How to ensure you aren't misled about banknote lifetimes
Dr Nikki Strickland, Global Director Marketing & Strategy, De La Rue
Banknote lifetime values need to be treated with care because it is easy to form misleading conclusions. The biggest challenge is ensuring that a fair comparison is being made and that comparisons are made on representative data. The before and after scenarios should be like-for-like. This article discusses the questions to ask when considering how a new banknote series compares to a previous series.
At a simplistic level there are different starting points to the measurement, so any comparison of note life should use the same point. Does the banknote life start when the banknote was produced or when it moves vaults or when it enters circulation? Is a banknote still considered to be aging if it is issued by the central bank but sitting in storage in the vaults of a commercial bank?
There are also different banknote lifetime end points. Mathematics can model the age at which a banknote is likely to have fallen below the acceptable fitness standard, but central banks assess each note themselves before it is declared as unfit. This means that the note must physically return to the central bank before it can be declared to be at the end of its useful life. When returned to the central bank the point at which the banknote is killed could be the point of sorting or the point of physical destruction. Again, any comparison of note life should use the same ending point measurement.
There is also a consideration about whether to model the banknote lifetime or to measure it for individual banknotes. Instinctively many central banks want to track individual banknotes with their serial numbers and to capture the moment at which each individual banknote begins and ends its life. A minority of issuing authorities, most notably the Dutch National Bank, have been looking at serial number capture for years. This approach has its merits and provides the reassurance of being ‘real’ data, although can be costly and involved to set up and run.
“Challenge us and other suppliers about our claims – are we making comparisons that we shouldn’t be making and have we extracted appropriate data points?”
Serial number tracking also has its limitations. It requires OCR sensors on sorting machines, serial numbers that can be read at speed (even on soiled/worn banknotes) and the appropriate infrastructure to securely capture and store the data. It also requires enough data points on enough notes to reach a robust conclusion. If your banknotes last for five years then you will only really have confidence that the note life is actually five years when the banknotes have been in circulation for well beyond that length of time. Some banknotes will enter circulation and get damaged quickly. Some banknotes will enter circulation and sit in a vault for a while or degrade slowly via normal wear and tear. Over time a database of note life data will build up and an appropriate distribution curve can be fitted to the data to provide the best note life value. It can be a frustrating wait for central banks seeking to compare the performance of a new series of banknotes to that of their previous series.
The alternative approach is to model the banknote lifetime mathematically. This can be achieved from simple data that many central banks publish publicly. Monthly data such as the number of new banknotes issued that month, the number of previously used fit notes re-issued, the number of banknotes declared to be unfit and the number of notes in circulation at the end of the month is all that is required for a steady state note life calculation. Evaluation by De La Rue revealed that monthly calculation of banknote lifetimes can provide banknote lifetime values that closely resemble those from actual sorting machine serial number assessment. The closest alignment came from a frequency distribution of the outputs of the monthly note life calculations. Irrespective of methodology, when comparing the life of two different series it is important to check that the values of the series being compared have been derived from the same method.
“Once you introduce a new banknote you need to let the cash cycle reach its new equilibrium. Otherwise you will overestimate the note life of your new banknote”
Once the basic like-for-like comparison criteria has been established there are additional questions to ask. The first question is whether the new series of banknotes have been in circulation long enough for the note life values to be trusted. Cash cycles can take a few years to return to equilibrium after the disruption of withdrawing an old series and introducing a new one. It is likely that the cash cycle was reasonably stable with the established series of banknotes. It is also likely that the previous cash cycle had a range of banknotes of different ages all circulating together. The act of introducing a new series can increase the proportion of new notes in circulation. If the notes in circulation are mainly new there is a period before they start to wear. This means that banknote lifetime measurements need to be treated with caution – these measurements are likely to indicate banknote lifetimes that are unrepresentatively high for the new series.
Central banks can form a view of when the cash cycle has stablised by monitoring the banknote lifetimes over time. They will typically see a flat line for notelife as a function of time for the old series. When the new series is introduced, the note life measurement will jump up. Over time the note life will come down again and a new flat line will be established. If the new series is more durable the banknote lifetime will have stablished at a new higher note life average. Once a reasonably flat line of note life over time is established the cash cycle is stable enough to compare different series.
The final stage of obtaining a meaningful comparison between banknote lifetimes is to consider other factors that may have changed. Has the fitness standards changed? Have sorting machines been calibrated recently? Our experience of supporting DLR Analytics™ users reveals several situations where sorting machines were not appropriately calibrated and were destroying fit banknote. Any meaningful comparison of banknote series lifetimes must ensure that the sorting machines were treating the older and newer series in an equivalent way.
“users of DLR Analytics™ can benchmark their banknote lifetimes to global and regional averages using a standardised methodology”
Then there are external factors. The pandemic has disrupted many cash cycles. 76 central banks, representing 90% of the cash cycle volume, shared public data showing the volume of banknotes increased from 523bn in circulation to 563.5bn – an increase of 7.7% in 2020. In some countries cash usage remained at pre-pandemic levels. In other countries cash use declined as people went into lock-down and/or switched to more digital payment types. Great care is needed with pandemic data (and there are many instances where it isn’t appropriate to include pandemic data in your comparisons).
De La Rue’s claim that polymer banknotes last 2.5 times longer than paper banknotes on average comes from over 12,000 data points, all based on central bank data. In some cash-cycles the improvement is five or six times greater. The data was scrubbed to ensure comparisons were only made between cash cycles that had finished transitioning to their new post-polymer equilibrium. The data came from every region of the world and was collated prior to the pandemic cash cycle disruption. It is significant because the cash cycles and circulating environments of the banknotes making up this data set vary (e.g. different specification of paper notes, different specification of polymer notes, different humidity/temperatures, different ways of handling cash, different sorting machines and fitness levels etc). However, despite all these variables the data revealed a statistically significant increase when transitioning to polymer banknotes. Today users of DLR Analytics™ can benchmark their banknote lifetimes to global and regional averages using a standardised methodology.
