Allstate, the largest publicly held personal lines property and casualty insurer in the US, deployed cognitive artificial intelligence (AI) agent Amelia in 2017. Since then, she has collaborated with live agents on more than three million calls, cut the duration of those calls and boosted the success rate of customer inquiries on the first call from 67% to 75%.[1]

Insurers around the world are turning to AI to cut costs, boost sales and improve service efficiency. From helping predict customer needs to detecting fraud in real-time and predicting claims values, AI is powering insurers all along the insurance value chain. 

According to the International Data Corporation, spending on cognitive and AI systems will reach US$77.6 billion in 2022 with a significant amount of that investment directed to conversational AI applications such as chatbots and deep learning and machine learning applications.[2] These investments are expected to save auto, property, life and health insurers almost US$1.3 billion while also reducing the time to settle claims and improving customer loyalty.[3]

Newer AI applications are now taking hold on the sales and distribution side to drive lead generation, automate targeted marketing, identification of sub-segment product profitability, support underwriting through the use of big data and growing sales by supporting intermediary strategies through guidance on agency business optimization, leads matching and customer propensity modeling. 

Insurers’ adoption of AI to drive sales is not coincidental. Increasing competition from born-on-the-web insurers and insurance aggregators that do not rely on large, expensive agency distribution channels are putting pressure on traditional insurers that do. In this environment, operationalizing AI to drive sales is critical but it is an area that is still not well understood by many insurers. 

How AI can bring value to distribution and sales

Insurers are data-rich organizations that have not necessarily used or made the most of that data in the past, particularly when it comes to driving sales, supporting sales channels and providing insights to intermediaries. 

This is changing thanks to two global trends: the increasing focus on the customer and the explosion of data being collected. AI-powered customer journey mapping and understanding customer behavior are proving to be critical tools to creating positive customer experiences and to building data sets with insights that are driving sales. For example, insurers are using advanced analytics and AI to derive insights from customer profiles in order to re-engage inactive customers and upselling or cross-selling to them.

In relatively mature markets with large agency channels, such as Singapore, several insurers are using AI to:  

  • recruit agents – What makes a good agent? How much will I earn if I become an agent?
  • determine the lifetime value of agents and customers – Who will be the best agents and customers of the future?
  • identify characteristics of successful agents and agency managers – Who makes a good agent or agency leader?
  • analyze customer personality types – What will customers buy? When in their life do they buy? How much do they typically spend?
  • profile-match agents to clients to ensure the greatest possibility of success – Which agent sells best to which customer? Who to pass leads to?

Where to get started?

KPMG has seen insurers typically adopt one of two approaches when introducing AI in their business:

  1. focus on data, data collection and building up data warehouses / lakes before focusing on the use cases;
  2. focus on use cases first through identification of business outcomes and then work back to the required data. 

The former approach assumes the CIO or CTO is the owner of the data and as a result, the AI adoption journey can become a technology-driven exercise focused on data collection and management. This also assumes that all data is equally valuable for the business. This approach has often resulted in less than optimal results due to lack of buy-in from the business. 

Those insurers who begin the AI journey with clear business priorities are enjoying greater success.  Identifying the business priorities will allow insurers to more easily identify which of the growing number of AI technologies is most appropriate and to pinpoint the data necessary to help them achieve business objectives. 

Also critical to success is working closely with stakeholders in the business to develop use cases based on desired business outcomes, such as using AI driven insights to support the achievement of key business performance indicators. By starting with clear business objectives and involving the stakeholders, who will benefit from the impact of introducing AI systems (i.e. the business people leading sales and distribution, marketing, operations or claims), the journey has automatic buy-in and the insights are relevant and immediately actionable. This is true regardless of where on the insurance value chain AI is being applied.

For those insurers that have yet to start the AI journey, there is no time to waste. Competitors that have adopted AI are already finding competitive advantages. 


Paul Brenchley

Partner and Insurance Advisory Leader, KPMG in Singapore

Paul leads the insurance advisory practice in KPMG in Singapore. He works with national and global insurance clients across a range of business challenges including strategy, growth, innovation, customer experience, operational and IT transformation, internal audit, risk management, regulation and compliance. Prior to joining KPMG in Singapore, Paul spent three years at KPMG in the UK’s insurance advisory team where he was a lead technical advisor on a number of risk management, strategy, IT, internal audit, performance and governance engagements.

Kaushik Raghunandan

Director, Digital + Innovation, KPMG in Singapore

Kaushik is KPMG in Singapore’s Director in the Digital + Innovation team and leads the Analytics and Artificial Intelligence (AI) business. Working with key alliance partners and KPMG’s Lighthouse data analytics team, he builds analytics and AI solutions that supports clients with the appropriate analytics and AI solutions, from conceptualization through to implementation, that aims to drive strategic value, solve their business challenges and help them gain a competitive edge.

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