By Max Kanaskar
Artificial intelligence is already seen as a positive force across the insurance sector.
In a survey of more than 300 industry professionals, 62 percent are piloting AI and machine learning programs, while 75 percent believe AI and machine learning offer carriers a competitive advantage.
But if we’re going to improve insurance from quote to claim, we must first recognize that AI solutions aren’t incremental advances, as with other technologies. In fact, AI ultimately means turning your business model on its head.
Why Is AI Different?
Consider how the insurance industry thinks about fraud—a problem the FBI estimates costs about $40 billion per year. To combat fraud, carriers have robust tools and processes to discover suspicious patterns.
Once suspicious activity is detected, investigators are tasked with determining the truth, disrupting the fraudsters, and mitigating the damage. Overall, it’s a reactive process that begins roughly two months after the fraud occurs; if there’s resolution, it generally takes about six months.
In contrast, AI-driven fraud detection is proactive. The idea is to detect fraud before it occurs. You still need people, but instead of asking them to investigate after the fact, they’re tasked with intervention.
Change Management Challenge
While shifting fraud detection from a reactive to proactive process is just one example of why AI is different, it speaks to the larger challenge of implementation. Put simply, you’re turning your business model on its head.
As change management practitioners know, even incremental evolutions are painful because people fear change and institutions are slow to evolve. That’s why AI is such a Herculean task.
We know that the bigger the transformation, the more important it is to have strong support from leadership and genuine investment from everyone else in the organization. The question AI presents is that with change at such a large scale, how do you achieve buy-in?
How Insurance Can Move Forward
While AI is transformative compared to other incremental technologies, there are areas where insurance carriers can execute discrete implementations. By prioritizing those areas where AI represents a smaller change, insurance companies can build consensus throughout the organization. Here are some areas to consider:
Matching prospects with agents. Currently, matching agents with prospects is on a first-come-first-served basis. AI can provide matching intelligence that looks for common ground (language preference, communication style, location, etc.) between agents and prospects.
Of course, AI matching is susceptible to issues around explainability and bias, but solving for those issues within a finite space will build valuable AI expertise.
Ingesting unstructured data. Consider the homeowner underwriting process. Inspection reports contain text, photos, and video. That unstructured data will determine if the underwriter needs further investigation, but the process is both manual and arbitrary. AI can analyze that data at scale and do a better job of determining which applications require human investigation.
First notice of loss for simple property claims. Currently, that’s a manual, time-consuming process. But for small claims, it’s possible for the policyholder to submit pictures of the damage so AI solutions can determine loss and settle the claim faster and without human intervention.
This is by no means an exhaustive list, but these three suggestions touch on quote, underwriting and claims. Ultimately, AI will transform insurance. But by running discrete AI programs that address obvious pain points within each business unit, the organization can glimpse its future.
Knowing what changes are possible and why they’re valuable goes a long way to reducing resistance and building buy-in.
Max Kanaskar is an AI advisor for CognitiveScale. Max leads CognitiveScale's efforts in driving market success and expansion in Financial Services. He leads various initiatives across the financial services vertical including business development, sales, marketing and delivery.
Prior to joining CognitiveScale, Max worked in strategy and management consulting industry focusing on insurance, banking and capital markets. His work has focused on leading strategic transformations, assessments, and delivery management.