Understand how market forces impact the outcome of pricing decisions
Gain insight into how high frequency price updates in the market are affecting business performance. Drive repricing decisions by integrating third party competitive data with pricing and portfolio analytics.
Understand price-to-sales elasticity effects with limited data
Using OFT1301 compliant data and advanced data science capabilities, develop high performing price-to-sales elasticity models.
Quickly explore pricing scenarios to support fast turn-around pricing cycles
Avoid inefficiency in the pricing process resulting from multiple data hand-offs between actuarial systems and analytical packages or Excel. Use flexible pricing tools and a rich suite of integrated analytics, allowing pricing teams to shorten pricing turn-around times.
Optimize premium rates to maximize growth while protecting margin and meet treating customers fairly (TCF) requirements
Use an extensive optimization toolkit to understand the impact of constraints, explore different pricing strategies, and arrive at a set of premium rates that best satisfy the limits set out by the TCF framework.
Understand key lapse drivers and optimize retention interventions
Bring together policyholder, customer interaction and third party data to better understand key lapse drivers and optimize the deployment of retention interventions to improve retention outcomes for a range of products.
Optimize the cross-sell and up-sell of features and products to existing customers
Understand which features and offers appeal to different customer segments by combining policyholder, customer offer and third party data to optimize cross-sell and up-sell offers.
Run rapid and cost effective actuarial models
Use an open and flexible low code model developer environment to create and run transparent modes.
Collaborate with actuarial and data science models in the same environment
Empower your Data Science and Actuarial teams to work collaboratively on the same platform.
Leverage open low cost APIs
Avoid data silos by connecting all necessary data. Feed actuarial outputs easily into other decision making processes.
Drive faster decisions by utilizing cloud based workflows within a scalable, collaborative platform
Harness the power, scalability and security of the AWS cloud, to reach decisions faster than existing tools allow.
Implement AI-enabled decision science capability and process that is modern, fast and proactive
Integrate key internal and external data sources into a repeatable and dynamic process.
Operationalize data-driven decision making
Consolidate disparate actuarial and data science tasks and tools into one decision science platform.
Drive decision making through data and AI
Shift away from ‘gut-feel’ or biased decision making towards data-driven decisions.
Improve efficiency, transparency and output of your team
Avoid silos by running processes on a centralized platform. Increase transparency and reduce the risk of error in outdated spreadsheets and legacy technology.
Make personalized care interventions to reduce claim costs in LTC
Build effective claim propensity models for the purposes of claim intervention optimization. Predict the likelihood of customers to make claims.
Combine policyholder and claim data with third party data in order to understand, at a granular level, the type of customers likely to make a specific type of claim in the short-term.
Optimize the up-sell of more features and products to existing customers
Combining policy admin and third party data, build a up-sell / cross-sell model to identify customers with a high propensity to buy additional products. This can be used to improve pricing by considering customer lifetime value metrics.
Improve retention outcomes through more effective interventions
Combining policy admin and third party data, build a lapse model to identify key drivers for customer lapse by product. This is used to assess the effectiveness of various retention intervention initiatives and optimize interventions that produce the highest value for cost.
Optimize buy out or exchange offers to customers
Supplementing customer demographic information to prior LTC benefit reduction initiatives can improve future benefit reduction plans. Build a predictive model to understand the key drivers of customers who opt-in for a price increase or specific benefit reduction to inform how to best optimize future benefit reduction options.
Optimize pricing and product features that are not guaranteed
For yearly renewable products such as Medical Supplement, combine policyholder data with third party data and analyze the impact of price changes on sales, morbidity, and lapse. Then use AI-enabled price optimization to strike the desired balance between new business sales and in-force value.
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Identify optimal customer segments and align product, pricing and distribution strategies to these segments
Using a combination of third party and internal data, pinpoint customer and advisor segments that are in closest alignment with customer, proposition and distribution strategy. Identify how these strategies can be improved based on opportunities identified in the data to grow market share. This solution can be applied to a wide range of products and channels.
Identify the most attractive customers in your target segments by deeply understanding customer profitability
Through the combination of granular actuarial analysis and competitive analysis, understand your company's sweet spot; where high profitability exists at an attractive price for customers. Monitor pricing performance to ensure pricing rates are consistently close to their optimal positions.
Improve the value generated through your pricing process by harnessing AI to analyze and monitor the impact of price positioning on sales
Based on your historical sales and competitive pricing positions, build a price sensitivity model to operationalize the process of assessing potential pricing impacts due to changes in sales volume, business mix, and profitability.
