Guest post by Timothy Partasevitch, Chief Growth Officer at Smart IT.
Smart IT is a custom software development and web development company headquartered in Minsk and San Francisco.
Customer service in the insurance industry leaves much to be desired. Clients traditionally had to put up with the lack of smooth operations and absence of personalization. That’s when they had to deal with traditional insurers. Until tech giants like Amazon, Facebook, Apple, and Google refined customer experience and expectations.
Since then, Insurtech has started to revolutionize the industry. It is offering unique experiences for clients wanting to acquire or issue a renewal with the help of AI. Everything may be simplified and expedited, from claims to underwriting to data analysis. The early effect of AI insurance will be focused on increasing efficiency and automating customer-facing coverage and claims procedures.
The disruption caused by COVID-19 shifted the timetables for AI adoption by considerably speeding up insurers’ digitalization. Here are some widely used insurance industry trends that would be shaping the future:
- Customer experience: Insurers use big data analytics to detect pain areas in client interactions and make the process more fluid and personalized.
- Fraud detection and back-office processing: Behind the scenes, insurers are employing AI to improve underwriting and automate basic repetitive operations like claims processing, allowing employees to focus on more strategic activities.
- Risk management and compliance: Artificial intelligence (AI) has shown to be particularly useful for automating compliance and boosting predictive risk analytics.
- Cybersecurity systems: Deep learning is also being seen as the way of the future for cybersecurity systems, allowing them to adapt at the same rate that cyber threats develop.
AI in the Health Insurance Industry
The amount of electronic health data, such as medical records and claims information, has exploded in recent years. At the same time, the health insurance sector has yet to figure out how to use this tremendous resource. In the health insurance market, predictive analytics is considered a way to improve patient outcomes. It also increases the efficiency of health claims processing, and lowers operational costs and patient premiums.
The benefits of using AI in the health insurance industry are:
- Increased data precision using NLP (Natural Language Processing): Deep integrations between diverse interfaces enable AI platforms that combine numerous healthcare datasets to discover hidden information. The data may be evaluated in text formats using the natural language processing or the NLP technique to provide useful insights.
- Enhanced customization: A recommendation engine might utilize machine learning to tailor the navigation process and provide the best-fit insurance offers for end customers.
- Strict anti-fraud measures: This would be based on patterns derived from the correlation of inaccurate billing trends, service underutilization records, and abnormalities in the database in question.
Benefits of Predictive Analytics
The process of discovering patterns in data and determining whether those patterns are likely to repeat is known as predictive analytics. Based on the possibility of previous patterns recurring, businesses and investors modify their resource allocation to take advantage of future occurrences. Below, we discuss three instances where predictive analytics might help health insurance companies.
Data-Driven Claims Decisions
Health insurance companies utilize predictive analytics to streamline their claims processing procedures in six ways:
- Resource allocation/triage
- Values for reserving/settling
- Identifying assertions that may be false
- Early notification of possibly large-scale losses
- Management of expenditures
- Studying the trends
The management of “outlier” claims that look normal but can evolve into high-value losses is a unique use of predictive analytics for health claims processing. Workers’ compensation claims, for example, can result in long-term impairment and permanency. Some seemingly modest claims, such as those involving soft tissue injuries, may deteriorate over time.
Insurers can detect “creeping catastrophic” (or creeping Cat) potential by using predictive analytics. It helps them to examine historical claims data for parallels and other features of such losses. Early in the claims process, strategies and resources targeted to limit losses from such claims can be implemented to decrease the risk of ballooning expenses.
Reduced Operating Expenses
Value-based insurance design (V-BID) is emphasized in healthcare legislation, such as the Affordable Care Act of 2010. V-BID aims to improve healthcare quality while lowering costs through financial incentives that promote efficiency and customer choice.
Many state governments and big insurers, including Blue Cross and Blue Shield plans, have adopted the V-BID method. Predictive analytics can help healthcare providers and insurers overcome the challenges of implementing value-based reimbursement models. Data analytics may help value-based care models in five ways:
- Identify treatment gaps and at-risk patients that are most likely to have a “crisis event”;
- Make incorrect usage of high-cost health services more visible;
- Address socioeconomic determinants of health, such as access to food and shelter, patient health literacy, and transportation;
- Create, test, and put into practice innovative processes and care models;
- Improve patient satisfaction and consumer satisfaction.
Improved Profitability and Expansion in New and Existing Markets
Studies have estimated that 5% of all patients account for roughly half of all healthcare costs in the U.S. Healthcare professionals use predictive health analytics to discover variables in their patients that are antecedents of chronic diseases and conditions. To decrease inpatient admissions and emergency department visits, health insurers increasingly rely on predictive analytics to identify and engage high-risk patients.
The Health Care Transformation Task Force’s initiative to build care management programs for high-need, high-cost groups is an example of predictive analytics used to identify high-risk patients. The health insurance software systems incorporate qualitative data from physicians and patients and quantitative data from claims, demographic data, and other publicly available sources.
The use of predictive analytics in the insurance business is still in its early stages. For insurers, the future of this technology offers increased efficiency, profitability, and higher consumer engagement.
While insurers are now focusing on life, health, and auto coverage using predictive analytics, other forms of underwriting have proven more challenging to adapt to this and other artificial intelligence healthcare technology. Therefore, artificial intelligence companies may need to focus on this particular aspect.
One area where insurers are expected to benefit from predictive analytics is acquiring information into client’s behaviour and preferences. Further, big data applications in insurance will help insurers to construct more detailed risk profiles of consumers. For example, allowing them to offer low-cost insurance to high-risk consumers rather than denying them coverage outright.
The insurance industry has been hesitant to accept new technologies compared to its digitally native rivals. The employment of this and other artificial intelligence technology in the sector is still in its early stages. Human actuaries and underwriters will undoubtedly continue to play an essential role in the insurance business. However, their responsibilities will alter as new technology and data sources become available.
The capacity to forecast outcomes and behavior changes is crucial to the insurance sector.
Predictive analytics tools and other artificial intelligence software will be the only way to exceed the competition in this crucial sector in the future. These tools will significantly influence all insurance providers and their consumers to become more accessible to corporate decision-makers.
Author: Timothy Partasevitch, Chief Growth Officer at Smart IT.
Tim is a sales and marketing specialist, who solves business challenges like an engineer by focusing on data insights, analysing what works, what doesn’t, and what can be improved from a technical and financial perspective. Over the years, he has supported the transformation of new clients into long-term partners and expanded services provided in the work space, ultimately facilitating revenue generation and business success. Tim strongly believes that you can’t be in charge of the outcome and results. However, you are 100% in charge of the input.