Enforcement report 2018/19
Affordability and customer protection
In recent years our Enforcement team has reviewed numerous cases where individuals have demonstrated gambling-related harm and yet have been able to continue to gamble without effective engagement.
Some of these individuals have funded their gambling activity through the misappropriation of monies from businesses, the taking out of unaffordable loans and misappropriating the funds from vulnerable people. Common to all these cases has been the ineffective controls framework used by the operators to identify and manage the risk.
Open source data exists which can help operators assess affordability for its GB customer base and improve its risk assessment and customer interventions.
According to the Office for National Statistics Annual Survey of Hours and Earning: 2017 provisional and 2016 revised results:
Section 4 – In April 2017, median gross weekly earnings for full-time employees in the UK was £550; and
Section 11 – In April 2017, the occupation group with the highest median weekly earnings for full time employees was managers, directors and senior officials, at £824 per week.
Based on the above, 50% of the full-time employees in the UK receive less than £29,000 gross earning per year and 50% of the full-time managers, directors and senior officials in the UK receive less than £43,000 gross earnings. These figures omit income tax, national insurance and cost of living, for example but not limited to, mortgage payments or rent, mobile phone contracts, travel costs, food and utility bills.
A “YouGov” survey, with a panel of 1 million members, asked the question “Approximately how much do you have left to spend each month after deducting taxes (including council tax) as well as expenditure on accommodation, utilities and food”. A split of the results by age is set out below:
The data for the above table was taken from YouGov Profiles, and collected from a National Representative sample of 127,642 panellists. All panellists were adults aged 18+ within the GB population. The question was asked between June 2018 to June 2019.
For each age group, the data suggests that most of the panel members have disposable income per month from a figure less than £125 up to £499. This is equivalent to less than £1,500 per year and £6,000 per year. However, even these disposable income figures do not take into consideration unavoidable monthly costs or annual costs such as transport, fuel, monthly contractual payments (mobile phones, cars, life insurance etc), vehicle maintenance (service, repairs and MOT), clothing and personal care.
The above disposable income data identifies clear benchmarks that should drive Social Responsibility (SR) triggers which will help to identify gambling-related harm by considering affordability. SR triggers should be set at a level so that most of the customer base is monitored based on the open source information.
To date we have seen nothing to indicate that gamblers have more disposable income than the general population and most people would consider it harmful if they were spending all their disposable income gambling. Benchmark triggers should be a starting point for engaging with customers and are not intended to definitively demonstrate a customer is suffering from gambling related harm – but they can help identify instances when an operator needs to understand more about a customer, their play and affordability.
Without adopting a framework based on such data, operators are at risk of not understanding whether customers are spending an affordable amount or whether the money is from a legitimate source.
Some of the casework has revealed operators omitting from their customer monitoring monies withdrawn and then apparently re-deposited, believing that no checks are required to mitigate any SR or Money Laundering risks. We have observed that this activity has been linked to SR issues where an individual is misappropriating monies and the monies re-deposited are fresh criminal spend. Operators should consider this and obtain evidence when appropriate to satisfy themselves that this is not the case.
We recognise that not all business models are the same and that operators have customers with different wealth and disposable incomes. But we do expect that the operator should be able to evidence this and have developed a framework that fully reflects and incorporates the diversity of its customers base.
As an example, we have observed operators attempting to assess affordability for wealthy customers by obtaining financial statements from Companies House and/or looking at property ownership. The subsequent customer triggers were then set at a level equal to the drawings from the companies or the net assets of the company and the value of the property combined. Operators applying this approach frequently fail to identify indicators of problem gambling.
In the examples some included financial statements, around which we make the following observations:
a.The company accounts were abbreviated and, as such, contained little detail as a standalone document to support the trigger levels decided upon.
b.The accounts were unaudited and carried a risk of not being free from material misstatement.
c.The companies had low cash levels and most of the net assets were tied up in the fixed assets, therefore the company had no liquid assets to support the level of spend set by the operator.
d.There was limited information on the profitability of the companies and evidence of salaries or dividends paid. Where this information was available, no consideration was applied to the customers’ tax liabilities on the drawings, personal circumstances or cost of living.
If an operator is going to set specific triggers for a customer base not representative of the general public, various documents sources should be relied upon, but they must contain sufficient information to substantiate the trigger level set.
In conclusion, we would recommend that operators revisit their framework on triggers and consider their customer base and their disposable income levels as a starting point for deciding benchmark triggers. This would help ensure vulnerable customers are identified as early as possible and interacted with appropriately.
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