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6 InsurTech Companies in India Featured in the Prestigious InsurTech100

3 minutes, 36 seconds read

Indian technology companies are leading InsurTech innovations and 6 firms have successfully secured a spot in the InsurTech100. FinTech Global’s InsurTech100 is an annual list of tech-startups- transforming the digital insurance landscape through innovative products and solutions. These top 100 InsurTechs are recognized by a panel of analysts and industry stalwarts from an exhaustive list of over 1000 technology firms, who are solving the most-pressing insurance challenges. Here are the InsurTech Companies in India who are pioneering the Global InsurTech revolution.

Acko

Acko is India’s first fully-digital general insurance company. Founded in 2017, it provides personalized pricing to customers through deep-data analytics. It studies customers’ interaction patterns and behaviours and accordingly suggests insurance products. 

Currently, Acko has insured over 40 million Indians, acquiring 8% of the car insurance policies bought online in India. It also introduced Ola Ride Insurance for lost baggage, laptops, missed flights, accidental medical expenses, and ambulance transportation cover. 

Artivatic

Artivatic provides an insurance SaaS platform to automate buyer onboarding, profiling, underwriting, and claims administration. Their solutions leverage cutting-edge technologies like NLP, ML, Deep Learning, Behavior Analysis, AI, and IoT.

Currently, the company is working with 16 clients which include Deloitte, KPMC, HCL, and Cynopia, among others.

Mantra Labs

Mantra Labs is an AI-first product & solutions firm solving the most pressing front & back-office challenges faced by Insurance carriers. Their product portfolio includes — FlowMagic, a visual-AI platform for insurer workflows; an AI-enabled chatbot for insurance; and an AI-driven lead conversion accelerator that maximizes opportunities from the sales funnel.

One of the oldest InsurTech companies in India, Mantra Labs has worked with leading insurers like Religare, DHFL Pramerica, Aditya Birla Health, and AIA Hongkong along with unicorn Internet startups like Ola, Myntra and Quikr. Mantra Labs also has strategic technology partnerships with MongoDB, IBM Watson, and Nvidia.

Pentation Analytics

Pentation Analytics provides state-of-the-art analytics applications targeting core insurance use cases. The company has introduced ‘Insurance Analytics Suite®’ which addresses retention/persistence, cross-sell, acquisition, and underwriting through advanced machine learning models. The product is adaptable to both cloud and on-premise applications. 

Pentation Analytics is partners with international technology companies like Hewlett Packard Enterprise, HortonWorks, Hitachi, among others.

PolicyBazaar

PolicyBazaar is India’s largest insurance marketplace. It allows users to view and compare different insurance policies online based on their preferences. Users can also buy, sell, and store policies online. The platform provides an end-to-end solution to track policies and claims assistance. The company hosts over 100 million visitors annually and records nearly 1,000,000 sales transactions/month. Currently, PolicyBazaar accounts for nearly 32% of India’s life cover & retail health business collectively. 

The company has support from an array of meticulous investors like SoftBank, InfoEdge (Naukri.com), Temasek, Tiger Global Management, True North, and Premji Invest. 

Toffee Insurance

Toffee Insurance is a new-age contextual microinsurance products firm. It’s customer-centric products deconstruct traditional underwriting and pack relevant policies according to individual requirements. The company is distributing plans through different channels like APIs, mobile, and SMS transactions. Their current portfolio includes cycle insurance, income protection insurance, daily commute insurance, and dengue insurance catering to individuals with monthly income less than USD 300. 

The company has succeeded in issuing policies to 115K+ Indians, of which 80% are first-time buyers. Currently, Toffee Insurance is partners with Hero Cycles, Wildcraft, Eko, and Apollo Hospitals and is backed by ICICI Prudential, Religare, HDFC Ergo, and Tata AIG Insurance among many others.

Changing market dynamics has brought a radical shift within the insurance industry. AI-driven technologies are making subtle changes to the way millennials and younger generations are thinking about Insurance as an immediate need. Insurtech is well poised above all else, to satisfy even the most unique coverage needs, removing traditional challenges like ownership from the mix.

With the growing popularity of digital channels, customers prefer self-service portals for quick access and instant solutions for their ever-changing financial and protection needs. Also, customers are now more aware of the potential threats than ever before and expect relevant products from insurers. “25% of business customers and fewer than 15% of retail policyholders believe they are covered comprehensively against emerging risks”(according to the World InsurTech Report 2019); indicating a rising need for consumer-centric and innovative insurance solutions to meet the new demand.

[Related: 10 Takeaways from the World InsurTech Report 2019]

In the year 2018, the InsurTech100 was secured by 7 InsurTech companies in India — Acko, Arvi, CoverFox, GramCover, PolicyBazaar, PolicyX, and Toffee Insurance as innovative InsurTechs.

