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Trivia question: Millennials don’t purchase new vehicles. True or False?

When developing a risk model, validation is an essential step in evaluating and verifying a model’s predictive performance. There are two types of data samples that can be used to validate a model. In-time validation or holdout sample: Random partitioning of the development sample is used to separate the data into a sample set for development and another set aside for validation. Out-of-time validation sample: Data from an entirely different period or customer campaign is used to determine the model’s performance. We live in a complicated world. Models can help reduce that complexity. Understanding a model’s predictive ability prior to implementation is critical to reducing risk and growing your bottom line. Learn more

As I mentioned in my previous blog, model validation is an essential step in evaluating a recently developed predictive model’s performance before finalizing and proceeding with implementation. An in-time validation sample is created to set aside a portion of the total model development sample so the predictive accuracy can be measured on a data sample not used to develop the model. However, if few records in the target performance group are available, splitting the total model development sample into the development and in-time validation samples will leave too few records in the target group for use during model development. An alternative approach to generating a validation sample is to use a resampling technique. There are many different types and variations of resampling methods. This blog will address a few common techniques. Jackknife technique — An iterative process whereby an observation is removed from each subsequent sample generation. So if there are N number of observations in the data, jackknifing calculates the model estimates on N - 1 different samples, with each sample having N - 1 observations. The model then is applied to each sample, and an average of the model predictions across all samples is derived to generate an overall measure of model performance and prediction accuracy. The jackknife technique can be broadened to a group of observations removed from each subsequent sample generation while giving equal opportunity for inclusion and exclusion to each observation in the data set. K-fold cross-validation — Generates multiple validation data sets from the holdout sample created for the model validation exercise, i.e., the holdout data is split into K subsets. The model then is applied to the K validation subsets, with each subset held out during the iterative process as the validation set while the model scores the remaining K-1 subsets. Again, an average of the predictions across the multiple validation samples is used to create an overall measure of model performance and prediction accuracy. Bootstrap technique — Generates subsets from the full model development data sample, with replacement, producing multiple samples generally of equal size. Thus, with a total sample size of N, this technique generates N random samples such that a single observation can be present in multiple subsets while another observation may not be present in any of the generated subsets. The generated samples are combined into a simulated larger data sample that then can be split into a development and an in-time, or holdout, validation sample. Before selecting a resampling technique, it’s important to check and verify data assumptions for each technique against the data sample selected for your model development, as some resampling techniques are more sensitive than others to violations of data assumptions. Learn more about how Experian Decision Analytics can help you with your custom model development.

There’s no question today’s consumers have high expectations. As financial services companies wrestle with the laws and consumer demands, here are a few points to consider: While digital delivery channels may be new, the underlying credit product remains the same. With digital delivery, adhere to credit regulations, but build in enhanced policies and technological protocols. Consult your legal, risk and compliance teams regularly. Embrace the multitude of delivery methods, including email, text, digital display and beyond. When using the latest technology, you need to work with the right partners. They can help you respect the data and consumer privacy laws, which is the foundation on which strategies should be built. Learn more

Data can be a powerful tool. But the key to data isn’t just accessing it. It’s interpreting it — and using it to make better decisions that benefit your business and your customers. Here are four key areas where business leaders can use data in more meaningful ways to impact decisions: Grow your business — Reveal patterns, trends and associations to better evaluate business opportunities and respond to market fluctuations. Improve efficiency — Optimize operations and improve use of time to acquire more customers for less. Manage fraud and credit risk — The better you know your customers, the less risk you’ll have. Validate manually entered information — Determine the best actions to deliver the most effective outcomes for both existing and future customers. According to Forbes, by the year 2020 about 1.7 megabytes of new information will be created every second for every human being.1 Get the most out of our data-driven economy to remain competitive. Learn more> 1Bernard Marr, “Your enterprise competes to win. Does your digital infrastructure?,” Forbes, September 2015.

