A Mindtree White Paper

Achieving pricing maturityin insurance

- A digitaltransformation roadmap

Achieving pricing maturity in insurance - A digital transformation roadmap

Summary

How leaders in pricing operate

The foundation – Consistent use of ‘unconstrained' generalized linear models in

underwriting processes

Institutionalizing AI in pricing

Dynamic pricing

Product simplification

Full-scale transformation

Program bird’s eye view

Benefits

Conclusion

Pricing optimization is the most significant driver of sales and underwriting profit, and an

imperative in these unprecedented times as Insurers struggle to provide value. Traditionally,

pricing has been a ‘walled tower’ exercise undertaken by underwriters and product manufacturers

based on periodic input from the market. Leaders in pricing however, are doing much more to

change this traditional approach as a recent study from McKinsey1 put forth. This whitepaper

operationalizes these findings into a roadmap for pricing innovation for organizations.

How leaders in pricing operateFrom creating events that help understand purchase causality to promoting a culture of pricing

awareness, industry leaders in pricing are changing the rules of the game. This approach in turn is

reaping rich dividends. In the era of low interest rates and commoditization of innovation, sharp

pricing driving underwriting profit is the best driver of operating profit and return on investment.

McKinsey outlines five levels of pricing maturity in their study, and here, we attempt to

operationalize a pathway to achieving them.

The post-COVID-19 pricing imperative for P&C insurers – McKinsey Insurance Practice

Summary

1

Insurance carriers can be classified by level of pricing innovation and transformation.

Consistentapplication of GLMs1

Consistentlyapplies “unconstrained”GLMs to all risk models and product types

Description

Levels of pricing sophistication

1 Generalized linear models.2 Unavailable in the United States.

Use of AI-based or machine-learing pricing tool

Product simplification forthe right pricing

Implementationof robo-pricing2

Full-scale pricing transformation

Implements AI-based, automated pricing model to leapfrog rate-making process using GLMs; alternatively, could build on existing GLMs

In addition to using GLMs and AI, understands real-time market dynamics and uses robotics for ongoing, automatic pricing including sales development

Adjusts product structure and eliminate highly unprofitable tariff cells and product modules; enables end-to-end product value chain optimization and behavioral pricing

Approaches pricing transformation comprehensively, including current capabilities, people culture, organizational design and structure, data and tech strategy

The foundation – consistent use of ‘unconstrained’ generalized linear models in underwriting

Underwriting used to be known as both an art and science. However, with the growth of predictive

analytics and machine learning, the verdict is clear – science is where the future lies. Moving beyond

simple linear rate cards, a solid foundation to move towards pricing maturity lies in using generalized

linear models with unconstrained variables in the pricing equation. This is a starting point in pricing

maturity and more than half of the P&C companies in the US and Europe have already established this as

a foundation (and therefore are in good stead). For those that have not, the adoption of a GLM

(Generalized Linear Model) software in underwriting is table stakes.

Institutionalizing AI in pricing Introducing Artificial Intelligence (AI) into the pricing process allows companies to leapfrog into

sophisticated pricing. Typically, historical data from policy admin and sales CRM system is used to train

the model and drive pricing changes in annual filings. An acceptable step taken in today’s environment is

to invest for future scale and build AI in the cloud from the get-go (as opposed to an offline solution). This

can lay the foundation for real-time inputs and dynamic pricing in the near future.

Figure 1: Pricing innovation and transformation maturity

Figure 2: The Mindtree AI practice - areas of expertise

Figure 3: Systems in play to integrate with a cloud-based AI solution for pricing

Machine Learning

Deep Learning

Data Science

Natural language Processing

AutomationNeural network

Autonomy

Computer VisionSarcasmDialog

Sentiment

IntentLinguistics

Speech Recognition

Classification

RecommendationPersonalization

Regression

Platforms AnalyticsBusiness Intelligence

Supervised learning

Unsupervised learning

BILLING

PURCHASE UNDERWRITING SERVICE CLAIMS RENEWAL

CRM

PRICING A.I.POLICY ADMIN SYSTEM

DIGITAL CX PLATFORM

SALES CONTACT CENTER

eSERVICE CLAIMS CX

ENTERPRISE DATA LAKE

SERVICE CONTACT CENTER

The evolution of pricing maturity leads to dynamic pricing. To enable an effective solution, it is

essential to draw upon a wide range of additional data to drive faster and meaningful learning.

Here, AI establishes pricing in real-time based on a continuous stream of relevant parameters from

sales channels coupled with historical data on purchases, claims and renewals. Dynamic offers are

made to prospects depending on their propensity to purchase as well as the risk they represent.

Examples of additional data may include demographics (age, marital status, gender) and web

metrics (media source, time on site, frequency of visit) from the digital channel, service levels (call

back time, time to answer) and engagement levels (call sentiment analysis, time on call) from the

contact center and additional customer behavior data from third-party services.

