AI and machine learning are propelling the insurance sector forward – willingly or unwillingly – while offering organizations the ability to make substantial improvements. The technologies can help to enhance customer service, claims processing, fraud detection, and much more. They also potentially transform underwriting, risk assessment, and personalized pricing.
For delving into these opportunities and challenges, Edosa Odaro’s expertise is invaluable. A twice-published, critically acclaimed author and a globally recognized expert in AI and data transformation, Mr. Odaro currently serves as the Chief Data and Analytics Officer at Tawuniya, the foremost insurer in the Middle East and North Africa (MENA) region. He was previously the Chief Data Officer at AIG.
Emerj Senior Editor Matthew DeMello recently spoke with Odaro about said opportunities. This article will focus on three key takeaways from our interview with Mr. Odaro:
- Embracing value-added services: Leveraging AI and machine learning to integrate additional value-added services and shift from a ‘payer-to-partner’ business model can help insurance companies boost customer engagement, gain competitive advantage, and create additional revenue streams.
- Enhancing data management and utilization: It is essential to identify business problems first and work backward to see if the organization has the necessary data with a ‘less is more’ approach to support potential AI use cases that increase customer engagement and drive digital transformation.
Listen to the full episode below:
Guest: Edosa Odaro, Chief Data and Analytics Officer, Tuwuniya
Expertise: Data science, analytics, strategic leadership, AI
Brief Recognition: Odaro began with Allianz Insurance in its data analysis department before transitioning to a data solutions architect consultant, a role he undertook for several well-known private and public enterprises for nearly two decades. He was the Chief Data Officer at both AXA Insurance and AIG for several years, during which time he wrote the first of his two books, Making Data Work. His most recent book, Value Driven Data, was published by KoganPage in August of 2023.
Closing Service Gaps and Enhancing Customer Experiences Through Value-Added Services
Odaro begins by addressing the shortcomings of AI adoption in insurance, drawing a parallel to the challenges previously faced by the banking industry. While noting that insurance has “come a long way” towards bridging these gaps, Odaro states there’s more work to do. Promisingly, he says there is a “huge” (his emphasis) opportunity for AI to close this chasm.
He stresses the importance of prioritizing customer engagement, faster response times, and proactive operations (e.g., predicting desirable insurance products/services) in use cases via enhanced data analysis, AI, and machine learning. Odaro emphasizes the need for insurers to embrace AI and machine learning to develop value-added services for a more interactive, engaging, and profitable customer experience.
He goes so far as to call value-added services the ‘Nirvana’ of insurance companies – to create something that your customers “actually want to interact with” instead of being “forced onto” them:
“Ultimately, from a customer standpoint, what you and I want is on the claim side. How do you look after me in those situations where I suffer a loss or fall into certain types of difficulties? And I think, really, what happens, if you look at banking, we look at what happens in other industries more broadly – is that you don’t want to be seen as a ‘Your call is very important to us…’ You want that instant service, if possible. Could you actually be proactive and actually step ahead of the game and either detect challenges or, beyond that, even prevent challenges?”
– Edosa Odaro, Chief Data and Analytics Officer at Tuwuniya
Value-added services are critical for an insurance company’s ability to stand out, engage with customers, and create additional revenue streams. Mr. Odaro points out the performance gap between the banking and insurance sectors in the realm of value-added services. Yet, he asserts that insurers have significant opportunities to enhance their services through improved data analysis, better data management, and the strategic application of AI.
Odaro emphasizes the shift from the traditional insurance sales model to a ‘payer-to-partner’ approach, which he argues has the added benefit of boosting customer engagement. He suggests that there is a substantial opportunity within the insurance sector to leverage AI for various use cases that enhance this relationship, such as:
- Personalizing customer interactions
- Tailoring insurance products to individual needs
- Enhancing risk assessment with predictive analytics
The payer-to-partner model would likely involve insurers using AI and predictive analytics to predict better and meet customer needs, providing services that are not just reactive (e.g., faster response times) but also proactive. Doing so could entail offering personalized insurance products, anticipating potential issues before they arise, and engaging with customers more meaningfully beyond the point of sale.
By doing so, insurance companies can transform their role from mere financial support to becoming integral partners in their customers’ lives, thereby increasing customer loyalty and satisfaction.
Enhancing Data Management and Utilization
In the rapidly evolving landscape of insurance, where AI and data analytics are becoming increasingly central, Mr. Odaro emphasizes the need for insurers to manage and effectively utilize data properly. One of the most significant challenges in the insurance space, says Oraro, is the industry’s need to transition from being policy-centric to customer-centric. This shift necessitates that insurance companies enhance their engagement with customers. The key to this transformation lies in the intelligent use of data.
Odaro also highlights the importance of understanding and measuring the risks associated with data management and AI implementation. He advises that insurers should first identify the problems they aim to solve and then work backward, assessing whether they already possess the necessary data.
However, Odaro emphasizes the focus of these efforts should be on something other than accumulating as much data as possible, but instead taking a more minimalist approach to refining existing datasets and employing better techniques to extract only data that is actionable to the business problem at hand:
“There’s confusion around this whole idea of big data, suggesting that it’s got to be bigger and more. From my perspective, you could be looking at the same dataset and using it in a more intelligent way and using better techniques that actually utilize that same dataset. That’s actually fundamentally right in saying that it’s not necessarily new stuff (data) but potentially looking at the old stuff in a different sort of way.”
– Edosa Odaro, Chief Data and Analytics Officer at Tuwuniya
In essence, insurers need to re-engineer their processes, removing friction and redundancies to increase efficiency. In doing so, they can enhance customer experiences, reduce response times, and offer more personalized services. Odaro states that, in many cases, the data necessary is often already present – and, when this is not the case, a data “problem” (e.g., data shortage, data quality, etc.) is often much less severe than initially thought.
Mr. Odaro states that proper management and utilization of data can offer insurance companies a real competitive advantage, precisely when aligned with customer needs and business goals.
