=Paper=
{{Paper
|id=Vol-1383/paper2
|storemode=property
|title=The Logic of Insurance: an Ontology-Centric Pricing Application
|pdfUrl=https://ceur-ws.org/Vol-1383/paper2.pdf
|volume=Vol-1383
|dblpUrl=https://dblp.org/rec/conf/semweb/LangevineB14
}}
==The Logic of Insurance: an Ontology-Centric Pricing Application==
https://ceur-ws.org/Vol-1383/paper2.pdf
The Logic of Insurance: an Ontology-Centric Pricing Application
Ludovic Langevine Paul Bone
Mission Critical IT Mission Critical Australia
llg@missioncriticalit.com pbo@missioncriticalit.com
We have developed a new pricing and scoring application for a large insurance company’s car insurance products. The
business logic of this application is entirely expressed in an OWL ontology and SWRL rules. The ontology and rules are not
merely documentation or a specification of the application, they form the business logic of the application. In other words the
application is ontology-centric. The application efficiently generates quotes, calculates the pricing of contracts and determines
the applicability of a product based on an acceptance policy.
We propose to report on our experience developing and deploying this system, as well as integrating it with legacy systems
and new applications using mainstream technologies. We will show the significant quality and agility benefits of an ontology-
centric approach that also delivers the expected performances on commodity hardware.
Scope and context of the project
In an insurance company, actuaries define how products can be configured, how they are priced and at which market segments
they are aimed. They document these details as reports and spreadsheets. IT people use these as the specifications of the
systems to be built, creating multiple scalable systems, such as: product configurators, pricing services, scoring systems and
acceptance checkers. These systems can be used through multiple channels, e.g. a call center, a Web site, insurance brokers,
or partner car retailers. In this scenario, the introduction of a new insurance product or changes to existing acceptance rules
or other aspects of existing products can take months and is subject to interpretation errors.
The development of Web insurance comparators and faster customer turnover represent challenges that insurance companies
have to address in order to compete effectively in the market. The time-to-market for new or altered insurance projects must
be as small as possible, this means the delay between the actuaries’ reports and actual changes to IT applications must be
reduced.
To meet this challenge, a French subsidiary of the UK-based insurer Aviva decided to externalize the business logic of its
car insurance products into an ontology. The ontology drives the behavior of an application which implements the products’
definitions. This application is then used by other systems such as the customer-facing Web site, and the sales-support
application used by the call center. Changes to insurance products are made by changing the ontology and rules which, after
testing, are used directly by the production application. This reduces the time-to-market dramatically.
Mission Critical IT was chosen to support the development of the ontology, to provide the needed technical components for
IT to leverage it and to assist the IT department in the integration with other systems. A key component of the application
architecture is Mission Critical IT’s ODASE1 platform for ontology-driven development. The application went live on the
10th June 2014.
Functionality
The application exposes three services:
• Pricing: execute the product policy defined by the actuaries to produce quotes (determine the eligible products
and their prices, apply the default selection based on questions answered by the customer) or compute the yearly
premiums of existing contracts on renewal. This pricing service includes a configuration service, as it returns the set
of optional insurance features a customer may chose and the default choices.
• Acceptance: depending on the available data about a customer (including incomplete data when some of the questions
have not been answered yet) the application executes the accepting policy to determine whether an insurance offer
can be made to the client or whether their contract can be renewed.
• Scoring: depending on the data about a new offer or a change of an existing contract, this service computes a statistical
score which is used by the backend to decide whether the customer should be asked for additional documentary
evidence (e.g. a statement from the previous insurer in the case of a new customer).
1
http://www.missioncriticalit.com/odase.html
1
Ontology-driven development and integration with other systems
The concepts and properties of the domain are expressed in OWL. Examples of concepts are Vehicle, Person, Guarantee and
Premium. The ontology contains 208 concepts and 598 properties. SWRL rules are used with the axioms to provide reasoning.
Two examples of rules are: a single rule defines the premium including taxes as the sum of the premium without taxes and the
applicable taxes; a set of rules define which optional guarantees can be offered to the customer for a given quote and which of
these should be selected by default.
The ontology was developed by two Cobol developers2 who were selected for their abstract reasoning and strong understand-
ing of the business. It is worth noting that the ontological approach was very well received by those developers who wanted
to upgrade their skills and use their deep knowledge of the business.
The ODASE platform includes a tool that generates a Java API from an ontology. It creates a Java class for each OWL concept
and Java getters and setters for each OWL property. OWL subclass axioms are translated into up- and down-casting methods.
This ontology API interacts with the RDF stores and the OWL/SWRL reasoner which enforces the business logic expressed
in the ontology at runtime. This API provides a type-safe and familiar interface to the business logic for Java developers.
The application is pure Java and runs as a plain servlet in a Tomcat container. The ontology files (OWL and RDF) and the
ontology API are packaged within the WAR artifact with the runtime libraries of the ODASE platform.
The servlet listens to requests with two protocols: HTTP REST requests for modern client applications (the company Web
site and external Web-based insurance comparators) and MQ Series requests from applications running on the legacy AS/400
system (backoffice and sales-support application for the call center). The amount of hand-written code specific to these
services is very small: less than 2,000 lines of Java code.
The REST requests use RDF/XML formatted data, which is already in the business model defined by the ontology. However
the MQ Series requests use Cobol data structures in the model of the legacy applications. A semantic integration with the
AS/400 legacy system has been developed through the generation of intermediation ontologies from the description of these
Cobol data structures.
The actuaries often work with statistical and spreadsheet software. Specific import tools have been developed to extract data
from Microsoft Excel spreadsheet and translate them into RDF instances. More than 5,000 instances have been generated this
way to provide the ontology with all the required parameters.
Performances
The use of runtime reasoners and interpreted rules for such complex computations and inference tasks was perceived as
a major risk for this project. To our opinion, this skepticism is mostly due to the lack of visibility of the Semantic Web
technologies. To mitigate this risk, performance tests were conducted throughout the project, under the supervision of the
client’s IT department. Finally two load-balanced Intel Xeon systems proved enough to cope with the load during the busiest
business hours.
ODASE parallelization mechanisms have been used to leverage the multiple cores of the servers. The query-based nature
of its reasoners allows good response-times: the most complex request is answered in less than 400 ms during peak times.
Quotes can be generated in 250 ms. The application now serves about 30,000 requests per day.
Benefits
Business — IT alignment Traditionally such a business has, at least, two separate views of the business model; one main-
tained by the business and one (or more) maintained for the sake of IT applications. This requires extra effort and problems
will occur when the models are out-of-sync. Now a single model can be maintained and shared between by experts and IT.
Agility IT and business people now share the ontology and understand the same concepts. The delivery of a new pricing or
a change to the acceptance rules can be achieved in a few hours. The delivery time of new products has also been reduced.
Changes in the ontology are reflected in the generated Java API when necessary and IT can modify applications as necessary,
which is less effort than implementing new products in a Java business model. In both cases time-to-market is reduced
dramatically.
Quality Only one bug reported in the pricing engine so far. This low instance of bugs is because the business logic has
been iteratively externalized in a high-level formal language: the application is built on top of an executable and testable
specification, rather than being the result of human interpretation of large and often inconsistent informal specifications.
Moreover, thanks to the declarative nature of the implementation of the business logic, any given output of the application can
be explained in business terms, pinpointing the set of axioms and rules which are responsible for the result using the ODASE
platform’s declarative debugging tool.
2
The developers were coached throughout the project by ontologists from Mission Critical IT.
2