one of the biggest insurance brokers in the United States, has over 25 years of experience. They offer services at over 40+ locations that include auto, motorcycle, boat, life, health, pets, and other insurance services.
As an insurance broker, our client also conducts underwriting services for numerous insurance providers across the US.
Insurers use underwriting to verify the authenticity of clients' insurance documents containing personal and financial information. This process is typically done manually but can be automated using AI-based technologies like machine learning, natural language processing, and cognitive computing.
With a high influx of customers, introducing automation to increase customer engagement and decrease manual workload has become paramount.
Our client’s project scope revolved around automating the underwriting tasks. It includes verification of customer information offered during claims registration and digitizing the same.
These documents included:
Underwriting can be a tedious process that traditionally involves manually verifying various documents and proofs. While assessing, an underwriter must be highly attentive as they primarily have to safeguard the company's risks.
The manual verification process for such a lengthy sales cycle demands ample time, energy, and workforce and yet leaves room for error, resulting in the loss of customers.
Our client’s prerequisite for underwriting was to meet this challenge with an Optical Character Recognition model that could quickly scan claimant’s documents. They include first notice of loss documents, bank statements, credit scores, and other printed or handwritten claim-processing documents. The model would transcribe the same and render a digitized copy.
Our client was looking to partner with an AI solutions partner with expertise and experience crafting tailored solutions for the insurance industry.
Their requirements included:
Maruti Techlabs had previously worked on AI projects within the insurance industry. We connected with the client’s team to discuss our expertise in enhancing their underwriting processes. Our experts wanted to gain deeper insights into their underwriting and related operational processes that could be addressed using AI solutions.
After learning their challenges and assessing our capabilities, both parties agreed to proceed with a time-constrained Proof of Concept (PoC) as a trial run.
Following a successful execution of the PoC, they proceeded with their automation venture. Subsequently, we learned the project's scope and developed a comprehensive timeline.
Our approach to understanding their business goals, validating the proposed solution through a time-limited Proof of Concept, and recommendations from industry peers were crucial factors for our client's decision-making process. These factors gave our client the confidence to proceed with Maruti Techlabs.
We created a Python-based OCR model that would look for inconsistencies, missing information, or errors, and these applications would be flagged for manual review. The unflagged documents would be sent to the underwriters for processing.
Our primary goal was to extract essential data from a handwritten or printed FNOL document and rectify it by cross-referencing it with the customer's registered credentials.
One major challenge our development team faced was feeding quantitative and qualitative data into the model to train the same effectively.
Here’s the step-wise process of implementing document processing using Optical Character Recognition:
We tested our model using the test data provided by the client. Once the model was tried and tested with numerous use cases, it was deployed to their online platforms.
At Maruti Techlabs, we like to ensure transparency in communication while working on any project.
We leveraged JIRA for sprint management, task creation, roadmap tracking, and backlog grooming for this project.
Other than this, we scheduled weekly calls on Google Meet to share the development progress and specifics about the project. Both parties used Slack to carry out the day-to-day communication.
We deployed the following team for the development phase of the project:
We follow Agile, Lean, & DevOps best practices to create a superior prototype that brings your users’ ideas to fruition through collaboration & rapid execution. Our top priority is quick reaction time & accessibility.
We really want to be your extended team, so apart from the regular meetings, you can be sure that each of our team members is one phone call, email, or message away.