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INSURANCE industry is our Top customer segment. We have & are helping several large Insurers – both General and Life, solve some of their most complex business problems around Customer Intelligence, Fraud Management and Operational Transformation amongst others.

Some of our Insurance customers have won prestigious industry awards and coveted accolades based on the cutting-edge solutions we delivered for them. Some even made it to select case study publications by NASSCOM.

We offer a whole host of solutions & services including Consulting, Implementation and Post-production support to Insurance companies in Big Data/Analytics/ AI/ML/Cloud space.

Having delivered several transformational projects to Insurance cos, we have acquired significant Insurance industry domain expertise that would be a big benefit for our future Insurance industry customers.

Insurance Use Cases

Product Development & Pricing

Product Feedback Analysis
Product Development
Product Pricing

Sales & Marketing

Lead Management
Customer Lifecycle Mgmt
Omnichannel Virtual Sales

Risk/Underwriting

Risk Category Prediction
Premium Estimation
Operations Optimization

Operations

Salesforce Optimization
Salesforce Productivity
Internal Process Automation

Claims Handling

Early Fraud Detection
Claims Productivity
Data Generation

Customer Service

Sentiment and Behavior Detection
Chatbot optimizations

In-service recommendations

Product Feedback Analysis

Business Objective: 

  • Analysing customer sentiment for the different products offered by the brand

Business Brief: 

  • Automatically analyze and categorize customer feedback  
  • Analyze speech (audio) and interviews (video) to understand customer sentiment and feedback
  • Summarize and interpret data from conversational channels (chatbots)

Solution Approach:

While the insurance products are developed after a lot of research and analysis, the feedback received from the customers regarding the product is equally important.

The solution uses the AI tools like Speech recognition, sentiment analysis, social media profiling, chatbot data, IVR data etc. to gather all information regarding the product and store it in a structured manner for the product team to analyse it further and introduce improvements to the product structure

The collected information can also be analysed using AI/ML for the engine to produce recommendations for the product

Product Development

Business Objective: 

  • Development of new products
  • Developing investment portfolio for existing products

Business Brief: 

  • Capture and analyze response to product recommendations
  • Identify segment-wise product uptake 
  • Identify product bundles 
  • Assessing market sentiment for a profitable launch of a new product or expansion of existing product line

Solution Approach:


Using all information gathered from Product Feedback Analysis to develop new products


The AI engine performs the in-house product analysis, performance and feedback analysis to compare it to the competitor products. It also takes into the account the industry performance, customer’s voice gathered via different interactions on various mediums.


Using this analysis, the engine throws out new product recommendations, which can be further analysed by the product development team


In addition to this the AI based solution can also analyse the internal product portfolio and create better portfolio combinations depending upon historical data

Product Pricing

Business Objective: 

  • Develop right product pricing based on various market factors
Business Brief: 
  • Traditional Pricing based on actuarial view of risk & cost allocation
  • Pricing models covering
    • Competition based pricing
    • Usage based pricing
    • Product – Channel pricing
    • Segment – Product pricing

Solution Approach:

The solution analyses the data for the various products and the customer response to same with respect to the product structure and pricing

The analysis on historical data is utilized to compare it with the competitor pricing and come up with recommendations for structure and pricing in below aspects

  • Usage based structuring and bundling of product
  • Channel based pricing
  • Segment based product bundling
  • Competitor based bundling and pricing

Lead Management

Business Objective: 

  • Acting on the leads in a timely manner
  • Presenting right offer to the leads
  • Passing lead to the right channel/ agent and closure of leads in shortest time possible

Business Brief: 

  • Customer 360 view of customer interactions across channels
  • Identify the right offer, right channel for customer
  • Automate customer communication
  • Close looped customer response for self-learning

Solution Approach:

Insurance has become a very fast passed environment where leads get won or lost within a matter of minutes. The lead wait time expectation of customers have reduced considerably and hence, this AI based lead management framework aims at cutting down on this latency and aligning right information and resources to cater to leads in shortest possible time and possibly in real time.

The solution achieves this by analysing the information provided by lead in real time and aligning right agent/ salesforce within the organization. If there is a wait time then, the interactive chatbot and conversational IVR will be put in touch with the lead to analyse the need further and answer the lead queries.

In time the solutions aims, with the help of ML algorithms, to reduce the need for agent interaction as much as possible. This will help in faster lead response and closure time

From initiation of lead/ communication to closure, entire set of events is close looped for a customer so that all players/ departments know exactly what has happened with the customer at any given point in time

Finally the solution enables extreme micro-segmentation of customers to generate most appropriate dynamic offer across channels

Customer Lifecycle Management

Business Objective: 

  • Intuitively manage the lifecycle of the customer from lead to exit stage

Business Brief: 

  • Customer 360 view of customer interactions across channels
  • Identify the right offer, right channel for customer
  • Automate customer communication
  • Close loop the customer response for self-learning

Solution Approach:

This solution aims at creating an AI/ ML enabled Communication Management Data Mart which contains all key KPIs associated with any customer.

