Sending Out an SMS: Grubb, Kelly, Nieboer, Osborne & Shaw (2025)

What this is. The paper’s core results, the identification strategy (two randomized controlled trials at major U.K. banks), the estimating specification (DiD with individual and month fixed effects), and the behavioral mechanism evidence: enough to know what it found and how, without reading all 48 pages. To replicate or extend, read the full source at the original.

TL;DR

The paper runs at-scale field experiments at two large U.K. retail banks (Banks A and B) with a combined sample of 1.1 million banking customers in 2017 to 2018. Customers are automatically enrolled into various overdraft text alert treatments; a control group receives only the alerts already required by regulation. All tested stand-alone alerts (both just-in-time and early-warning) significantly reduce the overdraft charges they target by 2% to 19%, with just-in-time alerts reducing unarranged overdraft and unpaid item (UOD and UI) charges by 17% to 19% and arranged overdraft (AOD) charges by 4% to 8%. These findings directly informed FCA regulation expanded to all U.K. banks in 2019. Incremental early-warning alerts layered on top of existing just-in-time alerts show no statistically detectable effect. The primary mechanism is that alerts prompt customers to log into their accounts and transfer funds on the same day, rather than cut spending. Alerts help customers across the income and overdraft-frequency distribution, with absolute benefits highest for heavy overdrafters.

Core results

Magnitudes are as reported; \*\*/\*\*\* = 5%/1% significance. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Stand-alone just-in-time alerts reduce total monthly charges by 3-19% across both banks	Table V Panel A, p. 487	Treatment 1 (Bank A AOD alert): -0.528 GBP/month total (8.0%)***; Treatment 4 (Bank B UOD+UI): -0.399 GBP/month (3.8%)***; Treatment 6 (Bank B UOD only): -0.557 GBP/month (19%)***
R2	Just-in-time AOD alerts reduce AOD charges 4-8%; early-warning AOD alert reduces them 2.4%	Table V Panel A, p. 487	Treatment 1: -0.529 GBP/month (8.4%)***; Treatment 2: -0.302 GBP/month (3.8%)***; Treatment 3: -0.194 GBP/month (2.4%)***
R3	Incremental early-warning alerts have statistically insignificant effects on charges once just-in-time alerts are in place	Table V Panel B, p. 488; Table VI col. 7, p. 489	Treatments 7-9 postmandate: point estimates near zero, not statistically significant; 95% CI rules out effects larger than 14p/month postmandate
R4	Alerts reduce days in overdraft by 4-21% per month across stand-alone treatments	Table VI, p. 489	Treatment 1: -0.499 days/month (8.7%)***; Treatment 4: -0.063 days/month (15%)***; Treatment 6: -0.081 days/month (21%)***
R5	Absolute benefit rises with overdraft propensity; low-income and heavy overdrafters both benefit from alerts	Tables VII-VIII, pp. 492-493	Frequent overdrafters: -1.30 GBP/month AOD (Treatment 1)***; Rare overdrafters: -0.09 GBP/month (39% effect size)***; all account-inflow terciles show significant reductions
R6	Alerts raise same-day account logins by 10-53% on the day they are received	Table X, p. 498	Treatment 1 (AOD just-in-time): +0.495 logins on day 0 (53% increase); Treatment 9 (early-warning, premandate): +0.27 logins; Treatment 10 (UI): +0.13 logins
R7	Primary mechanism is fund transfers, not spending cuts; each alert triggers up to 0.27 same-day transfers averaging GBP 465	Table XII, p. 500; Table XI, p. 499	Treatment 1: +0.272 transfers on day 0 (52% increase, average value GBP 465 per transfer, GBP 126 per alert); debit card reduction economically small (-0.08 transactions)
R8	Alerts reduce the probability of exceeding overdraft thresholds at all day counts from 0 to 15 days in a month	Table IX, pp. 496-497	Treatment 1: -3.71pp (11%) for any overdraft day; -3.03pp (11%) for over 3 days; -2.58pp (10%) for over 5 days; -1.74pp (8.4%) for over 10 days

Overall (paper’s conclusion). Just-in-time overdraft alerts provide large consumer benefits without offsetting harm: automatic enrollment is the key because active opt-in rates were below 8% prior to mandates. Alerts eliminate less than half of overdraft charges arising from inattention, so scope remains for further interventions. The FCA expanded the CMA’s mandate for just-in-time UOD and UI alerts to cover more banks in 2019 and added a mandate for just-in-time AOD alerts, based directly on these findings.

