Can you quantify the financial value of reading serial numbers for use in data analytics?

This article concentrates on the monetary value of data analytics.

Accountants measure value in monetary terms only, rather than achieving policy goals such as clean notes, and they will want evidence of whatever is claimed.  Before a project starts, this is not necessarily straight forward. 

At a high level one can use some broad brush numbers to scope the value.  Based on the Bank of England’s published numbers for 2017/18, they issued 1.104 billion banknotes.  The Bank spent £66 million producing 1.304 billion notes.  The simple calculation means their notes cost £50.61/1000.  Based on this, 

  • A one per cent reduction in new notes issued due to longer note life, saves the Bank £558,734 annually. 
  • If the Bank has 3.6 billion banknotes in circulation and 720 million banknotes in store (20%) for emergencies and work in progress, a one percent reduction in stock is worth £364,392 (one off saving).

We have listed where we think benefits would come below but reading the published literature on where companies have used data analytics, the changes achieved are almost universally significantly more than one percent.  We know the accountants may not buy that argument!

“Hard” Money BENEFITS

Good data will allow the straightforward development of cost/benefit analysis to justify changes to banknote specifications, security features or the cash cycle.  It enables the creation of predictions and validations based on actual data.  


  • Banknote stocks: It is normal for Central Banks to keep substantial stocks of banknotes.  This is partly because it is hard to anticipate the unexpected and partly because the ability to forecast banknote demand accurately is difficult.  The first requirement remains, the second should be significantly better with good data analytics.  This should allow the stock of banknotes to be reduced, particularly if the commercial delivery arrangement with the printer is also organised effectively.
  • Banknotes held in the supply chain: Separate from the overall challenge of forecasting total requirements, significant stocks need to be held at different points in the supply chain to ensure continuity of supply.  With good data, this can be optimised.  After processing, vault management software can be given data about the fitness status of banknotes in stock.  Central Banks can map which cash centres sorted the banknotes around the country, even linking them to the organisations that returned them for sorting.  If the Central Bank knows the fitness level of stocks and has at least some understanding of which organisations and cash centres have handled them, it should be possible to reduce stocks held in the supply chain.
  • Optimise the denominational structure: Understanding circulation velocity may reveal the need for changes to the denominational structure.  An efficiently configured structure will minimise the number of banknotes required.
  • Specifications: As a good picture of the life of banknotes becomes clear, particularly with all data being collected rather than just the primary reason for destruction, the Central Bank has the opportunity to set the banknote specification to meet the actual circulation conditions experienced by their banknotes.  This includes selecting security features that will perform in circulation.   The result should be savings from longer note life and better performance in circulation.


  • Clean notes: Today fitness acceptance thresholds are set on sorting equipment usually based on historic information from the cash centre and, sometimes, from secondary sources such as surveys.  Often the standards are subjectively decided.  In future it should be possible to set the desired clean note standard and manage the note life based on a deep understanding of the cash cycle.  It should be possible to model different scenarios and even to test them.  The sort of change this could enable is, for example, if a specific event is believed to increase note wear, the Central Bank can look at options to mitigate this.  It may choose to reduce circulation velocity by increasing the issue of new notes into an area (effectively increase the notes per head) by encouraging commercial banks to change the notes issued in ATMs. Conversely it may wish to carry out a local public education/teller campaign about how notes are handled.
  • Manage 3rd parties:  A good understanding about whether or how product from different substrate, print or components suppliers performs, offers the opportunity to manage those suppliers to achieve more consistent performance so that all notes achieve best in class performance.  This understanding answers the question about whether the banknotes of all printers or all substrate suppliers are performing equally in circulation.  When commercial 3rd parties such as commercial banks and cash in transit companies return notes for destruction, clarity on what they have returned is possible from the Central Bank’s sorters.  It may be that they are returning a higher level of fit notes than thought and that this can be managed better.
  • Reduce trial costs:  Running trials is now a straightforward every day task.  The costs and complexity will be minimal since the serial numbers used can be searched for and the results reviewed in the system without any other set up activities being needed.
  • Understanding quality fundamentals:  For the manufacturing professional, if they have the end of process data that shows where the note sits within the allowed specification tolerance, and if they have information from the sorters about how the banknote has performed in circulation, it will be possible to understand the impact the tolerance has on the notes performance.  As understanding grows, it may be possible to optimise the specification and its tolerances and enable more efficient manufacturing.  This is particularly important for banknotes where so many components and processes combine to create the finished article.


This article has focused solely on the sources of financial savings from data analytics.  It has not laid out the policy goals of an Issue Department and the direct benefit that data analytics can deliver to achieve these goals.  In addition, it has not discussed what has to be done to achieve these benefits or where they are derived from but this is touched on below at Annex A (where data is ideally collected from) and B (what each of those data collection points can show).  As a result, we recognise that this article only offers part of the benefits and none of the costs.  We would be happy to discuss this with Central Banks since we have no doubt that the benefits significantly outweigh the cost and work needed to achieve them.


