In the mid-1990s, the digital revolution went into overdrive. The advent of household internet and the general public’s comprehension of the two-digit language that is binary code are just two things that changed the way we consume information forever.
The consequence, however, is an ever-increasing division between existing analogue data, like handwritten paper documents, and cloud-stored, aggregated and organised digital data.
Decades on, this division still presents a significant problem when dealing with trust deeds, as they are virtually always manually produced and filed away without a digitised version.
Deeds as data
Up until the 1950s, data was completely analogue. It came in the form of documents produced by typewriter or handwritten which were stored in physical filing systems. This is where trust deeds and the problem they present come in.
Trust deeds have been in existence since the 1200s in medieval England and are growing in number as a means of financially structuring a business. As a result, trust deeds can range from ancient legacy pieces of paper to PDF documents which are either partially or entirely scanned.
This creates pressure on businesses to manually investigate and link trust to the trustees to establish who they are actually doing business with. The time and human resources this requires are high overheads and often businesses forgo the process as it is difficult and expensive.
The nature of technology and the data they produce and store need to evolve in order to function in a modern world.
Just as analogue photographs become obsolete by the advent of digital cameras in 2003, and digital cameras were largely superseded by smartphones in 2010, so is the form and medium of the trust deed evolving.
While a lot of trusts created in the past decade are at least produced in PDF format, they are still not a part of any centralised database nor complete with extensive mapping and thorough identification of trustees. On top of this, they still require further digitisation so they can be integrated with other systems when necessary.
More so, many trusts produced in previous decades remain as the most recent version of the document and exist only in paper or scanned form.
In order to bring the analogue trust deed into the twenty-first century, the involvement of ‘machine learning’ and optical character recognition is required.
Optical character recognition
Optical Character Recognition (OCR) is the innovative software technology which allows analogue records such as scanned paper documents, PDFs or digital images like the JPEG to be captured and converted into data. OCR instantly converts information that would otherwise have had to be reviewed by a human and manually transferred into digital data that is recognisable, editable and searchable.
The ability to recognise characters, like letters of the English alphabet, in a static image and convert into digital data means the manual entry process is rendered obsolete. Businesses are able to optimise this technology to convert information previously held in their paper records.
For trust deeds, this is specifically critical.
The concept of machine learning is where a computer will gain a higher level of capability over time after being exposed to ever more data.
New digital technologies are continuing the progression of data conversion, and with it, the difficulties of the past are becoming simplified, manageable and streamlined.
In the process of converting analogue data into digital data, machine learning is a great opportunity to increase efficiency when dealing with trusts.
As more trust deeds are scanned by computerised technology using OCR, we can learn about what pieces of data are more important than others such as:
- Account name
- Trust name
- Trust type
* Requirement of consent by the trust beneficiary:
- The disclosure of their personal information (in the trust deed) to illion
- The collection and use by illion of that personal information
We can also begin to search and identify common patterns to be applied to the software’s ever-evolving algorithm.
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