Automotive Insurance -
Insurances claims are always bureaucratic, with multiple steps along the way which can create a disparaging experience for the customer. To prevent this we have proposed a system where the customers of the client can record their needs and wants as a simple recording which then can be processed by automation to create a robust system that gives us many benefits which will bolster our relationship to not just the customer but also our place in the market.
Our client is an Indian automotive insurance company with an ever expanding customer base in the insurance market. For years the clients have provided support and services for brands and vehicles of all ranges.We have formulated a program which can automate the process of insurance claims via proposing the option to directly record them on-the-fly.
This process can very easily detect their wants and needs by understanding the semantic and speech patterns which are compared with the existing dialogue patterns used by the agents, which also helps the agents and the client to scrutinize claims for their legitimacy. Easier user extraction can be initialized, which streamlines the process of claim settlement by auto-filing and auto-filling methods.
The data derived can be used to create newer training methodologies and market strategies which could lend a major hand in developing the overall performance of the company.
Various challenges should be tackled for a very serious issue like this. Here are some important challenges that are faced by our model:
Cognitive Extraction - A clear understanding of behavioral patterns that could be extremely variant, and it also has to match with the protocols set by the authorities and the corporation.
Transcription Issues - Due to heavy accents and hardware issues, transcription process can be very erratic, proper semantic analysis is a must.
Real-time monitoring - One of the most key features of this model is it's real-time monitoring abilities, which can be harder to integrate considering the expansive nature of this system.
Behavioural Analysis - For customer scurtiny, properly emotional/behavioral analysis had to be done to identify the intentions of customers to differentiate between the honest and the dishonest ones while applying for a claim, this is a complicated task since it has to keep in check the various parameters that pertain to human emotions.
Our goal follows these major steps:
Create a method to record the claims requested by the customer and then using contextual and vocal analysis, we can find out points of improvement.
These can be then used to identify false claim patterns, customer support objectives and other important parameters.
We can also audit claims and agent engagement to meet compliance standards.
With the challenges in mind, we arrived at the following steps for a solution:
Auto-claim filling with all the parameters set is engaged using perfect semantic and vocal analysis.
Create areas for customer extraction via cognitive analysis.
Using the archived files we need to identify opportunities for cross-sell and up-sell utilizing natural language processing which understands the notions that instigate a sale, which can be streamlined to work in a real-time environment.
Enable the ability to audit the interactions between the client and their customers so that would meet compliance and security procedures.
With the aforementioned procedures we can easily derive a method to easily increase not just the customer influx but also their relationship with the company. This increases the efficiency and also maintains strong customer retention. The most important feature of using this model is the strict auditing principles embedded with the program, this posits an overall safety standard and maintains a positive key perfomance indicators. False claims can be identified and proper measures can be taken, saving a lot of time and resources for the company.
This creates a model that is an all-in-one solution for the clients's need; promising everything from monitoring to call metric analysis to marketing support tools.