A few years ago, I transferred-in an account for a client. As I looked through the positions to prepare recommendations about which positions to sell and which to keep, I noticed a handful of penny stocks. Actually, to call them penny stocks would be an exaggeration. They were each worth fractions of a penny and, of course, only traded over-the-counter.
I assumed that these were positions-gone-bad—stocks that had fallen far from grace, trophies to amateur overconfidence. I called my client to discuss removing them.
“…Oh, and one more thing. I’ll send you a form to remove these stocks from your account since they don’t trade and aren’t worth anything.”
“What?! No, don’t do that!” was his urgent reply. “Those are my lottery tickets! I put about a hundred bucks into each of them and I want to see if they pay off!”
I chuckled. “Alright, no problem, we’ll leave them, but I’m not going to follow them, okay? Just let me know if you change your mind.”
I didn’t know it then, but I gave him terrible advice that day. In fact, I should have been the one to tell him to put some money in those micro-penny stocks.
* * *
Before you excommunicate me as a heathen, at least hear me out. Let’s take a step back and remember where the advice “never gamble” comes from.
A standard utility function taught in the CFA Program curriculum (sometimes called quadratic utility) determines an investor’s happiness from her portfolio’s expected return, minus the variance (volatility) of those returns, times her risk aversion parameter. The more averse to risk, the more unhappy she is with variance (volatility).
In this model, all else equal, higher volatility is always bad. In this model we would never expect an investor to choose a high volatility, low-return portfolio (i.e., a gambling portfolio) when low-volatility, high-return portfolios are on offer. We have this expectation because this model assumes that the thing our investor wants to avoid is volatility.
By contrast, goals-based theories of choice take a different approach. Rather than define risk as volatility, goals-based utility defines risk as “not having the money you need when you need it,” to quote my friend Martin Tarlie. Risk, in goals-based investing, is not volatility, but the probability that you fail to achieve your goal.
Running with this more intuitive definition yields some surprising results because it changes the math of the portfolio choice problem. We move from an equation in which return and volatility are the only two variables, to a probability equation of which return and volatility are inputs, but not the only inputs.
All the variables which define our goal (minimum wealth level, time horizon, current wealth, etc), are also inputs in the probability equation. Lastly, when we remove the inexplicable academic assumption that investors can borrow and sell short without limit, then we find that the efficient frontier has an endpoint, the last efficient portfolio.
Here’s the catch: sometimes, investors have return requirements that are greater than what the last efficient portfolio can offer. When that happens, her probability of achievement is maximized by increasing variance rather than decreasing it, even if returns are lower.
And so we enter the world of rational gambles.
Rational gambles are those portfolios to the right of and below the last efficient portfolio, but for which the probability of achievement continues to rise. Irrational gambles are those for which the probability of achievement begins to fall. The plot below illustrates the point.
Inflation has been widely discussed in recent years, from supermarket aisles to newspapers. But what if what people think inflation is stems not only from grocery prices or energy bills, but from more? Our analysis in Dhamija et al (2026) shows house prices matter in this context, ie housing is salient. Using household surveys for the United States, we find that people tend to overweight their expectations about house prices when thinking about inflation with a coefficient of 25%–45%, significantly above the weight of house prices in the inflation index. Should central banks care about this? The short answer is yes.
Why expectations matter and why might house prices sneak into thinking about inflation?
Inflation expectations matter because they shape economic behaviour. When households expect prices to rise, they adjust their spending and saving decisions, as well as wage demands in ways that feed back into inflation itself. For this reason, central banks closely monitor measures of inflation expectations, and it has become increasingly important to understand how these are formed.
Several factors influence how households form their inflation expectations; this includes their prior beliefs, exposure to media, knowledge of monetary policy, cognitive abilities, and shopping experiences, among others (Coibion et al (2020)). However, it is not just frequently observed price changes, but also the less frequent, larger price changes that seem to matter. One such price is housing, irrespective of whether one is a homeowner or not.
House prices are widely reported, frequently discussed, and central to households’ financial well-being. Houses are typically the largest asset owned by a household and are associated with significant wealth and collateral effects. Housing is the largest expense for renters and homeowners alike. Changes in house prices are also highly salient as they often attract media attention and shape public debate about affordability and living standards. In the US, a large majority of the population are homeowners, and there is high geographic mobility, suggesting that house prices are closely monitored.
House prices are not directly included in headline inflation measures.
