How an insurance company can ensure that claim cycle time is reduced significantly
ZEPTO is an automated AI-driven Insight generator having an intuitive visualization area with analytics capabilities. This is mainly to make analytics a truly self-serviceable for small businesses in the financial services sector. ZEPTO’s predictive analytics feature uses statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened — to provide a best assessment of what will happen in the future.
Using Predictive Analytics, insurance firms can reduce loss ratio significantly below from the tolerance level. Also, it helps to set competitive prices in underwriting, analyse and estimate potential future losses, detect and prevent fraudulent claims, plan marketing campaigns and provide better evaluation of the customer’s profile. Although predictive analytics can be applied across various value chains, we will focus on reducing auto insurance claims in this article, as 80% of premium revenue is spent on claims. Will predictive analytics become an automated commodity, or will it need to be analysed with human decision making? This is a question that has been debated upon, however rest assured predictive analytics will completely change the landscape of how insurers conduct their business!
Predictive Analytics in Auto Insurance Claims — Overview
Claims are ‘the moments of truth’. An interesting fact in the auto insurance industry is: Many auto accidents happen on late Friday nights. Also, the drivers of these cars are of a certain age group. Specific areas of the city are more prone to such accidents. Mostly, these accidents happen on cloudy or rainy nights when the visibility is low. Certain make and models of cars have more damage than others in such accidents. This is understood by looking at the insurance data by drawing relations between different variables such as day of the incident, time, age group, and associating it with other external information such as location, behavior patterns, weather information, vehicle types, etc. Establishing association between the variables, understanding the pattern, modelling the pattern as a function of these variables, simulating the pattern on a larger data set to observe the emerging inferences and using these inferences in the decision making is the role of predictive analytics.
Why is the claim cycle tedious and time consuming?
Each claim must go through various levels of checks and validations to ensure a profitable business. The main reason is that any leakage in the claims due to fraud by customers or even a mistake by internal staff could largely impact the profits as claims constitute the larger portion of the expense for any insurance company. This makes the administration cost of processing a claim inherent and it is correlated with the length of the claim cycle.
How can ZEPTO help in reducing claim cycle time?
Most of the steps in the process are more clerical in nature and with the use of predictive analytics and AI technologies, some of those mundane tasks can be automated to reduce the claims cycle time drastically while delivering a superior experience to the policy holders.
Insurance companies can use ZEPTO’s Intimation Assistance Bot as the first communication point on recording the notification of loss/accident by the client and providing best direction for both the client and the Insurance Officials. A motor insurance company can significantly reduce its claim cycle time using ZEPTO’s AI Chatbot in the following way:
1. ZEPTO’s AI Chatbot will be the first-hand information receiver from the affected party/the policy holder.
2. It will gather information about the incident in a natural way (using NLP technology) like a human resource in a call center. Also, the information gathering will be uniquely tailored based on policy information of the policy holder
3. Our proprietary algorithms will run different fraud detection models to access the genuineness of the query
4. If the claim is less material and straightforward, ZEPTO’s AI will pay it out directly else direct the claim to the relevant resource in seconds
The AI chatbot is well equipped to respond to query on complex claims, decide what priority should be given to it and who it should be routed to for faster processing. After answering all of the bot’s follow-up questions, the customer will have submitted their claim in full to the insurance company. Then, the customer would then wait to be contacted by an insurance representative.
Also, using ZEPTO’s analytics engine we can also obtain the following operational insights to take corrective and preventive actions:
- How many adjusters are available to assess that particular type of claim?
- Are the adjusters utilized efficiently? What is the skill set of the adjusters? Are there any gaps that can be addressed by training or knowledge base?
- What is the idle time among adjusters? Are there any activities that let adjusters wait for information?
Different claims have varying cycle times. The pattern observed was that whenever a bodily injury had occurred in a claim, the cycle time increased. Also, the cycle time increased proportionately with the number of bodily injuries in a claim. This led to the understanding that by addressing the root cause of the cycle time increase in bodily injury claims, a significant decrease in the average cycle time can be achieved.
As a part of the early-bird program, we are giving the product out for free for a month for the first 100 pre-signups. If you’re a person looking to reduce the claim cycle or analyse data to make optimized decisions, feel free to sign up here :
