Tag: credit3d

For an industry that has grown accustomed to sustained year-over-year growth, recent trends are concerning. The automotive industry continued to make progress in the fourth quarter of 2016 as total automotive loan balances grew 8.6% over the previous year and exceeded $1 trillion. However, the positive trend is slowing and 2017 may be the first year since 2009 to see a market contraction. With interest rates on the rise and demand peaking, automotive lending will continue to become more competitive. Lenders can be successful in this environment, but must implement data-driven targeting strategies. Credit Unions Triumph Credit unions experienced the largest year-over-year growth in the fourth quarter of 2016, increasing 15% over the previous period. As lending faces increasing headwinds amid rising rates, credit unions can continue to play a greater role by offering members more competitive rates. For many consumers, a casual weekend trip to the auto mall turns into a big new purchase. Unfortunately, many get caught up in researching the vehicle and don’t think to shop for financing options until they’re in the F&I office. With approximately 25% share of total auto loan balances, credit unions have significant potential to recapture loans of existing members. Successful targeting starts with a review of your portfolio for opportunities with current members who have off-book loans that could be refinanced at a lower rate. After developing a strategy, many credit unions find success targeting these members with refinance offers. Helping members reduce monthly payments and interest expense provides an unexpected service that can deepen loyalty and engagement. But what criteria should you use to identify prospects? Target Receptive Consumers As originations continue to slow, marketing response rates will as well, leading to reduced marketing ROI. Maintaining performance is possible, but requires a proactive approach. Propensity models can help identify consumers who are more likely to respond, while estimated interest rates can provide insight on who is likely to benefit from refinance offers. Propensity models identify who is most likely to open a new trade. By focusing on these populations, you can cut a mail list in half or more while still focusing on the most viable prospects. It may be okay in a booming economy to send as many offers as possible, but as things slow down, getting more targeted can maintain campaign performance while saving resources for other projects. When it comes to recapture, consumers refinance to reduce their payment, interest rate, or both. Payments can often be reduced simply by ‘resetting’ the clock on a loan, or taking the remaining balance and resetting the term. Many consumers, however, will be aware of their current interest rate and only consider offers that reduce the rate as well. Estimated interest rates can provide valuable insight into a consumer’s current terms. By targeting those with high rates, you are more likely to make an offer that will be accepted. Successful targeting means getting the right message to the right consumers. Propensity models help identify “who” to target while estimated interest rates determine “what” to offer. Combining these two strategies will maximize results in even the most challenging markets. Lend Deeper with Trended Data Much of the growth in the auto market has been driven by relatively low-risk consumers, with more than 60% of outstanding balances rated prime and above. This means hypercompetition and great rates for the best consumers, while those in lower risk tiers are underserved. Many lenders are reluctant to compete for these consumers and avoid taking on additional risk for the portfolio. But trended data holds the key to finding consumers who are currently in a lower risk tier but carry significantly less risk than their current score suggests. In fact, historical data can provide much deeper insight on a consumer’s past use of credit. As an example, consider two consumers with the same risk score at a point in time. While they may be judged as carrying similar risk, trended data shows one has taken out two new trades in the past 6 months and has increasing utilization, while the other is consolidating and paying down balances. They may have the same risk score today, but what will the impact be on your future profitability? Most risk scores take a snapshot approach to gauging risk. While effective in general, it misses out on the nuance of consumers who are trending up or down based on recent behavior. Trended data attributes tell a deeper story and allow lenders to find underserved consumers who carry less risk than their current score suggests. Making timely offers to underserved consumers is a great way to grow your portfolio while managing risk. Uncertain Future The automotive industry has been a bright spot for the US economy for several years. It’s difficult to say what will happen in 2017, but there will likely be a continued slowing in originations. When markets get more competitive, data-driven targeting becomes even more important. Propensity models, estimated interest rates, and trended data should be part of every prescreen campaign. Those that integrate them now will likely shrug off any downturn and continue growing their portfolio by providing valuable and timely offers to their members.

Sometimes life throws you a curve ball. The unexpected medical bill. The catastrophic car repair. The busted home appliance. It happens, and the killer is that consumers don’t always have the savings or resources to cover an additional cost. They must make a choice. Which bills do they pay? Which bills go to the pile? Suddenly, a consumer’s steady payment behavior changes, and in some cases they lose control of their ability to fulfill their obligations altogether. These shifts in payment patterns aren’t always reflected in consumer credit scores. At a single point in time, consumers may look identical. However, when analyzing their past payment behaviors, differences emerge. With these insights, lenders can now determine the appropriate risk or marketing decisions. In the example below, we see that based on the trade-level data, Consumer A and Consumer B have the same credit score and balance. But once we see their payment pattern within their trended data, we can clearly see Consumer A is paying well over the minimum payments due and has a demonstrated ability to pay. A closer look at Consumer B, on the other hand, reveals that the payment amount as compared to the minimum payment amount is decreasing over time. In fact, over the last three months only the minimum payment has been made. So while Consumer B may be well within the portfolio risk tolerance, they are trending down. This could indicate payment stress. With this knowledge,  the lender could decide to hold off on offering Consumer B any new products until an improvement is seen in their payment pattern. Alternatively, Consumer A may be ripe for a new product offering. In another example, three consumers may appear identical when looking at their credit score and average monthly balance. But when you look at the trend of their historical bankcard balances as compared to their payments, you start to see very different behaviors. Consumer A is carrying their balances and only making the minimum payments. Consumer B is a hybrid of revolving and transacting, and Consumer C is paying off their balances each month. When we look at the total annual payments and their average percent of balance paid, we can see the biggest differences emerge. Having this deeper level of insight can assist lenders with determining which consumer is the best prospect for particular offerings. Consumer A would likely be most interested in a low- interest rate card, whereas Consumer C may be more interested in a rewards card. The combination of the credit score and trended data provides significant insight into predicting consumer credit behavior, ultimately leading to more profitable lending decisions across the customer lifecycle: Response – match the right offer with the right prospect to maximize response rates and improve campaign performance Risk – understand direction and velocity of payment performance to adequately manage risk exposure Retention – anticipate consumer preferences to build long-term loyalty All financial institutions can benefit from the value of trended data, whether you are a financial institution with significant analytical capabilities looking to develop custom models from the trended data or looking for proven pre-built solutions for immediate implementation.