Improve the speed and performance of the pricing function
Remove repetitive pricing processes to allow pricing actuaries to spend more time on high value-add tasks such as reviewing results and suggesting other strategies to explore. Harness AI and machine learning to perform complex tasks faster than is otherwise possible. Use a standard, customizable and vetted set of solvers to speed this process up.
Run rapid and cost effective actuarial models
Use an open and flexible low code model developer environment to create and run transparent modes.
Collaborate with actuarial and data science models in the same environment
Empower your Data Science and Actuarial teams to work collaboratively on the same platform.
Leverage open low cost APIs
Avoid data silos by connecting all necessary data. Feed actuarial outputs easily into other decision making processes.
Drive faster decisions by utilizing cloud based workflows within a scalable, collaborative platform
Harness the power, scalability and security of the AWS cloud, to reach decisions faster than existing tools allow.
Implement AI-enabled decision science capability and process that is modern, fast and proactive
Integrate key internal and external data sources into a repeatable and dynamic process.
Operationalize data-driven decision making
Consolidate disparate actuarial and data science tasks and tools into one decision science platform.
Drive decision making through data and AI
Shift away from ‘gut-feel’ or biased decision making towards data-driven decisions.
Improve efficiency, transparency and output of your team
Avoid silos by running processes on a centralized platform. Increase transparency and reduce the risk of error in outdated spreadsheets and legacy technology.
Customer Segmentation - Optimize the up-sell of more features and products to existing customers
Understand which features and offers appeal to different customer segments by combining policyholder, customer offer and third party data to optimize cross-sell and up-sell offers.
Optimize retention interventions
Deeply understand what customer, product and distributor factors are driving lapse behavior and find the overlap of highest preventable lapses and most valuable products in order to focus retention efforts. Analyze historic retention intervention effectiveness and identify the optimal intervention strategy. Monitor outcomes and changes in lapse behavior to maintain effectiveness.
Understand the role of price in driving customer purchase and lapse behavior
Isolate how price changes and positioning impact customer buying and lapse behavior. Use these insights to create data-driven pricing strategies that balance customer outcomes, growth and profitability.
Understand and optimize the financial dynamics of an in- force portfolio
Use sophisticated AI and machine learning to improve in-force pricing including reshaping age curves to achieve the optimal balance between customer retention and value.
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Set new business prices to achieve growth and margin targets
Based on your historical sales and competitive pricing positions, build a AI-driven price sensitivity model to provide your pricing team a systematic way to assess potential pricing impacts due to changes in sales volume, business mix, and profitability. Use a price optimization framework to drive pricing outcomes that balance the competing goals of growth and profitability.
Identify optimal customer segments and align product, pricing and distribution strategy to these segments
Using a combination of third party and internal data, pinpoint customer and advisor segments that are in closest alignment with customer, proposition and distribution strategy. Then identify how these strategies can be improved based on opportunities identified in the data to grow market share. This solution can be applied to a wide range of products and channels.
Identify the most attractive customers in your target segments by deeply understanding customer profitability
Deeply understand what customer and distributor characteristics are driving lapse and claims behavior and profitability for different types of customers through different channels and distributors. This understanding can significantly improve ROI on distribution.
Improve the speed and performance of your pricing function through the use of solvers and optimization techniques
Remove repetitive pricing processes to allow pricing actuaries to spend more time on high value-add tasks such as reviewing results and suggesting other strategies to explore. Use a standard, customizable and vetted set of solvers to speed this process up.
Monitor and measure whether pricing is performing as expected
Set prices with a clear understanding of the expected impacts on volume and mix of new business. Regularly monitor actual sales to identify unexpected outcomes and emerging changes in customer behavior, highlighting areas of opportunity and the need to trigger potential price changes.
Run rapid and cost effective actuarial models
Use an open and flexible low code model developer environment to create and run transparent modes.
Collaborate with actuarial and data science models in the same environment
Empower your Data Science and Actuarial teams to work collaboratively on the same platform.
Leverage open low cost APIs
Avoid data silos by connecting all necessary data. Feed actuarial outputs easily into other decision making processes.
Drive faster decisions by utilizing cloud based workflows within a scalable, collaborative platform
Harness the power, scalability and security of the AWS cloud, to reach decisions faster than existing tools allow.
Implement AI-enabled decision science capability and process that is modern, fast and proactive
Integrate key internal and external data sources into a repeatable and dynamic process.
Operationalize data-driven decision making
Consolidate disparate actuarial and data science tasks and tools into one decision science platform.
Drive decision making through data and AI
Shift away from ‘gut-feel’ or biased decision making towards data-driven decisions.
Improve efficiency, transparency and output of your team
Avoid silos by running processes on a centralized platform. Increase transparency and reduce the risk of error in outdated spreadsheets and legacy technology.