The early stages of establishing a startup are some of the most difficult. In fact, it is said 90 percent of startups fail. Challenges include forming the right team, raising capital, and constructing a business model. But no one will deny that one of the most important parts of a startup’s business strategy is the data and technology that underpin its solution. On the one hand, new startups don’t benefit from a wealth of historic data on their clients, prospects, and partners like their more established competitors. While this isn’t the end of the world, it does emphasize the importance of finding a trusted data partner to build those data insights into the design for their application or platform. By using a trusted third-party data provider, companies can ensure they receive reliable and accurate data to utilize in their products and services. On the other hand, startups have the luxury of not being bogged down and burdened by legacy systems and older tech. While building a solution from the ground up is never an easy feat, startups can generally move faster. They can benefit from the latest technology to build new apps and products, making them nimbler than the incumbents in the space. Cloud technology enables organizations to quickly get their business up and running. In addition, companies are exposing many of their data assets and services through application programming interfaces (APIs), allowing others to more easily create their own solutions. Rather than reinventing the wheel, companies can leverage existing services to build more complex solutions and launch faster. “We’ve talked to countless startups and businesses and know they want easy, fast, and secure access to our data assets and services,” said Alpa Jain, vice president of Experian’s API Center of Excellence. “That’s why we’ve launched our API Developer Portal.” The list of APIs available through Experian’s Developer Portal includes solutions like consumer credit data, commercial credit data, commercial public record information, data quality, vehicle history information, and more. Companies can browse the list of available APIs, create an account, and start utilizing the APIs for building out a product within minutes. “Our goal is to help companies unlock untapped market opportunities and grow,” said Jain. “Success with APIs requires a successful developer program and portal to accelerate developer productivity – we believe we’ve created both with our new portal experience."

Although it’s hard to imagine, some synthetic identities are being used for purposes other than fraud. Here are 3 types of common synthetic identities and why they’re created: Bad — To circumvent lag times and delays in establishing a legitimate identity and data footprint. Worse — To “repair” credit, hoping to start again with a higher credit rating under a new, assumed identity. Worst — To commit fraud by opening various accounts with no intention of paying those debts or service fees. While all these synthetic identity types are detrimental to the ecosystem shared by consumers, institutions and service providers, they should be separated by type — guiding appropriate treatment. Learn more in our new white paper produced with Whitepages Pro, Fighting synthetic identity theft: getting beyond Social Security numbers. Download now>

An introduction to the different types of validation samples Model validation is an essential step in evaluating and verifying a model’s performance during development before finalizing the design and proceeding with implementation. More specifically, during a predictive model’s development, the objective of a model validation is to measure the model’s accuracy in predicting the expected outcome. For a credit risk model, this may be predicting the likelihood of good or bad payment behavior, depending on the predefined outcome. Two general types of data samples can be used to complete a model validation. The first is known as the in-time, or holdout, validation sample and the second is known as the out-of-time validation sample. So, what’s the difference between an in-time and an out-of-time validation sample? An in-time validation sample sets aside part of the total sample made available for the model development. Random partitioning of the total sample is completed upfront, generally separating the data into a portion used for development and the remaining portion used for validation. For instance, the data may be randomly split, with 70 percent used for development and the other 30 percent used for validation. Other common data subset schemes include an 80/20, a 60/40 or even a 50/50 partitioning of the data, depending on the quantity of records available within each segment of your performance definition. Before selecting a data subset scheme to be used for model development, you should evaluate the number of records available in your target performance group, such as number of bad accounts. If you have too few records in your target performance group, a 50/50 split can leave you with insufficient performance data for use during model development. A separate blog post will present a few common options for creating alternative validation samples through a technique known as resampling. Once the data has been partitioned, the model is created using the development sample. The model is then applied to the holdout validation sample to determine the model’s predictive accuracy on data that wasn’t used to develop the model. The model’s predictive strength and accuracy can be measured in various ways by comparing the known and predefined performance outcome to the model’s predicted performance outcome. The out-of-time validation sample contains data from an entirely different time period or customer campaign than what was used for model development. Validating model performance on a different time period is beneficial to further evaluate the model’s robustness. Selecting a data sample from a more recent time period having a fully mature set of performance data allows the modeler to evaluate model performance on a data set that may more closely align with the current environment in which the model will be used. In this case, a more recent time period can be used to establish expectations and set baseline parameters for model performance, such as population stability indices and performance monitoring. Learn more about how Experian Decision Analytics can help you with your custom model development needs.

The business case for identity verification and risk assessment tools is most compelling when it includes a broad range of both direct and indirect factors. Here are 3 indirect measures we suggest you consider: Customer experience improvement — With 72% of businesses focused on service, according to Forrester Research,* the value of reduced friction can’t be overstated Reputation and brand protection — The monetary cost of fraud losses can be high, but the impact on customer relationships and brand integrity can be even higher. Compliance — Noncompliance costs an average of 2.65 times more than investing in a technology-based compliance solution. Justifying investment in fraud prevention technology can be challenging. A business case built on the right data can pave the way to upgrading your identity verification and risk assessment technology. Learn more in our buyer's guide>

