Rules-based fraud detection and risk management models are ubiquitous in financial institutions and fintechs. However, the utilization of artificial intelligence (AI), including machine learning (ML), is still not commonplace. 

The need to incorporate tools beyond rules-based fraud detection is here, as fraudsters have become more sophisticated in their tactics.

  • 70% of financial institutions surveyed last year lost over $500k due to fraud. 
  • In the same survey, 37% of fintechs and 31% of regional banks report losing between $1 million  – $10 million to fraud.

The reliability and sturdiness of rules offer many solid benefits, but the lack of agility results in the inability to keep up with the fast-moving fraud schemes. To address this limitation, financial firms can boost their defenses and augment rules-based models by incorporating AI/ML fraud detection.

Rules-based Models in Financial Institutions and Fintechs

Financial service companies have used rules-based fraud detection systems for many years. It has been a reliable and cost-effective method to detect and prevent fraud. The rules are created based on an expert’s knowledge and can process large amounts of data and consistently produce intended results. Plus, analysts can monitor activities and, if they spot an error, can often quickly adjust to rectify the situation. Alternatively, they can spot gaps and add new rules to address them.

Fraud rules are based on detecting and then implementing rules designed to address previously identified fraud patterns. However, banks and fintechs are challenged in creating and maintaining effective rules because while the fraud patterns may be similar, they are rarely identical. Rules are relatively short-lived in effectiveness, as the perpetrators will tweak their attack vectors to get around the new rules or attack another organization. As every practitioner knows, rules are hard to build and expensive to maintain. A challenge for many organizations is ensuring that rules are continuously updated to catch the never-ending stream of bad actors without blocking good customers from transacting with your organization.

Strengths of Rules-based Models

In specific instances, rules-based systems can be superior to more technologically advanced AI/ML systems. The advantages of a robust rules-based system include:

  • Transparency and Explainability – One of the significant benefits of this approach is the simplicity of understanding why a transaction was flagged. Rules-based fraud systems provide easier tracing and explainability than AI/ML-based systems. This benefit is especially important when interacting with regulators in explaining why an action occurred. The rules are simple, and staff can quickly debug why a rule was invoked.
  • Fast Implementation – Rules-based systems offer the advantage of a relatively fast, cost-effective, and tailored implementation. It typically requires fewer resources, data sets, and testing before implementation than AI/ML systems. In addition, they can more easily be tailored to the company’s business processes and fraud prevention strategies. The ability to align more easily with current workflows reduces the implementation time and resources needed. 
  • Flexibility to Modify – This simplicity also benefits the adaptability of the rules. While caution must be in place, typically, changes to rules can be done relatively quickly and without wide-ranging ramifications.
  • Reasonable Accuracy – By their nature, rule-based systems will operate precisely as they have been programmed. Combined with the transparency provided, it offers more precise fraud detection and results in less time spent analyzing and faster response.  

Limitations of Rules-based Models

Many attributes that are advantageous for using rules-based systems are also weaknesses when new fraudulent activity and patterns emerge. Below are the limits of rules-based models in fraud detection:

  • Lack of Adaptability: Novel fraud schemes not adhering to previously observed behaviors and actions will go undetected by rules-based systems. This is the critical weakness of rules-based approaches as increasingly sophisticated criminals keep changing and advancing their strategies. Rules-based approaches are geared toward less sophisticated and older fraud schemes. Financial service companies face significant risk exposure by only deploying rules-based models. Plus, they often go undetected until after substantial losses have been incurred. 
  • False Positives: Non-fraudulent transactions that are even slightly out of the norm can falsely be flagged as fraudulent.  As a result, rules-based systems are susceptible to producing false positives and subsequent time-consuming investigations by staff. For reference, a global study found that 95% of rules-based alerts were closed as “false positives.” Worse, these false positives can result in dissatisfied retail customers and even impair commercial prospects. 
  • Difficult to Scale: While volume increases in a data set can easily be consumed by rules-based systems, it creates challenges and additional costs for businesses to adapt to changes in datasets and additional rules. The personnel-intensive nature of the system is time-consuming and prone to errors as the number of rules increases. As the number of rules grows, the difficulties in maintaining and ensuring consistency can grow exponentially. Because of the cumbersomeness of maintaining, increasing levels of staff are needed to create and review new rules.
  • Limited Scope – Rule-based systems are exact and often contain “blind spots” – gaps in the rules that fraudsters can exploit. With set thresholds and rigid detection checks, they offer no prioritization or probability of risk output. These factors limit the scope of protection to only the confines of their original coding. Helps FIs and Fintechs Transform Fraud Prevention

Financial institutions and fintechs can generate significant synergies for improved fraud detection and prevention by incorporating AI/ML into rules-based environments. With a multi-layered approach, your organization can gain the benefits of both models – with a more robust, accurate, and agile solution.

At, we offer an all-in-one customizable tool that constantly updates with the latest fraud information. Our service is easily scalable as your business grows. Plus, we have a global reach to help clients detect, visualize, and investigate fraud from over 185 countries. 

Learn more about hybrid rules-based and AI fraud prevention in our eBook: Outsmart Financial Fraud with AI and Rules-Based Fraud Detection. And, schedule time today with our experts for a free demonstration of our AI/ML fraud detection and prevention services.