Latest / Fintech Conversations with Fexingo: Banking, Payments, and Financial Software Companies / How Fintechs Use Real-Time Energy Data for Dynamic Pricing
Transcript
- Lucas: If these conversations are useful for what you're building or running, stick around toward the end for a quick honest thing about how this show stays going. But first — let's talk about something I hadn't seen coming: using real-time energy data to set loan prices. Luna: Energy data? Like, how much power your house uses determines your mortgage rate? Lucas: Exactly that. And it's not a hypothetical. Octopus Energy in the UK has been quietly piloting a program where customers can opt in to share their smart meter data with lenders. In return, the lender uses that data to adjust the interest rate on a loan — typically a home improvement loan, but potentially a mortgage. Luna: So the lender is looking at real-time consumption patterns. What are they looking for? Lucas: They're looking for efficiency and predictability. If your home is well-insulated, your heating system is modern, and you tend to use energy during off-peak hours, that signals a lower risk of default — because you have lower utility bills, less financial strain. And it also signals environmental responsibility, which some lenders factor into ESG scoring. Luna: Interesting. But how much of a rate difference are we talking? A few basis points? Lucas: In the Octopus pilot, borrowers who share their smart meter data can see rates up to fifty basis points lower than the standard offer. That's significant on a hundred thousand pound loan over five years — you're saving hundreds of pounds. Luna: And the data is coming from smart meters? The ones that send readings every half hour? Lucas: Yes. Those are standard in the UK now. The data is pulled through an API platform called Kaluza — which was actually spun out of Octopus Energy. Kaluza built a 'data marketplace' where consumers can grant third parties access to their energy data on a granular, permissioned basis. It's very similar to open banking APIs for financial data. Luna: So it's opt-in, permissioned, and real-time. That feels like a natural extension of the open banking model. Lucas: Exactly. And it solves a real problem for lenders. Traditional credit scores don't capture your actual financial health in 2026. They miss your utility payment history, your cash flow patterns, your energy burden. Energy data adds a new dimension. Luna: But is there a risk of discrimination? If you live in an older, drafty building, you might have higher energy consumption through no fault of your own. That could mean a higher rate or denied credit. Lucas: That's the big concern — the 'green penalty' for people in inefficient housing. The UK has millions of pre-1930s homes with solid walls that are expensive to heat. If lenders penalize those borrowers, it could exacerbate inequality. Luna: So how do responsible fintechs avoid that? Lucas: Good ones adjust for the property's baseline. They look at consumption relative to similar homes, not absolute usage. They also factor in whether you're using energy at peak times — which is more about behavior than building quality. And some are using the data to offer targeted green loans: if you have high consumption, they'll offer you a lower rate if you use the money for insulation or a heat pump. Luna: That's a clever incentive structure. Use the data to identify inefficiency, then lend to fix it. Lucas: Right. And it aligns the lender's interest with the climate transition. A well-insulated home is less likely to default — and it reduces carbon emissions. That's why you're seeing traditional banks like Barclays and NatWest experiment with similar programs, though they're less advanced than Octopus. Luna: What about commercial real estate? Are we seeing energy data used for business loans? Lucas: Yes, and it's possibly bigger. Commercial buildings account for a huge share of energy use. Lenders like JPMorgan are exploring dynamic pricing on commercial mortgages based on building energy performance certificates and real-time smart meter data from tenants. A building with a high EPC rating and low peak demand could get a rate discount. Luna: So the data source is the same — smart meters. But the scale is different. Lucas: Exactly. And the commercial side is more about risk management. A building that's energy inefficient faces higher operating costs, which means tenants may leave, which means the borrower's income stream is less stable. So the lender prices that risk. Luna: It's like the building's energy efficiency becomes a proxy for creditworthiness. Lucas: That's the thesis. And it's not just buildings. We're starting to see electric vehicle charging data used for auto loans. If you charge your EV during off-peak hours, you're paying less for electricity — that frees up cash flow for your car payment. Some lenders in California are piloting programs that pull charging data from the car's API to adjust rates. Luna: That is wild. So your charging habits affect your APR? Lucas: Possibly, yes. But it's early. The bigger near-term opportunity is still home energy data for personal loans and mortgages. The UK is the most advanced market because of smart meter penetration — over sixty percent of households have one. The U.S. is behind, but some utilities are rolling out similar programs. Luna: And the regulatory framework? In the UK, the