There’s a lot of buzz surrounding machine learning and its potential. Businesses across industries are looking to AI to optimize their operations, improve customer experience, and streamline product development. However, many of them still don’t know if this version of AI is the right choice for their business.
Rules-Based Solutions Dominate
It may come as a surprise to many, but many anti-money laundering (AML), know your customer, credit rating, and fraud prevention processes are run by powerful business rules engines.
Perhaps not as flashy as machine learning, rules-based solutions have a proven track record, which is why organizations use them to generate data-driven decisions.
Rules-based solutions represent knowledge in conditional if-then statements, (known as business rules) for a particular decision.
For instance, in a rules-based lending solution, a client’s personal and financial data can be assessed against a clearly defined set of criteria, and if the data meets all the requirements, then a loan is offered.
What is a Business Rules Engine
A business rules engine is a powerful application that deploys business rules. Think of business rules as if-then statements that influence the behavior of an organization.
For instance, an online retailer could use the following business rule:
If a new customer spends over $150 on their first order, then they get a 10% discount with free shipping.
These simple but extremely powerful conditional statements help organizations of all sizes achieve their goals.
Rules-based solutions are powerful because they are built with the help of subject matter specialists. This is why rules-based solutions are sometimes referred to as expert systems.
Domain experts lay out all the necessary steps to reach a decision, including predicting outcomes.
Introducing Machine Learning
Machine learning relies on large datasets to make correct decisions.
An AI algorithm examines the dataset and uncovers relationships within that data, enabling it to make inferences and predict outcomes.
What makes machine learning so enticing is that after a while decisions are made without the direct influence of human programmers. Instead, the machine learning model is able to train itself as it studies the data.
However, without high-quality data machine learning is unable to function effectively. In other words, machine learning is only as good as its data.
The Black Box Problem
Machine learning systems suffer from the black box problem. This means that while the inputs are clear, over time the internal decision-making of the algorithms becomes hard for us to understand.
For most businesses, this presents a very serious problem since they need to have a clear view of all of their processes.
However, the opaque nature of machine learning algorithms can have legal ramifications as well.
The Right to Explanation
Most laws that regulate the finance industry include the right to explanation. Individuals are allowed to ask for an explanation regarding decisions that were produced by algorithms and that affect them financially or legally.
Someone who is denied a loan has the right to ask for an explanation from the lender. For instance, an explanation could be that the applicant failed to make all of their car payments on time.
Using business rules engines, companies have a clear audit trail and can drill down to the exact reason a certain decision was made. This also comes in handy when a user needs to update a rule in real-time in order to make changing market conditions.
The same cannot be said of machine learning solutions.
Business Rules Engines are the Right Choice
Both machine learning and rules-based solutions are designed to automate business operations and improve customer experience.
However, businesses that require accuracy, transparency, speed, and reliability have only one true option: business rules engines.