Consumer topline outcomes and metrics

Metric CFV1-M01: Reduction in the proportion of consumers who, in the last 2 years, have been offered a financial product or service they wanted, but at a price, or with terms and conditions, they felt to be ‘completely unreasonable’

Data source and links to further information

This metric was sourced from the FCA’s Financial Lives survey (FLS).

Find out more about how FLS data is used for our metrics in the ‘Key data sources: FCA Financial Lives survey’ section of our website. Further information on the FLS is also found in the FLS resources library, including questionnaires, data tables and reports.

How the metric was calculated

To build this metric, we used the following question from the FLS.

This metric was calculated as the proportion of consumers answering ‘Yes’ to AC7. This metric is a rebased statistic, meaning its calculation excludes respondents who answered ‘Don’t know’ to AC7.

Previous years

We took results from the 2020 survey as our baseline and included the 2017, 2022, 2023 and 2024 survey results below for comparison:

Interpretation of the metric values

This metric measured the extent to which consumers say that they have been offered a product or service they wanted, but at a price, or with terms and conditions, they felt to be ‘completely unreasonable’. It is an indicator of whether consumers perceive they are being offered a fair price and good quality financial products.

It is important to note that ‘completely unreasonable’ is used for the purposes of questionnaire design and doesn’t represent a threshold of what we view as fair value. The interpretation of ‘completely unreasonable’ will be influenced by consumers’ personal circumstances and biases. This could be considered a limitation, when looking at one wave of FLS. There will still be value in the statistic, when looking to see if it reduces between survey waves. The question is asking consumers about their experiences in the past 2 years and it takes time to show any changes that result from our work.

We considered that a decrease in the observed value for this metric would be a positive indicator for our desired outcome that consumers receive fair prices and good quality products.

Metric CFV1-M02: Increase in aggregate benefits from FCA policy work (2022-23 prices)

Data source and links to further information

How the metric was calculated

*We changed our methodology this year for reporting the benefits from our policy work. To be consistent with our approach to Cost Benefit Analyses (CBA) we counted the expected annualised benefits from interventions introduced over a 10-year reporting period. The figures in this the table have been restated using the new methodology and are given in 2024/25 prices.

For an explanation of this metric read our Positive Impact 2025

Metric CST1-M01: Increase in consumer satisfaction with their providers

Data source and links to further information

This metric was sourced from the FCA’s Financial Lives survey (FLS).

Find out more about how FLS data is used for our metrics in the ‘Key data sources: FCA Financial Lives survey’ section of the website. Further information on the FLS is also found in the FLS resources library, including questionnaires, data tables and reports.

How the metric was calculated

To estimate consumer satisfaction with providers of financial products and services, we calculated a composite satisfaction index. To do this, we looked at the weighted satisfaction scores for individual product or service providers, as reported in Figure 5.5 of the FLS 2024 report of key findings. which are then further weighted by product incidence. We included satisfaction with providers of the financial products and services shown in the table below:

Providers included in FLS composite satisfaction index

Consumers were asked to rate satisfaction with their own provider of these products or services. They were asked to give a score from 0 to 10, with 0 being completely dissatisfied, and 10 being completely satisfied. We first measured the mean satisfaction rating for each product or service provider in the table by applying the appropriate section or selected product weights produced for the FLS. These weights ensured that the sample analysed is representative of the population holding the given financial product.

Individual product/service scores were then further weighted by the incidence rate of that product. The incidence rate is defined as the estimated proportion of consumers who hold the product.   

In other words, the composite index is created as a weighted composite mean of products related to the 19 providers in the table above by:

[(weighted satisfaction score for product/service provider 1 x weighted incidence of product 1) + (weighted satisfaction score for product/service provider 2 x weighted incidence of product 2) + … + (weighted satisfaction score for product/service provider 19 x weighted incidence of product 19)]

As a result, satisfaction with providers of those products with higher incidence rates contributes proportionately more to the composite index.

This metric is a rebased statistic, meaning its calculation excludes the respondents who answered ‘Don’t know’ to the relevant questions (see table above).

Previous years

We have used the results of the 2020 survey as our baseline and also include the 2017, 2022, 2023 and 2024 survey results below for comparison:

Interpretation of the metric values

This metric provides an indicator of the degree of consumer satisfaction with providers of various financial products and services.

We consider that an increase in the observed value for this metric over time would be a positive indicator for our desired outcome.

