The insurance industry is experiencing change on multiple fronts at a rate that has not been witnessed for at least a generation.The rising cost of claims and the complexity of managing claims is one of the pressing challenges insurance companies are facing.Regulations and compliance is increasing, reduced cost of entry for new market players is threatening incumbents, and customer expectations on their engagement with agencies is drastically changing. Let me explain the issues in detail.
According to a report by the Manpower Group, 46% of US Insurance companies say they can’t find the people with the skills they need. The industry needs to bring at least 60,000 new agents and brokers every single year just to maintain the current size of the distribution. With an average age of 59 years and a median age of 45.6 years with attrition hovering around 10%, the industry will find itself running dry if the right skills are unavailable in the numbers required to run the business. Industry studies indicate Millennial’s interest in Insurance jobs is pretty weak. Companies are using more temporary staff 18% in 2019 up from 12% in 2018. The need for technology in insurance is expected to grow in the coming years.
Customer expectations of how they interact with agencies continue to raise, making it more important than that agencies provide a high-quality customer experience. The high expectations not limited to “born in digital” citizens (Millennial and those born in recent years) but also baby boomers. The challenge is to serve customers across different generations; some who may prefer to walk in and chat with an agent and others who may prefer self-service. With pervasive technologies,customer expectations across groups are 24/7 always-on responsive insurance agency that quickly responds to any query any time.
In recent years, issues related to protection of consumer privacy, data security and data sharing are gaining importance. European Union’s General Data Protection Regulation (GDPR) went into effect in EU countries in May 2018. One of the most important features of GDPR is the need for consent from consumers before their data is used. A commercial risk (a sanction could have serious consequences in terms of reputation) as well as financial losses.
For example, fines for GDPR noncompliance can go up to $23 Million Plus or 4% of the annual global turnover whichever is higher. California is the first state that has adopted a similar law in June 2018 – California Consumer Privacy Act 2018. The law goes into effect on January 1st 2020. The adoption of this law would mean that every consumer in California would be able to question and stop information being shared for any reason. The brokers would have to ensure that their client data (either in part or full) is not transmitted out of the US for any activities including data processing, accounting and other business processes related activities without the consent of the client.
AI/ML Adoption for Competitive Advantage
Automation of the policy life cycle, from data input to payment, has the potential to streamline policy management, as well as boost its efficiency and accuracy. When done right, digitalization will result in both lower costs and better customer experience. Incumbent insurance agencies can successfully compete against new entrants and grow their business by leveraging digital technologies. Technologies like Robotic process automation (RPA), machine learning (ML) & rule-based algorithms are being used to seamlessly automate a process. Process automation leads to elimination of human burden and error proof the process. Cost of poor service including rework, waste, waiting time and low productivity get eliminated. Automated policy process can be used to enable self-administration and enhance customer delight. These technologies also enable companies to use analytics to deepen customer engagement and process management.
Automation using RPA brings another advantage to the insurers. They gain a channel to serve high volume low value policies and pursue right cost based delivery mechanisms to target different customer segments. This would enable brokers and underwriters to spend more time on higher risk and profitable segments. Automation of repetitive process involving both on demand policies with no touch or minimum touch reduces costs and results in improved productivity.
Exdion has successfully developed and deployed suite of BOTS and AI tools to drive automation efficiencies and productivity improvements across many large insurance brokers. With adoption of AI/ML tools Policy administration and renewal need to stop being just a system of record but can transform into a system of engagement. Automated Policy Checking through Exdion Policy Check not only reduces costs, but significantly improves client experiences.
Deploying AI and ML tools for back-end gives you significant cost advantage. It costs much less compared to a Full-time employee (FTE) cost. Secondly, human errors which can range between 5 to 7% gets completely eliminated. Moreover, your employee would be able to spend time on high value non-boring activities. Employee satisfaction increases and employee attrition nose dives. Importantly, customer experience is a manageable and predictable process as both front and back-end operations work in tandem to seamlessly delight the customer.
