Data Science: The key to modern Corporate Governance

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Kyriakos ChristodoulidesBy Dr. Kyriakos Christodoulides (LinkedIn), Managing Founder of Novel Intelligence

In today’s volatile economic landscape, where supply chains are strained and inflation hinders financial performance, the stakes for corporate reputation and regulatory compliance have never been higher. At the same time, merely introducing isolated policies and procedures is increasingly being recognised as insufficient for improving, or even signalling active interest to ensuring compliance.

For instance, the UK’s Economic and Corporate Transparency Act (ECCTA) mandates organisations to demonstrate proactive fraud detection capabilities. As businesses confront an increasingly complex labyrinth of evolving risks and regulations, leveraging the power of data science emerges as a critical strategy for enhancing compliance and managing corporate risk.

This article explains the notion of data science and how it serves risk management and compliance.

The Basics: What’s Data science?

Data-science is a relatively new emerging discipline that utilises some of the most successful mathematical and statistical methods from every discipline that has ever existed. These quantitative methods enable us to see ‘how normal behaviour looks like’ and also detect behaviour that’s different to normal i.e opportunities and risk.

Data science expresses statistical and mathematical methods as sequential iterative processes – algorithms – and deploys them as software that helps convert procurement data to information that can improve your risk management and compliance efforts.

Data communication is a huge aspect of data science, and it includes data-visualisations and demands deep business understanding. Finally, as data-science is inspired by the scientific method, it is an iterative process that achieves continuous improvements.

In order to claim you are doing data-science and not a technical exercise, ALL of the above ingredients are absolutely necessary WHILE delivering VALUE. For clarity:

  • If you have not set a quantifiable goal, you are not doing data science
  • If you are not aligning data projects to your business goals, i.e being strategic, you are not doing data-science
  • If you are not leveraging subject matter expertise regarding the people, processes and technology of an organisation, you are not doing data science
  • If you are not continuously improving your data gains and procurement processes, you are not doing data-science

So, how is data-science directly related to enhanced prevention hence compliance?

Data Science and Prevention

The Fraud Triangle (FT) is sociology’s primordial framework for explaining the reasons behind an individual’s decision to commit fraud. The three FT components are:

  • Opportunity: These are the circumstances that allow fraud to occur, like for example, weak internal controls and unethical management.
  • Pressure: This is essentially the fraudster’s incentive and it could be related to wanting more money, hitting KPIs etc.
  • Rationalisation: This is the perpetrator’s justification. Common rationalisations are “upper management is doing it too”, unfair treatment or fear of losing one’s job etc.

Red flags regarding rationalisation and pressure do exist. However, opportunity is the only FT component an organisation can have an actual grip on. By supercharging internal controls, data science diminishes opportunity through efficient detection that in turn further discourages potential perpetrators.

Conclusion

In conclusion, as compliance and risk management increasingly converge, data science emerges as a vital tool in modern corporate governance. As businesses increasingly recognize the value of data-driven decision-making, investing in data science capabilities becomes not just a strategic imperative, but a fundamental driver of long-term success and resilience.