When Rules Are Cool – Why Rules-Based Tech is Better for Matching Use Cases

Rules-based technology for Counter-financing of terrorism (CFT)

Today, everyone is talking about AI – it seems that no matter what the use case is, AI is supposed to be the answer. So, many people are surprised that a cutting-edge RiskTech company like Facctum uses a rules-based technology instead of AI within its core.

Facctum uses rules-based technology because it is the most effective technology for the use case our solution is seeking to address – matching to identify financial criminals and sanctioned individuals and entities. We are doing new and exciting things with this approach and have overcome the disadvantages of established “old-fashioned” rules-based solutions.

A different data architecture

Rules-based approaches have earned a poor reputation because many rules-based solutions that run on older technology are in danger of collapsing under their own weight. These solutions have been around for many years, and layers of rules have built up as regulatory change has resulted in continuous new compliance demands, and as new risks have emerged. However, because of the way these older solutions are constructed, when new rules were added on top of old ones, the overall stack usually becomes unstable. Like a Jenga puzzle, it rapidly becomes challenging to modify a rule within the stack without a serious risk of the whole thing comes crashing down.

Facctum has solved this challenge by creating a data architecture for its financial crime software solution that lets firms have many complex rules in operation simultaneously while enabling firms to make changes to those rules without the fear of unintended consequences. This gives firms unprecedented flexibility and agility to address new regulatory obligations and emerging risks quickly and easily.

The importance of explainability

Facctum chose to build its financial crime solution using rules-based technology because we believe it is the best technology for the job. A data architecture that enables the implementation of many powerful rules in a connected way – without creating code complexity – leads to better outcomes. In fact, our rules-based approach delivers risk detection outcomes faster than an AI approach.

Good financial crime risk management derives from decisions that are accessible and explainable.  In this context, AI-based technologies struggle to provide an adequate explanation of why a customer or transaction might pose a compliance risk.  Poorly understood outcomes can cause compliance uncertainty, missed payment cut-offs and client friction. In contrast, using a rules-based approach with fully deterministic decisioning in an innovative data architecture provides clear risk decisions that are fully and quickly explainable. The key here is that the deterministic technology approach enables Facctum clients to trace back each decision to a rule or several rules. Firms have detailed transparency into the logic behind every decision that is taken. This is of critical importance for compliance teams today, who need to be able to support their decisions to regulators and customers.

In contrast, with AI, it can sometimes be quite difficult to unwind the thinking behind the decisions that the machine has taken. To explain AI decision-making to a customer or a regulator can prove to be even more difficult, as it can involve discussing the nature of the algorithms used and how the AI has applied those algorithms in an individual case.

Translating into Technology

Today, firms can employ Facctum rules-based data architecture to screen customers or transactions for a full spectrum of risks on an agile, accurate and highly scalable platform. Firms can align complex list management policies to operational workflow, and build, model and deploy matching and scoring rules for their specific risk profile.

True innovation is about using technology to deliver better outcomes for a specific use case. With Facctum, the innovation is in how the rules-based data architecture is constructed.

So, although “rules-based” does not sound as sexy as AI, the reality is that it meets the demands of the use case much better and delivers impressive results that stand up to scrutiny.