Artificial intelligence is becoming a key priority as insurance organizations navigate complexity in a fast-paced world.
Artificial intelligence (AI), along with generative AI (Gen AI), offers untapped value for those who are willing to embrace change and can navigate the associated challenges. Whether it’s automated claims, assessing risk, personalization of products and services, or fighting cybercrime, insurance organizations are increasingly looking towards AI to help tackle complex and time-intensive tasks, and to streamline processes. However, there is an urgent need to pick up the pace in adoption of the technology. Despite the use of AI in a handful of areas and pilots taking place elsewhere, insurance organizations are not gaining an advantage on competitors by using this technology more widely. Doing so could enable businesses to work faster, more flexibly and develop more sophisticated models in response to the evolving market.
So, how can insurers adopt AI effectively and unlock its full potential?
57%
of organizations see AI as the most important technology for achieving their ambitions over the next three years. [1]
58%
of insurance CEOs interviewed said it would take three to five years for generative AI to provide a return on investment with increased profitability, fraud detection and cyber-attack listed as the top benefits. [2]
Leadership teams acknowledge that AI could completely transform their operating models and ultimately, the customer experience. However, insurance organizations appear to be approaching the technology strategically and with cautious optimism.
Many insurers have already started introducing machine learning (ML) or other AI technologies to help improve specific business processes, such actuarial models and fraud prevention processes. In customer service, AI is being leveraged to confirm identity through voice recognition, to provide a timely response to online queries through use of chatbots, and to generate more sophisticated ‘next best actions’ for customer service agents.
With enough training data, algorithms can better analyze risk and predict outcomes, adding accuracy to risk models and pricing structures. Both traditional and Gen AI could empower organizations to enhance actuarial models, deliver personalized insurance cover, or even increase the pace of insurance claims. But the process of doing so appears to be slow, with testing and implementation processes often taking several months to complete.
There is also growing recognition among insurers that a successful AI journey will likely be intrinsically linked to the maturity of their digital transformation. AI thrives on quality data and is best supported by cloud-based infrastructure and agile operating models; firms that are yet to fully embrace this are becoming aware of the urgency to do so.
Often times, it’s not the machine learning technologies that limits our client’s ability to predict outcomes, it’s often limitations in the quality of data platforms, master data management and data science that prevents them from gaining the full value of AI. As these factors improve, our clients can unlock new insights to better understand their business and predict the impact of underwriting decisions.
Mike Helstrom
Principal, Insurance Technology Strategy Consulting , KPMG US
While the opportunities aligned with AI are significant, the associated risks are also increasing. Eighty-five percent of insurance CEOs believe that generative AI is a double-edge sword, in that it may not only aid in the detection of cyber-attacks but also provide new attack strategies for adversaries[4]. Organizations should be mindful of this when developing and integrating AI solutions:
However, avoiding AI altogether may also expose insurers to the risk of missing out on potential opportunities and benefits, and losing competitive advantage. Doing so presents opportunity risks through reducing organizational knowledge, cutting abilities to develop new products and processes, and the risk of being overtaken by more technologically confident peers.
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