There are 2 key limitations of this index. The first is that it is based on the provider satisfaction questions in the FLS and they do not cover all products. For example, only some credit products are covered. It also does not cover all retail sectors. For example, the FLS does not include questions about satisfaction with retail investment product providers.

The second key limitation is that a reduction in satisfaction with a provider of a high incidence product would make a larger impact on the composite score. This could potentially hide improved scores for providers of lower incidence products, or vice versa.

Metric CST1-M02: Reduction over time in upheld Financial Ombudsman Service complaints about unsuitable advice or mis-sold products and services

Data source and description

This metric measured the number of Financial Ombudsman-resolved complaints relating to unsuitable or mis-sold products that were upheld in favour of the consumer. It showed this number as a percentage of cases resolved by merits relating to unsuitable or mis-sold products. Throughout this section, when we use the words ‘upheld’ and ‘not upheld’ complaints, this refers to cases resolved by merits by the ombudsman service.

Find out more about the ombudsman service data and see what other metrics this data is used for.

Previous years

We monitored complaints including and excluding Payment Protection Insurance (PPI), those with any event date and those with recent event dates. Recent event dates are where the event that caused the complaint was during 2020 or 2021 (for complaints resolved in 2021) or during 2019 or 2020 (for complaints resolved in 2020).

We used data from 2021 as our baseline and also included the 2020 data below for comparison:

Complaints with any event date – including PPI

Complaints with any event date – excluding PPI

How the metric was calculated

The source data included categorisation of cases by product type and type of complaint. For the purposes of this metric, we counted cases that fall into any of the following complaint type categories used by the Financial Ombudsman, which we considered to be relevant to unsuitable advice or mis-selling:

Ombudsman service complaint type categories

Calculation 1: Number of upheld complaints. The total of all complaints cases captured in these categories that were resolved in the calendar year and where the complaint was upheld in the consumer’s favour.

Calculation 2: % uphold rate. The total upheld complaint cases from Calculation 1 divided by the total number of cases falling into the above categories where a decision was taken and the complaint was either upheld or not upheld by the Financial Ombudsman in the calendar year.

Interpretation of the metric

We considered a decrease in the number of upheld complaints to be a broad indicator of better advice being given to consumers and better treatment of consumers by firms. If we achieved our outcome that consumers are sold suitable products and services and receive good treatment, we expected to see a downward trend in upheld consumer complaints over the medium to long-term.

Although a high volume of complaints and a high uphold rate is considered to be bad, and low volumes and a low uphold rate good, there may be circumstances where an increase does not necessarily reflect an increase in poor treatment. For example, an increase as a result of greater consumer awareness of their rights to complain or our regulatory powers being extended to new products. A low number of complaints and upholds in absolute terms may suggest there are few problems in a market but may actually reflect that for some people, often in financial distress, complaining may not seem like an option. At other times, a high uphold rate might indicate positive engagement by a firm that settles complaints in line with the approach of the Financial Ombudsman.

These complaints do not represent all the complaints to firms that have been settled in favour of the consumer but those where the firm did not resolve the complaint itself and then the consumer referred it to the Financial Ombudsman.

The overall time between the event that caused the complaint and complaint resolution can vary considerably. The firm could resolve the complaint quickly and some cases referred to the Financial Ombudsman may be concluded within a few months of the event. In other cases, years could pass between the initial event and the referral to Financial Ombudsman. While it aims to give answers to complaints within 3 months, for more complex cases there could be a longer period before the Financial Ombudsman reaches a decision.

Link to further information

Further information about the complaint cases handled by the ombudsman service.

Metric CCO1-M01: Increase in the proportion of consumers who have confidence in the UK financial services industry

Data source and links to further information

This metric was sourced from the FCA’s Financial Lives survey (FLS).

Find out more about how FLS data is used for our metrics in the ‘Key data sources: FCA Financial Lives survey’ section of the website. Further information on the FLS is also found in the FLS resources library, including questionnaires, data tables and reports.

How the metric was calculated

To build this metric, we used the following question from the Financial Lives survey:

This metric was calculated as the proportion of consumers answering ‘Strongly agree’ or ‘Slightly agree’ to question AT2_b. This gave us the proportion of consumers who agree with the statement that ‘I have confidence in the UK financial services industry.’

This metric is a rebased statistic, meaning its calculation excludes the respondents who answered ‘Don’t know’ to AT2_b.

Previous years

We took results from the 2020 survey as our baseline and included the 2017, 2022, 2023 and 2024 survey results below for comparison:

Interpretation of the metric values

This metric provided an indicator of the level of consumer confidence in the UK financial services industry.

