Rules-based Vs. Advanced Analytics – Do You Have to Choose?

April 11, 2020

Artificial intelligence. Advanced, predictive analytics. Machine learning. These terms are heard with increasing frequency in the compliance space as solution providers seek new ways to evolve technology offerings to get ahead of compliance challenges. But does this mean that rules-based testing should be completely abandoned in favor of newer technology, or become their poor second cousin? In a nutshell: no. In real-life implementations, this is simply not practical. Here are a few reasons why.

Let’s use a fairly simple use case to explain. Say you’re implementing a fraud analytics solution for health insurance claims to examine the relationship between the policyholder and their dependents. A rules-based analytic may flag dependents classified as spouses that are outside an age range of 18–75 years old, or children over 25. This seems reasonable and will have relatively few false-positives.

Advanced analytics, on the other hand, may use an anomaly detection model to flag outliers without specifically writing a rule. This is likely to detect spouses outside the typical age range that policyholders in the data set predominantly have spouses in. Therefore, if the similar policyholders have spouses aged 19–55 then a 70-year-old spouse may be flagged as an outlier as the model learns from the population and segmentation of its policyholders. In some situations, the anomaly detection model will identify fraudulent cases that would go undetected by rules-based analytics.

Advanced analytics models have many other upsides. You can use other features in the data set, such as the age of the policyholder (or the age difference) in the model. That may provide more accurate outliers and will be reflected in the outlier score. For example, the larger the age difference the more likely the policy is an outlier.

Sometimes the best way to explain the advantages is to say that a good model can tell you what you do not know. There is tremendous value in that. This seems great, so why should you hold on to rules-based testing as well?

 

Why keep rules-based analytics

What happens if you don’t have enough data to train a model or most of the transactions for a customer segment are in fact breaches? The model won’t accurately detect or predict non-compliant behavior. Rules-based analytics are still useful in identifying non-compliant activity when you are clear what the compliance requirements are and they are not driven by behavior. An example of this is when a bank needs to report to the regulators when they accept or transmit funds about a certain amount.

So what’s the best approach? It may be best to fuse the two approaches using a scoring engine. Then, based on the score, a decision is made to block the activity (possibly in real-time), investigate or continue monitoring.

This allows both approaches to work together, which may be better than just using one or the other. Applying rules and advanced models together ensures that false-positives will be much lower. If you have a workflow and case management platform, then it is a good idea to feed the results of investigations back into the learning models for continuous improvement.

 

Adding advanced analytics to your program

There are many ways to advanced analytics to an organizations analytics programs. Data scientists use terms like machine learning, anomaly detection, fraud models, artificial intelligence and more, and knowing where to start can seem like a daunting task.

Here is a few terms that may be helpful to get started:

Anomaly Detection

Anomaly detection identifies items, events or behaviors that do not conform to an expected pattern or historical trend. When enhanced with context, such as segmentation, the ability to identify fraudulent transactions increases and in many cases, reduces the number of false-positives. Examples of what anomaly detection models may identify include:

  • Deviation in number, aggregate or frequency of transactions
  • Deviation in customer usage of products

 

Link Analysis

Link analysis produces insights about how different individuals and businesses are connected, the nature of the relationship (provider-retailer; employee-employer; friendship) and the financial transactions that occur between these parties. Examples of where link analysis connect people include:

  • Relationships through addresses and geographical proximity
  • Relationships through senders and receivers of transactions

 

Textual Analytics

Textual analytics is a natural language processing (NLP) technique that provides the capability to extract information from the text contained in unstructured data (information that either does not have a pre-defined format or is not organized in a pre-defined manner). Examples of areas where textual analytics is used include the following:

  • Reviewing customer satisfaction reviews and comparing how an organization’s product is performing against competitors
  • Reviewing police reports, newspaper articles, prosecutions, and classifieds to detect potential human trafficking cases

 

Machine Learning

Machine learning includes supervised, unsupervised, semi-supervised and reinforcement learning. In the case of supervised machine learning, the machine is first trained by providing labelled historical data that is and is not correlated with fraud cases.

After the training phase is complete, it is tested with data that was not seen during the training phase to determine whether the model is capable of genericizing and predicting if a newly exposed case is fraudulent or not.

Some examples of how machine learning can benefit financial institutions include:

  • Advanced transaction monitoring to glean patterns or identify potentially suspicious transactions
  • Learning from similar customers to identify unusual and fraudulent behavior in payment cards, loans, wires, transfers

There are varying views on whether analytics programs should include both rules and advanced analytics. In a straight shootout, behavioral and predictive analytics are sexier and promise better results but in the compliance space, simple rules are still relevant.

To learn more about the types of rules that can be implemented with Alessa, contact us.

 

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