Knowing a consumer’s credit information at a single point in time only tells part of the story. I often hear one of our Experian leaders share the example of two horses, running neck-in-neck, at the races. Who will win? Well, if you had multiple insights into those two horses – and could see the race in segments – you might notice one horse losing steam, and the other making great strides. In the world of credit consumers, the same metaphor can ring true. You might have two consumers with identical credit scores, but Consumer A has been making minimum payments for months and showing some payment stress, while Consumer B has been aggressively making larger pay-offs. Trended data adds that color to the story, and suddenly there is more intel on who to market to for future offers. To understand the whole story, lenders need the ability to assess a consumer’s credit behavior over time. Understanding how a consumer uses credit or pays back debt over time can help lenders: Offer the right products & terms to increase response rates Determine up sell and cross sell opportunities Prevent attrition Identify profitable customers Avoid consumers with payment stress Limit loss exposure The challenge with trended data, however, is finding a way to sort through the payment patterns in the midst of huge datasets. At the singular level, one consumer might have 10 trades. Trended data in turn reveals five historical payment fields and then you multiple all of this by 24 months and you suddenly have 1,200 data points. But let’s be real … a lender is not going to look at just one consumer as they consider their marketing or retention campaigns. They may look at 100,000 consumers. And on that scale you are now looking at sorting through 120M data points. So while a lender may think they need trended data – and there is definitely value in accessing it – they likely also need a solution to help them wade through it all, assessing and decisioning on those 120M data points. Tapping into something like Credit3D, which bundles in propensity scores, profitability models and trended attributes, is the solution that truly unveils the value of trended data insights. By layering in these solutions, lenders can clearly answer questions like: Who is likely to respond to an offer? How does a consumer use credit? How can I identify revolvers, transactors and consolidators? Is there a better way to understand risk or to conduct swap set analysis? How can I acquire profitable consumers? How do I increase wallet share and usage? Trended data sounds like a “no-brainer” and it definitely has the ability to shed light on that consumer credit horse race. Lenders, however, also need to have the appropriate analytics and systems to assess on the huge volume of data points. Need more information on Trended Data and Credit 3D? Contact Us

Personal loans have been booming for the past couple of years with double-digit growth year-over-year. But the party can’t last forever, right? In a recent Experian webinar, experts noted they have seen originations leveling off. In fact, numbers indicate it’s gone from leveled off to a slight year-over-year decline. They projected the first quarter of calendar year 2017 may also be down, but then we’ll see a peak again in the second quarter, which is typical with the seasonality often associated with personal loans. The landscape is changing. A recent data pull revealed a 9-point shift in the average VantageScore® credit score for originations from Q3 to Q4 of 2016. Lenders are digging deeper in order to keep their loan volumes up, and it is definitely a more competitive marketplace. The days where lenders were once able to grow their personal loan business with little effort are gone. Kelley Motley, Experian’s director of analytics, noted some of the personal loan origination volume shifts may be due to the rebound in the housing market and increased housing values, enabling super-prime and prime consumers to now also consider home equity loans and lines of credit, in lieu of personal loans. Still, the personal loan market is healthy. Lenders just need to be smart about their marketing efforts and utilize data to improve their response rates, expand their risk criteria to identify consumers trending upward in the credit ranks, and then retain them as their cash-flow and financial situations evolve. In the presentation, experts revealed a few interesting stats: 67% of those that open a personal installment loan had a revolving trade with a balance >$0 5% of consumer that close a personal loan reopen another within a few months of the original loan closure 68% of consumers that re-open a new personal loan within a short timeframe of closing another personal loan do so with the same company Together, these stats illustrate that individuals are largely leveraging personal loans to consolidate debt or perhaps fund an expense like a vacation or an unexpected event. Once the consumer comes into cash, they’ll pay off the loan, but consider revisiting a personal loan again if their financial situation warrants it. The calendar year Q2 peak has been consistent since the Great Recession. For many consumers, after racking up holiday debt and end-of-year expenses, the bills start coming in during the first quarter. With the high APRs often attached to revolving cards, there is a sense of urgency to consolidate and lock in a more reasonable rate. Others utilize the personal loan to fund weddings, vacations and home improvement projects.  Kyle Matthies, a senior product manager for Experian, reminded participants that most people don’t need your product, so it’s essential to leverage data find those that do. Utilizing propensity score and attributes, as well as tools to dig into ability-to-pay metrics and offer alignment can really fine-tune both an organization’s marketing and retention strategies. To learn more about the current state of personal loans, access our free webinar How lenders can capitalize on the growth in personal loans.