COVID-19 has accelerated the need for digital systems in the new work-from-home business paradigm. Most companies have been pushed to remodel and run fully online. The insurance sector is no exception.
In this article, we'll explore the rise of digital disruption in insurance, and how it is setting the tone for a complete redesigning of the insurance sector.
The 21st century is witnessing businesses from all spheres being thrown into the life-changing chaos induced by technology. The companies emerging from this disruption are not traditional businesses, but new-age solutions that offer agile and efficient services to customers, supported by a range of ever-evolving tech.
The financial sector, in particular, has mostly migrated to online services, which can be discerned by the rise in usage of mobile wallets, net banking, online trading, e-payments, etc.
The insurance industry, however, is slow and slightly reluctant about jumping on the bandwagon. A Deloitte survey of 200 EMEA Insurance executives reveals that most incumbents are skeptical of a fast-paced digital disruption, believing that a gradual advancement is enough to meet changing customer demands and the challenge of building a digital infrastructure.
Consequently, workflows in the Insurance sector still depend on manual processes, tons of paperwork, and excessive human intervention.
However, Deloitte states the industry is overestimating the time it has to morph into a digital ecosystem that offers personalized online solutions at scale. The Research conducted by Accenture reveals that out of the 18 industries studied, Insurance is the most susceptible to future disruption.
So let's look at how modern technology can change the sector.
AI technologies learn and improve over time as they process more data. In insurance, AI can automate several processes and reduce the role of manual tasks.
For example, neural networks can be used to detect fraud patterns in insurance claims or identity proofs. Similarly, these networks can also train chatbots to answer common customer queries through emails, phone messages, or chat boxes.
Machine learning can help in competitive pricing of insurance policies, personalizing product recommendations for customers, and optimizing risk and actuarial models to help form highly profitable policies.
Big data involves processing enormous volumes of data that have been acquired from various sources, to conduct predictive analysis so that businesses can make intelligent decisions.
For example, motor insurance companies install telematics devices in cars to observe the driving patterns of the policyholder. The behavioral data acquired is tallied with a pool of risk assessment factors, based on which the premium for that insurance policy is decided.
Claim settlement is another area where big data could be leveraged. Machine Learning algorithms can evaluate the loss or damage that has occurred to a policyholder by tapping into the sea of data available on claims and corresponding elements.
In fact, data science companies like DataRobot use ML to automate data cleansing and selecting which statistical method to use for which problem in the claims procedure.
RPA is used to automate repetitive and mundane tasks so that process cycles can be faster and smoother. Instead of wasting their capacities on redundant chores, insurance workers can focus on the bigger picture and create useful products to raise customer satisfaction.
Typically, an RPA software will extract information from a document, track errors, fill it up in relevant fields automatically, and verify the collected data.
In the context of insurance, you can use RPA to streamline an array of processes like registering and processing claims, underwriting, compliance procedures, form registration, fraud detection, etc.
Any object connected to the internet is an IoT device. These smart devices collect and store data extensively, facilitating customer behavior analysis, accurate risk assessment, and astute decision making.
For example, John Hancock distributed free Fitbits to customers to incentivize them to keep a track of their vitals. A customer who is conscious of his/her health will be less likely to file a claim.
Liberty Mutual collaborated with Google's Nest to install internet-connected smoke alarms in homes, enabling customers to keep a check on fire hazard risks and reduce their home insurance premiums.
From Telematics devices and drones to smart toothbrushes, disruptive IoT devices are increasingly being employed by progressive insurance companies to evaluate customer losses, influence customer behavior, and gather important data.
Straight through Processing (STP) involves automated processing of transactions to eliminate the need for human intervention. The main goal is to inculcate technology in day-to-day processes to improve efficiency.
We're living in a consumer-centric business world that relies on swift user experiences to satisfy its customers. STP is a necessity for businesses that want to thrive in such a world. While the finance and payments industry has already imbibed STP in their workflows, the insurance industry is striving to catch up.
In insurance, we see the most common use of STP in underwriting. A high degree of adoption is expected to be seen in the claims procedure in the coming times. However, an ideal scenario would be if insurance companies used straight-through processing during the entire lifecycle of an insurance policy, right from application through underwriting, issue, and settling claims.
Insurance companies should streamline and blend the entire process, so that data captured during the first step can easily flow into other activities like risk-assessment and price estimation, etc.
Trusted by many data-driven businesses, Docsumo is an Intelligent Document Processing solution that can help seamlessly automate the underwriting, compliance, and claims procedure for your insurance company.
Through Docsumo, you can streamline the most challenging aspects of your policy lifecycle in the following manner:
You can easily capture data from application forms in real-time, without having to write custom rules. Docsumo's OMR technology enables you to extract key-value pairs, verify the captured information from various documents, and identify signature blocks.
You can check KYC documents and easily identify any errors, frauds, or discrepancies before bringing the customers onboard. Docsumo assists you in proper verification as its functionality allows you to match identity from multiple documents, match selfies with IDs, and cross-check with external databases.
Docsumo enables you to effortlessly capture data from invoices, categorize line items, validate important details such as tax rates and claim amounts, all the while maintaining precision and speed.
With Docsumo's intelligent OCR technology, you can achieve over 98% accuracy and with all the data safety measures to safeguard the transfer and digitization of your data. Its reporting feature gives you detailed performance reports so that you can keep track of your team's progress in real-time.
With the intervention of disruptive technology, the insurance data processing systems will change radically, giving way to automated workflows. Insurers will be able to create beneficial policies that are more appealing and customer-driven, something insurance companies majorly struggle with.
It is evident that in the coming times, insurance companies need to proactively adapt to disruptive 'insurtech' if they want to stay ahead in the game.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.