Trends that will help or hurt you in 2023
3 fast emerging trends that are disrupting
Bill Rice
February 20, 2023
What would you list as the top trends in FinTech?
A simple Google search will give you a host of lists from prestigious consulting firms such as McKinsey and KPMG to a website cleverly named FinTech Ranking.
I thought I would throw my hat in the ring with my list.
Photo by Joshua Chun on Unsplash
1. NeoBanks
These banks and financial institutions offer all the traditional banking services without the traditional banking infrastructure — branches.
You’ve probably heard of a few of these: Chime, Varo, SoFi, and Acorns.
The trend of shedding the physical world for the virtual is not confined to banking.
In the mortgage world, consumer direct is our version.
Mortgage lenders have been wrestling with this disruption for nearly two decades when Quicken Loans pioneered this approach with a March 1998 Dan Gilbert email.
Even now, many mortgage lenders are still one foot in and one foot out of this strategy mixing distributed retail with consumer direct.
Like banks, many mortgage brands have spun out satellite brands to address this disruption while cautiously maintaining the 800-pound retail gorilla in the corner.
However, a crushing mortgage market and tightening resources are forcing hard choices. What stays and what goes?
What is your take? Drop your strategic opinion in the comments.
2. Product to Market Fit
Getting to product/market fit is a common Silicon Valley startup mantra.
Getting your product to satisfy strong market demand makes a lot of sense, but ironically the mortgage market is often tone-deaf.
Surprise! Consumers are not screaming to swap out their sub-3% 30-year fixed-rate mortgages for 7.8% refinance offers.
However, there are a lot of interesting economic and real estate trends that need mortgages.
Mounting consumer credit card debt and rising rates versus historically high home equity levels. Swapping 23% credit card debt for a 7.8% HELOC does sound compelling.
Housing prices and rents continue to rise as a result of strained inventories. Savvy real estate investors recognize the strong demand for short and long-term rentals and are aggressively looking for DSCR, fix and flip, AirBnB, and other investor-friendly loans.
Post-pandemic self-employment rates seem to continue rising. This is surging demand for bank statement loans and other self-employment-friendly lending programs.
What are other loan programs on the rise based on unique market trends? Drop your insights in the comments.
3. AI and Machine Learning
Every mortgage lender has made cuts.
That means that there are fewer people to do the work.
At the same time, AI and machine learning have surged into the market with the increased realization of their capability.
ChatGPT has become an almost annoying sensation.
This seemingly simple, intelligent chat interface surged into the public consciousness overnight. Why? Because ChatGPT shocked early adopters at how much human work and effort could be offset at a nearly indistinguishable (from a human) level of quality and expertise.
Meanwhile, as proof that this was truly cataclysmic disruption, Google and Facebook were sent racing to counterpunch with their own AI to avoid becoming instantly irrelevant. Only to expose their lagging efforts.
Let this be a lesson. Now is the time to start working on your AI and machine learning strategy.
To help you start this journey, I recommend my Executive Guide to Machine Learning Enabled Sales and Marketing Operations.
Personally, I think sales and marketing is the best place to start for the following reasons:
Recent layoffs made the deepest cuts to sales and marketing
Sales and marketing tech stacks are full of rich, predictive data
These operations are the closest to revenue impact
AI/ML and intelligent systems are being productized (i.e., off-the-shelf-ready with minimal IT resources required) in these areas first. See ProPair (AI/ML) and TrustEngine (Data-driven intelligence).
There has been quite a bit of effort put into back-office efficiency using AI/ML, but right now, frontend volume is the pain point
Where do you think the hotspots are for AI and machine learning in future lending? Leave a comment with your predictions.