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Predictive Analytics Revolutionizing Fraud Detection In Insurance: A Guide By Money 2.0 Conference

In the insurance world, companies are currently grappling with a huge problem: fraud. Fraudulent activities have been skyrocketing as fraudulent claims increase in the insurance sector. To combat this issue, many businesses have turned to predictive analytics to act as an early warning system.

To explore new ways to prevent fraud, Money 2.0 Conference will host panel discussions on novel approaches for insurance companies to double-check fraudulent claims. These sessions will aim to teach attendees about the benefits and applications of predictive analytics in combating insurance fraud. Not only that, it will highlight recent advancements made by leading experts, and one can also gather firsthand experience from distinguished speakers who are practicing actuaries.

Until then, read on to understand more about how predictive analytics can help you reduce your risk of fraud and increase the profits you take in from your policies.

What Is Insurance Fraud?

As mentioned by experts at the Spring Edition of the Money 2.0 Conference, insurance fraud is one of the fastest-growing crimes affecting policyholders, insurers, reinsurers, capital markets, and regulators. It has become much more sophisticated and less detectable than ever due to technological advancements, resources on the dark web, and vast amounts of data on social media sites such as Facebook or Instagram.

The traditional approach to catching insurance fraudsters involved a series of low-tech manual investigations that were time-consuming and labor-intensive. In short, fraud detection was reactive rather than proactive, with insurance companies waiting for losses to occur before responding with an investigation. Thankfully, there's been a significant shift toward predictive analysis techniques that can reduce insurance companies’ risk exposure by allowing them to investigate and identify fraudulent claims before they happen.

Understanding Insurance Fraud & Its Types

Numerous types of insurance fraud are present to date, but they all fall into two general categories — premium fraud and claim fraud, as mentioned by financial and insurance experts who attended the Money 2.0 Conference. Let’s get into details to understand them:

  • Premium fraud is misrepresenting oneself as a low-risk customer or claiming to have a more negligible risk than one does to pay less for insurance premiums.
  • Claim fraud occurs when an individual exaggerates their loss or injury to receive more money from an insurance company.

The most common form of fraud is staged accidents, where someone injures themselves and files a false claim. Staged accidents happen more often with PIP (Personal Injury Protection) policies that cover medical expenses after an accident and can be very difficult to detect.

Some other ways that people commit insurance fraud include inflating their losses during natural disasters such as floods or fires, making false claims on life insurance policies by pretending they're ill when they're not, etc.

In such scenarios, experts assert that predictive analytics has the potential to do a lot of the heavy lifting for insurers. They scan data from many sources, like past claims and social media posts, looking for patterns and warning signs that might state fraudulent activity. The experts of the Money 2.0 Conference add that while predictive analytics software isn't perfect, it's still much better at detecting fraud than any human could hope to be alone.

The Benefits Of Predictive Analysis
  • Predictive analysis has the potential to change the insurance industry by automating fraud detection, expediting manual claims handling, and providing much-needed data security with limited investment costs.
  • With the correct use of machine learning, predictive analysis can generate significant financial savings while increasing customer satisfaction.

The insights below, as highlighted by Money 2.0 Conference’s panelists, illustrate some examples of how predictive analytics is revolutionizing how we detect fraud in insurance:

  • The latest research reveals that companies will spend more than $6 trillion on cybersecurity over the next decade—much more than any other type of crime can cost. And yet, as technology advances exponentially and new technologies are introduced into our lives at an alarming rate, many companies still do not know how to protect themselves from cybercrime—fraudulent or otherwise.
  • Predictive analytics solves this problem because it offers robust data protection without requiring significant capital investment in more hardware or software systems.
  • It utilizes company assets such as server logs, application logs, user authentication logs, operating system logs, network traffic captures, etc. These assets already exist within most organizations but have not been leveraged due to the high costs associated with storing this data in a scalable format.
  • Data storage is expensive, but when you leverage predictive analytics, you only need to keep event logs that relate to business transactions. After determining which events correlate with a fraudulent activity using powerful algorithms and methods like correlation clustering or linear regression model (LRM), these events are deleted immediately after they're used to create predictions. Implementing predictive analysis doesn't add complexity or management overhead — only benefit!
Implementing Predictive Models & Analyses

According to Money 2.0 Conference’s experts, predictive models and analyses play a critical role in fraud and scam detection efforts by helping assess the probability of an insurance claim being fraudulent and identifying fraud when it's starting to happen or before it occurs.

Predictive models and analytics are becoming popular due to many advantages over other strategies. For example, traditional human-based detection methods are costly, time-consuming, and subject to human errors. Meanwhile, machine learning algorithms can be customized for specific business needs without costing extra resources. These approaches will be instrumental in assessing risk probabilities for insurers in the future — not only for losses but also for new opportunities like emerging markets where accurate data is scarce, and people are less trusting of institutions because they don't understand them.

Insurance stalwarts who attended the Money 2.0 Conference’s previous edition opined that these anti-fraud models and analysis tools help them predict what might happen next through predictive scoring. Simply put, predictive scoring assigns every individual or customer a score that indicates how likely they are to engage in certain behaviors. Risk scores can be applied at various levels, including the person level (e.g., identifying which individuals present the highest level of risk), account level (e.g., assigning high scores to customers who have engaged in recent risky behavior), and portfolio level (e.g., allocating capital based on which segments offer the best rates of return).


Predictive analysis is going to help insurance companies find out where their risks are. It will lead to healthier insurance prices for honest consumers and a more competitive business environment as insurers compete for customers who pay claims instead of those looking for cheaper rates regardless of risk.

Money 2.0 Conference’s upcoming Winter Edition in Las Vegas and Dubai will explore financial technology and fraud detection's past, present, and future with the brightest minds. The event is gearing up to bring together entrepreneurs from every corner of finance, including bitcoin, crowdfunding, banking apps, and data science, later this year in December.

09/19/2022 - 12:21
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Lakshay Mohanpuriya
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Author Bio

Lakshay has always been drawn to the world of finance. His fascination for it led him to join the organizing committee for the Money 2.0 Conference - an annual conference exploring FinTech trends, showing how to best manage one's money, identifying scams/fraud and fake investments spam, as well as highlighting ways to stay safe.