Financial Conduct Authority has been supportive of open banking. Are they extending that to energy? Lucas: They've issued guidance that energy data can be used for creditworthiness assessment as long as it's fair, transparent, and non-discriminatory. But there's no specific 'open energy' regulation yet. The industry is self-regulating through standards like the Energy Data Taskforce recommendations. Luna: So it's a bit of a wild west. But with guardrails. Lucas: Right. And the key guardrail is consent. Every data share is opt-in, granular, and revocable. The consumer sees exactly what data is being shared and for how long. Kaluza's platform even lets you set time limits — share for six months, then auto-revoke. Luna: That's more user control than we often see in financial data sharing. Lucas: Exactly. And it's part of why adoption has been decent. Octopus says about twenty percent of customers in the pilot opted in. That might not sound huge, but for a new product, that's strong. Luna: Now, how does this connect to the broader fintech trend of real-time data underwriting? We've done episodes on payroll data, rent data, subscription data. Energy data feels like the next logical frontier. Lucas: It absolutely is. And it's one of the few data sources that actually predicts financial distress — because energy is a non-discretionary expense. If you're struggling to pay your bills, that shows up in your energy data before it shows up in your credit report. You might start using more energy at home because you're staying in, or you might fall behind on payments. Luna: That's a really interesting point. It's a leading indicator. Lucas: Yes. And lenders are hungry for leading indicators. Traditional credit data is lagging — it tells you someone already missed a payment. Energy data can tell you they're about to. That's powerful for proactive lending — like offering a payment holiday or restructuring before the default occurs. Luna: So it's not just pricing. It's also loan management. Lucas: Exactly. Some fintechs are using energy data to trigger automatic loan modifications. If your consumption spikes and you're in a cold climate, that might indicate a heating system failure — they could offer an emergency repair loan at a preferential rate. It's contextual lending based on real-world events. Luna: That's a far cry from the old model of looking at a FICO score and calling it a day. Lucas: It's a different philosophy altogether. And it requires a lot of trust. The fintechs that succeed will be the ones that are transparent about how they use the data and give customers real value in return — not just a slightly lower rate, but also tools to understand and reduce their energy usage. Luna: So it's a two-way value exchange. The customer gets financial benefits and energy insights. The lender gets better risk data. Lucas: Right. And that's the model that scales. Octopus and Kaluza are proving it works at a small scale. The question is whether it can go mainstream — and whether regulators will allow it to become a standard part of underwriting. Luna: What's your prediction? Five years from now, will most mortgages be priced with real-time energy data? Lucas: I think in the UK and parts of Europe, yes. The infrastructure is there. Consumer willingness is growing. And the climate angle gives it political support. In the U.S., it'll take longer because of utility fragmentation and lower smart meter penetration. But I'd be surprised if by 2030 we don't see at least a few major lenders offering energy-linked pricing. Luna: That's a big shift. And it's happening quietly, without a lot of fanfare. Lucas: That's often how the most interesting fintech changes happen — not with a bang, but with an API call. Luna: Speaking of which, a quick honest thing. We keep this show free and ad-free. A handful of listeners chip in monthly through buy me a coffee dot com slash fexingo, and that literally funds making this many of these. Lucas: Yeah, it's a small group, but it makes a real difference. If you find these conversations useful, that's the only way we keep going. No pressure, just the truth. Luna: So back to energy data — one thing I want to circle back on: the privacy piece. Are there any examples of misuse we should watch out for? Lucas: The biggest risk is inferring behavior beyond what the borrower consented to. Your energy data can reveal when you're home, when you're sleeping, whether you have medical equipment running — that's deeply personal. If a lender uses that to make assumptions about your health or lifestyle, that's a violation. Luna: So the fintech needs to be very disciplined about what they look at and why. Lucas: Absolutely. The best practice is to only use aggregated or derived metrics — like average daily consumption, peak usage ratio, or payment history — not raw time series. And to never share data with third parties without explicit consent. The ones that do it right will earn trust. The ones that cut corners will get burned. Luna: And that trust is the whole foundation of this model. Lucas: It is. And it's why I think the fintechs that succeed won't be the ones with the fanciest algorithms. They'll be the ones that make the customer feel in control, informed, and better off. Energy data is a tool. Like any tool, it depends on who's using it and why. Luna: Alright, that's a good note to end on. Thanks, Lucas. Lucas: Thanks, Luna. Talk next time.