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Innovation is about changing the status quo in a way that adds value to stakeholders.  To demonstrate adding value requires measurement. With this clear definition of innovation, lets apply it to regulation.



The scope of this challenge is huge as the regulatory fabric impacts every person and every organisation on this planet. To understand the scope, it is important to define the regulatory fabric.

The regulatory fabric covers national laws, statutes, regulators’ handbooks, quality standards, such as ISO, and their deployment through policies, procedures and controls. In essence, the regulatory fabric consists of rule-based knowledge, mostly contained in documents. At the core of the regulatory fabric is rule-based knowledge designed to safeguard society, people and organisations. 

By treating rule-based knowledge as a market in its own right then the size of the opportunity is huge and is largely untapped in terms of digital transformation. The regulatory fabric impacts every industry, some more than others. For example, in Financial Services alone there are over 50,000 regulatory updates a year.



This regulatory challenge cannot be solved by machine learning as we cannot empower machines to upend and change the rule-based knowledge within the regulatory fabric. The reason for stating the obvious is that the market hype around machine learning has led to considerable misunderstanding.

This is quite a fundamental change to the current market, which is to lead with AI regardless of context. Clearly, rule-based knowledge found within the regulatory fabric needs to be controlled by humans. The outputs in the form of a new type of data are more suitable for machine learning to help identify inefficiencies, blockages, patterns and learnings at the edge. 

The very nature of the regulatory fabric is that it contains the rules by which society, people and organisations are governed. Though these rules are contained in documents, they are not easy to understand as the logic is often poorly structured and fragmented, such as through referencing to other regulation. This is not surprising as the tools for documenting the regulatory fabric are quite inferior for the task.

As a consequence, it is reasonable to conclude that automation simply cannot unravel the rules contained within these documents and synthesise the results into algorithmic cohesion. In other words, this is a synthesis challenge best handled by people as its too messy for automation to succeed.



It is important to understand the status quo. Every organisation is expected to synthesise regulations and other sources of complex knowledge into a written form known as standard operating procedures. This problem is further compounded as workers are expected to find and synthesise these standard operating procedures in context to their needs, regardless to their capacity or capability.

This is best illustrated by a recent review in the UK. After the Grenfell Tower fire, an independent review of building regulations was conducted. This included mapping the current regulations in the way they have been applied in practice to determine the strengths and weaknesses. This review was led by Dame Judith Hackitt. Her key finding from this review was best encapsulated by the following:


“it has become clear that the whole system of regulation, covering what is written down and the way in which it is enacted in practice, is not fit for purpose ...”


To view the status quo of the regulatory fabric in terms of a market, it would be classified as heavily fragmented, inefficient and lacking any cohesive symmetry.  The negative impact upon efficiency and effectiveness is profound. 



The regulatory fabric is complex by nature. Just the simple nature of cross-referencing to other regulations makes it more complicated. This complexity increases as the regulation links with other primary sources of knowledge such as statutes, directives, guidelines, standards and procedures, for example ISO, regulatory bodies and their handbooks and case law. Further complexity is amplified when extended to international regulation such as trade, tariffs, treaties and sanctions.

Knowledge rules found in the regulatory fabric are in essence decision-trees or flowcharts containing choices with options, that drive different pathways, each leading to distinctive outcomes.  The nature of a regulation is that under certain conditions there is a need to transverse to a different regulation. The more regulatory rules and the more cross-referencing to other regulatory decision-trees, the greater the permutation complexity and its impact upon the way decisions flow and their associated risks.

Regulatory rules are primarily deterministic. This means that when the precise set of conditions are met then the outcome, as defined by law or policy is predetermined. Just by the sheer density of regulatory content there are areas where there is ambiguity. In these cases, this starts to define the boundary for the codifiable rule-based knowledge and where ambiguity begins. In other words, the rule-based outcome can conclude that a handover to a human specialist is required.