We considered that an increase in the observed value for this metric over time would be a positive indicator for our desired outcome that consumers have confidence in the UK financial services industry.

Metric CCO2-M01: Stabilise and then reduce over time Financial Services Compensation Scheme (FSCS) compensation, claims and payments

Data source and description

These metrics measured the number of claims and the number and value of compensation payments to consumers as a result of failures of financial firms which could impact on consumer confidence in the financial system. The numbers for payments made include claims paid and decisions upheld. The data for this metric was sourced from FSCS annual publications.

Find out more about FSCS data and to see which other metrics this data is used for.

How the metric was calculated

Total FSCS claims, payments and compensation for financial year 2020/21 for the following classes of business:

1. Life distribution and investment intermediation
2. Investment provision
3. General insurance distribution
4. General insurance provision
5. Home finance intermediation
6. Debt management
7. Deposits

Previous years

We used data from 2020/21 as our baseline and included the 2018/19 and 2019/20 data below for comparison:

Interpretation of the metric values

In interpreting this metric, it is important to know that not all firm failures where the FSCS steps in are disorderly. Although we used FSCS data as an indicator for our outcome, life insurance and pensions provision and general insurance provision and deposits are prudentially regulated by the PRA and not the FCA. Also, the FSCS does not protect all FCA regulated activities. For example, most consumer credit activities are not protected, and there is limited protection available for investment-based crowdfunding. The FSCS does not protect the activities of electronic money (E-Money) and payment institutions.

Given some of these limitations and wider economic circumstances, this metric in particular should be considered alongside the other confidence metrics.

Movement in this metric in the short to medium term may reflect our adoption of new approaches to identify harm faster, so it needs to be carefully interpreted. Compensation payments reported in the latest calendar year may also have been caused by firms’ activity or failures in previous years.  It can take some time for these effects to flow through to the FSCS because claims lag behind misconduct and firm failures. This means that legacy redress issues, for example, may limit the timescales with which FSCS compensation claims in specific sectors can be reduced.

Links to further information

More information on FSCS.

Metric CCO2-M02: Monitoring the number of firm failures

Data source and description

This metric was sourced from FCA data. Our supervisory activities identify firms that have entered insolvency processes. We may also be notified by the firms themselves and/or insolvency practitioners. We refreshed the data for this metric monthly and reported it annually. Any firm failures that are not picked up through this were captured through a Companies House data sweep, ensuring our data set was as complete as possible.

How the metric was calculated

The metric is the total number of FCA regulated consumer firms that have entered an insolvency process in the calendar year. This does not include firms that have entered schemes of arrangement or company/partnership voluntary arrangements (compromises with creditors), firms that have entered members’ voluntary liquidation (a solvent process) or firms that have been dissolved through a compulsory or voluntary strike off. 

This metric aggregates the number of failed firms primarily operating in Consumer sectors.

Previous years

We used data from 2021 as our baseline and included the 2020 data below for comparison:

Interpretation of the metric values

This metric provided an indicator of the number of firm failures occurring in the financial services market. We regulate around 41,000 solo-regulated firms, for which we are the prudential regulator. In line with our statutory customer protection and market integrity objectives and our competition duty, the regulatory framework aims to mitigate harm while firms are a going concern, before any firm failure. The firms where we are the prudential regulator are not systemically important and should be able to fail, as orderly entrants and exits are part of a well-functioning market.

We expected this metric to be particularly affected by wider economic circumstances. For example, during difficult economic conditions, more firms are likely to fail.

Firm failures are a normal part of business, but can harm consumers, the effectiveness of markets and overall confidence in the UK’s financial system. We wanted to ensure that firms have sufficient financial resilience to enable them to exit the market through a solvent wind-down and avoid disorderly failure. We also sought to mitigate harm in failure as far as possible where firms entered an insolvency process. We minimised harm in a number of ways, for example by making sure that firms had proper measures to keep customer money safe.

Metric CCO3-M01: Proportion of consumers who have confidence that the UK financial services industry is working to tackle banking, pension and investment fraud

Data source and links to further information

This is a new metric sourced from the FCA’s Financial Lives survey (FLS).

Find out more about how FLS data is used for our metrics in the ‘Key data sources: FCA Financial Lives survey’ section of the website. Further information on the FLS is also found in the FLS resources library, including questionnaires, data tables and reports.

