Double up your insurance business by leveraging AI


Deploy AI-powered solutions to improve your operational efficiency, boost sales, eliminate human errors and increase customer satisfaction.

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AI models are transforming the way
Insurance operate

AI-powered solutions helps the insurance industry in creating intelligent workflows
for their processes and improve their overall bottom-line.


Speed up labor-intensive tasks in claims and underwriting by up to


Operational Costs

Bring down expenses involved in traditional manual methods by up to


Human Errors

Automate error-prone, human tasks across processes by up to


Process Times

Improve claims processing, fraud detection, policy-level data extraction in less than


Enhance Customer

Enable real-time interactions with customers to speed up delivery and improve end user experience by


Data Seamlessly

Derive insights from various emails, scanned documents, claim forms, policy documents at an accuracy of



We bring pre-built AI models for claim
processing, data extraction and underwriting

Our AI-powered solutions help you streamline and automate your operational workflows
to drive real business impact and lay a digital foundation for future growth.

Data Extraction

Imaginea Extract is an AI-powered data extraction solution that is built primarily for the insurance industry. With our solution, you can obtain, interpret, classify, and analyze unstructured data from various claims, quotes, and policy documents with significant accuracy.

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Fraud Detection

Imaginea Detect is an AI-powered fraud detection solution to segment claims, automatically identify fraud before they are escalated and improve customer experience with faster payout to authentic claims using predictive insights and real-time analysis.

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Quote Generation

Imaginea Quote is an AI-powered quote generation solution that automatically extracts data from various unstructured sources, enter them into client’s systems, compare and validate the data and communicate the generated quote to the customer.

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We specialize in providing differentiated solutions
for your entire insurance value chain

We understand the unique needs of the insurance industry and guide them with the right fit solutions using a combination
of AI and other advanced data processing tools like Machine Learning (ML), Natural Language Proessing (NLP),
Optical Character Recognition (OCR), Deep Learning and Neural Networks.


How AI changes your bottom-line

Taking the AI route in Insurance can fix crucial industry challenges to get the best value
out of data, streamline processes and improve customer experience.


Unleashing the potential of cognitive
technologies in insurance



Choose automation over monotony: intelligize your document indexing

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Data excellence in insurance using AI

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The value of AI in transforming the insurance claims experience

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How to manage dark data with intelligent automation?

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Global Insurance Brokers & Carriers trust us


1. How will AI benefit my business?

    • To implement AI, you should be specific on the need for implementation and the expectations from it. Key benefits:
      • Improve employee productivity – AI can help in automating routine tasks with zero/ minimal human touchpoint. With this, employees’ time and effort can be diverted for other more important tasks
      • Streamline Complex Decision Making – AI has made a transition to cognitive solutions, which help in solving complex issues. Its usage in areas like fraud detection and claims adjudication is helping businesses to find the right solutions to address their challenges much better. 
      • Reduced Errors – Automating regular tasks using AI helps in the reduction of manual errors. 
      • Data Analysis – Helps in data analysis, especially in processes that need to adapt to unidentified data patterns.
      • Increased Business Efficiency – AI-driven automation can help to ensure consistent performance round the clock. This can help reduce stress on employees who can focus on more complex tasks.
      • Enable Process Level Automation – AI in the automation pipeline enables end-to-end process level automation rather than the usual task level automation.

2. How long does it take to implement AI?

    • The project timeline can vary depending on what needs to be achieved. The success of the implementation hugely depends on the availability of large and varied data sets. Restricting to data extraction case studies, possible timelines are as follows:
      • Data Classification – Minimum 2-3 weeks to months (depends on multiple factors like feature information availability, already classified data sets, etc.)
      • Data Extraction – 2-3 months (depends on multiple factors like quality of document, OCR, accuracy requirement, etc.)
      • Decision making – 2-3 months 

3. How long does it take to read a document and extract critical data?

    • Data Extraction timeline depends on many factors:
      • Size of the document
      • Network bandwidth between clients sending the document to the processing station that processes the document (for file transfer, bigger the document, greater the time)
      • Number of pages
      • OCR parsing technique (depends on the quality of incoming document) [Analogy: Inkjet printer printing quality vs ppm]
      • Complexity of the document (number of fields, tables and elements that we have to extract and relations between them)
    • In short, extraction can be done with two different approaches:
      • Realtime – any document with just a few pages (<5 pages) would only take a few seconds (<10-20 secs) to extract data.
      • Background – any documents that are 100s of pages long and with high complexity can be done in batches.
    • Most of the time, for processes which rely on data from documents, the documents are pre-processed and the information is converted from unstructured or semi-structured to a structured format before the business process starts. For this, we recommend the ‘Background’ approach.

4. How will AI improve Insurance quote management?

    • Better customer profiling – identification of safer drivers through telematics data
    • Faster quote generation
    • Improved risk analysis for the clients and suggestion of appropriate risk cover
    • Personalized pricing by analyzing historical data and categorizing the customer

5. Is AI-driven fraud detection proactive in nature?

    • Fraud detection takes many forms. In finance, fraudulent activities, such as money laundering, do not happen on an event-by-event basis, but cumulatively. In such cases, the detection of whether money laundering is happening will occur well after several ostensibly legal transactions have taken place and been analyzed. It is not possible to detect the onset of money laundering right before the first suspicious transaction occurs since the suspicion is in retrospect. However, the cumulative effect of a transaction pattern can be to cast increased suspicion on a new transaction, which, if proved fraudulent, could trigger an alert. 
    • In the case of insurance fraud, all required data may be available at the time of claim. Therefore, an analysis can be triggered proactively before the claim is processed further. In other cases, a pattern of fraud emerges only after several claims have been completed. In such cases, while the completed cases have to be dealt with in retrospect, subsequent claims can be set up to trigger appropriate alerts before claims processing kicks in.

6. How do AI and ML help insurance companies to prevent fraudulent claims?

      • AI and ML help in analyzing inconsistency in data patterns provided by claimants. They also help in providing details on suspicious claims with potential liability and repair cost assessments, and suggesting procedures that can resolve and enhance fraud protection with their learning capabilities.
      • Today’s AI/ ML tools learn patterns of transactions that would be considered “normal” and treat fraudulent transactions as ones that seem “abnormal” when compared to known patterns. For example, consider a situation when a person who usually deposits only small amounts suddenly deposits a large amount, or one who usually receives only small amounts gets a large amount from a new source. Conventional approaches to pattern-based anomaly detection are limited to known patterns, whereas AI/ ML-based approaches can discover patterns on their own from transaction patterns.

7. How can AI be integrated with my existing system?

      • AI can be added to any existing pipeline since most AI pipelines are API-enabled. They can be process-specific or generic.
      • Training AI models usually involves data processing pipelines that do not interfere with business activity though they draw data from it. Once trained, a deployed AI model (typically) takes the form of an API-enabled service that can be integrated into regular systems using conventional engineering approaches. An additional business process to consider is setting up a schedule for model (re)training, validation, versioning, hosting and monitoring. Parts of this process can involve assigning personnel with the requisite business knowledge to provide input to the model training. The advantage is that this input work can be translated into repeated usage many folds, thus potentially saving cost. In certain specialized cases, models may need to be deployed to end-user devices such as mobile phones or web browsers, which would involve additional engineering activities including packaging, optimization and maintenance.

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