The AI part of the solution keeps these KPIs updated basis the customer touchpoints and various interactions happening via different mediums. It also tracks the customer behaviour in terms of campaign responses, preferred response timings, preferred communication channels, preferred payment modes, renewal behaviour, claims behaviour etc. Basis these, it creates a single 360-degree view of customer with all key KPIs at a single place.

This enables the marketing team to roll out right communication at the right time. The ML part of the solution tracks these communications and in time can start rolling out the relevant communications on its own.

Omnichannel Virtual Sales Agents

Business Objective: 

  • Rolling right product with right monetary value to the right customer at the right time

Business Brief: 

  • AI based sales agents to support customers during sales process
  • Integration with commonly used channels like WhatsApp, Website, Mobile app
  • Assist salesforce on the ground in sales process

Solution Approach:

With so many competitors and available product choices, rolling right offer of right value at the right time is extremely important. This solution aims at analysing the customer profile via inputs gathered from various channels like Call Centre, Marketing channels, Core customer and policy data, social media, website behaviour and information coming in from other channels, to generate the right offer for the customer and automatically intimate the customer.

This is achieved via machine learning algorithms to understand the customer response to historical offers and tailoring the new offers based on customer 360 degree view. This enables to have offers developed in a range with exact premium values rather than pre-fabricated offers.

The engine also studies how the past issuances with respect to such offers have panned out for similar customers and tweaks the offers for better revenue output.

The engine further keeps tracks of customer across all frequented channels and ensures that a uniform non-conflicting offer/ message is going to the customer

Risk Category Prediction

Business Objective: 

  • Analysing the customer inputs and documents with regards to issuance and arriving at the right risk value and associated premium amount

Business Brief: 

  • Consolidate historical risk data 
  • Recommend risk category based on customer segment
  • Utilize past claim data for prediction

Solution Approach

Risk plays a very important role in determining the policy premium and hence it is important to arrive at a right risk estimation for any issuance. This solution helps to analyse the documents provided by the customer and the various customer interactions over calls to arrive at the right risk value. This is achieved with the help of AI algorithms refined over time via ML. In addition the Risk Estimation Engine also looks out for the risk related parameters globally, as per information made available by the customer

Once the risk value is determined, the company’s internal premium allocation algorithm can arrive at the right premium value for the issuance. But the solution further enhances this by analysing the look alike past historical data and company’s profit/ revenue in those cases and accordingly tweak the premium to strike a balance between the company and customer needs

Premium Estimation

Business Objective: 

  • Analysing the customer inputs and documents with regards to issuance and arriving at the right risk value and associated premium amount

Business Brief: 

  • Predict premium based on past underwritten policies
  • Find Look-alike based on proposal attributes 
  • Utilize past claim data for prediction

Solution Approach:

Risk plays a very important role in determining the policy premium and hence it important to arrive at a right risk estimation for any issuance. This solution helps to analyse the documents provided by the customer and the various customer interactions over calls to arrive at the right risk value. This is achieved with the help of AI algorithms refined over time via ML. In addition the Risk Estimation Engine also looks out for the risk related parameters globally, as per information made available by the customer

Once the risk value is determined, the company’s internal premium allocation algorithm can arrive at the right premium value for the issuance. But the solution further enhances this by analysing the look alike past historical data and company’s profit/ revenue in those cases and accordingly tweak the premium to strike a balance between the company and customer needs

Operations Optimization

Business Objective: 

  • Reducing human intervention in the underwriting process
  • Automating underwriting steps which are rule based and repetitive in nature

Business Brief: 

  • Automation of underwriting process to take underwriting decisions. This will create an AI/ ML and NLP based system to take the underwriting decisions which are today taken by employees.

Solution Approach:

Underwriting is the key activity during policy issuance which determines whether a particular policy issuance will be good or bad for the organization. The solution aims at automating the series of activities involved in the underwriting process. While the Underwriting Engine will follow the established set of rules, it will also intuitively formulate decisions for any situations not encountered so far. In such cases the formulated decisions will be sent for manual review and after it had been ratified, AI engine will assimilate it as part of process going ahead

The solution will reduce human intervention to only special cases. The solution also aims at automating the medicals required for a policy by automatically connecting with the predefined set of hospitals and intuitively suggesting the same to the customers based on their locale and preferred time slot information gathered through documents and through automated systems calls done by the engine. This is where the speech recognition capabilities of the AI engine will come into play. Over time Machine Learning algorithms will start doing these tasks intuitively.