How the metric was calculated

To build this metric, we used the following question from the Financial Lives survey:

This metric was calculated as the proportion of consumers answering ‘Strongly agree’ or ‘Slightly agree’ to question AT2_d. This gave us the proportion of consumers who agree with the statement that ‘I am confident the UK financial services industry is working to tackle banking, pension and investment fraud.’

Previous years

We have used the results of the 2023 recontact survey as our baseline and also included the 2024 survey results below for comparison:

Interpretation of the metric values

This metric provides an indicator of the level of consumer confidence in the UK financial services industry working to tackle banking, pension and investment fraud.

We considered that an increase in the observed value for this metric over time would be a positive indicator for our desired outcome that consumers have confidence that the UK financial services industry is working to tackle banking, pension and investment fraud.

Metrics CCO3-M02: Increase in the proportion of Authorised Push Payment (APP) fraud losses that are reimbursed

Data source and description 

This metric is taken from the latest yearly UK Finance publication Annual Fraud Report 2025.

UK Finance is a trade association for the UK banking and financial services sector. It represents nearly 300 firms providing credit, banking, markets and payment-related services in the UK. UK Finance also publishes a Half Year Fraud Report. This data is available twice a year and we reported this metric annually.

How the metric was calculated

We published the data on Authorised Push Payment (APP) fraud losses that are reimbursed from UK Finance’s annual publication ‘Fraud - the Facts’. To calculate the % reimbursed we have divided the total reported Authorised Push Payment fraud case value reimbursed by the total reported Authorised Push Payment fraud case value.

Previous years

We used data from 2020 as our baseline and included the 2019 data below for comparison:

Interpretation of the metric value

The figure for APP fraud taken from UK Finance included reports on fraud falling within investment fraud as APP is the method through which this fraud is often perpetrated.

Working with our partners, our longer-term aim was to increase the proportion of APP losses that are reimbursed which would indicate we are meeting our confidence outcome. This is because a higher level of reimbursed losses is likely to increase consumer confidence in the UK financial system.

Links to further information

UK Finance publication Annual Fraud Report 2025

Metrics CCO3-M03: Slow the growth in or reduce investment fraud victims and losses

Data source and description

The metrics for investment fraud were sourced from National Fraud Intelligence Bureau (NFIB) dashboards. NFIB publishes data relating to victims of fraud and total fraud losses. Data is gathered from the police, members of the public and businesses who report into Action Fraud using its telephone service or online reporting tool. This data is available on a real-time basis and we reported on this metric annually.

How the metric was calculated

For investment fraud we aggregated the total amount of reports and total losses for the following typologies within NFIB data:

1. Pension Liberation
2. Fraud recovery
3. Share sales / boiler room
4. Pyramid / Ponzi schemes
5. Other Financial Investments

Interpretation of the metric values

There is likely to be a degree of misclassification of fraud within NFIB data and reports of types of fraud we identify as priorities, some of which may not relate to regulated activity. In addition to this, NFIB cannot guarantee that there is no double counting within the data.

We considered slowing the growth in or reducing reported fraud cases and losses in the UK to indicate we have met our confidence outcome. This is because a higher level of fraud cases is likely to reduce consumer confidence in the UK financial system. However, the vast majority of fraud is not reported and therefore the figures stated do not represent the true rates of fraud in the UK.

Links to further information

NFIB dashboards 

Metric CCO3-M04: Slow the growth in or reduce Authorised Push Payment (APP) fraud cases and losses

Data source and description  

The metrics for APP fraud are taken from the latest yearly UK Finance publication Annual Fraud Report (2025). UK Finance is a trade association for the UK banking and financial services sector. It represents nearly 300 firms providing credit, banking, markets and payment-related services in the UK. UK Finance also publishes a Half Year Fraud Report. This data is available twice a year and we reported on this metric annually.

How the metric was calculated  

For APP fraud, we publish data on total reports and total losses to APP fraud from UK Finance’s annual publication. 

Previous years

We use data from 2020 as our baseline and include the 2019 data below for comparison:

Interpretation of the metric values 

The figure for APP fraud taken from UK Finance will include reports on fraud falling within investment fraud as APP is the method through which these frauds are often perpetrated. There may therefore be some double counting present in the data related to the investment fraud priority area. Whilst our longer-term aim is to reduce the number of consumers falling victim to APP and total losses, in the short term the aim would be to slow down the growth of APP fraud. 

We considered slowing the growth in or reducing reported fraud cases and losses in the UK to indicate we are meeting our confidence outcome. This is because a higher level of fraud cases is likely to reduce consumer confidence in the UK financial system. It should be noted however that the vast majority of fraud is not reported and therefore the figures stated do not represent the true rates of fraud in the UK.

