We’ve seen how AI and NLP can help with Documents, Contracts and ERP systems, but can it help detect fraud?
Natural Language Processing with the help of Machine Learning is the current win-win combination used to detect fraud and misinterpreted information. One of the biggest challenges of the free and anonymous internet that we have constant access to and basically drives our life is “Fraud”.
Fraud has many forms – ranging from fake news spread on social media and doctored images/videos to manipulate hateful ideas. Even instilling fear with numerous hoax calls about social security blockades; and how you can be deported if you do not pay the fine immediately over the call!
A long-standing open problem of fraud has always existed in major industries like banking, medical, and insurance. Unfortunately, it is becoming increasingly common. Not to mention the magnitude and novelty of fraud attacks. With the surge in online transactions all over the world, systems are more vulnerable than ever.
But it’s not all bad news! An exponential increase in computing power and advances in statistical modeling have ensured that we are a step ahead of the attackers. New preventive measures have been made to counter fraud in real-time. Machine Learning has become the go-to strategy for creating and updating various supervised/unsupervised fraud detection algorithms. This has been a step up from traditional rule-based approaches which were more time/effort consuming and led to higher rates of false positives.
The team at 1000ml has applied many different analytical approaches in fraud detection applications. Today we will see how the industry is starting to leverage Natural Language Processing along with ML algorithms to counter fraud in various use-cases.
For the insurance sector, identification of fraudulent claims are one of the key factors of success. Insurance firms, working with brokers, agents and investigators have also deployed various IoT technologies and have vast records of social media, geographical, and user-sentiment data. It’s not feasible for human agents to review all claims manually with so much background information verification. This is where natural language processing can step up the scale as well response time through, what we call, Entity Mining and Sentiment Analysis.
Entity mining is the way to process patterns and assertions from a huge block of unstructured, text-based big data. Analyzing insurance applications derives insights from similar claims by creating a knowledge database. This also helps insurance investigators to detect fraudulent cases using common keywords or descriptions of an accident across different geo-locations by multiple claimants, which is possibly a red alert of organized fraud. Processing genuine claims faster also lead to better customer experience.
Sentiment Analysis – This is a method of identifying and categorizing opinions present in a body of text to determine whether attitude towards a particular context is positive, negative, or neutral. It is crucial for companies to keep a sense of the market and their customers (both current and potential) to stay a step ahead in planning new policies and outreach efforts. Knowing what the industry is talking about would also help in identifying any potential scam issues and warn your customers in advance.
Similarly in the banking industry, the use-cases of NLP are implemented at scale. Emerj, an artificial intelligence market research firm stated that NLP-based products make up 28.1% of the total AI Approaches across various product offerings. The biggest share of these NLP products is for Information Retrieval or document search based products.
Search capabilities to quickly find crucial highlights amongst stacks of digital documents can be crucial for external compliance tests. This is also important for restricting fraudulent behavior against customers by wealth managers and financial advisors.
While NLP technology is still under continuous research to solve the challenges of ambiguity, co-reference, synonymity as well as syntactic language-based rules to ensure large scale accuracy; we do have a lot of available NLP technology to deploy in various fraud analytics use-cases and derive value from terabytes of unstructured text.
Here’s a quick visual summary of the key points we discussed on how NLP can assist in fraud analytics:
Apogee Suite of NLP and AI tools made by 1000ml has helped Small and Medium Businesses in several industries, large Enterprises and Government Ministries gain an understanding of the Intelligence that exists within their documents, contracts, and generally, any content.
Our toolset – Apogee, Zenith and Mensa work together to allow for:
Check out our next webinar dates below to find out how 1000ml’s tool works with your organization’s systems to create opportunities for Robotic Process Automation (RPA) and automatic, self-learning data pipelines.