Additionally, the growth in telematics /IoT based insurance and pay-as-you-go product

development has led to further proliferation of once unknown data points that can monitor risk in

real-time and feed a dynamic pricing AI.

Dynamic pricing

Figure 4: Real-time feedback and potential data points that can feed the dynamic pricing AI model

Social Profile

Life Events

Online Behavior

DIGITAL PRICING A.I. CLAIMCONTACT CENTER

Demograohics

Web Metrics

Media Source

3rd Party Data

Engagement

Call Back Time

Qualifiers

Coverage

# of Incidents

Average Value

Risk Category

Deductible

Rate Appetite

Cross-sell

Channel

CLTV

Driving HistoryIOT 3rd PARTY DATA

Usage

Geolocation

RENEWAL

“What-if” analysis

About dynamic pricing

Figure 6: Systems in play to integrate for an AI led dynamic pricing model

Our interpretation of dynamic pricing, given the US’s regulatory laws, is more of a multi-faceted product

combination covering product versions, riders and coverage levels. These are facets of prior filed products

that are stitched together by AI, factoring in optimal uptake price and maximized underwriting

profitability.

PURCHASE UNDERWRITING SERVICE CLAIMS RENEWAL

CRM

PRICING A.I.

IoT PLATFORMS

POLICY ADMIN SYSTEM

eSERVICE CLAIMS CX

ENTERPRISE DATA LAKE

SALES CONTACT CENTER SERVICE CONTACT CENTERDIGITAL CX PLATFORM

BILLING

3RD PARTY DATA AGGERGATORS

Figure 5: Configurable parameters that impact end-customer pricing

VersionRiders

Coverage

Product simplificationAs pricing maturity evolves, the focus on profitable cells and products that meet emerging needs benefit

from richer and real-time inputs. Organizations with simplified portfolios benefit from focused targeting

and lower operational costs, driving further ROI. Mature organizations institutionalize the feedback loop

between pricing intelligence and new product development so that they can continuously optimize their

product portfolios. A further necessary step is to manage these products on modern policy administration

systems where feature development, policy issuance, billing, commission accounting and filing with state

entities is carried out using a ‘no-code’2 approach.

Mindtree case studyReal-time pricing platform for a leading European Airline

The ask: How to leverage AI at scale to optimize pricing in real time at a ‘per-seat’ level?

The solution: Mindtree launched an AI platform using distributed scaling architecture in the cloud,

integrated it with multiple real-time data sources and scaled the model to 17000+ onward and

destination markets, driving increases in revenue and yield.

Mindtree case studyProduct rationalization and simplification when moving to a Policy Admin System (PAS) for a large P&C

carrier.

The ask: Simplify and migrate a portfolio of over 150 different commercial P&C products to a modern

Policy Admin System (PAS).

The solution: Once the carrier rationalized the portfolio after a profitability review exercise, Mindtree

designed a template-based variance approach and launched a factory model to move these products to

the PAS. A configuration-based approach and Mindtree’s expert knowledge of the domain and technology

ensured product migration in three months vs. the earlier model of nine months.

2'no code': Platforms that allow developers and advanced skill non-technical users to develop

custom implementations using a “code-less” configuration console.

Full-scale transformation into a mature pricing-led organization involves further

cultural changes and an organization-wide governance structure that drives

everything from a periodic review of market insights, to pricing changes, to

championing investment in technology. All of these drive competitive advantage

in pricing.

Full-scale transformation

Mindtree envisions a multi-year program where infrastructure and technology are rolled out and

institutionalized across the organization. Maturity is built layer by layer and a long-term strategy is

embraced in order to extract benefits down the road.

Phase 1: Adopting unconstrained generalized linear models in pricing

More than 50% of P&C insurers have achieved this level of sophistication and various sophisticated models are provided through the likes of Willis Towers Watson, Milliman, Prophet and other actuarial software providers for those that need to adopt this level. These are historically on-prem products with newer versions presented either stand-alone or through packaged solutions in the cloud.

Program bird’s eye view

Again, actuarial software providers have gained ground in this, given their historical role. For example, “Emblem” by Willis Towers Watson comes packaged with machine learning models for companies with large datasets in customer and claims data.

The option exists of course, to build custom models where internal 'tribal' knowledge and data science can work together to build models fine-tuned to the company’s DNA and risk appetite. Tensor Flow, Watson Analytics and Sage Maker can be deployed in the cloud and integrated with a corporate data-lake by an IT services vendor to continuously train your pricing AIs.

A corporate-wide data lake strategy is also a necessary step either before or when you reach this stage. This sets the stage for consolidating data into one central area and simplifies programming models for the AI.

Phase 2: Using AI for pricing

A move to dynamic pricing models provides (and needs) insurance companies to significantly expand the amount of data they track. The creation and capture of additional marketing events is well supported by a sophisticated marketing operations program, which in turn benefits from back-end input. Mindtree defines a mature markops program along the dimensions of campaign management, operational execution and marketing analytics and supports the implementation of platforms like AEM and Salesforce Marketing Cloud.