Potential improvements to the metric

To have a long-lasting impact on the levels of fraud, a whole system response is needed, therefore we welcome the publication of the National Fraud Strategy.  We have reviewed our metrics in light of this Strategy and at this stage have not identified any scope for improvement.  

UK Finance publication Annual Fraud Report 2025

Metric CCO3-M05: Increase in proportion of applications rejected, withdrawn or refused by the FCA under Money Laundering Regulations (MLRs) or for financial crime reasons

Data source and description

This metric was sourced from FCA data. We gathered data on the rate at which applications for regulatory approval by firms are refused, withdrawn and rejected (known as RWR). MLR registrations are currently reported to HM Treasury annually.

The FCA has responsibility for supervising the anti-money laundering controls of businesses that offer certain services such as payments, leasing and types of commercial lending. Firms offering these services need to register with us as an Annex 1 financial institution.

We also included the RWR rate for cryptoasset applications under MLR as this area is a particular focus for the FCA.

How the metric was calculated

We analyse the reasons why applications for authorisation by firms are unsuccessful to produce the RWR. This metric covers the following:

• Rejections – submissions rejected due to extremely poor quality and the fee returned to the firm on the basis it does not amount to an ‘application’
• Withdrawals – where we influence a firm not to continue with the application following our engagement and scrutiny of the application
• Refusals – where we recommend refusal of the application, and this is agreed by the relevant decision maker and a Decision Notice is issued

Interpretation of the metric values

This metric was an indicator of the strength of our Gateway in reducing financial crime within the regulatory perimeter. It shows whether we are authorising firms that have high standards for minimising financial crime at the outset and being assertive in rejecting those that don’t.

We considered that an increase in the observed value for this metric over time would be a positive indicator for our desired outcome. This metric should be considered alongside the other financial crime confidence metrics because if, over time, financial crime falls we would not expect to have a continuing increase in the number of applications rejected, withdrawn or refused.

In addition, RWR may change for reasons not linked directly to our assertiveness. For example, our engagement with compliance consultants and the Association of Professional Compliance Consultants may improve application quality, leading to fewer unsuccessful applications.

Potential improvements to the metric

We are working to develop a similar metric for Variation of Permission applications and applications from individuals seeking to carry out/hold senior management functions.

Metric CAC1-M01: Reduction in the number of operational incidents

Data source and description

This metric was compiled from supervisory data that records notifications firms make to us. Under principle 11 firms must deal with us in an open and cooperative way and disclose to us anything that we would expect notice of. This includes any matter which could affect the firm’s ability to provide adequate services to its customers and which could result in serious detriment to a customer of the firm. The metric captures the total number of these notifications that relate to the operational resilience of a firm.

How the metric was calculated

The metric was calculated by adding the number of operational incidents that are reported to us.

1. Consumer: We aggregated the number of operational incidents for firms that are aligned primarily to ‘Consumer’ sectors. These are the Retail Banking, Retail Lending, General Insurance & Protection, Retail Investment and Consumer Finance portfolios of firms.
2. Wholesale markets: We aggregated the number of operational incidents for firms that are aligned primarily to ‘Wholesale markets’ sectors. These are the Wholesale Financial Markets and Investment Management portfolios of firms.

Previous years

We used data from 2021 as our baseline and included the 2020 data below for comparison:

The previously published data for 2021, 2022 and 2023 has been amended to account for changes to the FCA supervisory model. However, these corrections have not impacted the underlying trends identified from the data or our assessment of previous outcome metrics.

Interpretation of the metric values

Ensuring the UK financial sector is operationally resilient is important for maintaining both market access and the confidence of consumers, firms and others reliant on the smooth functioning of wholesale markets. Operational incidents can cause harm to market participants, pose a risk to market integrity, and could threaten the viability of individual firms or cause instability in the financial system.

Operational incidents (including technology and cyber) reported reflect a range of events from reduced service availability to confidential data loss. In aggregate, these incidents can have an impact on market access and/or the confidence of those who rely on the smooth functioning of wholesale markets.

For instance, operational incidents such as IT outages may reduce access and operational incidents that are publicly disclosed, due to a potential or observed impact on market functioning, and overlap with other criteria such as transparency, price discovery, which drive overall market confidence.