The consumer prices index (CPI) only reports the consumption part of housing services relevant to the cost of living index. In the US, housing services are captured through the CPI component Shelter, which accounts for approximately one-third of the index. The subcomponent of this attributed to homeowners is Owners’ Equivalent Rent (OER). To compute this, the Bureau of Labour Statistics surveys the rents in a region and weighs it by the proportion of homeowners. This is considered best practice and correctly reflects that the OER must represent the opportunity cost of rents at market value or the rent that homeowners implicitly pay to themselves to live in their home, not the asset-portfolio aspect of housing.
Chart 1: House price growth and CPI shelter inflation
Notes: This chart shows CPI shelter inflation and two sub-components: CPI-rent and CPI-OER from the Bureau of Labor Statistics. House price growth is the growth rate of the S&P/Case-Shiller US national home price index. The sample period runs from 1987 to 2022.
Since house prices are not directly part of the CPI, their influence is limited to indirect channels such as rents or OER.Chart 1 plots the S&P/Case-Shiller US National Home Price Index along with the relevant housing components of CPI from 1987–2022. Over this period, there have been some large swings in house prices, while the OER and other housing-related components of shelter are much more stable and have not kept up with the large house price swings. This shows that these indirect channels are likely to be small. As such, the impact of house price inflation on overall inflation is close to zero.
To capture this disconnect more precisely, we establish an ‘accounting benchmark’ to define how house price movements should, in theory, affect measured inflation. Using US data from 1987–2022, we regress actual house price growth on overall CPI inflation and its major components, including twelve leads and lags of house price growth. These coefficients are then weighed by their respective shares in the CPI. This gives the implied elasticity of overall inflation to house price inflation, and it ranges between 0.004 and 0.04 across different specifications, refer to Dhamija et al (2026) for details. That is, a one percentage point increase in house price inflation should raise CPI inflation by no more than 0.04 percentage points. Any estimated relationship substantially larger than that would imply overweighting by households. However, households as non-specialists may be unable to distinguish between the asset aspect of house prices and housing services. This could potentially lead to overweighting of house price expectations in overall inflation expectations.
But can households distinguish between houses as assets and housing services?
We use the Michigan Survey of Consumers (MSC) and the Federal Reserve Bank of New York’s Survey of Consumer Expectations (SCE) to examine household behaviour in the US. For each survey, we regress inflation expectations on house price expectations of households, controlling for individual demographics, region and time fixed effects, past house price growth, and rent expectations, among others. To further address potential endogeneity arising from common shocks and/or omitted variables, we instrument house price expectations with housing supply elasticity using the Wharton Land Use Regulatory Index and past expectations.
We find that a percentage point increase in households’ expected house price growth is associated with a 0.25 to 0.45 percentage point increase in their inflation expectations, holding all else equal. Relative to the benchmark, this indicates that households place disproportionate weight on house price expectations when forming expectations about inflation.
Our second identification strategy exploits variation in households across characteristics.
If households overweight house price inflation expectations, this bias should be less pronounced among individuals with stronger numeracy skills and those who are currently more attentive to housing market developments. We find that more educated households and those with higher numeracy skills place less weight on house price expectations when forming inflation beliefs. We also find that households that moved homes recently, and therefore potentially observed housing markets more prominently, overweight by more. Taken together, the results of both identification strategies provide strong evidence of individuals overweighting from house price expectations to their inflation expectations.
Does this household behaviour matter for monetary policy?
To address this question, in Dhamija et al (2026) we embed this household behaviour into a two-sector New Keynesian model where households assign disproportionate attention to inflation developments in one sector relative to its actual weight. The model provides a stylised framework representative of any two sectors such that it could be used more broadly to examine the monetary policy implications of overweighting any good. This also encompasses the results documented in prior literature, such as D’Acunto et al (2021) and Coibion and Gorodnichenko (2015) among others, related to groceries or fuel prices. We show that this overweighting behaviour distorts households’ intertemporal choices by creating a wedge between the actual and perceived expected inflation rate. This misperception carries through to consumption and saving decisions, generating a wedge between the true and perceived real interest rate, which can amplify or dampen the effects of monetary policy. This household behaviour, however, does not alter the firms’ price-setting. Deriving the welfare function or the central bank’s loss function shows that this overweighting does not introduce any new policy trade-offs for the central bank. This implies that it is sufficient for the central bank to set the nominal rate in line with the perceived expected inflation to stabilise any distortions from overweighting.