Theory / model

The paper has no formal structural model. The tested hypothesis is the inattention hypothesis: consumers overdraft not because they value credit above its cost, but because they are unaware of their current account balance at the time of transactions (Stango and Zinman (2009), Armstrong and Vickers (2012)). If inattention is the primary friction, then real-time text alerts that notify customers when their balance approaches or crosses an overdraft threshold should allow customers to take corrective action (transfer funds, cut spending) and avoid overdraft charges.

The only prior study of overdraft alert experiments is the contemporaneous work of Ben-David, Mintz, and Sade (2021), who study e-mail alerts sent by the personal finance application Mint. The present study finds larger effects (17-19% vs. 3-9%), consistent with text messages being opened by 99% of recipients while Mint e-mails are opened by only 28-31% of users (p. 472). In contrast to Stango and Zinman (2014), who find that raising overdraft fee salience via a survey question does not affect account inflows, this paper shows that just-in-time text alerts do affect inflows via same-day transfers.

A secondary hypothesis motivates early-warning alerts specifically: by warning customers before the overdraft threshold is crossed, they have more time to cut spending rather than react after the fact. The paper also considers the “waterbed effect” (Agarwal et al. (2015)): mandated reductions in overdraft charges might cause banks to raise other fees, partially offsetting consumer benefits. No evidence for or against this is available from the experimental data; it is left as a question for future work (p. 508).

The identification strategy is randomized assignment. Two large U.K. retail banks randomly assigned eligible customers (those with a valid mobile number who incurred overdraft charges and whose balance dropped below GBP 1,000 in the prior six months) to treatment and control groups. Bank A used stratified (block) randomization on key pretreatment variables; Bank B used simple random sampling. Balance on pretreatment observables is verified in Internet Appendix Table IA.XXII. The FCA served as a pre-registration authority: trial dates and sample sizes were agreed in a “Terms of Reference” document before the experiments began (Section IX of the Internet Appendix). An ethics review board approved the protocol (p. 478).

Method

The primary estimator is an intent-to-treat (ITT) difference-in-differences specification with individual and month fixed effects. The estimating equation is (equation 1, p. 484):

$$Y_{i,t} = \beta_1 \text{Treatment}_i \times I(t \geq 7) + \beta_2 \text{Treatment}_i \times I(t = 7) + \eta_i + \mu_t + \epsilon_{i,t} \tag{1}$$

where $Y_{i,t}$ is the outcome variable for individual $i$ in month $t$, $\text{Treatment}_i$ is an indicator equal to one if customer $i$ was assigned to the treatment group, $I(t \geq 7)$ is an indicator for the treatment period (months 7-11), $I(t = 7)$ is an indicator for the first treatment month (included separately because observed overdraft charges lag by approximately 25 days due to billing cycles), $\eta_i$ are individual fixed effects, $\mu_t$ are month fixed effects, and $\epsilon_{i,t}$ is an error term. Standard errors are clustered by individual and month. The parameter of interest is $\beta_1$, the ITT effect of being automatically enrolled into the treatment alert.

For treatments 4 and 6, which use a staggered-rollout design (design B2 in Figure 3), months 10 and 11 are excluded because all units are treated in those months, following the approach of Goodman-Bacon (2021) who shows that the standard DiD estimate is hard to interpret when treatment effects vary over time.

The behavioral-response analysis uses a second specification to measure same-day account activity following alerts (equation 2, p. 496):

$$Y_{i,t} = \sum_{k=-3}^{3} \gamma_k (k\text{-Days.after.predicted.alert}_{i,t}) + \lambda \text{Treatment}_i \times I(t \geq 7) + \sum_{k=-3}^{3} \beta_k (k\text{-Days.after.alert}_{i,t}) + \eta_i + \mu_t + \epsilon_{i,t} \tag{2}$$

where $k\text{-Days.after.alert}_{i,t}$ equals one if and only if day $t$ falls $k$ days after the first treatment alert customer $i$ received. The indicator for actual alert arrival is instrumented by the predicted alert arrival interacted with treatment (equation 3, p. 497):

$$k\text{-Days.after.predicted.alert}_{i,t} \times \text{Treatment}_i \times I(t \geq 7) \tag{3}$$

This IV is needed because receiving an alert is endogenous to opt-in and opt-out decisions, and predicted-alert indicators have measurement error. Customer ($\eta_i$) and day ($\mu_t$) fixed effects are included; standard errors are clustered by customer and day.