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Tabular Data Extraction from Invoice Documents

5 minutes, 12 seconds read

The task of extracting information from tables is a long-running problem statement in the world of machine learning and image processing. Although the latest accomplishments in the field of deep learning have seen a lot of success, tabular data extraction still remains a challenge due to the vast amount of ways in which tables are represented both visually and structurally. Below are some of the examples: 

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Invoice Documents

Many companies process their bills in the form of invoices which contain tables that hold information about the items along with their prices and quantities. This information is generally required to be stored in databases while these invoices get processed.

Traditionally, this information is required to be hand filled into a database software however, this approach has some drawbacks:

1. The whole process is time consuming.

2. Certain errors might get induced during the data entry process.

3. Extra cost of manual data entry.

 An invoice automation system can be deployed to address these shortcomings. The idea is to upload the invoice document and the system will read and generate the tabular information in the digital format making the whole process faster and more cost-effective for companies.

Fig. 6

Fig. 6 shows a sample invoice that contains some regular invoice details such as Invoice No, Invoice Date, Company details, and two tables holding transaction information. Now, our goal is to extract the information present in the two tables.

Tabular Information

The problem of extracting tables from invoices can be condensed into 2 main subtasks.

1. Table Detection

2. Tabular Structure Extraction.

 What is Table Detection?

 Table Detection is the process of identifying and locating tables that are present in a document, usually an image. There are multiple ways to detect tables in an image. Some of the approaches make use of image processing toolkits like OpenCV while some of the other approaches use statistical models on features extracted from the documents such as Text Position and Text Characteristics. Recently more deep learning approaches have been used to detect tables using trained neural networks similar to the ones used in Object Detection.

What is Table Structure Extraction?

Table Structure Extraction is the process of extracting the tabular information once the boundaries of the table are detected through Table Detection. The information within the rows and columns is then extracted and transferred to the desired format, usually CSV or Excel file.

Table Detection using Faster RCNN

Faster RCNN is a neural network model that comes from the RCNN family. It is the successor of Fast RCNN created by Ross Girshick in 2015. The name Faster RCNN is to signify an improvement over the previous model both in terms of training speed and detection speed. 

To read more about the model framework, one can access the paper Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

 There are many other object detection model architectures that are available for use today. Each model comes with certain advantages and disadvantages in terms of prediction accuracy, model parameter size, inference speed, etc.

For the task of detecting tables in invoice documents, we will select the Faster RCNN model with FPN(Feature Pyramid Network) as a feature extraction network. The model is pre-trained on the ImageNet corpus using ResNET 101 architecture. The ImageNet corpus is a public dataset that consists of more than 20,000 image categories of everyday objects.  We will therefore make use of a Pytorch framework to train and test the model.

The above mentioned model gives us a fast inference time and a high Mean Average Precision. It is preferred for cases where a quick real time detection is desired.

First, the model is to be trained using public datasets for Table Detection such as Marmot and UNLV datasets. Next, we further fine-tune the model with our custom labeled dataset. For the purpose of labeling, we will follow the COCO annotation format.

Once trained, the model displayed an accuracy close to 86% on our custom dataset. There are certain scenarios where the model fails to locate the tables such as cases containing watermarks and/or overlapping texts. Tables without borders are also missed in a few instances. However, the model has shown its ability to learn from examples and detect tables in multiple different invoice documents. 

Fig. 7

After running inference on the sample invoice from Fig 6, we can see two table boundaries being detected by the model in Fig 7. The first table gets detected with 100% accuracy and the second table is detected with 99% accuracy.

Table Structure Extraction

Once the boundaries of the table are detected by the model, an OCR (Optical Character Reader) mechanism is used to extract the text within the boundaries. The text is then processed using the information that is part of a unique table.

We were able to extract the correct structure of the table, including its headers and line items using logics derived from the invoices. The difficulty of this process depends on the type of invoice format at hand.

There are multiple challenges that one may encounter while building an algorithm to extract structure. Some of them are:

  1. The span of some table columns may overlap making it difficult to determine the boundaries between columns.
  2. The fonts and sizes present within tables may vary from one table to another. The algorithm should be able to accomodate for this variation.
  3. The tables might get split into two pages and detecting the continuation of a table might be challenging.

Certain deep learning approaches have also been published recently to determine the structure of a table. However, training them on custom datasets still remains a challenge. 

Fig 8

The final result is then stored in a CSV file and can be edited or stored according to one’s convenience as shown in Fig 8 which displays the first table information.

Conclusion

The deep learning approach to extracting information from structured documents is a step in the right direction. With high accuracy and low running time, the systems can only learn to perform better with more data. The recent and upcoming advancements in computer vision approaches have made processes such as invoice automation significantly accessible and robust.

About the author:

Prateek Sethi is a Data Scientist working at Mantra Labs. His work involves leveraging Artificial Intelligence to create data-driven solutions. Apart from his work he takes a keen interest in football and exploring the outdoors.

Further Reading:

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