The global insurance industry grew by 4.9 percent between 2018 and 2019. The US insurance industry is also among the largest globally, with written premiums peaking at $1 trillion in 2020. However, the industry's growth and a lower drive for innovation among industry stakeholders have also attracted criminals who perceive the industry as a low-lying yet lucrative target. Technologies like AI fraud detection exist, but they aren’t widely adopted yet — adding to the appeal for criminals.
Insurance fraud involves small-time and sophisticated criminals sponsored by rogue state elements or organized crime. The threat vectors to insurers are many, and the levels of coordination and technology used by criminals are outstanding.
To put this into perspective, the Coalition Against Insurance Fraud estimates that American consumers lose about $80 billion annually to insurance fraud, with medical insurance fraud the largest contributor at $60 billion. This only accounts for reported fraud, and the figure may be higher.
Thanks to advances in artificial intelligence (AI), it's now possible to catch fraudulent claims before they happen and protect both parties involved, with the added advantage of increased speed and more effective fraud detection.
Here's why AI for fraud detection is crucial in the fight against insurance fraud.
The Need for Faster Payouts And Why AI Fraud Detection May Be the Answer
Few things are as frustrating for a policyholder as having a claim drag through the system only to have it denied later on. It's also equally devastating for insurers who have to pay out fraudulent claims in addition to the genuine claims and face the possibility of a damaged reputation.
Unfortunately, this is all too common in the world of insurance, where fraudulent claims can cost both policyholders and insurers dearly.
The rise in fraud has led to an increase in insurance premium costs and delays in claims payment as insurers try to prevent possible fraudulent claim payments.
The Case for Faster Payouts: An Opportunity to Gain Competitive Advantage
Enabling faster payouts is quickly turning into a necessity if an insurer is keen on attracting and retaining clients. These are some of the reasons why:
- The insurance industry's growth has attracted new players keen on cashing in on the growing and profitable sector, further increasing competition.
- Another interesting observation is the shift in purchasing power and decision-making from baby boomers and Gen X to millennials and Gen Z. They also sway Gen X and baby boomers' purchase decisions. In a few years, this key factor will rest primarily with Gen Zers, a critical development that shouldn't be overlooked if a company intends to remain relevant.
- There's an aspect of increased competition that offers consumers more alternatives than a decade ago. Also, insurance companies now have to deal with tech-savvy millennials and Gen Z audiences who are less loyal to brands, keen on what influencers and previous customers have to say about a brand, and most likely to shift to alternatives whenever they have bad experiences or catch the slightest whiff of corporate irresponsibility (read: bad PR).
So insurance companies have to deal with cut-throat competition amid rampant fraudulent claims and new demands to satisfy this emerging and disruptive buyer segment.
Therefore, insurers must find innovative solutions to curb fraudulent claim payments without inconveniencing their clients. That means effective, accurate, and faster fraud detection solutions.
Other industries have incorporated AI and related technologies. They have reaped numerous benefits as a result. It is high time that proactive company leaders and key decision-makers consider AI fraud detection solutions to remain profitable and relevant in the increasingly competitive insurance market.
How AI Fraud Detection Works to Curb Fraudulent Claims
Here are three ways AI can help protect insurers from fraudulent claims:
1. Automated Fraud Detection
AI can quickly and easily identify subtle indicators in data that may indicate fraudulent activity.
This is achieved in two main ways:
- Prompt analysis using machine learning models and neural networks that can make automated
- Qualified decisions based on the data and the set decision rules/risk criteria
This reduces the amount of time spent on manual investigations and resolution.
2. Predictive Modeling
Companies can use advanced analytics and machine learning models to collect and analyze structured and unstructured data to identify patterns that may indicate fraudulent intent. This technology can provide early warning signs of potential fraud, and companies can take remedial steps.
3. Data Analysis
AI is incredibly efficient at scanning large data sets using machine learning. As a result, it can quickly identify patterns that human analysts might otherwise overlook. For example, if a policyholder has filed many false claims in the past, AI might be better able to pick up on this pattern and flag future claims as suspicious. Fraud monitoring with OSINT (Open Source Intelligence) data could offer an effective way to triage need for further review.
By understanding their customers' baseline behaviors, companies can better detect any changes that may suggest fraudulent behavior. This information can help them take preventive measures, i.e., process flagging for further review before a payout is made, which reduces the risk of fraud occurring in the first place.
Some of the benefits insurers can get from AI fraud detection solutions include:
- Faster payouts without compromising on fraud detection and prevention
- Increased customer satisfaction due to the added convenience of faster claim payouts
- Reduced operational costs
- Improved Know Your Customer (KYC) capabilities, which can be deployed to create a personalized customer experience beyond fraud detection
- Increased brand loyalty due to better customer experience, better pricing, and customized touches on insurance products
What to Look for When Evaluating an AI Fraud Detection Solution
There are some crucial factors companies should consider before settling on an AI fraud detection solution. These include:
The solution must be sufficiently trained with similar or actual insurer data to correctly identify fraudulent claims and prevent them from being paid out.
The solution should be flexible enough to cater to future company needs, whether it concerns increased data management and analysis needs or adapting to alternative data sources and regulations with relatively fewer cost implications.
The AI system should be easy to use and navigate so that users can quickly identify potential fraudsters. This also removes the burden of training and the time and cost implications for organizations.
White-Box Models Over Black-Box Models
While both solutions are equally capable of curbing fraudulent claims, white-box models or explainable AI provides companies with more insight into how and why the AI made key decisions like flagging down suspicious claims or authorizing others.
The additional insight from white-box models can be used to identify and rectify possible bias problems with the AI or defined rules, ensuring further refinement of fraud detection efforts.
Insurance fraud is on the rise. Criminals view it as lucrative and easy to get away with since most insurers still use obsolete fraud detection techniques.
As most industry stakeholders sleep on this revolutionary technology, forward-thinking companies can take advantage of AI fraud detection solutions to pivot themselves into being the most sought-after insurers. With this change, you can offer a better customer experience with faster claims payment and more effective and affordable premium pricing due to the significant reduction in losses to fraudulent claim payments.
Get started with Pilotbird's fraud analytics insurance solutions.
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