Using AI to drive growth in asset management

24 May 2020

Algospark recently gave a presentation to CEO's and industry pioneers at the YPO organization about how to fast track benefits from applied artificial intelligence in the asset management sector. We kept use cases broad so that they would be relevant across the asset management industry, i.e. from large retail focused wealth managers through to boutique fund managers.

Here is a summary of the use cases that we covered.

  •  Use Case 1 : Customer and idea clustering
    • Focus area: New sales opportunities,
    • Why: Increase sales funnel size and quality
    • Benefits: Sales increase by x%
  • Use Case 2: AI driven insight - news analytics
    • Improved trading performance
    • Why: Generate insights, focus on the right things at the right time
    • Benefits: Consistent analytics framework and analyst time saving of x%
  • Use Case 3: Outlier detection / compliance monitoring
    • Risk mitigation
    • Why: Spot bad early
    • Benefits: Regulatory compliance, reduce existential threats and compliance review time saving of x%

When evaluating the most effective way to implement AI solutions we nearly always advocate a business transformation approach using micro-services architecture. This ensures good business adoption and reduces time to solution. It also makes sure that the solutions are scaleable and flexible.

  • AI solutions:
    • High value add AI is rarely “off-the-shelf”
    • Needs to be defined by business priorities and impact assessed across: process, roles, data and technology
  • Transformation approach:
    • Define a future state
    • Build a change map, prioritised list of solution requirements, business case and project plan
    • Define and build a rapid working prototype (R, Python, Shiny, Django)
    • Develop the prototype in a sand-box for low early IT dependency
    • Use agile delivery to iteratively deliver every 2 weeks
  • Micro-services architecture:
    • Each service is self-contained and implement a single capability (avoid creating IT “Gordian knots”)
    • Interface across stand alone micro-services using API (Application Programming Interfaces)
    • Use a “pick and mix” approach of micro-services to deliver overall service functionality

Use Case 1 Overview: Customer and Idea Clustering

  • What: clustering customers and ideas to increase sales success and productivity
  • Why: improving sales idea success by x% and reducing overall ideas sales time by y%
  • How:
    • Clustering customers and ideas to prioritise target lists for investment ideas
    • Run through an appropriateness filter for idea / customer suitability (classifier)
    • Use recommender systems to determine probabilities of sales
  • Considerations:
    • Quality of CRM data (customer type, portfolio, objectives, recent activity)
    • CRM data architecture and API
    • Personal data protection using pseudonymisation
    • Data hosting and computation (legislation)
    • In-house data and analytics capabilities vs third party provider

Customer and ideas heatmap / dendrograms:

Key considerations for project delivery success:

  • Clear owner
  • Realistic assessment of in-house capabilities and support
  • Speed to insight considerations, not everything needs to be real-time
  • Process and focus of roles will need to change to realise benefits
  • Good data is good solutions: the impact of data governance, data architecture and maintenance
  • Data security
  • Regulatory regimes
  • Flexible technology (transition to API driven micro-services)

If you are interested in learning more about this use case, the other use cases, or successful approaches to AI implementation, please do not hesitate to get in touch.

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