Ascent White Paper Level Up Pricing Innovation Maturity and Value User Guide
- June 13, 2024
- ASCENT
Table of Contents
Ascent White Paper Level Up Pricing Innovation Maturity and Value
Product Information
This product is a modern pricing platform designed for insurance and reinsurance businesses. It addresses the problem of pricing silos and offers a better approach to pricing by providing reproducibility, data accessibility, and market responsiveness.
Features
- Calculation engine
- Underlying architecture
- Principles and properties of a modern pricing platform
- Data processing capabilities
- Calculation platform
About the Authors
Robert Rimmele is a Principal Architect at Ascent with expertise in
delivering technical consulting to global insurers and reinsurers. Konrad
Pfeffer is also a Principal Architect at Ascent with extensive experience in
software engineering and domain expertise in reinsurance and identity and
access management.
Product Usage Instructions
To use the pricing platform effectively, follow these steps
- Ensure that the necessary data for pricing is available in a central location or in a standard format.
- Access the calculation engine provided by the platform.
- Utilize the underlying architecture to perform data processing tasks.
- Familiarize yourself with the principles and properties of the modern pricing platform to understand its capabilities.
- Follow the data processing guidelines provided by the platform to ensure accurate calculations.
- Make use of the calculation platform to perform pricing calculations and scenario testing.
- Take advantage of the reproducibility and data accessibility features to analyze data and generate reports.
- Ensure that data is exchanged efficiently using the platform’s capabilities, reducing reliance on manual tasks and Excel files.
- Monitor market responsiveness and make adjustments to pricing strategies as needed.
By following these instructions, you can leverage the modern pricing platform to enhance pricing capabilities in your insurance or reinsurance business.
The problem with pricing silos.
In many insurance and reinsurance businesses, Excel is still one of the
most popular tools when it comes to pricing, market analysis, scenario testing
and calculations in general.
Sometimes pricing requires underwriters and actuaries to spend hours cleaning
and wrangling data before it is fit to use in pricing tools. Or sometimes the
entire calculation takes place in Excel, using formulas, macros and add-ins.
Excel is easy to use and very flexible for underwriters who need to work with
data and parameters to calculate alternative models, as it combines excellent
user interface capabilities and a broad range of formulas and features. Very
often, data is already exchanged via Excel files, so processing in Excel comes
in quite handy.
This approach, however, is inherently problematic.
Reproducibility and data accessibility.
Excel delivers frontend, database, and calculation engine in one application –
combining data and algorithms for pricing in a siloed environment (Figure 1).
So when Excel files are stored in different places, businesses are liable to
not only lose track of pricing results, but also the data and algorithms used
to produce the results. And in scenarios where pricing results have to be
reproducible (such as for regulatory requirements), this means keeping track
of multiple versions of data-related algorithms and their parameters.
Furthermore, data in Excel files is rarely available centrally or in a
standard format to be used later for more advanced data analytics, locking
away potential value in folder structures. The distribution of these files
also carries risk from a governance perspective, and make consolidated
reporting on real-time KPIs extremely difficult and fragile.
Level Up: Pricing Innovation, Maturity & Value.
Figure 1: Excel data silos.
Market responsiveness.
In a competitive, recovering economy, insurers and reinsurers need to maintain
focus on developing new products that can be brought to market quickly in
response to consumer needs, and/or that make better use of data. It’s
therefore critical that the pricing solution is able to incorporate modern
components like AI-based algorithms or new data (structured or unstructured,
from internal or external sources). Similarly, legacy pricing approaches that
entail manual processes and decentralized, non-standard data siloes prevent
insurers and reinsurers from being able to pivot or capitalize on more
granular or niche opportunities as they arise, as the cost and time involved
are prohibitive.
A better approach to pricing.
Calculation engine.
To improve pricing workflows, insurance and reinsurance businesses need to
rethink their approach to data and calculation. Important questions include
- How do we embed a calculation engine that supports a centralised pricing approach?
- How quickly can the calculation engine deliver new features in response to changes in pricing strategy? How do we avoid the requirement for new products or updates every time?
- How does the pricing engine help us reduce time-to-market for new insurance products?
- How should we approach large-scale calculations or bulk analysis on a portfolio?
Underlying architecture.
Pricing may be all about data and algorithms, but they need to maintain a
symbiotic coexistence with underlying software architecture to really deliver
the extensibility, scale, and flexibility that modern insurance and
reinsurance companies need.
Extensibility| The pricing solution needs to be able to update or
include new algorithms and combine calculation steps. This should not require
a fundamental change to the pricing applications to speed up product delivery.
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Scalability| The pricing solution needs to scale with increased
workloads. Pricing jobs can be long-running tasks that need to be managed to
make best use of the platform. Some calculations might have real-time
requirements, creating different challenges on the platform.
Flexibility| The pricing solution needs to be able to use variations and
versions of data and calculations. Underwriters need to be able to compare
versions of data sets, and also be able to use variations of data and pricing
parameters to determine the best outcome.
