With pressures from COVID-19 and the global economy, the insurance sector has been forced to evolve in all facets of operations.
Over the years, Insurance companies have made major investments in online, self-service, and mobile experiences, and customers have rewarded their provider(s) with high customer-sat scores and recurring business.
However, over time the customer expectations for self-help and extended service capabilities continued to grow while the insurers’ investments peaked as executives were anxious to see their promised ROI. Backend system limitations hampered some of the customer-facing initiatives, as the legacy technology uplift takes longer and is costlier than the façade. Then, add the unforeseen and unrivaled pandemic that forced employees out of the office and customers to seek sanctuary, leaving operating teams to feel the pinch of a perfect storm. Processes that are paper-based, or that cross operating silos are very difficult to execute outside of the office environment (e.g. end-to-end claims processing) and bottlenecked by legacy systems. Today, surveys indicate over 50% of IT budgets are used to simply maintain legacy systems rather than investing in forward-looking digitization and modernization efforts.
Robotic Process Automation (RPA), and Intelligent Automation (IA), drives to the core of manual inefficiencies as a way to leverage past system investments while providing the modernization much needed during our current business climate. Early automation adopters focused on low-hanging fruit at the task level, but even larger returns will be found by engineering for full end-to-end processes and leveraging best-of-breed automation with human hybrid interactions.
As process automation matures, more Artificial Intelligence (AI) and Machine Learning (ML) can be incorporated as more of the full process can be overseen by humans instead of performed by humans – thereby increasing quality, throughput, and overall satisfaction for both customers and employees.
Time consuming manual review/appeal processes while also managing rate and policy recommendations can be complicated.
Many of the same systems and data are repeatedly accessed when managing KYC programs and investigating behaviors for fraud.
Disparate systems and multiple process silos are navigated to complete the end-to-end claim process manually.
Many manual and tedious steps required to manage the lifecycle: New, canceled, and policy changes ‐ not to mention credit checks.
Ensure the right person can access the right information in real time. Legacy information silos can be difficult to navigate and even provide disparate and competing views of “the truth” for both customers and employees. Identifying the human decisions and processes required to gather the pertinent data is invaluable input for true process automation. Even if human/robot hybrid solutions are implemented early – there quickly become key common goals for simplification and rules-based decisions that can be standardized in order to simplify and automate as much as possible over time.
Know your Customer (KYC) requires gathering data from multiple disparate systems, of which many may be 3rd party sources. These multiple logins, various systems, and repetitive data extraction is time consuming and error prone due to the sheer volume of information. Improved compliance and reduced fraud are top C-suite mandates, all while reducing customer handle time and improving customer satisfaction. Many of these goals may be competing for the same resource mindshare and provide a golden opportunity for automation.
Intelligent automation using RPA allows humans to deal with exceptions and anomalies while the straight-through processing and mundane data management can be automated, effectively improving human satisfaction and reducing fraud, cost, and errors by allowing humans to focus more on the high-value or high-risk interactions.
Managing policies, claims, refunds, and even litigation requires handling mountains of personally identifiable information (PII). Even as agents and representatives are heavily trained in handling this confidential data, there are ever expanding touchpoints with more people, systems, and processes that result in potential areas of exposure. With a focus on efficiency and accuracy as the most common automation use cases, there is a strong movement to focus Automation on the capture and handling of PII as a key step in reducing exposure/leakage risk. Once PII is in the enterprise system, data is handled well with a long track record of success – but the data capture and entry points can be automated to further remove the additional risks.
Robots can read, process, and enter information quickly and accurately without errantly exposing confidential details. This allows humans to interact with the now obfuscated data once the legacy/master systems have been engaged and data has been encrypted and tracked.
Unifying customer communication to provide relevant, accurate, and timely updates is a critical component to managing customer expectations. Customers that receive ongoing information about their claims, questions, or open issues rarely generate additional call/email traffic for the call centers; and engaged customers are exponentially more likely to report a positive overall experience. With the multitude of legacy systems and siloed business events that transpire for a single customer incident, Intelligent Automation (RPA) automatically solves the need to communicate proactively without replacing legacy investments.
Automations can attach to key events and extract the details that need to be communicated with minimal human intervention. This non-invasive and parallel approach provides the right customer value, removes tedious employee activity, and improves the proactive communication needed by both customers and agents.
Reviews, appeals, adjuster decision support, automated responses for rate inquiries, policy review and coverage recommendations, 3rd party provider actions, payment renewals/cancellations, delinquency management, single/multi line discounting.
Regulatory reporting, sensitive data management, refund investigation, reduce litigation costs, improve research efficiency, claim status investigation, credit dispute process management.
Throughout the lifecycle — pre-analysis, automated/manual reviews, determination, disposition. Claim pre-analysis, simple claim automation, self-service claim inquiries, claim triage and allocation, claim fraud management, claim adjustment/payment, auto-generate litigation package.
New policy setup, proactive policy review for payment or coverage, proactive risk profile review, automatic payment setup, underwriting policy and ID issuance, policy cancellations, Group benefit setup, underwriting suitability assessment, credit and reference checks.
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