Chart 2: Optimal response to a markup shock in the overweighted sector in models with overweighting (black) and without overweighting (red dashed)
Notes: The chart shows how key variables change in response to a one percent increase in the markup in the overweighted sector. Values are shown as changes from normal levels (steady state). The interest rate is shown in percentage points. The solid black line is the version of the model which incorporates overweighting, and the red dashed line is the version without overweighting (the standard case).
To illustrate this, in Chart 2, we examine how a central bank responds when inflation increases due to a markup shock in the overweighted sector. A markup shock is an increase in firms’ profit margins that increases inflation and decreases output. Since people put extra weight on price changes in this sector, inflation expectations rise more than they would otherwise. To keep overall inflation on track, the central bank therefore needs to raise the policy rate by more. With an appropriately stronger response, the economy ends up on essentially the same path as it would if households did not place extra weight on that sector.
Conclusion and policy implications
Recent research on salience demonstrates that individuals disproportionately emphasise frequently observed prices and large price movements when forming inflation expectations, even when these items carry low weight in official inflation indices. In Dhamija et al (2026), we identify a novel channel through house price expectations. We further show that inflation shocks in any overweighted sector have outsized effects on expectations and macroeconomic outcomes.
The policy implications of our work are twofold. First, our results make a case for central banks to monitor the housing sector due to its salience; this is beyond the usual, very important, financial stability concerns. Second, the knowledge of this household behaviour is imperative for central banks as movements in expected inflation in overweighted sectors are disproportionately more important for monetary policy. When households overemphasise price movements in one (salient) sector, the perceived inflation rate deviates from actual inflation. This requires central banks to respond more strongly to such sectoral inflation shocks, ie set the nominal interest rate in line with the perceived inflation expectations to undo any distortions. Our results may also have implications for central bank communication, which could be explored in future research. Going forward, we plan to examine whether house price expectations receive disproportionate weight in the formation of inflation expectations in the UK and other countries.
Vedanta Dhamija works in the Bank’s Monetary Policy Strategy Division, Ricardo Nunes is a Professor of Economics at the University of Surrey and Roshni Tara works in the Bank’s Economic Outlook Division.
If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.
Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.
Share the post “Under one roof: housing and inflation expectations”
Most agency founders think becoming CEO is the finish line. Jason Swenk says it is actually one of the traps. In this episode, John Jantsch sits down with Jason Swenk, founder of Agency Mastery and author of Operator to Owner, to walk through the five stages every agency founder has to climb and why so many get stuck long before they reach the top.
Jason built and sold his own digital agency after working with brands like AT&T, Hitachi, and LegalZoom. Now he works with seven and eight figure agency founders who are still doing too much, holding on too long, and wondering why the business cannot run without them. The conversation covers the identity shift required at each stage, why founders are usually the worst managers, and what it actually looks like when you finally get out of your own way.
This one is for agency owners and consultants who know the business depends on them too much and are ready to do something about it.
About Jason Swenk
Jason Swenk is the founder of Agency Mastery and host of the Smart Agency Masterclass Podcast. He built his own digital agency from scratch, working with clients including AT&T, Hitachi, and LegalZoom, before selling it. He now advises seven and eight figure agency founders on building businesses that run without them. His book, Operator to Owner, maps the five stages every agency founder must navigate to build a business they actually own. Find the book and a free diagnostic at operator2ownerrevolution.com.
Key Takeaways
Being the CEO is not the finish line. Most founders mistake the operator or manager stage for success and never push through to genuine ownership.
The agency owning you is a choice you keep making. You started a business to escape the nine to five and accidentally created a 24 by seven. Getting out requires an intentional identity shift, not just better systems.
Founders are usually terrible managers. Hiring people without systems, clarity, or defined outcomes is why you end up doing their work on top of your own.
The bottleneck is almost always the founder. Until you build decision-making layers that let your team act without coming to you, you are the ceiling on your own growth.
You held on to sales too long. Almost every agency founder does. And competing with your own sales team for leads is not a strategy.
Do not hire a salesperson before you have a system. Giving someone a quota with no context, no stories, and no process is like prompting an AI with no instructions.
You do not have to reach owner level. Architect is a legitimate destination. Know what stage you want to reach and build toward that intentionally.
Picking a niche takes time and that is fine. Treat it like a Vegas buffet. Try things, notice what works, and ask yourself who you would serve on a performance-only basis.
AI adds work before it removes it. If you do not build decision systems and layers first, AI will amplify your bottleneck, not eliminate it.