Salesforce Optimization

Business Objective: 

  • Reducing the attrition in the hired salesforce
  • Finding the right skillset and recruiting the right mix of employees for better on-ground salesforce productivity

Business Brief: 

  • Automate recruitment process based on salesforce and employee performance  and productivity
  • Auto recommendation of recruitment profiles based on social profiles and recruitment platforms data

Solution Approach:

This solution aims at analysing the past recruitment pattern of the company and subsequent salesforce performance/ tenure/ behaviour within the organization.

Using AI engine to analyse the traits and attributes that are working best in the salesforce for any given region and finding the similar set of profiles

Tapping into social media and recruitment platforms to run through the available profiles and recommending the right profiles to the HR for further analysis and recruitment

Salesforce Productivity

Business Objective: 

  • Improving the on-ground salesforce output and productivity
  • Dynamic target setting for the salesforce
  • Workload distribution

Business Brief: 

  • Prediction of upcoming workload based on past trends and historical data
  • Workload distribution based on employee performance and 

Solution Approach:

This solution analyses the past workload distribution and patterns and assigns workload and case load to the on-ground salesforce and employee workforce accordingly. To achieve this, it utilizes machine learning algorithms to effectively divide the workload

To further improve the productivity of agents, it analyses the quality of leads based on lead demographics and similar historical data and assigns the leads in prioritized fashion to the on-ground agents

Intuitive target setting for the agents and employees depending the market conditions, employee productivity and company performance. This is achieved via AI and text analysis to scour over the Internet data regarding market sentiment and internal data on employees and salesforce

Internal Process Automations

Business Objective: 

  • Automation of generation and distribution of regularly and most commonly used KPIs, MIS and reports
  • Intuitive creation of new KPIs based on industry standards, internal requirement, service requests etc.

Business Brief: 

  • Automation of regular reports and MIS for daily work processing
  • Prediction and formulation of new KPIs based on the ongoing business trend and what will be needed 

Solution Approach:

Reporting the company KPIs and performance is a key activity in any Insurance sector as it allows the organization to generate actionable insights. Using the automation capabilities of AI, this solutions creates a Reporting Framework which takes the reporting requirement of entire organization in the form of KPIs and auto generates the required MIS/ KPIs/ Reports on top of a generic reporting structure

The AI and ML also keeps looking through the industry standards on key KPIs with the help of digital and web crawling and formulates the same KPIs in advance.

Using AI and ML to formulate new KPIs based various internal reporting requests raised on company’s internal service desk/ emails etc.

Early Fraud Detection

Business Objective: 

  • Claims fraud is becoming a major issue resulting in business loss for organizations
  • Finding the early indicators based on customer demographics and other customer interactions over time
  • Objective is to either detect chances of fraud even before policy is issued or as early as possible once the policy has been issued

Business Brief: 

  • AI engine to detect possibility of frauds based on past trends and customer behavior
  • Using NLP and sentiment analysis to detect customer’s inclination towards claim

Solution Approach:

The solution amalgamates the data from different sources and merging with the fraud analytics engine to generate scores for the customers either at the time of policy issuance or after the policy has been issued

The AI engine will also scour through the social media for similar fraud instances with respect to the customer demographics and customer profile. The algorithm will try to generate red flags with respect to the customer for chances of a claims fraud happening

This will be further augmented with AI capabilities with respect through the speech/ text pattern recognition, locale data, sentiment analysis to determine whether the reported claim could be a potential fraudulent activity

The solution can be used to flag customer claims or even flag the customer at the time of policy issuance for potential future fraudulent claims activity

Claims Productivity

Business Objective: 

  • Significant reduction in claims handling time
  • Reduction of errors and manual intervention in processing the claims data
  • Prioritization of claims cases

Business Brief: 

  • Analysis of claims data, photos and documents to figure out correctness and severity of the claims, and prioritize accordingly for processing
  • AI service agent to cater to first level of claims handling and direct customer towards providing all required information for speedy claims processing
  • NLP and AI/ML to convert the speech and conversations regarding claims to access the severity of claims damage

Solution Approach:

STAGE 1
Information about the circumstances associated with a claim is very crucial. Hence AI/ ML powered chatbots and IVRs could help in collecting relevant first level information to assist with the speedy resolution. The CLAIMS Engine running behind this will not only collect the document but also start creating a structured repository which could then later be used for claims processing
STAGE 1
STAGE 2
Analysing the severity of the damage in the claims is very important as it relates directly to the claims amount. Normally this is a very tedious process but with the help of image analysis, this process can be sped up and can be utilized to accurately gauge the severity of the raised claims. This can further be augment with speech/ sentiment analysis and NLP to convert the spoken conversations to add to the claims data.
STAGE 2
STAGE 3
The accumulated data can be utilized to prefill any pertaining documentation with regards to the claims, reducing the workload for the case officers
STAGE 3
STAGE 4
The data collected for the various claims will be utilized to prioritize the cases which should received early attention as opposed to other cases
STAGE 4

