Regex vs. AI for Commercial Insurance

Artificial Intelligence - May 15 2019

For data-complex and risk-adverse industries like insurance, being able to access data locked away in file stores and data lakes is critical for effective decision making. Data collection and analysis is at the heart of insurance business processes. Real-time data extraction enables insurers to automate and standardize time-consuming labor-intensive processes. With insurers being under pressure to deliver a better customer experience, they are being forced to examine existing processes and adopt new methods of doing business.

With the adoption of technology solutions, insurers can re-imagine front-office and back-office processes. But given the plethora of technology available, it can be difficult to understand what it is and how to use it.

With all the hype around the new kid on the block  Artificial Intelligence – understanding how it compares with technologies like Robotic Process Automation (RPA) and Regular Expression and the potential use cases can be overwhelming. 

There are many different types of Artificial Intelligence – and not all AI is created equal.

Taking a Closer Look

Regular Expression – better known as Regex – is pattern matching. As regular expressions are regular, it means they are used to match well-defined patterns. If you need a kind of fuzzy matching, regular expressions are not valid for this business case. For a “find similar text or intent" algorithm, forget about regular expressions and instead use artificial intelligence specifically natural language processing.

Regular expression or pattern matching tools identify words and phrases with common patterns.  A typical use case for Regex is find and replace, however as it is not able to understand syntax, grammar, or context, Regex solutions tend to operate on a very small well-defined problem set. Basically, Regex solutions are using pattern matching to merely identify similarities and differences in patterns of words and phrases. They are not able to identify words and phrases taking into consideration syntax, grammar and ultimately context.

On the other hand, AI solutions using Natural Language Processing (NLP) and Machine Learning (ML) can conduct an intelligent analysis of unstructured text. NLP understands the language as it allows computers to undertake linguistic analysis and in practical terms “read” a document just like a human – only hundreds of times faster and with greater accuracy. AI solutions enable commercial insurance brokers and carriers to unlock trapped knowledge in unstructured insurance documents like submissions, applications, policies, quotes, and binders faster and with greater accuracy than a human.

For instance, NLP performs functions such as clustering, semantic analysis, classification, topic analysis, keyword/key-phrase extraction, and summarization. In addition, extracting data, recognizing domain specific named entities, identifying entity relationships and metadata fields are also possible in NLP.

NLP algorithms rely on multiple methodologies to understand the ambiguities of human language, including part-of-speech tagging, tokenization, vectorization, disambiguation, domain-specific entity and relations extraction, semantic analysis, computational statistics as well as natural language understanding.

The value of NLP based entity extraction lies in its ability to assign meaning to the words or phrases it extracts based on context. This moves beyond simple regular expression methodologies, pattern recognition or pattern matching.  For example, with NLP it can understand the context, so we do not mistake Orange, California (a city) with an orange (a piece of fruit) or orange the color.  

Named Entity Recognition

Some NLP solutions can also categorize insurance-specific named entities such as limits, premiums, deductibles, types of coverage, exclusions, endorsements, territories of coverage, outstanding conditions, statements of value, and loss run reports, instantly during the data extraction process. This capability coupled with a business rules engine can be applied to automate high volume, repetitive processes such as policy checking, quote comparison, submission triage and others, enabling skilled knowledge workers to make rapid fact-based business decisions.

Conclusion

Is there a use case for Regex and AI? Yes, there are specific use cases for both technologies and you will more than likely find Regex being used in more traditional word processing solutions. However, given AI solutions get better over time through supervised and unsupervised machine learning, artificial intelligence has leapfrogged the capabilities of Regular Expression solutions by leaps and bounds.

For data-rich industries like commercial insurance, AI solutions using natural language processing and machine learning can extract, interpret and analyze unstructured data quickly and accurately providing insurers and brokers with access to more pertinent data than ever before. Using this data, insurers can assess risk and price better enabling them to compete more effectively. Likewise, brokers can provide their customers with contract certainty and bespoke service tailored specific to their needs.

 

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