 By Kalyanaraman Gopalakrishnan, VP – Insurance Practice, Fulcrum Digital Inc.
Fraud, both detected and undetected, is a key concern area for everyone pivoting to digital lifestyles.
According to an article published recently that talks about the global insurance industry, insurance
fraud costs U.S. consumers at least $80 billion every year. It also estimates that workers’
compensation insurance fraud alone costs insurers and employers $30 billion a year.
Insurance fraud is a persistent problem that has not shown signs of slowing down. It is sometimes
misinterpreted as a crime with no victims. Consumers, on the other hand, incur greater premiums
and slower claims processing as a result of these crimes, in addition to the significant monetary and
reputational losses suffered by insurance companies.
The ongoing Covid-19 pandemic is expected to increase cases of insurance fraud as reports already
suggest the rise in Covid-19 related scams. A study released by the State of Insurance Fraud
Technology found that AI has become an increasingly important tool for fraud detection, as conmen
are leveraging data online and on social media for such fraudulent activities. The good news is that
India’s insurance industry has been able to curb fraudulent activities by digitizing fraud investigation.
In a survey, 68% of respondents said their organizations were using digital solutions for
investigations, while 19% said they were in various stages of planning the transition to digital.
Machine learning, predictive analytics, data mining methods are increasingly used for fraud
detection, as timely detection is key, considering there is deterrent for fraudsters. Here are ways in
which technology can help with the detection of fraud at the early stages.
Blockchain
A database network referred to as Blockchain, records transactional data in real-time. What this
technology also does is it highlights concerns in terms of security, privacy, and control. This
technology has also been hailed as an ideal solution to counter insurance fraud. A Blockchain ledger
keeps a permanent record of transactions that is automatically synced without the use of a
centralizing third party. It’s a process where every block links to a previous block, and they all have
time/date stamps. If a hacker attempts to change information on one of the blockchain copies, the
other versions would reject it as contradictory.
Not just to protect our data but blockchain is also leveraged for preventing identity fraud in
insurance practices. In today’s paradigm of passive, wholesale data sharing blockchain helps in
segmenting data so that only those who need it have access to it. Custom permissions can be set
depending on how data is stored on the blockchain. Your insurance provider, for example, may have
access to your product policy, whereas your bank may just have access to your financial information.
Nonetheless, while Blockchain has received a lot of attention in recent years from a variety of
industries, it does come with some risks and restrictions. Cyber-attacks remain a prominent issue:
Blockchain poisoning, for example, is an attack that involves loading private data or illegal materials
onto a network that renders it useless due to the conflict with local laws.
Anomaly detection
Anomaly detection is one of the key trends in cybersecurity practices, with numerous use cases such
as fraud prevention. In the case of insurance fraud, machine learning (ML) models helps in
identifying what a normal claim looks like to establish a baseline. Once that baseline is defined, they

can identify abnormalities and notify insurers. During claim processes, anomaly detection helps in
examining legitimate customer claims. This creates a model of how a typical claim appears, which it
applies to larger data sets. It can also be used by insurers to discover questionable conduct among
users on their network.
For an example, to detect fraud in large sets of insurance claims based on cases that are suspected
to be fraudulent, the anomaly detection technique analyses past insurance claims to evaluate the
possibility of each record being fraudulent. In transactional cases, if someone is not a frequent user
of debit/ credit cards, but if large sums of money are transacted to purchase policies one after
another from his/or her account within one day, banks will be able to identify anomalies and may
block respective cards. However, these irregularities are not necessarily always indicative of
intentional wrongdoing. Accidents and mistakes may happen even when no one is trying to defraud
you.
Predictive analytics
As per MarketWatch, the Global Predictive Analytics market size will reach USD 34.1 billion by 2027.
Valued approximately at USD 6.9 billion in 2019, it is anticipated to grow with a healthy growth rate
of more than 22.17% over the forecast period 2020-2027.
Many of us consider predictive analytics as being one of the most important measures in trying to
combat insurance fraud. Like anomaly detection, predictive analytics involves training artificial
intelligence or machine learning algorithms using historic data, in order for them to ultimately
forecast future incidents. The ability to determine vulnerabilities in the claim process is clearly
appealing to insurers, who would be able to save time by acting to avoid fraud rather than reacting
to it.
Predictive analytics helps in retaining a level of reactiveness rather than proactiveness. This solution
relies on the use of historic data, which means that new schemes are unlikely to be detected as the
models have not been trained to recognize them yet.
Speed up claims processing with chatbots
Reporting damage or theft to any insurance company generally initiates claim processing.
Traditionally, it was done through brokers. However, with technological advancements policy
holders could now leverage chatbots on insurance company’s website/mobile app to file the first
notice of loss (FNOL). Chatbots would direct them to take photos and videos of the damage, which
potentially lessens time for the fraudsters to change the data. These natural language processing
(NLP) driven customer assistants speed up claim processing, without the requirement of human
intervention.
Technology has become a day-to-day necessity for us as it has made our lives easier. Be it the usage
of chatbots to interact with companies or home assistants like Alexa to manage something as simple
as changing the audio loop. In the insurance sector, while the usage of technology started in
customer support, continually refining machine learning algorithms have expanded its applications
to multiple aspects, such as claim management, fraud detection, risk assessment, and pricing. While
technology has changed the way the insurance industry works, it must be noted that this is not a
replacement for human intervention but is aimed at making lives and processes easier.