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Intelligent Claims Automation Is Reshaping Malaysia’s Insurance Sector

Malaysia, drawn by its strong economic growth, expanding middle-class income and rising insurance penetration levels, is witnessing a new era of innovation – with AI leading the charge in bringing new and intelligent technologies to the mass-market.

According to Bank Negara, the country’s regulator of banks and insurers, life insurance penetration rate stood at 56% in 2018. Foreign insurers have been highly keen in this market despite lingering regulatory uncertainty over the sector’s foreign ownership rules, currently set at a 70% cap.

While ‘motor’ remains the largest class of insurance with a market share of 45.6%, followed by fire at 19.2% and marine, aviation and transit (MAT) at 8.2%; Takaful has been outpacing conventional insurance in the Islamic peninsula.

(Takaful refers to Islamic insurance products.)
Islamic insurance penetration rate in the country will likely touch 16% in 2019. In financial dealings, ‘takaful’ firms follow religious guidelines including bans on interest and monetary speculation and a prohibition on investing in industries such as alcohol and gambling.

Growth in the takaful business in Malaysia, the world’s second largest Islamic insurance market after Saudi Arabia, is backed by government efforts to reach out to the general consumer with affordable insurance coverage and the potential use of better technology as a disruptor.

AI is already poised to play a crucial role in Malaysia’s next big step. By 2021, Artificial Intelligence will allow the rate of innovation to almost double (1.8x) and increase employee productivity improvements by 60% in Malaysia, according to an AI study put forth by Microsoft & IDC-ASEAN Research Group.

While seven in 10 business leaders polled agreed that AI was instrumental for their organisation’s competitiveness, only 26% have embarked on their AI journeys. Those that have adopted AI expect it to increase their competitiveness by 2.2 times in 2021. Though, everyone agrees – every single interaction from here on is going to be digital.

Mckinsey Claims Automation Benefits

Malaysia is also moving towards a cashless society with infrastructure being put into place to facilitate e-payments which have more than doubled per capita from 2011 to 2019. For this, banking solutions in the region have ramped up digital investments so customers can take advantage of convenient and secure banking.

Intelligent Claims Automation

For insurers, claims settlement represents a large customer service touch point. However, taking a customer seamlessly through the claims resolution process is not always going to be simple.

Being an AI-driven insurtech enterprise means being able to fully utilize data and optimize business processes with powerful algorithms, creating the space for data-driven decision making. With AI, the claims process can be augmented using chatbots to convey support and status of a claim, and Machine Learning (ML) that can study large-volume patterns to reveal insights and detect fraud. Claims automation can be achieved at part or whole of the settlement process.

Claims Management Process

The Malaysian Insurance market is already witnessed to big insurers rolling out innovative products for customers, such as “Ask Sara” – AIA’s AI-powered enquiry channel that provides instant, real-time answers to agents anytime via Facebook Messenger. Integrating sensors into the value chain has also provided greater rewards with predictive modelling and data analytics, like Katsana – a telematics company that is enabling insurers to provide usage-based insurance based on driver’s performance data. These measures allow for safer, accurate and more affordable risk-based pricing for consumers.

The attitudes of the insurers and younger generations are shifting alongside their Asian peers, to a seemingly more AI-involved future. While the general insurance trade has witnessed nearly stagnant growth over the past several years, AI can help lower overheads and variable costs that will enable insurers to roll out affordable coverage, including to the under-served segment.


Enterprises benefit from our AI-first thinking.
We build AI roadmaps from scratch, guiding you all the way through your next transformational journey.

To learn how, drop us a line here: hello@mantralabsglobal.com


International Insurance Landscape

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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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