Ideally serial numbers, and related detector data, would be captured at a number of points,

  • Single Note Examination Machine packet data with serial numbers entered as the notes are delivered into the Bank’s vault.
  • Serial numbers when notes are issued.
  • Whenever the notes are put through a sorter.
  • When the notes are checked immediately prior to destruction.

This can be combined with other data sources such as supply contracts, specifications, design changes, ATM usage, inflation rates, interest rates, significant economic or societal events etc.

The more data that you capture, the more complete the picture you have.  We have assumed that a Central Bank will capture data,

  • For each denomination
  • For each series in circulation   (by knowing which serial numbers are used on which series)
  • By print factory                          (by recording which serial numbers are used for each supplier)
  • By substrate supplier                (by recording which serial numbers are used for each supplier)
  • By sorting centre (or sorting machine)

The serial number and detector data captured provides the snapshot by date for each source.  This could be note by note but is assumed that the aggregated view is always the starting point.  The data captured shows the trend over time and “spikes” where different results stand out from the normal distribution.  It allows inter-connections and comparisons to be considered.  The historic data can be used for forward projections based on the past or supplying good quality input data to other models.

The scope of the possible benefits increases in line with the number of the data sources listed above where good data is collected.  With fewer collection points or incomplete data, then the overview provided by the data is reduced and the data becomes “sampling”, albeit with very high volumes, much higher than other types of sampling.  Even in these circumstances, the good, consistent quality of this “partial” data, combined with the very large number of notes analysed, will mean that the confidence in results from any modelling carried out will be significantly higher than modelling using other approaches. 

We believe that with increasingly capable detectors and wide spread networking of banknote handling equipment, the supply of circulation data with serial numbers to the Central Bank from the wider cash cycle is achievable at low cost.  There is considerable work to be done around the practicalities and costs of making this happen, but this future is already starting to be a reality.


NoteChain can monitor data about notes at a number of stages in a notes’ life.  Each data source increases the ability to understand note life and note performance.  The different data collection points are,

  • Printer data
    • Provides the serial numbers per packet at a date.  If Issue data is not being captured, printer data can be used as part of estimating the issue date (Assumptive Issue is the name we give to this).
    • If the printer data is supplied with full detector data, this can be used to gain deep insights into the quality performance of the banknotes.  If tolerances were tighter/looser, for example, what would be the impact?
    • Within the printer data, it is assumed that once printed, the note enters into stock.  Although NoteChain is not a vault management software, this overview of note movement out of the printer stock does allow insights into definitions of what might be regarded as “emergency stock” or “work in progress” stock.  Significant changes in stock holdings can also be investigated relative to the “normal” flows of notes out of stock.
  • Issue data
    • Records the date that each serial number is issued for the first time.  This provides the starting point from which to measure note life and changes in the banknote over time.
    • If the issue date is not recorded, the software has the facility to assume when the banknote was issued.  This “Assumptive Issue” data is calculated based on when the note was first sorted.
  • Circulation data
    • When a note is sorted, the serial number and detector data can be captured.  Based on this it is possible to understand
      • Which notes are circulating, which are not: a sense of the active and non-active populations
      • The velocity of circulation: how hard are notes working
      • How circulation velocity changes: we can estimate at what point notes cease to be fit (after how many sorts) and assess the circulation population in this context
      • Note life can be calculated with a high level of confidence based on the date of first sorting and the frequency of subsequent sorting.
      • How the fitness of the note changes over time
    • If circulation data can be linked to other criteria such as the specification, where the note was made, where it was issued, where the note has been seen, what economic, social, environmental and political events were happening, it is possible to build connections and hypothesis about the factors driving the note life and performance.
  • Destruction data
    • Records the serial numbers and detector data immediately prior to destruction.  This allows,
      • Note life to be calculated.  If the issue date is not known, then the “Assumptive Issue” calculation can be used.
      • NoteChain monitors the state of the note when it is destroyed as well as the primary cause that triggered the destruction.  This data allows the user to monitor the causes of destruction and its performance across all fitness criteria.  It is also provides the basis for accurate scenario planning to understand the impact of changes to the specification or note issue policy.
      • If circulation data has been captured, then, when it is linked to the destruction data, a fuller picture of the note’s life and performance can be seen.  
    • If notes are not seen prior to destruction, NoteChain allows the user to assume that the note has been destroyed, “Assumptive Destruction”.  As with “Assumptive Issue”, if a note that had been assumed to have been destroyed is sorted, the underlying assumptions are automatically amended.
    • If destruction data can be linked to other criteria such as the specification, where the note was made, where it was issued, where the note has been seen, what economic, social, environmental and political events were happening, it is possible to build connections and hypothesis about the factors driving the note life and performance.

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This post was written by Tracy Kennish-Ward

For more information contact:

Phillip Ball

Development Director
D: +44 (0) 1242 225 520
M: +44 (0) 7821 892 372

John Winchcombe

Currency Performance Consultant
D: +44 (0) 1242 225 520
M: +44 (0) 7701 357 027