The vast majority of rule-based knowledge representing the regulatory fabric is found in documents containing numerous pages of content.  The tools used for producing rule-based knowledge in document form are typically inferior and are not fit for purpose for the following reasons:  

Weak Algorithms

Specifying both the logic and the narrative in terms of clear choices, pathways and outcomes is highly problematic leading to poor structure and fragmentation. In other words, the documented rule-based knowledge often results with weak algorithms. 

Too Complicated

The more choices within the rules the greater the number of permutations of pathways and outcomes. This geometric growth can easily lead to permutation complexity that negates the ability for most people to remember the rules accurately, even after training.

Requires Individual Synthesis

The documents containing the rule-based knowledge typically tends to be written in a way that requires the user to synthesise the content in context to their needs. This is both time consuming and more prone to misinterpretation. Often the more pages that need to be read, the longer it takes to synthesise the contextual knowledge required. 

Poor Transparency and Traceability

The user decision journey through the selected pathways within a document remains invisible creating a black box of the experience, which compromises governance, risk and compliance.  In other words, the way rule-based documents are used are hidden from plain sight.

Lacks Measurable Data

There is no data captured of the user decision journey through rule-based knowledge thus preventing the ability to measure, thus compromising management, supervision, learnings and improvement cycles.   



There are now the tools to transcend rule-based knowledge from the intangible characteristics of content into tangible, working and measurable knowledge assets. This involves a circulatory process consisting of create, share, measure and evolve. 

By using chatbots there is now the ability to simplify and streamline rule-based knowledge found within the regulatory fabric, complemented by an audit trail for transparency, traceability and new forms of measurements. The means for achieving this transition is a shift from the monologue of document-based content to the dialogue of rule-based knowledge.  

This requires a different way of thinking. The innovation requires the solution to be knowledge-driven and not data-driven. Being knowledge-driven fundamentally challenges the convention of software development that is based on it being data driven over the past 60 years. 

The nearest correlation to this different way of thinking can be found through Stephen Wolfram’s publication New Kind of Science whereby it was discovered nature uses simple programs to handle complexity. By implication nature does not use databases it uses simple programs. Wolfram’s insights concluded complicated programs cannot cope with the complexity of constant change, whereas simple programs can. This underlying revelation is counter intuitive to the traditional thinking of technologists. 

Being knowledge-driven does require handling multi-level knowledge, whereby each level supports multi-criteria decision-making that drives different pathways and outcomes. The user decision journey may involve just one level or many levels as it is dependent upon context.



This innovative approach is a paradigm shift from treating the regulatory fabric as an intangible to managing it as a tangible, working and measurable knowledge asset. The new value created is profound:   

Balance Sheet

Reduce provisions on balance sheet due to non-compliance.


Provide better protection of the brand, balance sheet and people.


Strengthen governance, risk and compliance through transparency and traceability.


Gain a 10x plus improvement in productivity for using the knowledge within the regulatory fabric compared to documents, the current status quo.


Materially increase self-sufficiency with citizens, customers and partners.


Instant upskilling for agility and adaptability with lower switching costs.


Sense early and respond quickly (new metrics).

Intellectual Property

Increase through knowledge assets and new forms of data insights.


Option to generate new forms of revenues using the knowledge assets.



By treating the regulatory fabric foremost as a rule-based knowledge challenge, the innovation has the potential to change the status quo in a way that adds value to stakeholders. 


“Rules are easier to arbitrage than principles. The more prescriptive and the more precise the code, the less people will think about what they are doing. If it’s principles-based and less prescriptive then market participants will have to think about whether their actions are consistent with the principles of the Code.”

Guy Debelle, Deputy Governor of the Reserve Bank of Australia. Opening remarks at the launch of the FX Global Code, FX Code Press Conference, London. (May 2017)


But like all innovation the dependency is upon early adopters gaining a momentum so that the paradigm shift becomes unstoppable.




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Freddie McMahon

Freddie McMahon

Director Strategy and Innovation

DF2020 Ltd

Member since

04 Aug 2017



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