Disruptive innovation in small-business insurance
Analytics-driven insurance solutions can help mitigate the evolving challenges produced by the COVID-19 pandemic.
According to the American Bankruptcy Institute, a trade organization involved in bankruptcy proceedings for attorneys and professionals, 255,000 businesses filed for bankruptcy protection during the first five months of 2020. Most of those filings happened in March when coronavirus lockdowns unrolled across the United States.
By May, the total number of Chapter 11 bankruptcies was 48% higher than the previous year.
The ramifications of these closures and shutdowns include soaring unemployment rates, as illustrated in this graphic from the Bureau of Labor Statistics.
Several government relief programs were specially designed to help small businesses survive. As of May 30, 4.4 million loans totaling $510.2 billion were made as part of the Paycheck Protection Program (PPP), according to the U.S. Small Business Association. However, these grants are not sufficient enough for small-business owners to cover their overhead costs, which includes rents, employee salaries, utility bills, and the expenses associated with instituting social distancing.
The prevailing circumstances are likely to disturb insurance carrier operating models, marketing strategies and expense ratios. Insurers that are not able to reduce costs to match falling premiums are facing severe expense ratio implications. Business interruption insurance carriers also are struggling to manage higher than expected losses along with the legal changes to their denial of many pandemic-related claims. This landscape not only poses a financial challenge to the insurance industry but also an operational one as claims continue to spike and legislators press for faster settlements.
Analytical solutions can help small-business insurance carriers face these key challenges and achieve long-term efficiencies by reducing the impact on their current portfolios and updating claims management.
COVID-19 and portfolio management
With the current fluctuations across industries and states, insurers need to closely monitor the changes in their portfolio and reconsider their existing guidelines and policy documents. Here are just some of the challenges insurers currently face:
- Present-day portfolio assessment;
- Rigorous policy triage;
- Increased demand for certain products (such as professional liability insurance for health care workers or data breach coverage due to work from home option for employees etc.);
- Constrained marketing; and
- Demand for digitization.
In order to mitigate and manage these challenges, insurers need to set and monitor new key performance indicators (KPIs) for COVID-19. After analyzing Analytical Practices of several industry leaders, it is suggested that the following analytical interventions can help insurers effectively mitigate the financial impacts of COVID-19:
Portfolio analytics: Interactive and user friendly dashboards designed in tools like Tableau and Power-BI would help the business in analyzing trends such as pre- and post-COVID19 trends across all KPIs, the extent of disruption in response flow, conversion and volume of business, and changes in industry/ state mix by marketing channel and by campaigns.
Customer segmentation and marketing mix models: Analyze the customer profiling and the existing models by bringing COVID-19 factors such as macroeconomic data, competitor information, and industry data. This would help in making required adjustments to the current marketing spend per the changes in the customer base.
Retention models: Revisiting the retention models with COVID19 factors would help in identifying high CLTV (Customer Life time value) customers in financial crises and offering them insurance concessions in the form of late fee removal, extended period for paying the policy premiums, adjusted renewal premiums thereby driving longer-term customer relationship.
Redesign creatives: Insurance companies need to revisit and continuously update their creatives and messaging as per the changing circumstances and execute controlled experiments to understand the impact of creatives on the customers and the impact on its brand would help in that.
Competitor analysis: It is also imperative to keep a close eye on what the competitors are doing, what marketing strategies are they adopting, what new products/coverages are they investing in, and considering these times as new normal, how are they adapting to these changing times.
Customer engagement through digitization: Carriers need to focus on preparing their agents with remote competencies to drive customer engagement as well as stimulate faster submissions, processing of payments, claims and enhance the overall customer experience. They would need to invest in comprehensive digital analytics solutions to:
- Track and diagnose the digital behavior flow of customers on their website to improve the customer’s website experience, increase traffic on the website and reduce the drop-out rate
- Identify the right portfolio or what sort of innovative products the Carrier should invest in
- Perform diagnostic analysis on the journey of different types of visitors and design required digital interventions.