Timestamps
[00:01] Opening hook: being CEO of your agency might be the trap you mistook for the finish line.
[00:40] The moment Jason’s wife told him to shut the agency down and get a job, and the two questions from a NASCAR interview that changed everything.
[02:25] The five stages: operator, manager, architect, CEO, and owner, and why most founders stall in the first two.
[04:24] The rubber band effect: why founders sabotage their own teams to feel important again.
[06:20] What the agency actually needs from you at each stage changes. Most founders never update their job description.
[08:29] Why hiring a salesperson never works until you have systems and stories behind them.
[11:34] Throwing your team into the deep end without floaties, and why fender benders are acceptable but train wrecks are not.
[13:34] The E-Myth reference and why most agency owners start a business to be free and end up less free than before.
[14:08] The niche question: why forcing a niche too early backfires and how to find the right one over time.
[16:11] What a true owner’s week actually looks like day to day.
[17:52] The one thing Jason held on to too long and what finally changed when he let it go.
[19:46] One move agency owners can make in the next 30 days based on which stage they are in right now.
Memorable Quotes
“We start an agency to leave the nine to five and end up starting a 24 by seven. It does not make any sense.”
“It is not about who you need to hire. It is about who you need to become.”
“If you are not evolving, you are not doing anything. Especially now, more than ever.”
“I held on to sales too long. I was even competing with my own sales team, which is completely unfair.”
“If you had to be paid on performance only, who would you do it for and what would you do for them? That is how you find your niche.”
Get the book and take the free stage diagnostic at operator2ownerrevolution.com.
Desjardins economists say surging energy prices tied to the Iran conflict are expected to push inflation higher through 2026, adding new uncertainty to the Bank of Canada’s rate path and mortgage-rate outlook.
The FDIC pegs the national average 12-month CD rate at just 1.53% APY. Meanwhile, a handful of online banks are paying 3.50% APY right now on the same 1-year term. That’s more than double the average.
On a $10,000 deposit, the gap between those two rates is bigger than most people realize.
Here’s the math, and why shopping around for CD rates matters.
What $10,000 earns at 3.50% APY for 12 months
A 12-month CD at 3.50% APY is straightforward math. You deposit $10,000, leave it alone for a year, and the bank pays you a fixed return on top.
Here’s exactly what that looks like at maturity:
Starting Deposit
APY
Interest Earned
Ending Balance
$10,000
3.50%
$350
$10,350
Data source: Author’s calculations.
That’s $350 in your pocket for doing nothing but parking the money and not touching it. For context, the national average 12-month CD rate is just 1.53% APY in May 2026 — so earning 3.50% is more than double what most banks are paying right now.
When a 12-month CD actually makes sense
A 1-year CD is a good choice when two things are true: you don’t need the money for a full year, and you want to lock in today’s rate before it potentially drops.
That second part is important. Rates have been slipping slowly in 2025 and 2026, and if the Fed cuts core interest rates later this year, savings APYs are likely to follow. A CD freezes your APY for the full term, so a 3.50% rate today will still be in place 11 months from now — even if savings accounts have fallen to sub 3.00%.
CDs also work well for money you’ve already earmarked for something specific. For example, a wedding next spring. Or a down payment in 18 months time. Locking up your cash isn’t a downside when you weren’t going to touch it anyway.
Keep in mind, most CDs charge an early withdrawal penalty of about three to six months of interest if you pull the money out early. So treat the term seriously before you commit.
When a high-yield savings account might be the better move
I’ll be honest — I don’t personally own any CDs right now. My high-yield savings account is paying nearly the same top rates, I can move the money whenever I want, and I don’t have a specific goal I’m saving toward in the next 12 months.
For a lot of people, that flexibility wins. Even though savings accounts are exposed to interest rate drops at any time, it might be worth making a little less interest to retain full access to your cash.
If you prefer that immediate access option, some of today’s best high yield savings accounts are paying upwards of 3.50% APY, or higher.
The bottom line
A 3.50% APY 12-month CD earns $350 on a $10,000 deposit — nearly $200 more than what the national average rate of 1.53% would earn. That gap is exactly why rate shopping matters before you commit your cash to any CD.
The difference between an average rate and a top rate isn’t pennies — it’s real money sitting on the table for anyone willing to spend 10 minutes comparing offers. See today’s top CD rates and find the best fit
If you thought El Niño was bad, it just got supersized, and the resulting extreme weather patterns could wreak havoc for investors who own or plan to own property in the eight states in its path.
