In its 21st CEO Survey1 pertaining to the insurance industry, PwC states that to drive innovation, companies have to become 'bionic' organizations where humans communicate with machines to improve business outcomes.

In the survey, 53 percent CEOs of insurance organizations said they aim to modernize the work environment with digital tools, and collaborative physical environments to help employees develop the skills necessary to deliver in such a model. Forty-nine percent CEOs are looking at strategic alliances or joint ventures with InsurTech companies to speed up their alignment with the digital marketplace.

Insurers have accepted the impact of Robotic Process Automation (RPA) and Artificial Intelligence (AI) as forces of disruption and innovation, and are mapping the transformation of their organizations to accommodate these changes.

Traditional automation is limited to automating simple, repeatable tasks in back-end processes. RPA combined with AI can be used to identify and deal with exceptions, analyze large volumes of data generated by internal and external sources, and translate them into insights that can trigger specific actions.2

For insurers, RPA and AI are no longer measures toward only cost reduction and efficiency. These technologies are now used to drive personalization, deliver speedy service and ensure higher degrees of self-service. We take a look at some leading examples and use cases across critical insurance processes.

Underwriting & Pricing: Customer-centric StrategiesUnderwriting & Pricing: Customer-centric Strategies

Connected devices such as sensors and wearables are leveraging the power of RPA, analytics and AI to give insurers real-time insights into customer-specific risks. This is opening up avenues for personalized, flexible covers. It is also helping companies move from being risk insurers to risk mitigators, with bundled services for alerts, repairs and rewards.

Underwriting is a key focus area for RPA and AI initiatives. It is estimated that the market for technologies for underwriting processes is set to grow by 60 percent by 2020.3 RPA is now applied in the assessment of loss runs, which forms the basis for underwriting and pricing of products. RPA-infused loss runs,4 with automated reporting of several years of claims history of customers, can reveal otherwise undiscoverable insights related to previous losses. For example, LexisNexis' Data Prefill5 solution helps insurers reduce costs, and increase speed and accuracy of quoting and underwriting by pre-populating insurance applications using only a few customer data points.

Cyber insurance is a growing market with breaches expected to cost businesses over USD 8 Trillion in the next five years.6 Insurers are now offering RPA-backed pre-loss mitigation services to help detect and prevent suspicious activities. Examples include automated password defense solutions, online cybersecurity training, and social media and dark web scanning.

Marketing & Sales: Improving Brand ProtectionMarketing & Sales: Improving Brand Protection

There is an 'advice gap' in the insurance market7 created by disintermediation, mass online selling and falling premiums due to online websites that offer price comparisons. AI is expected to fill the gap created due to the falling profitability of most personal lines.

For instance, U.K.-based startup SPIXII is a virtual insurance manager that chats with customers to understand their insurance needs and recommend the best coverage for them.8 The chatbot can be accessed via most messaging platforms as well as a native app, and can converse in six languages.

Robo-advisors are also being leveraged to mine non-traditional data sources such as social media to offer need-based, personalized recommendations that can lead to higher cross-selling and upselling rates. When informed of a customer's travel plans, the SPIXII chatbot can detect whether the customer intends to participate in activities (such as skiing or paragliding) that are not covered, and recommend an additional cover for the same.

Insurers are also leveraging robots to help their network of agents find product information and sales collateral to enhance sales. For instance, a leading commercial insurer in the U.S. has launched a virtual assistant specifically to assist its agents seeking information on business insurance products.

Insurance companies today have to manage their brands' online presence while complying with stringent regulations. RPA helps maintain logs of posted messages, in real time, and complete with context to enable compliance reviews.9

An automated pre-approval workflow ensures that employee posts are first passed into the compliance system where they are automatically sent for approval. Messages that are approved are posted directly, while others are formally reviewed and appropriate actions taken.

Policy Administration & Servicing: Enhancing Customer ExperiencePolicy Administration & Servicing: Enhancing Customer Experience

RPA leverages AI and advanced analytics to link back-end processes to the front end in a smart, contextual manner. This augments the productivity and effectiveness of customer-facing agents by helping them access the right information across back-end systems faster, thus improving resolution rates and customer experience.

Agent-facing chatbots can identify the customer before the agent even answers the call, process queries in natural language and make proactive suggestions based on the customer's profile. Customerfacing chatbots can answer questions about policy status and payments, enable automated policy renewals, and even alert customers in case of pre-loss warnings based on sensor data, and facilitate preventive repair and maintenance.

In terms of reporting and compliance requirements, RPA overcomes barriers of disparate systems and multiple formats, and creates records of all actions and transactions to quicken reviews, reconciliations and compliance checks.

ClearPay,10 a Canadian payments and reconciliation services provider for insurance companies (carriers and brokers), is using RPA and AI to fully integrate the settlements process and provide real-time information on payments in standardized reports.

The data is transferred in real time from broker management systems and assembled in standardized reporting formats to help brokers fulfill contractual reporting requirements and enable faster reconciliation by carriers.

Claims & Fraud Detection: Driving Speed, Transparency & AccuracyClaims & Fraud Detection: Driving Speed, Transparency & Accuracy

With customers' claims experience emerging as one of the most important metrics for insurers, it is perhaps not surprising that the estimated market for technologies for improvements in the claims process is expected to surpass USD 72 Billion by 2020.11

RPA can cut down the cost of a claims journey by as much as 30 percent.12 RPA and AI can enable self-service, and bring in transparency and speed in the claims process. Digital interfaces, backed by chatbots, can offer personalized guidance on the submission of important information and provide clear feedback to customers on the next steps.

While the use of drones in property and casualty insurance to assess damages is a well-established use case by now, InsurTech is now exploring the use of 'machine vision' in claims settlement.

A leading U.S. insurer has been experimenting with an app-based feature that uses smartphone cameras to help customers involved in accidents assess the damage to their car in real time. The app leverages AI that has been trained on thousands of images, and can also provide cost estimates for repairs.

Other AI products help drive down insurer costs by assessing the most accurate claims settlements. U.K.-based software company Tractable has an AI review program that checks thousands of repair estimates every minute, flagging unnecessary repairs and arresting auto claims leakage.13

Fraud detection and prevention is perhaps the process area with the widest adoption of RPA and AI technology across the industry. Solutions are being leveraged across the spectrum of claims assessment and fraud detection to enable early detection of trends and suspicious claims.

InsurTech company Guidewire14 delivers predictive analytics backed with RPA and machine-learning to score claims in real time, routing and assigning claims in the workflow, and identifying claims that may require litigation or subrogation. In effect, it accurately fast tracks correct claims, delivering higher process returns.

Shift Technology, another InsurTech company, has a software that delivers actionable insights on which indicators make a claim suspect.15 The model takes the context of the claim in consideration to intelligently increase or decrease the weight of suspicious indicators.

Even as insurers get their AI and RPA strategies in place, there is growing focus on concurrent transformation in employee skills. This is necessary to ensure a long-term, continuously evolving framework of innovation and productivity.

Higher business involvement, establishing a center of excellence, and a more practical and calibrated approach toward adoption — from proof of concept to business case and pilots — will be some of the important features of successful adoptions.16

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