Alejandro Martinez is CEO U.S. & Canada of SDG Group. Sharing insights on How Data & Analytics Generate Business Impact and Value.
The recent presentation of the latest version of the OpenAI language model, GPT-4, has raised a wave of expectations. Beyond the hype about the potential and impressive applications of artificial intelligence (AI) and natural language processing (NLP), there are real-world use cases for AI that are especially useful in the day-to-day operations of organizations such as fraud detection in the insurance and banking industries.
These industries have a great need to deal with fraud in a proactive and technologically sophisticated way, and they can find a great ally in AI and, more specifically, in solutions based on NLP. Relying on machine learning (ML) and NLP can provide better handling of legacy systems as well as siloed data sources.
NLP is the area of AI that focuses on the analysis of both spoken and written human language. It allows us to structure, interpret, exploit and incorporate a huge amount of language-related data into production processes. At a very high level, the actions we can carry out with NLP can be classified into three large blocks: word analysis, text analysis and text generation.
NLP And Banking: A Winning Combination For Fraud Detection
Banks operating in different countries must comply with multiple data privacy regulations (despite being from one bank, the data is subject to the regulatory requirements of the region or country in which it resides). In this situation (which is typical for many banks), detecting fraud by processing documentary data poses a significant challenge.
Solutions based on blockchain and multiparty secure computing (MPC) allow different decentralized data sources to work securely within a joint project. By adopting solutions like this, banks can have access to information such as know-your-customer (KYC) and customer due diligence (CDD) processes.
This is where NLP comes into play, which facilitates checks on watch lists and sanctions in near real time. Encrypted searches and document processing provide valuable insights for fraud detection and further investigations.
Doubling The Fraud Detection In The Insurance Industry With NLP
On a daily basis, the insurance industry faces a very high percentage of claims that are likely to be fraudulent. In the U.S., insurance fraud costs $309 billion a year; this equates to almost $1,000 for every single U.S. citizen.
Predictive solutions can be developed to estimate the propensity for fraud in the opening of claims in different lines of business—for example, auto or home insurance. By combining customer-provided information at the opening of a claim (a transcribed call or form) with the insurance agent’s report that includes impressions of the customer’s information (which corroborates Forrester’s argument that knowledge-based intelligence is still an essential capability for NLP), ML platforms oriented to NLP solutions can combine different predictive models and text mining to obtain the fraud risk associated with the claim in real time.
In short, these are two real examples of NLP’s applications in different sectors that expand the security focus of companies. Undoubtedly, neither of these applications will make headlines despite being an incredible and innovative breakthrough in the fight against fraud.
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