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2019 Demo Days: Your Front-Row Seat to Our Solutions

Records are made to be broken, and we are seeing a lot of MeridianLink’s previous records being broken left and right. Since the User Forum held earlier this month that shattered the prior attendance record with more than 700 people joining us to learn more about how we can connect them to better, interest in our products and services has also never been higher.
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Top 5 Indirect Lending Mistakes to Avoid

Spring cleaning, most people don't enjoy it (unless you’re obsessive compulsive – not that there’s anything wrong with that), but it’s hard to not enjoy the benefits. Reduced clutter, more opportunity to utilize that free space and a tidier living area – these are the end goals.  Now is also the time to do some spring cleaning or evaluations regarding the efficiency and performance of your indirect lending program. Below, we have provided the five most common mistakes lenders make when operating their indirect lending programs:
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How to Avoid Silo Analytics for Improved Credit Decisioning (Part 2)

Following last week’s blog post regarding silo analytics, we’re going to take it one step further this week.  As opposed to last week’s topic, which centered on using a single piece of (or incomplete) information to represent a holistic view of the consumer, silo analytics also refers to decentralized or fragmented process for analytics. In this example, each department within the same organization (such as marketing, credit risk, acquisitions and collections) often rebuilds its analytic infrastructure from data gathering to the creation of analytic attributes rather than partnering across divisions to improve the speed to implementation. For the second part of this two-part series, this week's blog post will take a deeper look at what institutions can do to avoid such a decentralized and fragmented process.
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How to Avoid Silo Analytics for Improved Credit Decisioning (Part 1 of 2)

on Wed, Mar 06,2019 @ 12:59 PM | By Chris Carlson | decision analytics credit decisioning
Analytics can be an extremely profitable investment – assuming efficiency and completion. Financial institutions, whether they handle it in house or turn things over to a trusted provider, need to know if they’re victims of leveraging or being served silo analytics to dictate their credit decisioning.
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Why a One-Size-Fits-All Approach is a Bad Plan for Profitable Credit Decisioning

Have you ever worn something that’s one-size-fits-all? I bet it either wasn’t very comfortable or didn’t quite look right – or both. It may have served a simplistic function, but that’s likely where the satisfaction ended. I recently came across a health article that claimed the same thing applies to health and nutrition.   Fitness expert, "Biggest Loser" trainer and author Jen Widerstrom said it's important to create a diet and fitness plan based on your personality because one size does not fit all. Widerstrom related her relationship with clients to a teacher in the classroom, CNBC reported. While some students may be strong readers, others may be better in math. Knowing a student's strengths and weaknesses will help the teacher develop a successful lesson plan.   Similar logic applies to lenders and the scorecards used for credit decisioning.
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Credit Decisioning’s Three Lines of Defense for Risk Management

For all of the football fans who subscribe to our blog, Sunday's big game will have your full attention. Even if you're not a football fan, the odds are pretty likely that you'll check it in some form or fashion to see the commercials or whoever performs at halftime. In last year's game, one team took a calculated risk by running a trick play that ultimately led it to victory (Philly special). Success on the football field, much like the real world, involves limiting risk while also at times using it to an advantage. Risk can come from anywhere. It can change at any moment and often be difficult to predict. For many financial institutions these days, risk management and control are split across multiple departments within an organization. Because these departments need structure, as well as checks and balances to properly management risk, the most successful institutions use a risk management strategy/model based on three lines of defense. This approach is an effective way to assign duties and coordinate various teams involved in the risk management and control process.
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How to Leverage Analytics for Better Dealer Management and Indirect Lending

In the indirect lending space for vehicles, building good dealer relationships can be the difference between receiving a steady flow of quality loan applications or getting the scraps. This often involves dropping by once in a while with donuts and maintaining a strong on-going dialogue on how to better work together. However, lenders can also use analytics to gather more insight about their dealers. This insight can help them build better relationships with those dealers that are most beneficial to work with – as well as better manage those that are under-performing. Here are three key areas where analytics can prove to be very beneficial for your dealer management services:
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Custom Scorecards: Your Ticket to More Success

Generic credit scores on the market are a widely used tool by lenders to mitigate risk by knowing which applicants have a high likelihood of going delinquent on their payments. Each generic risk score on the market has been developed on broader populations that were built long ago on old consumer profiles. Lenders should consider if the generic score works on their specific portfolio type with their own unique customer characteristics that are indicative of their own customer payment behaviors when choosing a generic scorecard.
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A Closer Look at CECL Compliance and What's to Come

The 2008 financial crisis brought more attention to one crucial aspect of the financial system, which was the delay in the recognition of losses by financial institutions. At the peak of the crisis and under stressed conditions, it became clear that the methodologies and procedures in place at that time (and still in place today within some financial institutions) to estimate reserves resulted in inaccurate, underestimated and untimely allowances for losses.
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