Why using Rules-Based technology should not be dismissed

Dr. Ben Larwood, Chief Architect at Facctum


Over recent years AI has grown hugely in popularity and is seen as the go-to answer for almost everything, particularly in the finance industry. According to recent statistics in the Economist Intelligence Unit adoption study, 54% of Financial Services organizations with 5,000+ employees have adopted AI. Moreover, the Deloitte Insights report found that 70% of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores and detect fraud. It clearly demonstrates that banks are convinced that AI is the way forward in order to achieve their business goals and aspirations.

While there is no question that AI comes with its benefits, many financial and business institutions are ignoring other avenues, such as utilising a rules-based technology. A rules-based technology approach is regarded highly by industry professionals as the most effective technology in seeking solutions, such as matching to identify financial criminals and sanctioned individuals and entities.

A different building blocks of data

The reputation of rules-based approaches has suffered over the years because many of them run on older, less capable technology, which puts them at risk of collapsing. As regulatory change has resulted in continuous new compliance demands, and as new risks have arisen, layers of rules have built up within these older solutions. Due to the design of these older solutions, the new rules that are added on top of existing ones add instability to the stack (like a Jenga puzzle). Within such a stack, modifying one rule without affecting the outer rules becomes increasingly difficult, before it all collapses.

However, a rules-based technology approach can allow firms to run multiple complex rules at once and still be free to make any further changes to the rules without the possibility of any unwanted consequences. As a result, this gives companies a huge amount of flexibility and agility to tackle new regulatory obligations head-on and any new emerging risks efficiently and quickly.

Dr. Ben Larwood

Explainability is key

One of the main reasons why financial institutions should chose to use a rules-based technology is because it is believed to be the best for fighting financial crime. With 62% of large banks reporting an increases in financial crime in 2022, according to PYMNTS, it has never been more important for firms to be using the right technology to battle this issue. Using a rules-based technology allows the implementation of many powerful rules in a connected way – without creating a complex code structure.

In order to achieve high quality financial crime risk management, it is important this this stems from decisions that are accessible and explainable. A downside of using AI-based technologies is they lack explainability, of why certain transactions or customers may be vulnerable to certain risks. As such, these outcomes are poorly understood and therefore can lead to compliance uncertainty, for example, missed payment cut-offs and client friction. Using a rules-based approach with fully deterministic decisioning and innovative data architecture, on the other hand, provides a clear risk decision that can be explained fully and quickly. This allows clients to be able to track every decision to a single or several rules. As a result, companies have a detailed understanding of the logic behind their decisions. This is especially vital as compliance teams must be able to support their decisions to regulators and customers.

This is where AI can fall short. It is often hard to explain and unravel the process behind the decisions AI has taken, for example explaining the decisions and the nature of certain algorithms and how the AI has used these for a particular case.

True technology innovation

So while a rules-based approach isn’t seen as the go-to trend at the moment in the regtech space, true innovation is about utilising technology to deliver the best possible results. A rules-based approach ultimately will always meet the demands of the use case much better and will deliver high-quality results that stand up to scrutiny.

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