The principles and properties of a modern pricing platform.
Based on our extensive experience with some of the world’s leading insurers and reinsurers, we’ve developed a set of principles and properties that should guide the development of any modern, competitive pricing platform.
Principles
- Data-first approach : Data must become a first-class citizen in the pricing solution. Data must be available centrally so that it can be used by different systems and must remain available for later use with more advanced algorithms.
- Flow-based algorithm s: The calculation engine should use a flow-based algorithm that breaks the calculation exercise down into discrete steps. The flow combines data and processing steps to produce results. Think of a processing step as a formula, a script or a calculation kernel implemented in programming languages like R or Python. Figure 2 (p. 8) shows the principle of a calculation flow, including data and processing components. The pricing solution should allow the addition of new data and processing steps without fundamental change to an application of a digital service.
- Calculation flow : The calculation flow should be separated from software products used for data input and output. This makes the solution usable
by different pricing tools and software products offered to clients. This also supports scalability when the pricing can be distributed across multiple servers, typically using a cloud platform that enables compute power to be dialed up and down as needed.
Level Up: Pricing Innovation, Maturity & Value.
Figure 2: Calculation flow.
Data.
The use of both structured and unstructured data is constantly evolving. Data
files used should be kept in central repositories like a data lake to ensure
they are available for future or wider use. Structured data extracted from
documents or Excel files should also be made available for consumption by
multiple applications and services – a good example might be the use of loss
data across many clients for market analysis purposes. Underwriters often end
up with multiple versions of data that reflect serial updates. A pricing
solution must be able to keep track of different versions efficiently and
provide the capabilities to compare them to understand what has changed.
Similarly, underwriters might want to create variations of data or pricing
parameters for as-if calculations to determine the impact on pricing results.
Being able to tag versions to data structures creates auditability, allowing
businesses to track and reproduce pricing results, fulfilling regulatory
requirements at the same time.
The calculation flow mentioned above helps to keep track of data lineage:
which data set was calculated with what version of pricing algorithm to
produce the actual results. This allows great control over dependencies and
when results need to be recalculated.
Processing
Calculations take input data and execute the processing steps as a flow. The
generic nature of the flow offers several advantages
- It decouples software tools and digital services from the calculation models. Calculation kernels can be updated without changing applications, avoiding expensive and lengthy release cycles.
- It is extensible. A workflow approach also allows for extending the calculation flow with additional processing steps. Algorithms can be implemented in traditional ways, like R scripts and Python, but can also involve modern AI-based approaches. By extending the calculation graph with alternative calculation flows, different results can be calculated, compared and weighted if required.
- It enables better simulation. Being able to inject processing steps into a calculation is particularly useful for simulations.
Consider this scenario : to test the effect of an increased inflation rate, a software system injects an additional processing step into the calculation flow and calculates the effect on the data. With the separation of the calculation flow from any software frontend, this can be done not only on a single data constellation, but also in bulk to determine the effect on an entire market or business model.
Calculation platform.
The separation of the calculation flow from a specific application or digital
service also opens up new capabilities
- New or updated calculation models can be defined without changes to a software system
- Large-scale calculations with large sets of data or long-running algorithms become possible
- Calculations and simulations of entire portfolios of contract or market data can be achieved.
A modern pricing solution supports both real-time and long-running pricing
tasks, scaling to accommodate big workloads. Some models can involve
significant requirements in terms of computing power, memory consumption and
processing time. The underlying pricing platform must be able to monitor,
audit, track, distribute, start and stop pricing jobs.
The solution needs to be able to distribute calculation steps across multiple
nodes, adding computing power when needed and shutting down nodes when idle to
minimise cost.
In summary
Modern pricing platforms, built on the right principles, have the power to
transform the way insurers and reinsurers operate, innovate, compete and
respond to changes in the environment around them. Whether driven by
regulatory requirements or client demand, the ability to absorb change in a
cost-effective, non-disruptive way will be a critical success measure as the
industry continues to evolve at pace.
If you are developing your own pricing platform, it’s important to understand
the specific properties that will maximise your investment. The principles and
characteristics described in this paper should help steer your approach and
improve your likely ROI, helping you deliver flexibility and scale with the
ability to extend, include and combine sources of value. And if you are
looking for a team of experts who have delivered solutions for Tier 1 insurers
and reinsurers, we are just one email away.
How Ascent helps Insurers and Reinsurers
Ascent helps leading global insurers and reinsurers to build new capabilities
and accelerate digital maturity in the cloud. Over the years we’ve worked on a
wide range of projects in the industry, from underwriting process automation
and exposure data transformation to innovative applications and complete
technology replatforming.
You can find out more about our experience here
- Data & analytics: Helping you predict, prevent and personalize at scale.
- Process digitisation & automation: Free your underwriters, actuaries and agents.
- Legacy system & application modernisation: Transform performance, scale, governance.
- Technology replatforming & cloud service adoption: Industrialise, centralise & standardise across the value chain.
- CX-driven innovation : Build richer products and more informed experiences for your customers