Fraudulent claims were, conventionally, detected by claims adjusters or fraud investigators who cross-reference limited fraud indicators and trends to identify claims with a plausible fraudulent activity that needs to be further investigated.

Though insurance companies deal with and maintain a ton of data including customer information, vendor information, and claim history, it is impossible for a human to go through such vast data, process it, and infer actionable insights in a short span of time and that is exactly where we leverage the power of artificial intelligence and machine learning to assist us in performing such complex tasks.

“Artificial intelligence is helping insurers save millions of dollars by preventing leakage due to fraudulent claims”

What sets apart Artificial Intelligence is its capability to process massive data sets, the use of advanced analytics to transform the data into meaningful insights, and the ability of these intelligent machines to learn and adapt on its own from user experience, data, and conditioning.

The setting up of these technologies require massive amounts of data to be fed into the system and therefore the first choice for its initial application phase for most insurers who have adopted this technology have been in personal lines insurance where the required amount of data is available due to it being mass-marketed insurance.

The two most common classification of AIs application in fraud management is the detection of anomalies and predictive analysis:

Detection of anomalies

Algorithms used in artificial intelligence can analyze large amounts of data, faster than the blink of an eye, and detect patterns.

These systems leverage on the data available from millions of past claims over the years, to learn the nature of a claims cycle and compare it with data available of the reported cases of fraudulent claims to identify trends and relations.

Upon deployment, whenever a claim deviates from the usual pattern, it is immediately notified to a claims investigator.

The beauty of machine learning that is incorporated into this technology is that it further learns based on the actions taken by the investigators and evolves rapidly based on the changing requirements of the society.

Predictive analysis

Predictive analysis is an artificial intelligence technology that is fast evolving and has started gaining popularity in the insurance domain due to its application in fraud prevention.

The development and implementation of this technology need lots and lots of data to train the software on fraud prevention through machine learning models, and this data is not a scarce entity for most insurers that have been storing claims data and even maintaining huge databases of fraudulent claims.

In the application of this technology, subject matter experts train the software by feeding it thousands of case scenarios of detected frauds and train the software to detect it. Once the application has been exposed to a considerable amount of data and has been trained to detect fraud, the application starts to recognize and discern fraudulent activities on its own.

The sophistication of this technology is increased as we train the intelligence using varied data sources through acquiring data from third parties, geographic data mapping, social media, and text mining.

Prescriptive analytics is often used in tandem with predictive analytics to provide users with meaningful insights and suggestions based on the predictive correlations received through predictive analytics. The same sets of data could be used to train both these models for effective implementation.

It is astonishing how these machines could analyze massive loads of information from multiple sources and draw on insights and relations from hundreds of thousands of claims data within moments to predict fraudulent activities.

Examples of applications of predictive analytics in fraud management:

As artificial intelligence draws upon multiple data sources, it could run through fraud databases to flag claims by users that have previously been reported or suspected of fraud to trigger a detailed investigation.

Claims databases have extensive information about the cost of repairs, replacements, or procedures and could detect cases where the amount claimed for a particular head is higher than the usual range. Further, as the cost of these products and services change in the industry, the application naturally adapts and modifies its values accordingly.

Predictive analytics could even scrape information from social media about users to identify risky behavior that can be relied on for underwriting of risks.

Though the scope of predictive analytics is huge in fraud management, the extent of its usage is to be decided based on the governing regulations in the country, privacy protection acts, acceptability of its usage by consumers among other factors.

Many insurance companies have already adopted AI and have seen drastic improvements in fraud prevention:

In the case of Anadolu Sigorta, one of the biggest insurance companies in Turkey, the successful implementation of predictive analysis has resulted in a 210% increase in ROI.

The French insurer AXA has invested in AI for claims fraud as well as internal frauds. The technology has been reportedly successful in identifying duties and roles based on user behavior and has aided in ensuring data security and prevention of claims fraud.

SignOn AI in insurance

Artificial intelligence and its development have been at the core of SignOn since its inception and we have a reputable recognition as an insure-tech service provider and proudly associate with one of the leading names in the global insurance industries serving multiple geographies.

SignOn is currently working on artificial intelligence technologies that are poised to change the face of the insurance industry.

We are always open to inquiries and discussions. Please feel free to contact us to know more.

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