Bilt Is Building a Data Moat

I recently got my Bilt 2.0 credit card and started migrating most of my recurring expenses to it. Mortgage, HOA, utilities, everyday spend. Understanding the rewards system took longer than I expected. At some point it felt less like reading card terms and more like cramming for a qualifier exam.

That friction made me curious. So I looked harder at what Bilt is actually building underneath the rewards layer.

The Blind Spot Every Credit Card Company Has

Traditional credit cards have always had a structural gap: housing.

Chase and Amex have built remarkably detailed pictures of consumer behavior across dining, travel, retail, and entertainment. Their data is sophisticated enough to predict life events, creditworthiness, and purchasing patterns with high accuracy.

But our single largest monthly expense - housing - has moved through ACH transfers and paper checks for decades. Rent and mortgage payments have been effectively invisible to any consumer analytics layer. For the average American household, housing represents 30-40% of monthly expenditure, and that entire slice was a black box.

Bilt has closed that gap. And at a $10.75B valuation, the market is clearly paying attention.

The Architecture

What makes Bilt interesting as a data play is not the housing piece alone. It is how each additional layer compounds the picture.

Their Banyan acquisition is the most interesting piece. Banyan captures item-level SKU receipt data, what the industry calls Tier 3 fidelity. Most transaction data tells you where someone spent money and how much. Tier 3 tells you exactly what they bought. The difference between knowing someone spent $200 at Whole Foods and knowing they bought organic produce, a specific wine label, and a particular brand of protein powder. That granularity is rare. Most loyalty programs never get close to it.

The next layer is open-banking integrations. Bilt lets users connect external credit cards to the app, which means Bilt can see spend on non-Bilt cards too, not just what flows through their own ecosystem.

Put those three layers together: housing costs, connections to external cards, and Tier 3 receipt fidelity. That combination gives Bilt a high-resolution, 360-degree view of consumer spend that no other platform has assembled. They see the fixed overhead, the variable spending, and the exact product preferences, all in one closed loop.

In AI infrastructure, the datasets that matter aren’t just large. They’re structurally difficult to replicate. The housing-to-retail connection has never been assembled at scale before. Bilt built it using consumer incentives.

The Consumer Tradeoff

When I posted about this on LinkedIn, the comments surfaced a fair question: what’s in it for the consumer? Why would anyone give one platform this much visibility into their financial life?

The rewards are genuinely differentiated. Earning points on rent or mortgage payments at up to 1x, with those points transferring to airline and hotel partners at 1:1, is something no other card offers. For someone carrying a $3,000 monthly mortgage, that is real value that simply did not exist before Bilt.

The broader tradeoff between data visibility and rewards value exists across the entire rewards industry. Bilt’s version is more pronounced simply because the data surface is larger, which is a direct function of how well they’ve built the product. The complexity of the 2.0 rewards structure, the thing that sent me back to the documentation repeatedly, also builds lock-in. A consumer who has invested hours understanding and optimizing their Bilt setup is not switching platforms easily.

Why the Banyan Acquisition Matters

The Banyan deal deserves more attention than it has received. Item-level receipt data at scale is not just valuable for consumer analytics. It opens up applications that aggregate transaction data simply cannot reach, particularly as the platform scales and the dataset deepens.

Supply chain forecasting becomes more accurate when you move from category-level to SKU-level demand signals. And connected to housing cost anchors, the neighborhood-level purchasing intelligence that emerges is the kind of data that commercial real estate and urban planning applications have wanted for years. The accuracy of those signals will improve as penetration grows, which gives Bilt a compounding data advantage over time rather than a static one.

What Comes Next

The question that stays with me is not whether Bilt has built something valuable. They clearly have.

The question is what the monetization path looks like beyond interchange fees and the existing rewards partnerships.

The obvious directions are consumer lending products, where a comprehensive financial picture translates directly into more accurate underwriting, and data products built on the neighborhood-level purchasing intelligence the platform is accumulating.

At $10.75B, the market is pricing in a platform, not only a credit card company. The data architecture being assembled suggests there may be significant upside from here.

If you are building or investing in fintech, data infrastructure, or consumer platforms and want to compare notes, I would welcome the conversation.