Asset and Fund Management

Supporting asset and investment managers to become fully data-driven

Our approach.

Business process analysis

We evaluate operations at a high level to understand a business’s strategies and apply analytics to these key areas. The methodologies are created around value creation and productivity gains through automation, focussing on delivering tangible, financial outcomes for our clients, their teams, and their portfolios.

Data engineering

It’s important to ensure that the analytical foundations are solid before embarking on more sophisticated analytics projects. That means ensuring that data is collected, organised, and automated in a way that feeds real-time data to your business to drive increased revenue and profitability.

Advanced analytics

We use advanced analytics including statistics, machine learning, and operations research to predict behaviour and carry out complex segmentation and process optimisation.

These models focus on: behavioural prediction, process optimisation, recommendation algorithms, and automated marketing segmentation.

AI and machine learning

Using code-based algorithms we build predictive models which secure our hypotheses, this allows our clients to have a competitive advantage in their business models. We contextualise the troves of data from our clients by using machine-learning algorithms to deliver actionable insights to the decision-makers.

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Our services.

Excel automation

Excel has historically been the backbone of most financial services, but quite often the bane of many analysts. With so many investment decisions underpinned by Excel calculations, the accuracy and speed of obtaining and calculating this data is essential.

We aim to simplify and automate this process to assist our clients in investment decisions. This work involves:

  • Automated data engineering:
    Using either off-the-shelf ETL tools or custom python extraction workflows to automatically pull data from multiple resources.

  • Automated transformation:
    Encoding all Excel logic (using DBT) to transform new data automatically when it is received.

  • Automated reporting:
    We can automate these within weeks freeing up finance team effort to focus on the decisions that matter.

Back office optimisation and automation

We apply advanced data analytics and automation to help asset managers optimize complex back office processes. By integrating data from multiple systems into a centralised data warehouse. QuantSpark builds machine learning and statistical models that automate complex tasks that would otherwise take entire teams of accountants to complete.

This improves efficiency, accuracy and scalability, allowing analysts to focus their expertise on high-value work.

QuantSpark's "no-touch" and "low-touch" automation solutions seamlessly integrate into existing systems with minimal disruption.

No-touch automation works behind the scenes without any user interaction. Low-touch automation lightly augments current workflows using machine learning to reduce repetitive manual work.

By taking an incremental approach focused on minimising invasiveness, QuantSpark can automate key tasks and unlock major efficiency gains.

Custom analytics platform development

For our largest clients with complex workflows, we develop bespoke cloud-based analytics platforms that leverage the best of data science and software engineering.

We can deliver long-lasting impact through automation and integration with existing tools and systems.

Recent examples of custom development include:

  • Building a bespoke Analytics Platform for a leading Asset Manager that scrapes 100+ Excel valuation models, seamlessly pulls data into a cloud-based data warehouse, and then visualises and analyses investment trends and decisions.

  • Building an LLM tool to process 5,000+ internal investment reports.