Pricing Engine
Product Description

Pricing Engine supports the rapid development of products. Changing a formula is parameterization, and so is adding new accumulations of data - e.g. cumulative turnover over a period. There is no development necessary, let alone regression testing of the whole monolithic core banking system.

Features
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Key features and benefits

Flexibility
No development need
No dependency
Pricing simulation
Flexibility

New pricing rules can be created, tested, and applied in production rapidly. Changes to source systems will be necessary only if new source data is needed to complete the price calculations. Price changes are decoupled from the rest of the application architecture, massively reducing the complexity of regression testing.

Agility in Rule Creation: Our Pricing Engine allows users to quickly create new pricing rules without extensive coding. Business analysts or pricing managers can define rules based on market conditions, customer segments, or promotional strategies without relying heavily on the IT department.
Changes to the core pricing logic are made within the pricing engine product itself, which means there is minimal need to alter the underlying source systems unless new data elements are required. This isolation ensures that the pricing engine can evolve rapidly without necessitating frequent and complex changes to other IT systems.
Organizations can handle increased pricing complexity or larger volumes of transactions without overhauling their existing IT infrastructure.
If new data sources are required to support more advanced or nuanced pricing rules, our PE product can integrate these data sources flexibly.
Organizations can continuously innovate their pricing models without significant rework or system overhauls.
No development need

Development is not necessary in PE in case of changes to or creation of new rules, or even the addition of new fields in the incoming data feeds.

If the business (or the regulator) thinks of "just a simple new field" that they want to use in the pricing formula, this means only parameterization in our solution no matter what they think up. This is because we have no predefined, rigid structures.

Our Pricing Engine is designed to offer unparalleled flexibility and ease of use, particularly when implementing changes or new requirements related to pricing rules and data fields. It allows for the adjustment of pricing rules through parameterization rather than traditional coding. This means that users can modify existing rules, or create new ones by simply adjusting parameters in the system's interface. There is no need for software developers to write new code, which drastically reduces the time and cost associated with implementing changes.
Our product is built to handle dynamic data inputs. When new fields are added to incoming data feeds, the system can incorporate these fields without requiring structural changes. This flexibility means the system can evolve alongside changing data requirements without necessitating development.
One of the key strengths of Joan Prys is its lack of predefined, rigid structures. When a new field is introduced, integrating it into existing pricing formulas involves updating configuration settings within the PE product. Users can specify how the new field should be used in the pricing calculations through intuitive parameter settings.
No dependency

No dependency is enforced or expected on pricing parameters or data structures that make integrating existing data structures as simple as possible.

Our data representation is very open and flexible on purpose. We faced situations where it was difficult to match the bank's vocabulary to the wording and notions in vendor products. We have no predetermined phrases e.g. accounts/contracts/passive products/retail accounts/SME accounts/micro-accounts. Similarly, we do not have predetermined phrases for customers/parties/retail or corporate/large corp/etc. These trivial things have proven to lead to serious challenges.

We designed our product to seamlessly integrate with your existing data structures. Whether you use custom data models or industry-standard formats, our solution can adapt accordingly.
This adaptability is particularly beneficial for financial institutions having complex and unique data architectures. Instead of forcing you to restructure your data to fit our system, our product accommodates your current setup.
One of the significant challenges with many vendor products is the mismatch between the vendor’s terminology and the bank’s internal vocabulary. Our product addresses this by al
Pricing simulation

The most exciting and forward-looking aspect of our solution is the possibility of simulation. We store all the information used for pricing and the pricing formulas retrospectively, enabling what-if analysis going back in time. Or going into a simulated future using various made-up scenarios. An analyst can use all this information to come up with better pricing strategies, and an AI can be used to suggest better strategies optimizing for pre-set conditions (e.g. max profit, min churn, etc).

Our solution allows analysts to perform what-if analysis on historical data. By adjusting different variables and pricing parameters, analysts can simulate how alternative strategies might have played out.
This retrospective analysis helps identify the strengths and weaknesses of past strategies, providing valuable insights into what worked and what didn’t. Beyond analyzing the past, our product enables the creation of simulated future scenarios. Analysts can craft hypothetical situations using various assumptions about market trends, customer behavior, economic conditions, and competitive actions.
This forward-looking approach helps in testing the potential impact of different pricing strategies before implementing them in the real world. Institutions that can rapidly test and optimize their pricing strategies gain a competitive edge in the market.
The agility to adapt to market changes and customer needs allows for more responsive and customer-centric pricing models.
Flexibility
Flexibility

New pricing rules can be created, tested, and applied in production rapidly. Changes to source systems will be necessary only if new source data is needed to complete the price calculations. Price changes are decoupled from the rest of the application architecture, massively reducing the complexity of regression testing.