Super El Niño Blasts Into an Insurance Market Already Under Strain
According to meteorologists, El Niño—the complex weather pattern stemming from warming waters in the Pacific Ocean—could redouble in force this year, increasing the odds of extreme weather such as storms, flooding, and hurricanes across the Sunbelt in some of the most coveted new developments and investment property locations in the U.S.
Newsweekreports that insurance costs could increase dramatically in anticipation of the arrival of Super El Niño, decimating cash flow in states including:
Alabama
Arizona
Southern California
Florida
Louisiana
Mississippi
New Mexico
Texas
Insurance Rates Have Increased 46% in Five Years
Super El Niño couldn’t come at a worse time for homeowners and investors, who have seen insurance costs escalate precipitously in recent years.
The Los Angeles Times, using data from Insurify, an online insurance comparison site, reports that average U.S. homeowner’s insurance premiums are projected to rise another 4% in 2026 to about $3,057 after increasing 12% in 2025 and a massive 46% since 2021, with extreme weather and rebuilding costs cited as key drivers.
Home insurance prices, the Times reported, were outpacing both inflation and income growth. This means for investors, cap rates and cash flow have been wrecked.
Flood insurance, which averages around $1,100 nationwide through the federally backed National Flood Insurance Program (NFIP) administered by FEMA, rising to over $2,000 in high-risk areas, is usually purchased separately. However, it is hit or miss because weather patterns remain unpredictable.
Natalie Lord, a principal climate scientist for global flood risk intelligence provider Fathom, told Newsweek:
“It’s hard to tell exactly where the highest risk is going to be until it actually happens. But I think that probably does mean whilst insurance premiums in general have been increasing, the markets in those states are likely to see greater than the country average increases in premiums to try to counteract some of those losses [from El Niño]. The issue will be not all of those states are likely to see extreme losses.”
The Cost to Investors in High-Risk Areas
The impact is clearly visible in recent data. Newsweek reports that Arizona saw the steepest homeowner’s insurance cost increase in the country, with average premiums up 94% between 2021 and 2025, while Florida, Texas, and Louisiana also saw steep increases due to severe weather losses and reconstruction costs.
For homeowners looking to trade the icy North for the balmy Sunbelt and lower their cost of living, Fidelity Investments calculated that the sunnier outlook doesn’t extend to the overall cost of living, which is evened out for property owners by insurance costs.
Citing Bankrate data, Florida was the worst of all Sunbelt states, with a combined annual outlay for homeowners (based on a $300,000 home) and car insurance totaling a hefty $9,550.
For investors looking to flip homes, the Rust Belt eclipses the Sunbelt, which has been hit hard by the “affordability economy,” with insurance costs no doubt playing their part. According toFortune, U.S. housing is experiencing a historic “reversion to the mean.” Or to put it more plainly: “The formerly sizzling metros have gone cold, and the unsexy plodders are back in vogue.”
According to a report from First Street, a climate-risk research firm, rising premiums, insurance deserts, and buyers skirting high-risk areas will drive a $1.47 trillion decline in home values by 2055.
Picking Investments With Insurance in Mind Is an Increasingly Nuanced Process
Picking areas to invest in while accounting for insurance costs is increasingly difficult, as wild weather patterns upend the underwriting business. TheNew York Times reported last year that insurance now accounts for more than 20% of home insurance premium increases since 2017.
A Basic Cash Flow Calculation With Today’s Insurance Rates
Bankrate’s 2026 ranking finds that Louisiana homeowners pay an average of $6,274 per year. That amounts to $523 a month, the highest state average in the country.
For a simple back-of-the-envelope cash flow calculation on a $300,000 property, this equates to:
Monthly rent:$2,200 (from tenant)
Other operating expenses (repairs, management, etc.): $400
Mortgage payment (principal and interest): $1,000
Property taxes: $400
Homeowner’s insurance: $600 (about $7,200 per year, slightly above the $6,274 statewide average but well within the range for Orleans Parish)
Total housing cost (mortgage, taxes, insurance): $2,000
The insurance share of the housing cost: $600 ÷ $2,000 = 30%
So, in simple cash flow terms:
Gross rent: $2,200
Operating expenses (not counting mortgage, tax, and insurance): $400
Net before mortgage, tax, and insurance: $1,800
Subtract mortgage ($1,000), tax ($400), and insurance ($600): $2,000
Cash flow after all housing costs: $2,200 – $400 – $1,000 – $400 – $600 = $200
In any other market, a mortgage payment of $1,000 and incoming rent of $2,200 would scream “deal!”—assuming the neighborhood was halfway decent. However, to make only $200 in monthly cash flow, which could easily be wiped out by an unexpected repair not factored into the monthly expenses, is a marginal deal at best.
