The dream of good management is that each part of an organization both performs their role flawlessly, and always maximizes the value or effectiveness of the entire organization. In reality most organizational silos optimize themselves. In the absence of an intelligent system that actually enables cross-functional optimization, most organizations tend to communicate goals and drive alignment simply using PowerPoint and meeting.
But the reality is that every decision is cross-functional in that they inevitably effect the entire company in some way. In the competitive environment of this decade and those to come, companies that don’t mutually optimize every decision and action run the risk of falling more and more behind.
One of the most essential elements of the vision of the Computational Enterprise is the ultimate goal of cross-organization mutual-optimization.
The mutual-optimization of the five mega-services that make up computational life insurance can bring about an unprecedented level of cooperation and effective decision making. With Computational Insurance the interrelated goals of increased revenue, lower claims, higher customer satisfaction, long term financial stability, and adaptation to an ever-changing world can be considered when making every core decision; with clear insight of how each lever movement will impact the other elements of the organization.
For example, by mutually optimizing Client Experience with underwriting we can ensure a rapid decision with all needed information and accurate pricing. Knowing the questions up front that an underwriting system needs avoids unnecessary delays.
And by mutually optimizing claims and underwriting, hidden risks can be identified that had not been considered previously. Instead of creating rules to capture risks derived from claims, having machine learning identify the patterns in claims that may impact the suitability of each application is a level of analysis unachievable previously.
With actuarial guidance the computational insurer can be optimized to the future. Making decisions on historical data, inherently biases a system to repeat the past. But executive actuarial guidance can drive the company to the future. For instance, certain impairments such as HIV and diabetes had much worse prognosis only a short time ago. Actuarial guidance optimizes to the future prognosis returning a more accurate result and increasing the ROI.
The five boxes in the diagram above represent both how a typical insurance company is organized but also the different perspectives those teams bring to the business. An underwriting system that only uses underwriting data to train a machine learning system will be destined to repeat the mistakes of the past. Incorporating claims data will utilize the ramifications of underwriting decisions and effectively incorporate the results of the underwriting decisions. Actuarial guidance directs and guides the company to the futures, ensuring that predictions of impairment prognosis or other changes in longevity can be fully incorporated into the decisions that build the portfolio of business. Capital management puts the guard rails on the system ensuring that decisions are made within the business and regulatory constraints. Lastly Customer intelligence ensures decisions are made with the most thorough and accurate picture of customers. It is when all of these perspectives and business insights are aligned and optimized in real time that an organization can truly create a new operational paradigm that can achieve unprecedented levels of business success.