QuanTech

QuanTech is an emerging area that combines quant (or analytics) with technology - offering an integrated approach to modelling, modern data engineering practices and cloud computing.  Its aim is to first disrupt, second modernize and then streamline the delivery of quant/analytics services in a firm – saving time and cost and avoiding unnecessary hassle.

QuanTech is a cousin of FinTech and RegTech

FinTech is an emerging industry that applies technology to improve activities in finance
RegTech, is a new technology that uses information technology to enhance regulatory processes. Often regarded as a subcategory under FinTech, RegTech puts a particular emphasis on regulatory monitoring, reporting and compliance and is thus benefiting the finance industry
Similarly, QuanTech is an emerging framework and set of practices that uses technology to improve analytics (in the heavily regulated financial sector and also wider). All models are part of the analytics including AI and ML models
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It is about quickly operationalising models

At its core QuanTech uses iterative engineering and modelling practices and automation at every layer to build, test, deploy, productionize models. This is in contrast to traditional approaches where the processes are sequential and rigid, teams operate within fixed boundaries and projects take long timeframes to complete.

Meet the QuanTech network

The network is good way for specialists to engage in discussions on topics of interest and increase the amount of connections and knowledge. There are opportunities to participate in exciting projects and create new methodologies, prototypes and tools.

A group Slack channel exists

Example of QuanTech topics

CI/CD practices

How to embed CI/CD practices into the traditional model lifecycle to reduce validation time.

Validity of models

What type of automated testing can help in continuously assessing and assuring the validity of models with known datasets.

Automation

How to automate data quality checks to continuously verify data lineage and pinpoint transformation errors. This will help in significantly reducing downstream problems that require time consuming investigations, e.g. in a regulatory stress testing process of the Bank of England
(STDF).

Microservices

How to wrapping quant models with microservices to allow for a polyglot ecosystem. Models can be built using the best languages and frameworks that suit the need and still be integrated with
other parts of a large application using this approach (e.g. new credit risk models may be in Python, dated trading book models in C++ and both libraries need to be “called” in a stress testing application).

Kogito

Is Kogito a good option for encoding model risk management workflows?

And much more

Once you get here, all is set up, and you can fully focus on enjoying your work and daily life. Consider the process and collaboration to be smooth.

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QuanTech in detail

QuanTech is an emerging area that combines quant (or analytics) with technology - offering an integrated approach to modelling, modern data engineering practices and cloud computing.

1

Modelling/ModelOps/MLOps

  • Modelling comprising the full lifecycle from build, validate, implement, productionise, monitor to risk manage and govern. It includes all models (traditional, AI, ML)
  • ModelOps is the operationalization of all models, i.e. efficient lifecycle implementation 
  • MLOps is the operationalization of ML models and hence is a subset of ModelOps. In some contexts MLOps is used to include the entire lifecycle of ML models
2

DataOps/Data engineering

  • DataOps is used to describe a process-oriented methodology and its automated implementation with the aim of improving the entire data lifecycle from data preparation to reporting
  • One goal is to shorten the cycle time of data analytics in alignment with business goals
  • DatOps recognizes the dependencies between the two teams of data analytics and technology operations
3

Software architecture

  • Software architecture describes a set of aspects and decisions that are important to a software, i.e the organization of the system, how the system parts communicate with each other, external dependencies, guidelines and implementation technologies, risks, performance, security, etc. 
  • It is a blueprint of the software and defines how the software will function. It becomes a basis for communication between the stakeholders
4

DevOps

  • DevOps is a set of practices that combines software development (Dev) and IT operations (Ops)
  • DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of software. This merging of software development and IT operations improves velocity, quality, predictability and scale of software engineering and deployment
  • Several DevOps aspects come from the Agile methodology

A classic example – stress testing

There are many real world situations that require all four aspects of QuanTech to work in unison. A classic example is the delivery of firm-wide stress testing orchestration tool in a large financial institution requires:

  • Thousands of models and an efficient model lifecycle 
  • Data ETL from a large number of disparate sources and with frequent updates
  • The different sub-systems (trading book, mortgages, credit cards, central risk, …) need to communicate with each other and manage dependencies
  • Continuous integration and delivery (CI/CD) and automation are required to able to provide the required speed to users.
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