Should insurance climb above $600/month, which is highly likely given anticipated cost increases, it will further erode the economics of ownership and investment.
If you want to calculate these numbers for a deal you’re working on or for one of your own properties, check out BiggerPockets’ Investment Calculators and get an accurate report in minutes.
Final Thoughts
The usual cash flow metrics these days are being upended by rising costs across the board—including purchase prices, interest rates, and, notably, insurance. What’s often not factored into these cash flow equations is the financial stress tenants face amid the affordability crisis.
With deals increasingly hanging by a thread, a missed rental payment or two is increasingly likely and could tip a deal into negative cash flow in a heartbeat.
For investors, earning the most money from the fewest doors has to be the safest path, simply because it mitigates risk. Yes, appreciation and leverage are great concepts in theory, but not in today’s market unless you have a lot of cash to absorb potential losses.
Justin Hotard was appointed as Nokia’s President and CEO on April 1, 2025. Prior to Nokia, he was at Intel as Executive Vice President and General Manager, Data Center & AI Group. Between 2015 and 2024, Justin worked for Hewlett Packard Enterprise (HPE). His last role was Executive Vice President and General Manager, HighPerformance Computing, AI & Labs. In this role, he delivered the world’s first exascale supercomputer for the US Department of Energy, and he positioned the company to be at the forefront of AI, quantum computing and sustainability research. Previously, Justin held several leadership positions at NCR Corporation and Symbol Technologies. He started his career at Motorola, where he was an engineer deploying mobile networks for US carriers.
Lloyds Banking Group has released new analysis revealing how digital banking product development and tech advancements could deliver a £100 billion economic boost to UK households over the next decade. The research, published on 11 May 2026, estimates this equates to roughly £3,500 in added value for the average family through smarter financial decisions enabled by technology.
At the center of the research findings is a striking gap in financial confidence.
Only half of UK adults currently describe themselves as financially empowered, while more than four in ten see no straightforward route to greater control over their money—even with additional help.
Yet 57 per cent believe that improved digital tools, clearer information, and personalized guidance could shift their situation dramatically.
The research report identifies seven practical areas where digital banking can drive real change: investing surplus cash, managing debt more effectively, switching mortgages at the right time, accessing better credit, choosing suitable insurance, building financial skills, and making everyday money decisions with greater insight.
For instance, UK households are currently sitting on between £430 billion and £610 billion in cash held beyond emergency savings.
If just 15 per cent of this were gradually moved into balanced investment products via seamless digital prompts, consumers could gain around £40 billion in compounded returns over ten years.
Mortgage behaviour offers another clear opportunity. Many homeowners remain slow to switch to better rates despite this being their largest monthly expense.
Digital eligibility checkers and instant pre-approval platforms could cut friction and deliver average annual savings of £1,600 per household—substantially more for those with larger loans.
Lower-income families stand to benefit disproportionately in relative terms.
The modeling suggests they could capture up to £31 billion of the total prize through tools that improve debt management, widen credit choices, and strengthen day-to-day money management.
Benefits would flow across all income groups, but the largest absolute gains would come from households with savings and mortgages.
Jas Singh, CEO of Consumer Relationships at Lloyds Banking Group, emphasised the transformative potential: advances in digital tools can help people understand their finances better and feel more confident in the choices they make.
He added that realising the full £100 billion opportunity will require industry-wide collaboration to ensure digital solutions are accessible, inclusive, and genuinely useful for everyone who needs them.
Professor John Gathergood of the University of Nottingham, who led the research, noted that the seven use cases examined illustrate where smarter, more personalised digital services can make a tangible difference to household prosperity.
Progress will not happen overnight, but steady adoption of AI-driven features—such as budgeting alerts and investment recommendations—could steadily close the empowerment gap.
The findings arrive as banks continue to invest heavily in technology. Lloyds itself generated £50 million in value from generative AI in 2025 and expects more than £100 million in 2026.
For the wider economy, the takeaway is evident. The so-called next wave of digital banking is not just about convenience—it is about unlocking meaningful financial wellbeing for millions of households. Collective action to design inclusive tools will determine how much of the £100 billion opportunity actually reaches UK consumers.