The economy remains steady, maintaining a positive outlook even though the GDP growth slowed in the first quarter. Real estate is holding ground even as rates rise. We’ve reached a 7-year high in 30-year fixed-rate mortgages, which could have a longer-term effect on this market. Bankcard may be reaching its limit — outstanding balances hit $764 billion and delinquency rates continue to rise. While auto originations were flat in Q1, performance is improving as focus moves away from subprime lending. The economy remains steady as we transition from 2017. Keep an eye on inflation and interest rates in regard to their possible short-term economic impact. Learn more about these and other economic trends with the on-demand recording of the webinar. Watch now

According to our recent research for the State of Alternative Credit Data, more lenders are using alternative credit data to determine if a consumer is a good or bad credit risk. In fact, when it comes to making decisions: More than 50% of lenders verify income, employment and assets as well as check public records before making a credit decision. 78% of lenders believe factoring in alternative data allows them to extend credit to consumers who otherwise would be declined. 70% of consumers are willing to provide additional financial information to a lender if it increases their chance for approval or improves their interest rate. The alternative financial services space continues to grow with products like payday loans, rent-to-own products, short-term loans and more. By including alternative financial data, all types of lenders can explore both universe expansion and risk mitigation. State of Alternative Credit Data

There is a delicate balance in delivering a digital experience that instills confidence while providing easy and convenient account access. When it comes to a frictionless, secure customer experience, our 2018 Global Fraud and Identity Report research showed: 52% of businesses have chosen to prioritize the user experience over detecting the mitigating fraud. 78% of consumers will create an account to complete ecommerce purchased because it is a trusted brand/website. 60% of consumers will follow through with a transaction even if they have forgotten their user name or password. Consumers believe that having simple, instant and easy-to-access verification methods are important to their experience when shopping online. Are your providing this? 2018 Global Fraud and Identity Report

Hispanics are not only the fastest growing minority in the United States, but according to the Hispanic Wealth Project’s (HWP) 2017 State of Hispanic Homeownership Report, they would prefer to own a home rather than rent. Hispanic Millennials—who are entering their home-buying years—are particularly eager for homeownership. This group is educated, are entrepreneurs and business owners that over index on mobile use, and 9 of 10 say wanting to own a home is part of their Hispanic DNA. For them, it’s not a matter of if but when and how they will become homeowners. An optimistic outlook is also a trait of Hispanic Millennials, who generally are more positive about the future than the average Millennial. They are also confident in their ability to handle different types of tasks that are part of their day-to-day lives. And at 35 percent, the share of bilingual Hispanic Millennials with a household income of $100,000 or more is consistent with U.S. Millennials as a whole Homeownership challenges Yet, despite their optimism and goal of homeownership, Hispanic homeownership at 46.2 percent lags when compared to the overall U.S. home ownership rate of 63.9 percent in 2017. There are signs the gap could narrow; Hispanics are the only demographic to have increased their rate of homeownership for the past three years. Moreover, the report shows Hispanics are responsible for 46.5 percent of net U.S. homeownership gains since 2000. Still, the 2017 State of Hispanic Homeownership Report notes that a shortage of affordable housing, prolonged natural disasters in states with a significant Hispanic presence (California, Florida, Texas), and uncertainty over immigration policy could hinder Hispanic homeownership growth. An opportunity to reach Hispanics It seems most Hispanic Millennials will strive for homeownership at some point in their life, as they believe owning a home is best for their family’s future. With no convincing needed, there is a tremendous opportunity for mortgage providers to look deeper into the reasons behind Hispanic Millennials’ optimism to determine how to insert themselves into that dynamic. Research highlights the importance of creating interest in financial advice and making this a potential means of gaining trust. Hispanic Millennials who gain a better understanding of the benefits—not only for them but for generations to come—and costs of owning a home may translate their confidence into action.

In my first blog post on the topic of customer segmentation, I shared with readers that segmentation is the process of dividing customers or prospects into groupings based on similar behaviors. The more similar or homogeneous the customer grouping, the less variation across the customer segments are included in each segment’s custom model development. A thoughtful segmentation analysis contains two phases: generation of potential segments, and the evaluation of those segments. Although several potential segments may be identified, not all segments will necessarily require a separate scorecard. Separate scorecards should be built only if there is real benefit to be gained through the use of multiple scorecards applied to partitioned portions of the population. The meaningful evaluation of the potential segments is therefore an essential step. There are many ways to evaluate the performance of a multiple-scorecard scheme compared with a single-scorecard scheme. Regardless of the method used, separate scorecards are only justified if a segment-based scorecard significantly outperforms a scorecard based on a broader population. To do this, Experian® builds a scorecard for each potential segment and evaluates the performance improvement compared with the broader population scorecard. This step is then repeated for each potential segmentation scheme. Once potential customer segments have been evaluated and the segmentation scheme finalized, the next step is to begin the model development. Learn more about how Experian Decision Analytics can help you with your segmentation or custom model development needs.