Rule-Based AI vs Machine Learning in Finance: Key Differences and Use Cases
Artificial Intelligence (AI) is transforming the financial sector, with two primary approaches being especially impactful: rule-based AI and machine learning (ML). While both aim to enhance efficiency and decision-making, they differ significantly in terms of how they operate, their strengths, challenges, and use cases.
Therefore, to better understand when either should be used, and in what way, for optimal results, we’re going to make a side-by-side comparison of these two approaches within the context of finance.
Understanding the fundamentals
Machine learning and rule-based AI models differ in a few fundamental ways; namely, how they make decisions, how they are developed and changed, and what they need to operate. Therefore, it’s essential to first understand how each model functions before we can compare and contrast the two and see how they fit into the financial world.
Rule-based AI
Rule-based AI operates on predefined "if-then" logic, where decisions are made based on a set of established rules crafted by human experts. These rules are often referred to as business rules and the software putting these rules into action is called a rules engine.
In practice, this means that a rule-based AI needs to be fully set up before it can start functioning. The developers or configuration team need to consider all outcomes of a given process and create the appropriate logic branches for the AI model to make decisions during every step.
These systems are also deterministic, meaning that they produce consistent outputs for given inputs. So, if one of the business rules states if “a customer is on a watchlist”, then “perform Enhanced Due Diligence”, regardless of all other factors, every customer that is on a watchlist will go through EDD.
This makes rule-based AI widely used and often preferred by regulators in processes where variability can create risk, such as in compliance. Therefore, they are particularly effective in environments where processes are well-understood and do not require adaptation to new data.
Rule engines often expose rule definitions through domain specific languages or no-code editors to let non-developers update logic quickly and keep the software up to date and compliant.
Machine learning (ML)
The main difference between rule-based AI and machine learning is that machine learning is a data-driven system that can adapt when retrained or updated. Depending on the type of ML used, the model can even learn from new data and self-adjust in order to accomplish its goal more efficiently.
ML algorithms can also identify patterns and make predictions or decisions based on historical data. This adaptability makes ML suitable for complex, data-rich environments where patterns are not easy to predict or code via an if/then system
For example, machine learning can contextualise customer behaviour during transaction monitoring. Let’s say that a person suddenly makes an overseas transaction. An if/then system might flag this as suspicious.
But a monitoring system that takes advantage of machine learning can identify that this customer has made transactions from this exact location in the past and that they’ve probably gone on a trip as opposed to being a victim of fraud.
Key differences between rule-based AI and machine learning
The biggest difference between rule-based and machine learning AI is that the former is predefined and relatively rigid while the latter is dynamic and adaptable. But this gives little insight into how the two models actually compare to each other. Therefore, here are the main diverging points between the two technologies.
Adaptability
Generally, machine learning AI models will be a lot more adaptable than rule-based technologies. This is because when presented with new data, the model can change dynamically to better fulfil its task. This is especially true when talking about self-supervised learning models.
Rule-based models on the other hand can be quite rigid. For example, a hard-coded rule-based model will have to have large sections of its code redone in order to accommodate for new changes. With that being said, there are ways to circumvent this issue.
For example, Atfinity’s AI-powered rule engine is built up in a no-code environment. Therefore, our configuration team can make changes many times faster than older systems.
Transparency
A major point of contention when it comes to ML models is that their decision-making process can be opaque, especially for complex models such as deep neural networks. This means that depending on the data used to train the model, it can practice certain biases that are difficult to detect and uproot.
In fields such as finance, this can lead to compliance issues as well as a loss of trust from the public. However, there are established approaches to make ML more interpretable, such as using intrinsically interpretable models (like decision trees or rule learners) or applying post-hoc explainability methods.
Rule-based models on the other hand are intrinsically transparent. Since every decision is made in accordance with explicit logic, as long as the system is properly set up, even very sensitive processes can be handled by a rule engine with minimal risk of non-compliance.
Maintenance
Hard-coded rule-based AI will typically require high maintenance costs, as the code must be rewritten or added to and then thoroughly tested to adapt to regulation changes. However, in-between major shifts, these models require very little upkeep as both the inputs and outputs are consistent.
Machine learning models likewise can demand a lot of resources to initially set up, due to training costs and the necessary infrastructure. However, ML also comes with significant maintenance efforts.
This includes monitoring for data drift, retraining, validating performance, testing for bias, and maintaining model governance procedures. This ongoing upkeep can be resource-intensive but is essential to keep ML compliant and effective.
