​Data analytics is a new technology that is gaining acceptance and growing to fulfil its potential in the world of banknotes and the cash cycle. Central Banks have always collected and used data to model, forecast and manage their banknote needs. The serial number data of banknotes can used to answer well questions about topics such as cash cycle velocity and flows, cash performance in circulation, quality levels, specification performance and optimization, supplier performance, etc. The ability to access information on banknote performance and ​the cash cycle based on high quality, high volume, detailed data down to the level of individual banknotes captured from the cash cycle is a new development allowing the full capability of data analytics. The use of Serial Number Reading (SNR) linked to sensor data. This is possible now due to the widespread use of optical cameras as part of the core technology platform in a very wide range of banknote handling equipment.

​Central banks have reacted differently to these pressures. The “controlled" style central banks (e.g. Germany, Belgium and several eastern European countries) have reinforced their dominant role in the cycle by keeping processes in-house and negating the need for high-volume processing systems at commercial banks and Cash in Transit companies (CITs). At the other end of the spectrum, the “minimalist" central banks have withdrawn from the daily cash cycle entirely by delegating to the commercial sector. The “utility" and “hybrid" models sit in between these two poles, with varying degrees of process outsourcing, automation and digitalization.