Tag: credit analysis

In 2017, a meaningful jump in consumer sentiment bolstered spending, and caused the spread between disposable personal income and consumer spending to reach an all-time high. This increase in spread was mostly financed through consumer debt, which according to the Federal Reserve Bank of New York has brought total consumer debt to a new peak of $12.8 Trillion surpassing the prior peak in 2008. The Experian eighth annual State of Credit report greatly supported the consumer behavior trends observed for the past year. Spanning the generations  It is no surprise that generation Z (the “Great Recession Generation”) is conservative and prudent in their approach to credit because they are the most familiar with the post financial crisis economy. Results showed Millennials experienced a drop in overall debt, and an increase in mortgage debt reflects the national homeownership affordability challenge facing this generation. As first time homebuyers, millennials have to relatively tighten their spending as they dedicate an ever-growing portion of their income to housing. On the other end of the spectrum, the results of the study showed that Baby Boomers’ had sizable debt (including mortgage debt), which reflects the generation’s intent to stay active in their communities and in their homes much longer than prior generations have done. A recent Harvard study reported that by 2035, one out of three American households will be headed by an individual 65 years of age or older, compared to current ratio of one out of five households. What’s on the horizon? It is reasonable to assume that these trends may continue into 2018, as the underlying conditions continue to persist. A closer eye should be kept on student and auto loans due to the significant increase in portfolio size and increasing default rates compare to other debt.   Editor’s note: This post was written by Fadel N. Lawandy, Director of the C. Larry Hoag Center for Real Estate and Finance and the Janes Financial Center at the George L. Argyros School of Business and Economics, Chapman University. Fadel joined the George L. Argyors School of Business and Economics, Chapman University after retiring as a Portfolio Manager from Morgan Stanly Smith Barney in 2009. He has two decades of experience in the financial industry with banking, credit management, commercial/residential real estate acquisition and financing, corporate finance, mergers and acquisitions, quantitative and qualitative analysis and research, and portfolio management. Fadel currently serves as the Chairman of the Board and President of CFA Society Orange County, and is an active member of the CFA Institute.

Expert offers insights into turnkey big data access   The data is out there – and there is a lot of it. In the world of credit, there are more than 220 million credit-active consumers. Bolt on insights from the alternative financial services space and that number climbs even higher. So, what can analysts do with this information? With technology and the rise of data scientists, there are certainly opportunities to dig in and explore. To learn more, we chatted with Chris Fricks, data and product expert, responsible for Experian’s Analytical Sandbox™. 1. With the launch of Experian’s all-new Ascend platform, one of the key benefits is full-file access to our Sandbox environment. What exactly can clients access and are there specific tools they need to dig into the data? Clients will have access to monthly snapshots of 12-plus years of the full suite of Experian scores, attributes, and raw credit data covering the full national consumer base. Along with the data access, clients can interact and manipulate the data with the analytic tools they prefer. For example, a client can log into the environment through a standard Citrix portal and land on a Windows desktop. From there, they can access applications like SAS, R, Python, or Tableau to interrogate the data assets and derive the necessary value. 2. How are clients benefiting from this access? What are the top use cases you are seeing? Clients are now able to speed analytic findings to market and iterate through the analytics lifecycle much faster. We are seeing clients are engaging in new model development, reject inferencing, and industry/peer benchmarking. One of the more advanced use cases is related to machine learning – think of artificial intelligence for data analytics. In this instance, we have tools like H2O, a robust source of data for users to draw on, and a platform that is optimized to bring it all together in a cohesive, easy-to-use manner. 3. Our Experian database has details on 220 million credit-active consumers. Is this data anonymized, and how are we ensuring sensitive details are secure? We use the data from our credit database, but we’ve assigned unique consumer-level and trade-level encrypted pins to ensure security.  Once the encrypted PINs are assigned, they remain the same over time. Then all PII is scrubbed and everything is rendered de-identifiable from an individual consumer and lender perspective. Our pinning technique allows users to accurately track individual trades and consumers through time, but also prevents any match back to individual consumers and lenders. 4. I imagine having access to so much data could be overwhelming for clients. Is more necessarily better? You’re right. Access to our full credit file can be a lot to handle. While general users will not “actively” use the full file daily, statisticians and data scientists will see an advantage to having access to the larger universe. For example, if a statistician only has access to 10% of the Sandbox and wants to look at a specific region of the country, they may find their self in a situation with limited data that it is no longer statistically significant. By accessing the full file, they can sample down based on the full population from the region they are concerned with analyzing. 5. Who are the best-suited individuals to dig into the Sandbox environment and assess trends and findings? The environment is designed to serve the front-line analysts responsible for coding and analytics that gets reported out to various levels of leadership. It also enables the socialization of those findings with leadership, helping them to interact and give feedback on what they are seeing. Learn more about Experian’s Analytical Sandbox and request a demo.

You’ve been tasked with developing a new model or enhancing an existing one, but the available data doesn’t include performance across the entire population of prospective customers. Sound familiar? A standard practice is to infer customer performance by using reject inference, but how can you improve your reject inference design? Reject inference is a technique used to classify the performance outcome of prospective customers within the declined or nonbooked population so this population’s performance reflects its performance had it been booked. A common method is to develop a parceling model using credit bureau attributes pulled at the time of application. This type of data, known as pre-diction data, can be used to predict the outcome of the customer prospect based on a data sample containing observations with known performance. Since the objective of a reject inference model is to classify, not necessarily predict, the outcome of the nonbooked population, data pulled at the end of the performance window can be used to develop the model, provided the accounts being classified are excluded from the attributes used to build the model. This type of data is known as post-diction data. Reject inference parceling models built using post-diction data generally have much higher model performance metrics, such as the KS statistic, also known as the Kolmogorov-Smirnov test, or the Gini coefficient, compared with reject inference parceling models built using pre-diction data. Use of post-diction data within a reject inference model design can boost the reliability of the nonbooked population performance classification. The additional lift in performance of the reject inference model can translate into improvements within the final model design. Post-diction credit bureau data can be easily obtained from Experian along with pre-diction data typically used for predictive model development. The Experian Decision Analytics team can help get you started.