AUTO INSURANCE TRACKING COMPANY

Executive Summary

Verifacto, a leading provider of insurance tracking and risk management company wanted to enable their clients to better manage risk, collections and customer interactions using technology. They partnered with ITTStar to develop a holistic solution that would help them accelerate business operations and drive customer satisfaction. Leveraging the industry leading capability of AWS, ITTStar built an AI driven BOT to increase their productivity, automate workflows and drive operational efficiency across functions.

Customer challenge

Verifacto processed all customer queries manually. They engaged in daily tasks such as sending emails and text messages to remind customers of their monthly bill payments using manual processes. This generated a requirement to hire data-operators and tele-callers, leading to an increase in operational costs.

In order to streamline these common work activities, Verifacto was looking for a partner to help them solve the above challenge through the use of technology. Their objective was to increase productivity, automate simple tasks, and drive operational efficiencies across functions. They wanted their customers to quickly be able to answer their clients’ queries quickly and for agents to address the more complex inquiries in a timely manner both leading to customer satisfaction.

Partner Solution

To solve this business challenge, Verifacto brought ITTStar onboard. After understanding and evaluating the customer needs, ITTStar suggested to invest in an AI driven BOT using cloud infrastructure which would help drive operational efficiencies and also have other benefits such as being cost-effective, easy-to-use, agile and scalable. ITTStar approached this project using the below methodology:

• Phase 1: Discovery – ITTStar commenced the project with an as-is scenario analysis by diving deep into dataset exploration, qualifying of available data, reviewing historical SMS and Emaills, and understanding of all current workflows.

• Phase 2: Solution Designing – Basis the above analysis, ITTStar designed an automated AI based BOT to enable handling of all end to end customer queries, information storage and understanding of customer behavior

• Phase 3: Develop & Deploy – ITTStar developed and deployed a sophisticated model after Dataset Transformation & Aggregation. It started with automating SMS and Email conversations with customers for proof of insurance , payment reminders & customer loan payment defaults. AWS Lambda was then created to read utterance and fetch responses from the BOT. All fallouts or unhandled utterances were stored in DynamoDB for reprocessing.

Why AWS?

AWS provides cloud native easy-to-use serverless architecture to customers to improve and extend their existing capabilities for enhanced performance. On doing an end-end analysis of customer’s existing infrastructure, ITTStar discovered that the customer was already using AWS infrastructure. Hence, Amazon Lex & Amazon Comprehend were identified to use as they would integrate easily into all their existing applications and would also scale automatically. AWS also provides developers with an easy-to-use modular architecture and comprehensive APIs to enable building and deploying conversational BOTs on various platforms including mobile. These modular architectures were used to custom build the BOT for the client on AWS

Impact & Results

ITTStar’s state-of-the-art approach led to the following benefits for Verifacto:

• Automation of all customer conversations & easy identification of customers
• 50% reduction in manual co-ordination with borrowers via text & email
• Close monitoring & reduction of business risk
• Increased productivity & operational efficiency
• Analysis of customer behavior leading to opportunity to predict and forecast customer payments, interactions and defaults if any
• Ability to communicate and solve customer queries in a timely manner
• Free up time and bandwidth for agents to solve more complex inquiries
• Enhanced customer satisfaction & experience