Agility in Rule Creation: Our Pricing Engine allows users to quickly create new pricing rules without extensive coding. Business analysts or pricing managers can define rules based on market conditions, customer segments, or promotional strategies without relying heavily on the IT department.
Changes to the core pricing logic are made within the pricing engine product itself, which means there is minimal need to alter the underlying source systems unless new data elements are required. This isolation ensures that the pricing engine can evolve rapidly without necessitating frequent and complex changes to other IT systems.
Organizations can handle increased pricing complexity or larger volumes of transactions without overhauling their existing IT infrastructure.
If new data sources are required to support more advanced or nuanced pricing rules, our PE product can integrate these data sources flexibly.
Organizations can continuously innovate their pricing models without significant rework or system overhauls.
No development need
No development need

Development is not necessary in PE in case of changes to or creation of new rules, or even the addition of new fields in the incoming data feeds.

If the business (or the regulator) thinks of "just a simple new field" that they want to use in the pricing formula, this means only parameterization in our solution no matter what they think up. This is because we have no predefined, rigid structures.

Our Pricing Engine is designed to offer unparalleled flexibility and ease of use, particularly when implementing changes or new requirements related to pricing rules and data fields. It allows for the adjustment of pricing rules through parameterization rather than traditional coding. This means that users can modify existing rules, or create new ones by simply adjusting parameters in the system's interface. There is no need for software developers to write new code, which drastically reduces the time and cost associated with implementing changes.
Our product is built to handle dynamic data inputs. When new fields are added to incoming data feeds, the system can incorporate these fields without requiring structural changes. This flexibility means the system can evolve alongside changing data requirements without necessitating development.
One of the key strengths of Joan Prys is its lack of predefined, rigid structures. When a new field is introduced, integrating it into existing pricing formulas involves updating configuration settings within the PE product. Users can specify how the new field should be used in the pricing calculations through intuitive parameter settings.
No dependency
No dependency

No dependency is enforced or expected on pricing parameters or data structures that make integrating existing data structures as simple as possible.

Our data representation is very open and flexible on purpose. We faced situations where it was difficult to match the bank's vocabulary to the wording and notions in vendor products. We have no predetermined phrases e.g. accounts/contracts/passive products/retail accounts/SME accounts/micro-accounts. Similarly, we do not have predetermined phrases for customers/parties/retail or corporate/large corp/etc. These trivial things have proven to lead to serious challenges.

We designed our product to seamlessly integrate with your existing data structures. Whether you use custom data models or industry-standard formats, our solution can adapt accordingly.
This adaptability is particularly beneficial for financial institutions having complex and unique data architectures. Instead of forcing you to restructure your data to fit our system, our product accommodates your current setup.
One of the significant challenges with many vendor products is the mismatch between the vendor’s terminology and the bank’s internal vocabulary. Our product addresses this by al
Pricing simulation
Pricing simulation

The most exciting and forward-looking aspect of our solution is the possibility of simulation. We store all the information used for pricing and the pricing formulas retrospectively, enabling what-if analysis going back in time. Or going into a simulated future using various made-up scenarios. An analyst can use all this information to come up with better pricing strategies, and an AI can be used to suggest better strategies optimizing for pre-set conditions (e.g. max profit, min churn, etc).

Our solution allows analysts to perform what-if analysis on historical data. By adjusting different variables and pricing parameters, analysts can simulate how alternative strategies might have played out.
This retrospective analysis helps identify the strengths and weaknesses of past strategies, providing valuable insights into what worked and what didn’t. Beyond analyzing the past, our product enables the creation of simulated future scenarios. Analysts can craft hypothetical situations using various assumptions about market trends, customer behavior, economic conditions, and competitive actions.
This forward-looking approach helps in testing the potential impact of different pricing strategies before implementing them in the real world. Institutions that can rapidly test and optimize their pricing strategies gain a competitive edge in the market.
The agility to adapt to market changes and customer needs allows for more responsive and customer-centric pricing models.

Other benefits

High availability

Pricing Engine never experiences downtime, whether it’s operating system or infrastructure upgrades, new product rollouts, rule creation, or even the addition of new formulas or fields.

Scalable
From a few thousand accounts to tens of millions of accounts. Our pricing engine is scalable even during the day leading to lower operating costs. This is due to the technologies and development patterns we used in our solution.
Event driven
Apache Kafka is our preferred integration technology. Our solution supports sync or async decoupled integration via Kafka streams or REST APIs.
Platform agnostic
We offer PE as an on-prem solution, a SaaS, or both. Built on top of cloud-provider-supported managed services supporting multiple cloud providers, leading to minimal infrastructure operations overhead and lower TCO. We also support on-prem installations or even cloud-on-prem hybrid solutions.
Fast
Enabling the calculation of end-of-period closing prices of millions of accounts in 10-20 minutes. Our typical response times are around 10 ms regardless of the amount of data (e.g. number of accounts) or transactions per second. If you need more performance, you can scale up the underlying infrastructure.

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