Cognitive Advantage for the Insurance and Health Insurance industry in Switzerland

Benefits for Insurance and Health Insurance companies in Switzerland are beyond cost savings

Insurance and Health Insurance are becoming commoditised industries. In order to attract and retain clients, Insurance and Health Insurance businesses are focusing a lot of attention on the frontend and on improving the customer experience, deriving a single view of the customer and their transaction history, ensuring that customer's interaction with the Insurance or Health Insurance are satisfactory regardless of the channel.

The potential business benefits of artificial intelligence technologies are much broader than the cost savings that might be implied by the term 'automation’. It includes:

• Faster actions and decisions (e.g. for fraud detection)
• Better outcomes (e.g. for portfolio optimisation)
• Greater efficiency (i.e. better use of highly skilled people or expensive equipment)
• Lower costs (e.g reducing labour costs with automated telephone customer service)
• Greater scale (i.e. performing large-scale tasks impractical to perform manually, seasonal activities)
• Product and service innovation (from adding new features to creating entirely new products)
• Client Data protection (prevent data leakage from manual data handling)
• Re-internalize services (automate outsource services)
• Compliance (automate compliance and regulatory activities)
  

Intelligent Automation is unlocking disruptive business models and enabling personalized client relationships: the Cognitive Advantage

IA is opening a vast range of opportunities for the Insurance or Health Insurance in Switzerland:

• optimize their processes by reallocating the employee to core added-value tasks
• tailor the client relationship by implementing progressively the segment of one
• augment workforce capabilities, enabling their customer rep to handle with better quality their client specific queries and needs
• preparing the customer self-service approach (24/7, assisted by virtual agents for common requests)

Pioneering enterprises are already redefining their new organization based on IA:

• Insurers are using IA to answer the queries of potential customers in real time, and to increase sales conversion rates. 
• Insurers are leveraging IA to automate Claim processing (review medical reports for evaluating personal injury claims)
• Health Insurers are addressing seasonality related to contract re-negotiation with virtual agents to attract new customers
• Health Insurers are using IA to optimize underwriting process

Other examples to be leveraged from the Financial Services Industry

• Wealth Management firms are using IA to review and analyze portfolio data, determine meaningful metrics, and to generate natural-language reports for their customers on the performance of each of their funds. 
• Global banks are leveraging IA to improve the regulatory compliance processes by monitoring all electronic communications of employees for indicators of noncompliant activities

How to onboard the Intelligent Automation transformation?

1. Assess for automation opportunities (What?)

Begin with an assessment of your process landscape to identify opportunity areas: Good candidate processes are those which require manual interaction with a computer interface, are largely rules-based, consume a significant amount of time, and are performed at frequent intervals. Less important is whether the process is performed by an individual or multiple people—RCA is good for addressing work that is distributed across people or departments. Ideal candidate processes (particularly for an initial pilot) are low risk, yet have potential for significant reduction in effort. Address pain points as you go. Work with the process owner to determine the readiness for automation of individual processes. How well understood is the process? Has it, or can it be documented in detail? Thorough analysis up front can help avoid surprises down the line.

2. Build your business case (Why?)

Estimate the benefits of automation on a process-by-process basis. If resources can be relieved of tasks through automation, where will their freed-up time be spent? How does improved accuracy or increased speed translate into value?
At this stage, consider automating some pilot processes to prove the concept, and allow precise measurement of the benefits. Pilots can also help obtain buy-in from your stakeholders, improve understanding of both the potential and the limitations of automation, and identify the key success factors in your environment. Good pilot candidates do not need to be large-volume processes, but they must be performed with some frequency; it is difficult to measure the benefits of automating a monthly process during a six-week pilot.

3. Determine your automation-operating model (How?)

Decide whether you wish to establish a strategic automation capability within your organization, or simply desire an automated outcome with the minimum of effort: this will affect your automation operating model and determine your sourcing options.

If building an internal capability for automation, consider who will be responsible for assessing, mapping, and prioritizing new processes for automation; who will develop and test the automated jobs; and who will manage and monitor the software robots as they perform them—particularly where they span intra-organizational boundaries. As a tool that targets relieving mundane tasks, RCA is likely to give better results in the hands of skilled users: many organizations develop an Automation Center of Excellence to facilitate training and the sharing of knowledge and best practices.

4. Identify your automation partner(s) (Who?)

Consider your sourcing options, and which type(s) of automation partner you are looking for. You may already have worked with a partner to pilot some processes—but you are not wedded to that partner.

You should approach the selection of your automation partner(s) as you would any other strategic technology or sourcing procurement, considering both your current and future needs. Some providers have more experience in particular industries and others in certain types of processes. If procuring an automation tool, prepare evaluation criteria appropriate to your requirements. Vendor demonstrations are valuable, but it is a good practice to supplement vendor demos with client references to confirm on-the-ground realities and appreciate complexities and challenges.
Consider which pricing model(s) will best align with your business objectives, and compare pricing carefully.

5. Prepare the automation roadmap (When?)

Your road map for automation should look beyond the initial deployment and set out how automation will grow within your organization.

Like other transformation programs, communications, training, and change management are all critical. Before you begin the automation journey, you must confirm that impacted stakeholders clearly understand the what, why, and how of automation. Your employees can also help identify candidate processes for an automation platform, which employees can use to describe their processes and recommend them for consideration.

Your road map should also account for any supporting initiatives on which successful RCA is dependent. For example, a commonly encountered obstacle is poor data quality preventing successful autonomous transaction processing. Where known data quality issues exist, you should incorporate remediation activities into your plan.

What is Intelligent Automation?

A useful definition of Intelligent Automation (IA) is the theory and development of computer systems able to perform tasks that normally require human intelligence. Robotics Cognitive Automation (RCA), a synonym to IA, is the application of technology allowing employees in a company to configure computer software or a 'robot' to reason, collect and extract knowledge, recognize patterns, learn and adapt to new situations or environments.

RCA leverages recent software abilities made possible by breakthroughs in computer power, including natural language processing, machine learning, machine vision and speech recognition.