Ease of implementation
When it comes to implementation, both models present challenges. For ML models, businesses must supply them with a large quantity of training data. This task can be rather difficult in areas such as finance since customer data can’t be used freely.
Rule-based models on the other hand require a lengthy setup if done from scratch. For banks with multiple branches in different countries and multiple products, setting up all the different business rules may require a lot of resources.
For this reason, many rely on technologies such as no-code platforms and enlist third-party vendors such as Atfinity instead of making the software themselves.
Use cases in finance
The use case of rule-powered AI are predictable tasks with predefined inputs and outputs. ML AI on the other hand, thrives in data-rich, complex environments. Therefore, the two models are used for drastically different processes in the finance world.
Rule-based AI applications
- Regulatory compliance and reporting: Ensures adherence to financial regulations by automating checks against predefined rules. For example, the appropriate level of Customer Due Diligence depending on the customer risk profile.
- Customer onboarding: Smart rule-based automations can be put in place to efficiently guide customers through the onboarding process, triggering different KYC/KYB procedures as needed, and allowing for the entire process to be fully digitised.
- Loan approval processes: Evaluates loan applications based on fixed criteria such as credit scores and income levels. With the right business rules in place, the entire loan origination can be automated in this manner, with only defined grey areas being handled by humans.
- Fraud detection (basic): Identifies fraudulent activities by flagging transactions that violate predefined rules, such as transactions exceeding a certain amount.
Machine learning applications
- Advanced fraud detection: ML models analyse transaction patterns to detect anomalies and potential fraud in real-time. This allows these models to handle much more complex fraud attempts as well as keep up with new approaches from threat actors.
- Credit scoring and risk assessment: Machine learning models can assess creditworthiness by analysing a wide range of data points, including non-traditional indicators, leading to more robust and accurate risk profiles.
- Algorithmic trading: ML models can analyse market data and execute trades at high speeds, identifying patterns that may not be obvious or even evident to human traders.
- Customer service automation: ML AI models allow for advanced chatbots, robo-advisors and virtual assistants for personalised customer interactions and support.
Advantages and limitations
Rule-based AI
Advantages:
- High transparency and explainability.
- Predictable and consistent outputs.
Limitations:
- Lacks independent adaptability to new or unforeseen scenarios.
- Maintenance can be labour-intensive due to manual rule updates if hard-coded.
Machine learning
Advantages:
- Capable of handling complex and unstructured data.
- Improves over time with exposure to more data.
Limitations:
- Decisions can be difficult to interpret ("black box" issue).
- Requires large volumes of quality data for effective training.
Rule learning: a hybrid approach
Lastly, I’d like to mention hybrid learning models, as it represents a powerful hybrid of rule-based AI and machine learning. It combines the interpretability of rule-based systems with the data-driven adaptability of machine learning.
Fundamentally, in rule learning, the system automatically generates “if-then” decision rules by analysing historical data, rather than relying on predefined logic from human experts. In other words, these systems can adapt to change and self-improve just like machine learning systems while also being transparent and explainable like rule-based systems.
However, like other forms of AI, rule learning does come with its own set of limitations and challenges. Namely, generated rules may become too specific and complex, hampering maintenance efforts, dataset preset biases can still occur, etc.
Conclusion
Both rule-based AI and machine learning offer valuable tools for the financial industry, each with its strengths and ideal applications. Rule-based AI is best suited for tasks requiring transparency and consistency, while machine learning excels in dynamic environments where adaptability and data-driven insights are paramount.
With Atfinity’s AI-powered Rule Engine, you get a solution that adapts quickly to new requirements, automates processes, and ensures full control and traceability. This means you not only maintain compliance but also gain speed and efficiency.
Next step: Discover how Atfinity can optimise your onboarding, compliance, and risk management processes. Book your demo today.
FAQ
What is the difference between rule-based and AI learning?
Rule-based AI functions in accordance with a set of rules written by humans, typically utilising an if/then logic, while machine learning includes learning from data during training/retraining.
What is rule learning in machine learning?
Rule learning in machine learning refers to a subfield of machine learning where the algorithm learns interpretable "if-then" rules from data. These rules help the model make decisions or predictions in a way that is more understandable to humans, essentially bridging the gap between traditional rule-based systems and black-box machine learning models.
What does black box mean in machine learning?
A black box machine learning model is any model where its decision-making isn’t transparent or understandable to humans, including the people who made the model. This is because these models are created by algorithms from vast amounts of data and humans have no insight into how exactly the model combines and uses different variables to make individual decisions.
