time Spending and recurring expenses: Everyday spending patterns and ongoing financial obligations Account balances and liquidity: Available funds and changes in account balances Debt obligations: Loan payments and other recurring debt commitments Cash flow patterns: How money moves into and out of accounts over time Traditional credit information provides a rich history of how consumers have managed credit. Cash flow insights add context about their current financial activity and capacity, providing lenders with a more complete view of financial health. Cash flow insights can also add predictive value to risk assessment. Experian analysis shows Cashflow Score can deliver up to a 25% lift in predictive performance over conventional credit scores when tailored to targeted risk tiers.3 How lenders can put cash flow insights to work at scale As interest in cash flow insights grows, the conversation is moving beyond what the data can reveal to how financial institutions can use it consistently and responsibly. That requires standardized consumer reporting, clear governance and decision-ready insights that can work within established lending processes. The launch of Experian Cashflow Data Bureau at Vision 2026 reflects this shift from access to adoption. The new consumer reporting agency operates under the Fair Credit Reporting Act (FCRA) and is designed to help financial institutions adopt consumer-permissioned cash flow insights at scale. A key part of that foundation is the cash flow consumer report, which organizes consumer-permissioned banking information into a standardized consumer report regulated under the FCRA. This gives lenders a consistent foundation for incorporating cash flow information into lending decisions while providing consumers with access to the information in their report. Turning greater visibility into better decisions The next evolution in credit isn’t about choosing between traditional credit and cash flow data. Each provides a different perspective. Traditional credit information offers deep insight into how consumers have managed credit over time. Cash flow insights can add context about financial activity and capacity. Bringing those perspectives together can give lenders greater visibility into consumers’ financial health and help them better distinguish between risk and resilience, support more informed decisions and identify opportunities to responsibly expand access to credit. As cash flow adoption continues to grow, its value will come from how effectively it complements the information lenders already use. The opportunity isn't simply to see more data. It's to turn that additional visibility into smarter, more informed decisions. Explore how Experian Cashflow Data Bureau is helping financial institutions put consumer-permissioned cash flow insights into practice at scale. Learn more [1] Federal Reserve Bank of New York, The Market for Credit Building: Demand, Product Characteristics and Consumer Uses, 2026. [2] Experian commissioned Atomik Research to conduct an online survey of 2,000 U.S. adults age 18+. Fieldwork took place July 16–21, 2026. Margin of error: +/- 2.2 percentage points at a 95% confidence level. [3] Based on Experian analysis when Cashflow Score is compared with conventional credit scores, tailored to targeted risk tiers. Predictability based on KS.

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