However, we have done much over the last 3 years to strengthen the operational resilience regime. For example, we have introduced a new set of operational resilience rules, consulted on a new framework to report incidents to us and engaged much more with industry, both domestically and internationally. Firms are more aware of our expectations to report operational disruptions to us, and we believe that this in part explains the general increase in the volume of incidents. However, we also consider that more incidents are being reported to us from a sector where there is an increasing reliance on third parties and interconnectedness, and at a time where firms also continue to face a high, and growing, level of cyber threats and operational resilience risks, against a complex geopolitical backdrop.

Links to further information

Find out more about the relevant FCA rules that underpin this metric.

Metric CAC2-M01: Reduction in the proportion of consumers who were declined a product or service in the last 2 years, and, in their view, this was due to non-financial factors such as their age, health or ethnicity

Data source and links to further information

This metric was sourced from the FCA’s Financial Lives survey (FLS).

Find out more about how FLS data is used for our metrics in the ‘Key data sources: FCA Financial Lives survey’ section of the website. Further information on the FLS is also found in the FLS resources library, including questionnaires, data tables and reports.

How the metric was calculated

To build this metric, we used the following questions from the Financial Lives survey:

This metric was calculated as the proportion answering ‘Yes’ to AC1NEW (ie selecting any one of the products listed as answer options for this question), and with any of the non-financial reasons selected at AC9.   This gave us the proportion of consumers who believe they were declined a financial product or service due to these non-financial reasons. For the 2017, 2020 and 2022 waves, as well as for the 2023 recontact survey these were:

• Age, health, postcode, gender, sexuality, religion, ethnicity and the criminal record of someone in the household.

For the 2024 survey, the following changes were made to the definition of ‘non-financial’ reasons:

• Gender was updated to ‘sex and/or gender’
• Health was split into physical and mental health
• New non-financial reasons added were: financial knowledge, disability status, social or political views/ affiliations.

This change may contribute to movement in the results for this metric. 

In our 2020 survey which provides the baseline results, 6% of UK adults were declined a product at AC1NEW. Of these, 18% put that down to the reasons described as non-financial. So, 1% of all UK adults were declined a product or service, in their view, for non-financial reasons.

Previous years

We took results from the 2020 survey as our baseline and included the 2017, 2022, 2023 and 2024 survey results below for comparison:

Interpretation of the metric values

This metric provided an indicator of the extent to which consumers, in their view, have been denied access to a financial product or service for a reason or reasons other than financial ones. We recognise that there may be products or services that are unsuitable for certain consumers because they are designed for a specific target market, but we monitored this metric as an indicator of the potential level of financial exclusion caused by firm conduct.

We considered that a decrease in the observed value for this metric over time would be a positive indicator for our desired outcome that consumer needs are met through low exclusion.

Metric CAC2-M02: Reduction in the proportion of consumers who do not hold certain key products

Data source and links to further information

This metric was sourced from the FCA’s Financial Lives survey (FLS).

Find out more about how FLS data is used for our metrics in the ‘Key data sources: FCA Financial Lives survey’ section of the website. Further information on the FLS is also found in the FLS resources library, including questionnaires, data tables and reports.

How the metric was calculated

This metric measured the proportion of consumers who do not hold certain key products. As it is difficult to judge whether wider holding of some financial products is desirable, we focused on products that consumers typically need. This does not mean other products are not important, but this metric provided an insight for 3 key product categories – day-to-day accounts (mainly current accounts), private pension provision and general insurance. Please note, an additional check question was added in FLS 2024 to the pensions product holding questions, as set out on slide 18 of our FLS 2024 pensions report. More specifically, for adults aged 22-69 who said they did not have a pension into which contributions were not currently being made, we prompted them to think about any old pensions they may have had from past jobs that they could have forgotten about. This change will have contributed slightly to the drop in the result of those with no private pension provision.

The percentages of people who do not hold a product in the given category are calculated using product holdings data from the FLS. For a full list of product holdings, see the Financial Lives 2024 ‘Product holdings’ annex (Annex A)

Previous years

We took results from the 2020 survey as our baseline and included the 2017, 2022, 2023 and 2024 survey results below for comparison:

Interpretation of the metric values

This metric provided an indicator of the extent to which consumers do not hold certain key financial products and was therefore an indicator of the potential level of financial exclusion.

A consideration in interpreting these values is that we do not know the reason why consumers do not hold the products concerned. There could be economic, social, or other factors at play that influence a consumer’s decision not to hold such products. Note that we do not regulate state or employer pensions. The Pensions Regulator is the UK regulator of work-based pension schemes.

We considered that a decrease in the observed value for this metric over time would be a positive indicator for our desired outcome that consumer needs are met through low exclusion.