Empirical specifications

Main treatment effects (R1-R2, Table V). The headline specification is equation (1) estimated separately for each of the 11 treatments, with outcomes: (i) observed AOD charges, (ii) UOD and UI charges (sum), and (iii) total charges. Observed charges are deducted from individual accounts once per billing cycle. Effect sizes are reported as $-100 \cdot \hat{\beta}_1 / \text{baseline mean}$ where baseline mean is the control group mean in the treatment period. Sample sizes range from 64,654 customers (Treatment 6) to 274,471 (Treatment 10). Adjusted $R^2$ ranges from 0.36 to 0.78, reflecting the absorbing power of individual fixed effects.

Secondary outcomes (R3-R4, Table VI). The same specification is applied to inferred charges, number of unpaid items, days in overdraft per month, number of overdraft episodes (by length), and number of zero-day overdraft episodes. For treatment 9, pre- and post-mandate periods are estimated separately (design A) to distinguish the stand-alone from incremental alert effect.

Heterogeneity (R5, Tables VII-VIII). The main specification is re-estimated within subsamples split by (i) pretreatment account inflows tercile (proxy for income: GBP 0-1,500, GBP 1,500-3,000, GBP 3,000+/month) and (ii) pretreatment overdraft propensity (rare, occasional, frequent overdrafters based on the pretreatment median of positive charges).

Distribution of days in overdraft (R8, Table IX). The specification is re-estimated with the binary outcome that days in overdraft exceed thresholds $d \in \{0, 3, 5, 10, 15, 20, 25, \text{full month}\}$. Effects are reported in percentage points. Standard errors cluster by customer and month. Stand-alone treatments only.

Behavioral response (R6-R7, Tables X-XII). Equations (2)-(3) estimated at Bank A on daily outcomes: number of account logins, number of debit card transactions, and number of account transfers, in the six-day window around alert arrival. IV first-stage F-statistics exceed 10,300,300. Effects are relative to a baseline day defined as four or more days before the first predicted treatment alert.

Datasets used

Dataset	Role in paper	Wiki page
FCA-commissioned bank transaction panel (Banks A and B), 2017-2018	RCT treatment and control data: monthly overdraft charges (AOD, UOD, UI), daily account logins, debit card transactions, transfers; 1.1 million customers, 11 months	No page yet (proprietary-confidential FCA data)
FCA representative 6-bank panel, 2015	Descriptive statistics on U.K. current account market: 1,366,355 accounts, overdraft charges by bank (Table II)	No page yet (proprietary-confidential FCA data)
U.K. Competition and Markets Authority (CMA) 2016 data	Context on overdraft usage: over two-thirds of accounts have an overdraft facility; 50% to 60% of days in overdraft avoidable via savings or credit cards	No page yet
Follow-up consumer survey (linked to Banks A and B experiments)	Survey of enrolled customers on alert attitudes, actions taken, and reasons for opting out (Internet Appendix Section I)	No page yet

Sample: treatment period is November 2017 to March 2018 (months 7-11); pretreatment period is May to October 2017 (months 1-6). The FCA-commissioned data include all current accounts held by sampled customers and all their transactions during the sample period.

When to read the full paper

Read the original if you are: designing or evaluating overdraft alert mandates in any jurisdiction; studying the role of inattention in consumer borrowing behavior; building on randomized experiments at U.K. banks under FCA auspices (the Internet Appendix contains the full treatment table, survey details, and all additional robustness tables); or interested in how at-scale field experiments can directly inform regulation. The behavioral-response tables (Tables X-XII) are the most detailed evidence on how alerts mechanically reduce charges.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(1), February 2025. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Grubb, Michael D., Darragh Kelly, Jeroen Nieboer, Matthew Osborne, and Jonathan Shaw. “Sending Out an SMS: Automatic Enrollment Experiments for Overdraft Alerts.” The Journal of Finance 80, no. 1 (February 2025): 467–514. DOI: 10.1111/jofi.13404. © 2024 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.