AI technologies have significantly reformed information systems by making them more adaptive to humans. Such technologies have improved the interaction between humans and computer systems.
This way AI in the insurance industry has transformed the claims management process by making it faster, better, and with fewer mistakes. Insurers now have more evolved claims management and fraud detection systems.
From machine learning to smart chatbots, the ever-widening potential of Artificial Intelligence in the insurance industry is already being utilised in multiple ways. This blog explores the ways Ai has simplified insurance claims management and fraud detection.
The Importance of Insurance Claims Processing
Efficient insurance claims management elevates the profitability of insurers and the satisfaction of policyholders. A report by Deloitte reveals that claims processing accounts for approximately 70% of expenses for insurance companies. Proper handling of insurance claims is associated with efficient insurance fraud prevention and detection since such activities tend to occur at the time of claims processing.
However, a study highlights that fraudulent claims account for around 10% of total expenditure. This means around 90% of claims management is about solving the problem of a customer who has experienced a tragic incident. Hence, it’s not surprising that 87% of customers consider the effectiveness of claims processing as a criterion for switching insurance service providers.
Types of Insurance Claims Fraud Insurers Should Watch Out For
Insurance claims fraud consists of different types of fraud activities. Here’s a little peek into these categories below.
False Claims: This is when an insured person makes a fraudulent claim about an incident that never occurred, such as an accident or injury.
Duplicate Claims: The insured person submits more than one claim from the same provider for the same service date and service.
Exaggerated Claims: The insured person increases the value of their claim. For instance, someone may claim a valuable item stolen from their home was worth $5000 when it was worth only a fraction of that amount.
AI-Powered Technologies That improves Claims Processing and Fraud Detection
There are many technologies branching out of AI that assist in claims processing and fraud detection. Mentioned below are some of the AI-powered technologies that have been proven crucial for the insurance claim processes.
- Chatbots
NLP-driven chatbots can simplify the First Notification Of Loss (FNOL) and payment arrangement steps of claims processing. Chatbots can be incorporated into customer service, on the website or in the mobile app of the insurance company.
Chatbots are essentially the first point of contact for policyholders when they want to make a claim. They can guide the consumers to take videos and photos of the claim and inform them of the required documents they need to submit, speeding up the submission of the FNOL. Chatbots also allow insurers to contact policyholders to arrange for payments, or to answer their queries. Either way, chatbots effectively automate customer relations.
- Advanced Analytics
Advanced analytics are algorithms that help users make accurate predictions. Such technologies are ideal for finding and interpreting correlations. Hence, such systems are useful for the initial claim investigation, policy checks, payment calculation and the steps of claim processing.
Insurers can utilise adjusters’ and experts’ reports as input data for advanced analytics to determine the actual claim costs. Advanced analytics are also implemented for fraud detection as it identifies patterns between fraudulent acts. More specifically, the implementation of behavioural analytics can help evaluate whether or not the claim complies with the terms of the policy. Such tools monitor browsing histories, clicks and location. This enables the insurers to understand whether policyholders’ claims are authentic or not.
- Optical Character Recognition (OCR)
This is an iteration of NLP that derives its meaning from handwritten documents and is used to categorise them. This technology improves initial claim processes and policy check steps of claims processing.
Both the investigation of initial claims and policy checks involve processing handwritten documents such as witness statements, policyholder statements, police and medical reports, and more. OCR enables insurers to automate the careful extraction of data from such documents and focus on the parts of claim processing that require human intelligence.
- Computer Vision Technology
Computer vision models derive results from visual inputs such as images and videos. This way, the technology can assist with the initial claim investigation step. Additionally, computer vision models can assess the cost of loss by evaluating data from videos and photographs taken by policyholders o claims adjusters. They can help insurance companies predict liabilities and organize their financial resources accordingly.
Some insurance companies are already using autonomous things for the initial claim investigation. The intelligent drones which are powered by computer vision models assess the insured object. Although such technologies have barely been adopted in the insurance sector today, McKinsey predicts that by 2030 we will notice more intelligent drones performing claims investigations.
Ways AI is Implemented in Insurance Claim Fraud Detection and Prevention
Among the wide variety of uses of machine learning in insurance companies, the tools used in claims fraud detection are worth mentioning. Such systems help insurers modernise, increase efficiency, and lower overhead expenses. Mentioned below are several ways AI is utilized in insurance claims fraud detection.
- Utilising NLP to Analyse Historical Data
A crucial benefit of implementing AI and machine learning into the claims filing process lies in Natural Language Processing (NLP). Other than processing huge volumes of information around the clock, NLP can evaluate historical data of fraudulent claims and the individual policyholder’s past claims and behaviour by monitoring recorded conversations and other textual data types, such as emails.
Without adopting AI in insurance claims fraud detection, it would be inefficient or even impossible to replicate the above-mentioned processes for human workers alone. The AI algorithms decipher the individual’s claim history and whether a specific request seems ordinary or suspicious by monitoring the historical trends.
Automating this entire process allows workers to focus on other activities. Additionally, it enhances customer experiences with quicker response times and more informed customer service.
- Real-time Notifications
Every day, multiple fraudulent claims are made out of the thousands of other legitimate filed. Monitoring this continuous flow of fraudulent claims proves tedious for the staff. The process of manually evaluating each new suit while trying to identify unusual patterns or questionable requests tends to be time-consuming and inefficient.
When implemented, AI systems run in the background and constantly assess the habits and behaviours of claims applicants and policyholders. This allows the algorithms to easily flag potentially fraudulent activity and provide real-time alerts to the insurer when a claim requires further investigation.
The sooner insurance firms are aware of potentially fraudulent activity, the better protected they are from paying out the requested amount and incurring the subsequent loss. AI in claims fraud detection helps improve the insurer’s resource efficiency and saves insurance companies millions of dollars every year.
With early detection of fraud risks, NLP to evaluate historical claims data, and real-time alerts, insurance firms can utilise AI and protect them against claims fraud.
- Advanced Text Analytics and Data Mining
AI and machine learning algorithms are capable of assessing quantitative and numerical data and dismissing any unstructured information. AI technologies can provide concrete and actionable insights from textual and unstructured information like claims applications, adjuster notes, social media searches, etc.
With such advanced capabilities, AI is streamlining the overall insurance claims process and helping firms access smarter fraud detection without added labour or expenses. AI in claims fraud detection allows firms to quickly evaluate both structured and unstructured data from internal and external sources which lead to better protection for the firm. The more details on policyholders that can be accessed and examined, the better firms will understand their risk exposure to insurance claims fraud.
- Predictive Analytics for Insurance Fraud Prevention
The first form of defence against insurance claims fraud lies in predictive analytics for early detection and fraud prevention. With each new customer touch-point and further details gathering, predictive analytics can assess the fraud and risk of the policyholders. Additionally, the technology also expedites the early detection of potentially malicious activity based on their profile and behaviour patterns.
The AI algorithms present an accurate claim score and reason code for each claim initiated. This allows insurers to determine if the request is suspicious and requires further monitoring. Such systems are utilised to alert the staff about possible fraudulent claims before processing and pay-outs. This enables insurance companies to allocate better resources to investigate claims that have been flagged as potentially fraudulent, saving them considerable time and money.
Partings thoughts,
AI will continue to transform the insurance industry as more firms adapt to machine learning and AI algorithms in their claims processes. While the insurance industry embraces advanced technologies into their systems, it’ll bring more efficiency and accuracy in claims fraud detection in the long run.