Data Generation

Business Objective: 

  • Generating right data sets for the internal analytics and MIS/ reports

Business Brief: 

  • Using AI/ ML to create data sets based on structured and unstructured data within the organization. This can be used by the employee workforce to take quicker decisions regarding claims
  • Using NLP to augment the customer sentiment and speech data to the existing data against the customer

Solution Approach:

Using AI/ ML to create data sets based on structured and unstructured data within the organization. Employee can use these datasets workforce to take quicker decisions regarding claims

Using NLP to augment the customer sentiment and speech data to the existing data against the customer

AI to keep track of different internal MIS and reports and accordingly keep altering/ augmenting the data sets to future use

Sentiment and Behavior Detection

Business Objective: 

  • Understanding the customer sentiment and stance is very crucial in any CRM interaction
  • Previous recommendations that might have been correct basis customer’s past behaviour might not hold true based on customers’ current sentiment and behaviour
  • Enabling the call centre agents by providing on the fly audio/ screen feed with regards to the next step with the customer during call

Business Brief: 

  • Using AI to detect the customer sentiment during customer calls and chat sessions. Directing better responses to the customer basis sentiment for faster resolution
  • Live audio feed recommendations/ screen-based recommendations to agents to improve customer communication basis sentiment analysis

Solution Approach:

Being dynamic during any interaction with the customer is the best approach. Providing right answer, right input and right feedback is very important for customer satisfaction as well speedy resolution to the customer query

Using Speech and sentiment analysis on the fly to understand the customers’ state of mind during conversation.

Using AI and Machine learning to augment this with the analysis of customers’ past interactions and other similar interactions to create a robust on the fly Agent Feedback Engine.

This engine will provide dynamic real time feedback to the servicing agents with regards to what should be told to the customers and what should be avoided

This will potentially make the conversation more goal oriented and productive for the customer, and also avoid any business loss for the brand

Chatbot Optimizations

Business Objective: 

  • Increasing the number of call centre agents with respect to the growing customer volume is not practically viable to business due to involved Operational Costs.
  • This inevitably results in longer and increasing average wait times for the customers, leading to dissatisfaction and eventual business loss
  • Improving the flexibility of the existing chatbots and IVR solutions to make it more intelligent and conversational oriented

Business Brief: 

  • AI/ML based chatbots to cater to initial stages of customer query. This will reduce subsequent manual workload
  • Creating sets of answers and chat options based on historical data analysis for faster and more relevant communication to the customer

Solution Approach:

The solution here is focused to make the chatbots and IVRs as the primary contact point. Analysis of past customer interaction calls and historical data shows that it follows that if right data is gathered even before the call is routed to the call centre agents, the query resolution happens faster.

The solutions aims to utilize the wait time of the customer by routing to an intelligent AI/ NLP powered IVR engine. While this engine will have fixed set of responses like any IVR engine, it will also have the capability to analyse the customer speech and sentiment on the fly to probe for relevant information.

The idea is to resolve the customer query through IVR/ Chatbots eventually and eliminate the need of routing the call to a call centre agent

The inclusion of AI, ML and NLP algorithms will make the Chabot/ IVR engine intuitive enough to ask intelligent questions and direct the customer to the right solution

In-service recommendations

Business Objective: 

  • Enabling the CRM to provide on the fly dynamic recommendations to the customers depending upon the core customer data and the current sentiment of customer
  • Providing the right recommendation of next best product or digital service to the customer at the right time.
  • Ensuring there is no conflict in the recommendations with respect to the previous interactions with the customer

Business Brief: 

  • Analyzing the need of the customer through audio and speech analysis. Providing macro/ micro recommendations to the agents/ customers in real time to increase business 

Solution Approach:

STAGE 1
CRM is the core central hub where majority of the customer interactions and communications will happen for any Insurance organization.
STAGE 1
STAGE 2
Enabling the in-service recommendations will require triangulation of data from a lot of sources.
STAGE 2
STAGE 3
With the help of AI/ ML algorithms, the system will collect data from various sources like core customer and policy database, past CRM history, marketing campaigns data, digital behaviour, social profile etc. to enable the system infer the current needs of the customer
STAGE 3
STAGE 4
During the conversation with the customer, NLP will be utilized to perform on the fly sentiment analysis and speech recognition to check if the customer will respond favourably to the pre-formulated recommendations. If not, the same will be re-calculated on the fly and presented to the calling agent enabling to provide better assistance to the customers
STAGE 4
STAGE 5
This will ensure that the customer will always get the most relevant recommendations every time they connect with the customer service desk
STAGE 5
STAGE 6
The recommendations will be a mix of cross-sell/ upsell, digital adoption, putting customer in touch with right agents and employee to improve their interaction with the brand
STAGE 6

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