Mortgage Business Intelligence Mistakes to Avoid

Common Mortgage Business Intelligence Mistakes and Pitfalls

In recent years, business intelligence has become all the rage among mortgage lenders. And why not? The mortgage industry has become increasingly complex and hyper-competitive.

When the economy changes, lenders have to be agile in response to new dynamics in the marketplace. Business intelligence and advanced analytics can give you an edge over your competition and help you guide your business through good times and bad.

That said, many businesses adopt a business intelligence strategy without considering some of the common pitfalls that lead to wasted money, missed opportunities, and even failure

Most Common Mortgage Business Intelligence Mistakes

  • Do-It-Yourself (DIY) to “Re-invent the wheel”
  • Omitting Accounting Data
  • Measuring the Wrong Thing
  • Overfitting Predictive Models
  • Not taking a Holistic Approach
  • Not Getting Buy-In From the Entire Organization

Tips to Avoid These Mistakes

Do-It-Yourself (DIY) to “Re-invent the wheel”

There are many off the shelf solutions available that cater to the mortgage industry. Why re-create their efforts?

While it may be tempting to use tools like Power BI or Tableau, you still need to program them. This involves either hiring a data analyst, data scientist, or using your existing staff to deploy your solution.

In addition to a data scientist, you’ll need to make sure that you have extractors in place to pull data from your Loan Origination System (LOS) and your accounting system. This is a programming effort that not only costs money but also slows the time where you can achieve a return on your investment.

Data scientists are in high demand, so the competition for hiring them is difficult and consequently expensive.

To make matters worse, they’ll need to have some in-depth understanding of the mortgage business. Finding a competent data scientist with domain expertise in the mortgage business is extremely hard.

Therefore, consider a turnkey system that allows you to plug in your data to get an immediate return on your investment. Pick a platform that already integrates best practices for the mortgage industry.

Omitting Financial Data From Your Accounting System

Sales and production data are the backbone of any mortgage business intelligence system. However, without financial information from your accounting system efforts will fall woefully short.

Profitability is the reason your enterprise exists. You’ll need data from your general ledger to evaluate things like cost per loan, branch profitability, breakeven point, etc. Without these, you are missing out on the most important factor in your business intelligence strategy.

Measuring the Wrong Thing

This sounds obvious, but many enterprises focus on the wrong metrics. Many think they’re measuring one thing but getting something else altogether.

Here’s an example:
You want to measure branch contribution to corporate overhead. The branch in question has a large sales volume, so you assume they are one of the better branches in your organization. However, if you can’t account for their expenses, you may be drawing the wrong conclusions. In this case, Sales Volume is not the best indicator of performance.

This happens when data scientists don’t have a good grasp on the mortgage business. Alternately, analysts may opt for a particular metric because it’s easy to extract and is therefore readily accessible.

Either way, measuring the wrong thing at the expense of a critical metric can render your business intelligence efforts ineffective and misleading.

Overfitting Predictive Models

Predictive analytics are a powerful part of a business intelligence strategy. This involves building models that are made up of independent variables. These are the ingredients that go into a formula that predicts something like sales, profitability, throughput, etc. These types of models help you to understand how these “ingredients” affect what you’re trying to measure.

However, these variables may in fact be related to one another (i.e.. correlated). In other words, they’re measuring the same thing.

Another problem is including too many variables. This makes your model fragile and hard to understand. When you include these variables in your model, you are “overfitting” your model, which may render it useless.

Make sure that you have a thorough understanding of statistical modeling to avoid this mistake. Better yet, consider a platform like Telemetry BI that has already developed these models for you.

Not Taking a Holistic Approach

You must measure all aspects of your business to get a true picture of your enterprise. This includes sales, operations, and finance. If you miss out on any of these, your business intelligence platform may lead you to the wrong conclusion. For example, you may become overly focused on one aspect while ignoring others.

This is related to the problem mentioned earlier of not including data from your accounting system. Measuring this aspect in required to help you understand profitability, efficiency, cost-per loan, etc.

Not Getting Buy-In From the Entire Organization

This is more of a management problem than anything else. Recognizing that different departments within your business have different metrics and data needs is the first step toward building consensus on a data business intelligence solution.

You should survey each of these departments and make sure that they are philosophically on-board with a data-driven approach to managing their business. Once you agree to that, you can focus on actual metrics.

You don’t need 100% consensus on all of the metrics, but you do need to ensure that every one agrees on the goals and metrics that measure their performance.

For your staff to be accountable for achieving goals, they must first understand and agree to those goals. They must be committed to adhering to the standards that you establish, which in turn will be measured in your business intelligence platform.

Next, you need to get people to agree on how to access and act upon the analytics that your system will provide. It is important to set peoples expectations with respect to what the system will deliver. Just as important, is understanding how they would like to consume the information. Most people expect to log on to a system and interact with the data online. However, others prefer to avoid yet another set of logins and passwords and would rather have their reports delivered to their email. Make sure that your system can accommodate both methods of transmitting information. Telemetry BI, for example, is both a web-based system and a platform for delivering custom reports to executives, branch managers, and loan officer emails.

Conclusion

These are just a few of the pitfalls to avoid when you’re looking at a mortgage business intelligence system.

Mortgage business intelligence mistakes can end up wasting you time and money. Consider Telemetry BI as your solution. This mortgage business intelligence platform is founded on mortgage industry best practices. It was developed by domain experts and mortgage lending veterans exclusively for the mortgage lending industry. It encompasses all aspects of a mortgage operation including the financial data from your general ledger.

There is no software to install and you simply pay for the platform each month with no implementation fees. Out-of-the-box, turnkey, best practices with simple pricing. Learn more about Telemetry BI or contact us to schedule a demo.

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