Insurance fraud has been around since the beginning of insurance organizations. These are varied and complex crimes that often go unnoticed and cost the insurance industry billions a year. Only in the U.S., the loss on fraudulent insurance claims last year reached $34 billion. Different types of insurance are prone to different crimes, however, in most cases, it manifests deliberate damage to the insured item or the purpose to obtain goods without paying. Detecting insurance fraud can be difficult since not every claim can be investigated thoroughly.
Traditional Analysis vs. Machine Learning Fraud Detection
Ideally, an insurance agent would have the capacity to investigate each case and conclude whether it is genuine or not. However, this process is not only time consuming, but costly. Sourcing and funding the skilled labor required to review each of the thousands of claims that are filed a day is simply unfeasible.
The most efficient strategy so far is a computerized system. However, the technologies available in the past would only allow for rudimentary analysis with limited accuracy. Even given the identification of a potentially fraudulent claim, an insurance agent would then need to engage in further investigation.
Red flags are known as fraudulent indicators in traditional computerized systems.
Traditional computerized systems searched for red flags, otherwise known as fraudulent indicators. These were preprogrammed, meaning the fraudulent claims had to fit into a particular template or else they would not be recognized. More advanced technologies have since been developed and allow for a more dynamic analysis of insurance claim data.
Machine Learning: A Big Step in Fraud Detection
Machine Learning is a part of Artificial Intelligence (AI). The idea behind Artificial Intelligence is to create a computerized system that can engage in complex analysis and not only replace human input but improve upon it. Machine Learning applies AI and “gives” systems the ability to learn and improve from experience, with no extra programming. In order to do this, systems analyze large, labeled data sets. AI may take over menial tasks and free human agents to do more complex analysis.
What are the benefits that Machine Learning can bring for the evaluation and resolution of insurance frauds?
- All claims suspected of fraud will be more accurately detected.
- Data is processed in very short periods of time.
- The system can demonstrate where connections can exist between various factors that may be imperceptible to human eyes.
- The continuous revision of this type of schemes and the application of variations in data analysis will allow anticipating the discovery of new fraud schemes.
As we have seen, fraud detection is a knowledge-intensive activity that allows classifying correctly whether the transaction or claim is legitimate or fraudulent.
The popular form of machine learning applied to the insurance industry is called deep anomaly detection. Anomaly detection works by analyzing normal, genuine claims made by the customer and forming a model of what a typical claim looks like. This model is then applied to large data sets.
Other aspects of artificial intelligence can also be built with this method of anomaly detection. One such development is predictive analytics, which can be used to design the program to further reduce the labor load. Predictive analytics work through a similar method of machine learning; however, the initial data set will be more specific. The program will build a model that not only searches for the features of typical or atypical claims but also analyzes the features of an atypical claim that it designated a fraudulent indicator.
Startups in The Fraud Detection Industry
To go deeper into the subject and see the technology that disruptive startups are applying to detect frauds, we have chatted with Steven Schwartz, VP, Strategy & Insurance Practice from Cytegic - Automated Cyber Risk Officer (ACRO). Cytegic leverages forward-looking, contextual and quantified global threat intelligence with internal, technologically validated assessment data to automatically identify an organization’s business assets, cyber risk and financial impact at any degree of granularity.
“The threat analysis we perform consists of two components, one of which is the client's human input, where they're answering questions and it ultimately correlates back to fifty-one security controls, which gives us the ability to dynamically map and translate a given clients risk through a variety of different security frameworks.”
Watch the full video where Steven Schwarts explains how Cytegic helps its clients to detect financial frauds and what technology lies behind this process:
The Technology Behind Fraud Detection
In the world of Data Science, there are a great number of other methodologies and algorithms that accurately leverage large amounts of user data. Each of them has proven to effectively perform in some particular scenarios and situations. Machine Learning experts divide them into two main scenarios depending on the available dataset:
Scenario 1: The dataset has a sufficient number of fraud examples.
In this case, classic machine learning or statistics-based techniques are applied to detect fraudulent attacks. This involves training a machine learning model or employing adequate algorithms to estimate transaction legitimacy. We’ll go through the most commonly used algorithms below.
Scenario 2: The dataset has no (or just a very little number of) fraud examples.
In the case that none of any previous information on fraudulent transactions was stored, the learning model is built based on examples of legitimate transactions.
Before jumping into the commonly used learning models applied to fraud detection, it’s to say that the majority has the same purpose of usage and differs only by its mathematical characteristics. Hence available data becomes a decisive factor when choosing appropriate learning models rather than an algorithm itself.
Random Forest or random decision forests. This algorithm ensembles decision trees and accurately analyzes missing data, noise, outliers and errors. It is fast on train and score and, as a consequence, has become one of the preferable among fraud detection professionals.
Artificial Neural Networks (ANN). This system simulates the function of the brain to perform tasks by learning from the past, extract rules and predict future activity based on the current situation. It can predict whether the transaction is fraudulent or not by classifying an input into predefined groups.
Support Vector Machines (SVMs). It’s an excellent prediction tool that can resolve a wide range of learning problems, such as handwritten digit recognition, classification of web pages and face detection. This method is capable of detecting fraudulent activity at the time of transaction.
K-Nearest Neighbors (KNN). Also known as the “lazy learning” algorithm due to its simplicity: instead of making calculations once the data is introduced, it just stores it for further classification. The KNN algorithm rests on feature similarity and its proximity. When the nearest neighbor is fraudulent, the transaction is classified as fraudulent and when the nearest neighbor is legal, it is classified as legal.
Logistic Regression is a prediction algorithm borrowed by machine learning from the fields of statistics. It's widely used for credit card fraud detection and credit scoring.
Finally, the goal of artificial intelligence in the field of insurance fraud is to make it easier for human agents to find and investigate fraudulent claims and transactions, rather than sifting through tons of claims in an exhausting and time-consuming way.
The use of advanced technology will increase the credibility of the insurer and, as a result, they would be able to establish better loyalty relationships with their clients.
Many insurance providers and organizations are limited due to the exploitation of insurance fraud and the expenses of human agents. The profits anticipated from the implementation of machine learning technologies will undoubtedly allow any organization to grow.
Our corporate partners are already using automated fraud detection solutions in their operations to prevent fraudulent behaviors such as false claims, account takeover, payment fraud, and phishing scams. Machine learning allows our partners to minimize their losses and increase their competitiveness.
At Plug and Play’s Insurtech accelerator we match large corporations with top-tier startups that are changing the future of the insurance industry. Join our platform today.