Automated Intake Applications: A Massive Growth Opportunity for Insurers
Insurance | 6 min READ
    
What Are Automated Intake Applications (AIA)?
Yet in today’s time and age, the application intake process for insurance is inconvenient, highly manual, and error-prone. While pdfs have substituted paper-based submissions, and they are shared through emails or a digital portal – the approach and processes haven’t changed much over the years. Professional experts go through every submission and highlight the appropriate data elements to assess the risk. They have to then manually re-orient the elements and enter them into the core insurance systems. This would be much reliable and convenient with the use of AI-based solutions and automation in insurance.
Shyam Somani
Shyam Somani

Head

Insurance Solutions

Birlasoft

Tej Sarup
Tej Sarup

Practice Head

AI & Big Data

Birlasoft

 
Artificial Intelligence in commercial insurance opens a plethora of opportunities for professionals to ramp up efficiency, productivity, and workplace satisfaction. Automated Intake Applications (AIAs) enable insurance agents to bring an organized flow into the entire process. Streamline the intake of submissions or application documents and faster converting them into data to feed in their submission and underwriting systems.
These solutions are facilitated with the use of AI, natural language processing (NLP), and named entity recognition (NER) for the comprehension of insurance documents, analysis of unstructured data, and classification of insurance-centric data elements such as name, city, country, state, limits, premiums, deductibles, coverage type, endorsements, nominees, exclusions, loss run reports, statements of value, and more.
How Do AIA Work?
The process of Automated Intake Application involves careful scooping out of data from the most popular structured as well as unstructured data sources your organization relies on. These reliable sources are the stakeholders such as medical providers, claimants, policyholders, brokers, and agents.
Usually, with the help of AI and Machine Learning, an automated process is put in place which takes these structured and unstructured data through a multi-step process, generally comprising of the following steps:
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  • Labeling: Recognize the type of document
  • Splitting: Set a category and order for the related pages
  • Indexing: Scoop out information and link it to a policy number
  • Alerting: Notifying the areas of manual intervention and review
  • Routing: Deliver processed applications to appropriate stakeholders
  • Reporting: Offer an intuitive dashboard with insights on the routed document
Automated Intake Application – Underlying Process
Automated Intake Application – Underlying Process

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Successful insurers and underwriters automate their workflows and application processes related to a set of pre-defined events like an acknowledgment, requests for additions or missing information, and updates on approval or rejection on submissions.
An insurer leverages AI and machine learning models to make value-driven decisions and reduce the chances of unfair actions. Every output from these models improves the accuracy and precision of decision-making. Let’s look at this example of property and casualty insurance, a submission coverage for a property built in 1980 versus another property that shows no history of losses in the last five years. The AI model will make sure that the latter gets a reward of premium credit, thus delivering exceptional customer experiences. Had there been no AI model in place, insurers might miss considering these aspects when there’s a massive volume of application submissions.
Robotic Process Automation is another way to simplify, distribute, service, underwrite and improve productivity along with revenue growth. This process streamlines the application intake through a thoughtfully crafted strategy clearly aligned with business objectives.
Why Should Insurers Adopt AIA?
Automated Intake Applications: A Massive Growth Opportunity for Insurers
Application intake is where the insurance process begins, and automating this first step inevitably smoothens out the entire operations, thereafter, ironing out the challenges. Once automation sets in, manual efforts can be redirected to more strategic areas, such as evaluating customer insights to innovate path-breaking insurance products. Not just one, AIA comes with many more benefits. In this context, let’s look at the benefits and the KPIs to measure successful integration:
Top benefits
The four primary benefits to automating the application intake process are:
Get deeper visibility and insights
The dashboard view of analytics of a particular document builds an understanding of the use of data flow, insights on business operations to make it evenly functional, metrics on quality, and turnaround time to improve the efficiency and management of customer service-level agreements internally and work in cooperation with the regulatory compliance.
Expedite key processes and enhance workflows:
Insurers and companies achieve a great margin of operational cost savings in further processes such as claims, underwriting, and policy administration through faster data access.
Automation that is aligned with your business
Insurance Intake Automation created through Machine Learning models self-train and adapt to the patterns and nuances of the operations and workflows, so insurers are able to complete work faster with timely accuracy.
Deliver superlative customer experience
Improved turnaround time for policy quotes, claim notifications, and ad-hoc changes or reforms. Thus, delivering satisfaction to both internal and external stakeholders and living up to their expectations.
Benefits of Automated Intake Applications in Insurance
Benefits of Automated Intake Applications in Insurance

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KPIs to Track Success
SLA adherence: Success measured against a well-defined set of parameters with service level agreements for individual stakeholders. Assessing if the process has met those parameters and added value to the customer journey while improving the overall operations.
Human hours saved: Productivity and efficiency achieved with reducing pre-clearance time, incoming submissions tracking time with more human hours vested in culling out information to better customer experience.
Improved turnaround time: Tracking the turnaround time on priority submissions and processing applications at the quickest.
Profitability: Evaluating decisions and outcomes based on the process goals and how appropriately it fits in to achieve organizational growth.
Cost savings: Application intake automation reduces operational costs. Keeping a tab on the entire operations and expenses and how much it has reduced contributing to overall process improvement.
How to Measure Success of Automated Intake Applications in Insurance
How to Measure Success of Automated Intake Applications in Insurance

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Examples of Automated Intake Applications in Insurance
Broker Submission Intake Automation
Keeping track and processing records of broker information is an essential part of the overall insurance operations and workflow. Manually entering the details and is a time-consuming and monotonous job. The process is highly manual in nature even today. Broker submissions are sent through emails and using a centralized digital portal the details are processed manually. Intake and maintaining a manual record of this submission might take hours, days, or even weeks, depending on the nature of complexity and the associated risk.
AI-led underwriting systems play a major role here and prove to be critical for churning out customer insights and driving the process better.
Automated Claims Intake
Fast and efficient claims processing begins with the seamless claims intake process ironing out the friction from the first go. Combining the technology of robotic process automation (RPA), machine learning, and human skills to smoothen out and pace up the entire claims-related operations and workflows.
Automated claims intake reduces the amount of manual work considerably, improving accuracy to a greater extent. Allowing companies to intake and triage the claims faster by collecting relevant information, drive up customer experience with faster back-office functions, fewer errors, redundancies, and reduced back and forth communication with customers.
Examples of Problems in Insurance Solved by AIA
Examples of Problems in Insurance Solved by AIA

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Automated Intake Application for insurers is a welcoming change creating a wide scope for massive growth. McKinsey re-establishes the importance of integrating value-driven, end-to-end automation from step one of the workflow. The integration can be further enhanced by including new touchpoints for data extraction and building a sophisticated, intelligent system that either strengthen cases of manual intervention or better management of highly complex applications.
As insurers seek to upgrade and modernize every bit of the insurance operations, they must locate a sweet spot for art versus science, autonomy versus control, and automation versus human intervention. Despite the existing challenges with the existing infrastructure, insurers must assess carefully and go all out to build a new era with intelligent automation. The point is to increase throughput while being efficient right from the very beginning. The beginning is your step one, which is processing submission ingestion through intake of applications.
 
 
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