Agency of Future
Agencies will adopt available Insurtech AI/ML and other software solutions to automate human centered process and drive efficiencies. Figure showcases how the agency of future will deploy AI/ML, analytics and personalization tools to drive efficiencies and enhance customer experience.
As a first step, agencies will adopt auto download of loss runs using APIs at predetermined intervals thus saving time spent following up multiple carriers. Agencies will automate Pre-Renewal Coverage Review by Client using smart AI/ML solutions. This will help agencies save administrative efforts in collating information from multiple spreadsheets. AI/ML solutions for Pre-Renewal review will automate policy renewal process track changes made by CSRs and provide accurate renewal exposure information to clients. Agencies will use Cloud and other tools to seamlessly integrate and update information as per market specific forms.
Quote generation process will benefit from increased adoption of Natural Language processing (NLP) based recommendation engines or self-learning BOTS or VPA that will help in selection of Carrier based on pre-ordained set of criteria. VPA or the recommendation engine on the line of Alexa or Siri can select a Carrier based on exposure in target industry, LOB or even specific region. Agencies can use AI tools to summarize & compare quotes. AI tools can also be used to perform coverage gap analysis and recommend policy upgrades and present in a standard proposal format to clients.
Agencies in future can use Portals for quote selection and request for Binder. Agencies can enable RES update collaboratively by Client and CSR and use e-signature to complete transactions. Data can be sent automatically to Carriers for Binding based on the Proposal Status. Smart agencies will use Big Data and analytics to provide on demand endorsement to facilitate policy servicing. Contract compliance review the final step in the policy life cycle can be automated to assess E & O exposures.
Agencies of future will also use predictive analytics to forecast catastrophic events and offer appropriate coverage (cross sell) before carriers stop providing coverage during the onset of these events. They could use predictive analytics to send out notifications to clients proactively.
Transitioning into Future
Transitioning to an agency of future is not a radical step, but is an evolutionary one. An incumbent insurance agency can transition to a new age agency by diligently planning and managing change management. However, there are many misconceptions about adopting AI/ML for insurance process. Firstly, AI/ML investments are expensive and disruptive. AI/ML interventions are evolutionary and do not cost much. Fact, many of these projects are financially very attractive, their payback period can be as less as a quarter. Companies adopting AI/ML solutions like ExdionRNU, Exdion Policy Check, etc have realized significant savings in terms of reduction of hires (both FTE and temp to meet the demand).
Second, AI/ML jobs does not replace jobs, but actually simplify work, improve the quality of work and help employees realize better work-life balance. Companies that have adopted AI/ML solutions have realized their CSR’s work quality has improved. Repetitive and boring work has been removed from their chores and they are able to spend quality time on more business growth areas today. To make AI/ML adoptions smooth and result oriented, involve employees in the project. Make them owners of adopting and driving the adoption of tools and incentivize them for outcomes. Use AI/ML adoption to elevate the roles of your staff so their job content is enhanced and leadership development happens.
As you prioritize the AI initiatives, do not ignore data, operations and customer service.Without clearly defined goals, flexible operating model capable of experimentation and without leadership or champions, AI tools alone are unlikely to fulfil the growth promise and drive service excellence. If Insurance agencies are to seize the potential of automation, they must invest in right IT architecture, talent and culture. Create a dedicated cross-functional team that operates in a start up mode. Let them quickly design and deliver solutions that can be tested with customers. It is not necessary for the agency to develop all of the digital tools at one go. Rather a step-by-step approach helps the agency manage the risk of adoption in an evolutionary way.
Partnering with vendors with proven expertise help insurance agencies traverse the automation journey in a smooth manner. Check whether the AI/ML solution is designed to scale and learn with new scenarios. To gain from AI led process improvements, Insurance companies must have a clear vision of what the future must be. Remember to balance investment, outcomes and impact of the project.
Conclusion
AI/ML technologies enable people and processes to accomplish more with technology, win back customers through better services at competitive price points.Remember even partial automation of the policy life cycle can lower overall costs. Furthermore, partial automation may enable an Insurance company to differentiate itself – and even give it a first-mover advantage.