IoT is rapidly becoming a necessary component in any P&C offering. Regardless of whether companies currently offer behavioral pricing based products to customers (which will rapidly emerge post the COVID-19 experience), launching safe driving or home monitoring pilots is a must have to build the dynamic pricing products of the future.

Phase 3: Dynamic pricing

We don’t see this necessarily as a phased event, but rather a continuous process companies should embrace to optimize profitability. Modern policy administration platforms like Duck Creek are essential tools in ensuring that desired changes are quickly rolled out. Inheritance models and default templates in Duck Creek significantly reduce turnaround times for filing new products.

Phase 4: Portfolio optimization

BenefitsAdoption of a pricing maturity approach yields results at every stage of the maturity continuum. There is

an immediate impact on loss ratio improvement by moving to GLMS and AI-based pricing, and real-time

pricing drives higher overall profitability. Full scale transformation has a significant impact on the

combined ratio, probably because companies embrace a pricing focus at every level of the organization

and view investment in training, technology and talent through this lens. Additional benefits in premium

increase, retention and anti-selection (the lack thereof) are also seen.

Consistentapplication of GLMs1

Consistentlyapplies “unconstrained”GLMs to all risk models and product types

Description

Level of pricing sophistication

Use of AI-based or machine-learing pricing tool

Product simplification forthe right pricing

Implementationof robo-pricing2

Full-scale pricing transformation

Implements AI-based, automated pricing model to leapfrog rate-making process using GLMs; alternatively, could build on existing GLMs

In addition to using GLMs and AI, understands real-time market dynamics and uses robotics for ongoing, automatic pricing including sales development

Adjusts product structure and eliminate highly unprofitable tariff cells and product modules; enables end-to-end product value chain optimization and behavioral pricing

Approaches pricing transformation comprehensively, including current capabilities, people culture, organizational design and structure, data and tech strategy

Implementation of GLMs

Building / Augmenting Data Lake

Create and capture measurable events

Feedback led portfolio rationalization Faster Portfolio Optimization in PAS

Build organization wide pricing focused culture Optimize Pricing Governance

Real-time Pricing A.I. A.I. in the cloud

Implementing Pricing A.I.

Integration of 3rd party data

Figure 7: A representative view of a multi-year technology program to achieve pricing excellence

Consistentapplication of GLMs1

Improved loss ratio of new business by

0.8-1.5 pp3

Improved loss ratio of renewal business by

0.8-1.5 pp

Improved loss ratio of new business by

2.1-4.2 ppImproved loss ratio of renewal business by

0.6-1.3 pp

Higher new business profitability by

2-4 pp of combined ratio

Higher new business premiums of

10-15%Improved retention by

10-12%

Double-digit growth rates of new business year over year, while loss and cost ratio are improved by

1 pp

3-6ppof combined ratio improvement

3-4%Additional GWP4

growth

Reduces severe cross-subsidization of more than

20%and therefore anti-selection for

10-15%of the portfolio

Impactobserved

Level of pricing sophistication

Use of AI-based or machine-learing pricing tool

Product simplification forthe right pricing

Implementationof robo-pricing2

Full-scale pricing transformation

ConclusionTechnology is an essential ingredient in moving an insurer to pricing maturity. Companies should consider

that a point solution alone will not solve the problem. Instead this calls for an investment in a technology

roadmap that delivers an infrastructure capable of executing this at scale. The good news is that

investment in policy admin systems, AI solutions and marketing operation platforms (for starters) – all

considerably important to any CIO – yield immediate benefits for pricing maturity (provided this is

contemplated in the roadmap). If anything, this should cement purchase and implementation decisions in

these focus areas to prepare insurers for adapting to changing times.

Figure 8: Quantification of benefits by McKinsey Inc.

General Manager

Riddhish has a deep background in Digital, Insurance and Direct-to-Consumer marketing. His

career experience spans across various roles, including Global Head of Broad Market, Head of

Digital Experience and VP of eCommerce. He has wide-spread experience in building

consulting practices, re-launching brands in the market and collaborating with key

stakeholders across geographies. Riddhish is an MBA alumnus of SP Jain in Mumbai and has a

degree in Chemical Engineering from the University of Pune.

Riddhish Trivedi

About MindtreeMindtree [NSE: MINDTREE] is a global technology consulting and services company, helping enterprises marry scale with agility to achieve competitive advantage. “Born digital,” in 1999 and now a Larsen & Toubro Group Company, Mindtree applies its deep domain knowledge to 280+ enterprise client engagements to break down silos, make sense of digital complexity and bring new initiatives to market faster. We enable IT to move at the speed of business, leveraging emerging technologies and the efficiencies of Continuous Delivery to spur business innovation. Operating in more than 15 countries across the world, we’re consistently regarded as one of the best places to work, embodied every day by our winning culture made up of over 21,800 entrepreneurial, collaborative and dedicated “Mindtree Minds”.

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