Data Science Support and Service

Data Science

Contextual financial data science and analytical business intelligence (BI) solutions for investment banks, institutional asset managers, hedge funds, private markets, commercial banks, consulting firms and corporates.

Financial Data Science Solutions

Global technology megatrends, regulatory developments, the competitive landscape and the rise of alternative data have been driving a paradigm shift in the capital markets ecosystem. Demand to find new avenues of alpha creation powered by data science techniques is at unprecedented levels. Acuity has been providing highly bespoke quantitative and technology-powered solutions to our buy-side and sell-side clients since 2005. To address growing demand in recent years, we launched a dedicated data science practice early in 2019 to provide unique and contextual insights based on artificial intelligence (AI) and machine learning (ML).

Financial Data Science Services – Support We Offer

  • Traditional data: financials, market data, operational metrics, macroeconomic data

  • Proprietary data: in-house data lake, subscribed data sources

  • Alternative data: individuals, businesses and machines

  • Exploratory data analysis: identify missing values, duplicates, imbalances

  • Data transformation: cleansing, structuring, enrichment, normalising, data splitting, tagging

  • Features engineering: using domain knowledge

  • Supervised machine learning

  • Unsupervised machine learning

  • Deep learning

  • Natural language processing, unstructured data mining

  • Stochastic/statistical modelling

  • Bespoke reporting: data, dashboards, data lakes, bespoke emails, reports

  • Proprietary outputs: signals on client-defined criteria, bespoke indices

  • Insights that are descriptive, diagnostic, predictive or prescriptive for different contexts

Financial Data Science Services – Support We Offer

  • Traditional data: financials, market data, operational metrics, macroeconomic data

  • Proprietary data: in-house data lake, subscribed data sources

  • Alternative data: individuals, businesses and machines

  • Exploratory data analysis: identify missing values, duplicates, imbalances

  • Data transformation: cleansing, structuring, enrichment, normalising, data splitting, tagging

  • Features engineering: using domain knowledge

  • Supervised machine learning

  • Unsupervised machine learning

  • Deep learning

  • Natural language processing, unstructured data mining

  • Stochastic/statistical modelling

  • Bespoke reporting: data, dashboards, data lakes, bespoke emails, reports

  • Proprietary outputs: signals on client-defined criteria, bespoke indices

  • Insights that are descriptive, diagnostic, predictive or prescriptive for different contexts

How we are different

Domain-centric solutions: 70+ data science professionals work closely with 1,000+ in-house fundamental analysts to create bespoke and contextual outcomes across asset classes

Meticulous scoping and solutioning process to identify key client challenges and formulate a milestone- and incremental gain-based framework with clear timelines

Ability to deliver scalable architecture and outcomes by partnering with our in-house team of 250+ technologists with proven experience in working with capital markets since 2003

What we have done

US asset manager: Non-linear factor modelling for systematic investment strategies
What we are proud of

10,000+

assets analysed

5+

external data pipelines built

Global asset manager (AuM: USD700bn+): Natural language processing for ESG investing
What we are proud of

 30

data fields analysed

10

alternative datasets sourced

US private credit fund: Automated and self-service analytics for enhanced portfolio insights
What we are proud of

100+

funds

6

strategies

Leading South African bank: Alternative data analysis to differentiate equity research
What we are proud of

500m+

real-estate property data points

4

websites

Support for Consulting and Corporate Firms
Cross-functional analytics center of excellence for a US-based bank
What we are proud of

200mn+

data fields analysed

8mn+

alternative datasets sourced

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