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Embedding machine learning

INSURANCE ANALYTICS
EMBEDDING MACHINE
LEARNING ACROSS THE
INSURANCE VALUE CHAIN


Insurance markets are already highly competitive, but still the pressure continues to rise. New entrants are starting to challenge existing business models, and technology is now advancing at an exponential rate rather than in a linear fashion. These factors represent a serious challenge for traditional insurers, 86 percent of whom believe they must innovate at an increasingly rapid pace simply to retain a competitive edge.

In the face of such competition, it is vital for insurers to develop an analytics-driven approach to understanding who their most valuable customers are—and then to tailor their interactions with those customers to produce optimal results. Insurers must identify which marketing and customer service actions (both during the renewal cycle and at other times) will generate the most profitable business outcomes and help retain high-value customers.

The valuable intelligence generated by analytics enables insurers to provide relevant, tailored offers to their customers—at the right time, through the right channel and at the right price. The marketing decisions resulting from this deeper understanding of the customer are not simply helping to deliver better, more differentiated customer service experiences. They are also generating additional revenue streams by upselling and cross-selling insurance to interested customers.


Discover how to navigate through the five stages of the insurance customer journey, using applied machine learning to achieve greater success.

Watch Improving The Customer Experience with Analytics

RE-ENGINEERING THE INSURANCE
LIFECYCLE WITH MACHINE LEARNING.

Machine learning algorithms offer a step change in extracting the most value from data today from customer acquisition to renewal.

Win

WIN

Identify high-value prospects to ‘win’, and increase the likelihood of converting these new customers by personalising their web quote journeys.

Modern consumers like to shop around. They also expect insurers to offer the same type of slick, seamless purchasing experiences they enjoy from other consumer-facing brands. To achieve this, insurers must harness the full value of all available acquisition data in order to identify high-value customers and tailor the quote journey to suit each individual.

GROW

Grow your customers’ average product holding by understanding and predicting their insurance needs and the ways in which they want to be marketed to.

Predictive analytics can help insurers identify which products and services their customers want, but it cannot tell them what marketing actions should be taken to help increase average product holding. However, by moving up the analytics maturity curve to prescriptive analytics, insurers can identify which channel, creative approach and or financial incentive will help them grow ancillary revenues.

Service

SERVICE

Harness the power of every customer interaction to better understand and predict your customers’ insurance wants and needs as they progress through the policy lifecycle.

Machine learning is helping insurers empower their call centers – and add intelligence across other channels too – in order to improve customer experience and offer more relevant products and services throughout the policy lifecycle.

Claim

CLAIM

Power claims processes with predictive decision-making, in order to help identify opportunities to reduce claims losses and to increase automation within the claims journey.

Claims handling is the ‘shop window’ of insurance, and it provides insurers with an opportunity to enhance the image of their brand. For example, by using predictive insights to offer car hire, personal injury and assistance services – and by integrating these insights within the existing supplier network – insurers can cuts costs, reduce handling time and deliver seamless claims journeys for their customers.

Retain

RETAIN

Use artificial intelligence (AI) to understand how your customers are likely to react to different pre-renewal strategies in order to retain high-value business.

By experimenting with price changes, outbound contact methods, creative approach and incentives, insurers can use AI to decide which pre-renewal strategy to apply to an individual customer to increase the chances of renewal.

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ANALYTICS BLOGS

Max Richter

Max Richter

European Insurance
Analytics Practice Lead

Max leads Accenture's Insurance Analytics practice across Europe, bringing over 18 years' experience in Insurance business and technology transformation including implementing advance analytics on both local and global/group level. His primary focus today is helping Insurers deal with the impact of disruptive trends and technologies as well as maximizing the value from advance analytics.

Steve Watson

Steve Watson

InsurTech Innovation
Lab Stream Lead

Steve Watson has been with Accenture for over 12 years. During his career with Accenture, he has worked across Insurance as well as Banking in core SI Delivery, Advisory and Sales related roles.

Daniel Presutti

Daniel Presutti

Senior Managing Director – Insurance Lead for Europe,
Africa and Latin America

Daniele Presutti is a Senior Managing Director for Accenture, the Insurance Lead for Europe, Africa and Latin America, the Global Life Insurance Lead, and the Client Service Group Lead for Insurance in Italy, Central Europe and Greece. He also has extensive experience in developing and implementing transformational business and IT strategies. Daniele joined Accenture in 1987.

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Max Richter

Max Richter

Managing Director – European
Insurance Analytics Practice Lead

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Matthew O’Kane

Matthew O’Kane

Managing Director – UK and Ireland,
Financial Services Analytics Practice Lead

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Sharad Sachdev

Sharad Sachdev

Senior Managing Director – Insurance
Analytics Lead for North America

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Daniele Presutti

Daniele Presutti

Senior Managing Director – Insurance Lead
for Europe, Africa and Latin America

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