The Benefits of Analytics for Insurance

One of the best pieces of career advice I received was shortly after joining Allianz as a Catastrophe Analyst, following several years of employment with a top risk modeling firm. During my first one-to-one meeting with the Global Head of Catastrophe Management, I asked how best to achieve success with this new company and she simply stated, “Learn how the underwriters think.”

Although much has been written on the latest advancements within the Insurtech space, leaders in this area rarely discuss the paradigm shift that needs to happen in the minds of analytics teams - as well as corporate organizations as a whole - necessary to establish a truly data-driven business. While insurance companies initially feared Insurtechs would slowly take over their market share, partnering with these firms is now widely seen as the tool needed to drive growth and development.

While most agree it is not easy for an organization to suddenly make the switch to become more data-driven, but once enabled, the benefits are worthwhile. By understanding what data is available and then integrating it across an organization to make informed decisions, businesses can thrive in today’s evolving and competitive landscape.

Analytics with Purpose

The first shift needed to take place is within the mindset of the analytics talent. Too often top employees treat their jobs as an extension of their graduate school dissertation, which results in siloed thinking, costly projects, and under-utilized reports. Instead, analytics work should have a business purpose, and all must develop a crisp understanding of how their results are used to make a business decision. Programs such as mandatory shadowing, job rotation, and investing time in understanding the details could be effective in achieving this change in mindset.

Leaders should also invest time to ensure teams understand the key business KPIs, their components, and their relations to the broader strategy. Analytics teams are often skilled in delivering their piece but fail to understand the puzzle they are part of. We often speak about upskilling business teams in data and analytics. Upskilling analytics teams in business concepts is just as important.

Analytics embedded in Business

When it comes to data, it is important to establish a single source of truth. It is also crucial to continuously improve data quality at its point of entry. As low-quality data cascades through the organization, fixing it becomes exponentially costlier. However, too often IT, business, and analytics teams work in separate units with few touch points scattered as meetings throughout the year. This creates silos, multiple versions of the truth, and reports that may not serve immediate needs of the business.

“By understanding what data is available and then integrating it across an organization to make informed decisions, businesses can thrive in today’s evolving and competitive landscape.”

The solution is to create an analytics ecosystem across the organization with two distinct parts: 1) a nuclear analytics team within the business units;

2) a Chief Analytics Officer at the Board level. The nuclear analytics teams ideally would have a reporting line to the Line of Business (LoB) leadership, comprised of business analysts, modelers, as well as dedicated data and systems specialists who serve as the bridge between business and IT. These teams ensure that the triangle of business, IT, and analytics work continuously and in tandem.

In a McKinsey report issued in 2021, titled “How data and analytics are redefining excellence in P&C underwriting,” Javanmardian et. al. indicate that it is important to “keep the effort anchored in the C-suite. Delegating down can dilute long-term aspirations.” A Chief Analytics Officer (CAO) is defined as the executive who turns data into decision.

The CAO not only has the mandate of overseeing data strategy (Chief Data Officer’s job) and creating business intelligence models (Portfolio Management), but he/she will have the authority to work closely with the CIO to establish the infrastructure required for analytics. Informed by the nuclear analytics teams embedded in each LoB, the CAO serves as a key enabler for business and a guide for IT.

Involve the Frontlines

Adoption by underwriters is the foundation for a successful data driven culture. It is important to engage the frontlines and ensure their buy in. A key benefit of embedding the nuclear analytics team within the business is to bring the agenda close to the front lines. Creating tailored analytics for each LoB will also increases support from the LoB’s leadership. Even if we think data quality should never become part of an underwriter’s annual performance review, underwriters and operations should know the critical data that they are accountable for.

In closing, the immediate benefits of a data-driven business model become obvious to any executive in the insurance industry. I would argue that one added advantage of the model and recommendations presented here is a refreshed talent pool to fill top jobs. Traditional thinking in the insurance industry often sees an underwriter, claims, or distribution professional as the next CEO. An analytics executive trained to think like an underwriter is the kind of leader that can guide an insurance company in today’s digital world. The time to invest in that talent pool is now.