Introduction

Growing competition and increasing demands from customers are forcing Insurance agencies to consider investments in advanced AI, ML and other automation solutions. For many reasons, such investments are often difficult to justify by means of traditional economic analysis alone. As a result, it is often necessary to consider agency wide benefits associated with these technologies in order to justify their adoption. Insurance agencies often tend to compare investments in AI/ML, and automation with status quo. This fallacy does not consider the risks and opportunity costs associated with the decision of doing nothing, including loss of business, competition gaining market share, etc.

Some agencies evaluate AI and automation investments using traditional economic models such as ROI, and Payback period. Limitation of these models is that they can’t capture non-quantifiable benefits such as improved customer experience, simplification of work and its effect on associates, etc. Yet another challenge, is that the decision makers often restrict the evaluation at one particular level within the agency. Automation of policy quote investment is evaluated only at the operation level and ignore the benefits this will have on overall agency’s competitive strength. For example, Incumbent companies invest in AI and automation to offer unique pay-per use models for areas such as car insurance based on the miles the client actually drives, data to optimize how they use their car and instant access to diagnostics in case of any accident or breakdown. Armed with these new technologies, Incumbent Insurance agencies can move away from traditional cohort-based pricing to personalized pricing. They can now build smooth and efficient customer facing, middle and backward operations and offer completely DIY on-demand insurance and peer-to-peer insurance models allowing customers complete freedom to select whose premium will be used for claim settlement.

Often, it is the types of benefit listed above that “push a project over the edge” of justification hurdle and make investments in AI, ML and automation visibly favorable. It is necessary therefore to consider the system wide benefits associated to justify their adoption. Such an analysis will not only consider the traditional cost factors involved in such projects, but also the secondary, and system wide benefits that can be obtained with these investments.

System wide benefits value analysis

What Insurance executives need are methods that are easy to use, easy to understand and not time consuming. We present a simple model, System wide benefits value analysis (SWBVA). This approach has been extensively used by manufacturing companies to assess advanced manufacturing technologies like Robots, Flexible Manufacturing Systems (FMS), etc and we show how the same approach could be used by Insurance agencies.

AI and automation investments help agencies realize three levels of benefits: 1) strategic, 2) operational and 3) tactical benefits as shown in Figure 1. Strategic benefits are long-term, broad goals, address “why” behind a particular course of action and are relevant for Board and CXO levels. These benefits may be related to financial, market, customer or agency capabilities. Operational and tactical benefits are short term effects an investment’s output, usually requiring reworking the existing systems, process and people.  Operational and tactical benefits are narrow and focused goals that can be easily measurable, and relate to “how” part of reaching the strategic objectives.

Most common “strategic benefits”that can be derived from AI and automation are:

  • Cost advantage: Direct & indirect costs and benefits
  • Demand Scalability
  • Competitive strength (response to customer needs ahead of competition, prevent competition in gaining market share)
  • Delight Customers:  manage “moments of truth” and drive great customer experience

Figure 1: System wide benefits analysis of AI, ML and automation

 

Operational benefits

Operational benefits range from cost reduction, operational optimization, Improved customer experience, etc more relevant to heads of units or operations where automation or AI adoption is carried out.For example, claim processing is a time-consuming process which warrants gathering information from multiple systems. With human workforce this can lead to delays and rework. With AI and automation investments agencies realize speed and accuracy in settling claims. This will help them drive efficiencies and retain their competitive advantage.

Most common operational benefits sought from adopting AI, ML and automation tools are:

  • Employee productivity (decreased manpower cost)
  • Low risk and quick pay back investment, realize benefits in a week, super quick ROI
  • Increased customer self-service (reduced cost and higher stickiness of customer)
  • Strong compliance: meet ISO 27001, PCI DSS and HIPPA standards
  • Great Accuracy (Realize high quality and reliable policy checking “process” )
  • Standardization of process (eliminate variability and integrate adjacent operations)
  • Shorter cycle times (better management of operations)
  • Product flexibility (changeover to produce a new (set of) checklist very economically and quickly)
  • Volume flexibility (the ability to operate profitably at different policy checking volumes)
  • Quick response to customer demand (reduced delivery time)
  • Demand flexibility (Cover small, mid & large market businesses at different cost points, profitably)
  • At the forefront of tech adoption (Bragging value with Peers)

Tactical benefits

The direct and indirect benefits derived by the employees involved in the work space where automation or AI is adopted are tactical benefits. Automation and AI solutions removethe burden of repetitive and process intensive tasks allowing the employees to perform more skills-based tasks, with higher levels of decision making and creativity. This in turn will improve employee job satisfaction and help agencies leverage their associates in better ways. Most common tactical benefits are:

  • Quicker Turn Around
  • Compliance to Corporate standards
  • Prevent backlog
  • Improved quality of work (higher morale, drop in absenteeism)

Looking ahead

To gain from AI and automation solutions Insurers must look beyond financial criteria. Insurance executives must evaluate the outputs and impacts automation and AI investments will have at different levels. AI and automation are enablers of a strategy. Without clearly defined goals and without strong leadership, AI and automation alone are unlike to fulfil the promise of growth and drive customer experience. Remember to balance investment, outcomes and impact of the project.

Bibliography

Azzone, G., Bertele, U., 1991. Planning and controlling investments in computer-based automation. Cost Management 28–36.

Christenson, C.M., 1997. The Innovator’s Dilemma When New Technologies Cause Great Firms to Fail. Harvard Business School Press, Boston

Downing, T., 1989. Eight new ways to evaluate automation. Mechanical Engineering 111 (7), 82–86.

Troxler, J.W., Blank, L., 1989. A comprehensive methodology for manufacturing system evaluation and comparison. Journal of Manufacturing Systems 8 (3), 175–183

 

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L.S Ram: Managing: Director & CEO

Ram is one of the co-founders of Exdion and has served as President and CEO since inception. Under his leadership, Exdion has consistently set new standards in the field of process management and quality excellence. He brings over 30+ years of experience and holds a post-graduate degree in Computer Science and is a Chartered Accountant. He is also a Certified Information Systems Auditor from ISACA, USA and a Certified Associate of Insurance Services awarded by “The Institutes” (AICPCU), USA.