Digital transformation, with artificial intelligence and machine learning technologies at its core, has created unprecedented challenges and opportunities across all industries. The financial sector will be profoundly affected as demonstrated by numerous use cases including:

  • Trading and portfolio management applications include asset allocation; security selection; risk management.
  • Operations-focused (or ‘back-office’) applications include capital optimization; model risk management; and market impact analysis.
  • Customer-focused (or ‘front-office’) uses cases include credit scoring; insurance; and client-facing chatbots.
  • Regulatory compliance (‘RegTech’) uses by financial firms or by public authorities for supervision (‘SupTech’).

Challenges to Successful Deployments
While we have seen many use cases of AI and machine, we are at the early stages of adoption with less than 10% penetration in the financial services sector. Technical and business challenges for sustainable implementations include:

Algorithm Improvements 
  • Alternative Data Sources 
  • Explain-ability
  • Ethics 
  • Talent 
  • Process 


“America’s largest companies, many of whom are struggling with a skills gap in filling technical positions, must improve their capacity for internal training and education to compete for talent in today’s economy and fulfill their responsibilities to their employees.” 
- Larry Fink, CEO of Blackrock 


Our Answer: Financial Data Professional Charter

The Financial Data Professional Institute (FDPI) provides a curriculum and a level of discipline and professionalism coincident with the alternative data explosion. It sets the foundation for financial analysts to develop specific skills such as working with data sets, managing a team of data scientists, communicating results to various stakeholders. In addition to helping financial professionals develop new skill sets, the FDPI curriculum elevates the role of privacy and ethics in the development and execution of any financial data science projects.

The Financial Data Professional (FDP) designation reflects the same rigorous standards set by other global designations such as CAIA, CFA, and GARP. Organizations that employ them, realize critical value propositions including:

  • Competitive advantage 
  • Trustworthy AI and machine learning tools and outcomes 
  • Multidisciplinary teams 
  • Regulation and compliance 
  • Standards and best practices 


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