Policy Contract Review with NLP Engine: Insurers can leverage natural language processing (NLP) solutions to review existing policy contracts. The NLP engines can be designed with the help of legal experts, underwriters and claims adjusters. The themes of the NLP engine are formed by carefully analyzing the existing laws, and tracking the policy and coverage documents to carefully determine the potential coverage, exclusions and inclusions. Some of the important factors considered by the model are the size of business, industry, exclusion of specific words from the contract, etc. Thus, it can be leveraged to identify the policies where a potential payout might happen. The NLP solution gives an edge to the above-mentioned process by integrating the new law changes and accelerating the portfolio review process.
Claims handling and management
Insurance regulators of at least 11 states have issued orders, notices or requests to insurance organizations concerning claims handling, according to the National Association of Insurance Commissioners (NAIC).
Insurance organizations have been asked to expedite claims payment by using such tools as remote adjustment options, virtual inspections and satellite imagery. Additionally, financial strain is highly likely with the grim economic outlook, and this could be an opportunity to leverage analytics to optimize claims handling and settlement processes.
The need of the hour is to improve and act upon digital and analytically driven no/low touch claims work flow. Such an intervention should:
- Enable faster, efficient assignment of claims based on complexity;
- Should leverage all existing sources of data to determine claim payout. This includes unstructured text notes;
- Check for accuracy and fraud. Identify such cases earlier and more effectively;
- Support claims adjuster and legal teams with changes in the law; and
- Integrate with existing claims process of an insurance company.
Identifying the right candidate claims for no/low touch model has been a challenge for the industry due to apprehensions about fraudsters taking advantage of such a model. As a response to COVID-19, claims organizations need to act swiftly and use various analytical levers.
While the threshold for candidate claims varies by line of business, $5,000 in indemnity value is the typical threshold used in the P&C Insurance industry. The top-left half of this grid forms an ‘inefficiency triangle,’ i.e. the claims in this area had more in ALAE (Allocated Loss Adjustment Expenses) than they had in Indemnity. In theory, the organization would have been better-off by auto-adjudicating all of these claims and saving the ALAE. However, in practice, this will not always be the case and there will be scenarios where high expenses are incurred to evaluate fraudulent claims or defending against litigation arising from an unreasonably high demand, for example. This is where machine learning models can help in identifying the right candidates for no/low touch processing.
Managing existing claims
The claims volume for certain coverages is expected to spike e.g. for workers’ compensation and business interruption. To handle this volume spike, there is an opportunity to leverage the macroeconomic scenario and free up resources from other coverages, e.g. those currently handling third-party liability claims and auto bodily injury claims, which usually take a longer time to settle due to the process used to agreeing at a settlement amount.
In an injury claim, for example, the compensation for ‘pain and suffering’ to the injured third-party is often determined by active negotiations between the claims adjuster and claimant.
Due to COVID-19, the demand curve for the settlement amount is likely to shift downward due to reduced bargaining power of the claimants. As depicted in the right side chart, claims organizations can respond by shifting the offer curve towards left, or by making more offers and sooner rather than later. This can be an opportunity for the insurance companies to clear the claims backlog at a fair settlement value avoiding unnecessary delays due to extensive negotiations. For the claimants, there is an opportunity to receive the much needed cash earlier than they would have received otherwise.
In order to effectively shift the offer curve towards left, it is important for claims organizations to prioritize the right claims leveraging machine learning models such as:
- Settlement propensity model can be built using claim and negotiation related attributes to predict the probability of a claim eventually settling without the plaintiff attorney’s involvement and the claims with high scores can be prioritized for settlement.
- Attorney representation model can be built for claims with severe injuries to provide additional guidance in prioritizing complex claims and building a defense strategy.
The takeaway for insurance organizations
An organization’s ability and agility to adopt these analytical frameworks and models relatively quickly could determine how well they are able to respond to the COVID-19 challenges. While the tactical solutions can help in the short term, alignment with the long term Analytics strategy should be ensured to make incremental progress in the right direction. This is also an opportunity for claims organizations to make transformational moves towards adopting Advanced Analytics and achieve long term efficiencies.
Amit Sharma (Amit.Sharma3@exlservice.com) is vice president of insurance analytics at EXL Service. Shubham Dangayach (Shubham.Dangayach@exlservice.com), Pratima Bakshi (Pratima.Bakshi@exlservice.com) and Varun Balutia (Varun.Balutia@exlservice.com) are all insurance analytics engagement managers at EXL Service.
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