Advanced Analytics Turbo Charge Advisor Efficiency, Performance

Authors Sudeep Doshi, Ju-Hon Kwek and Joseph Lai state that asset managers are applying advanced analytics in three main “vectors”:

1) Asset acquisition in sales and marketing: The authors found that applying advanced analytics to the distribution and service models, allowed for:

• Behavioral segmentation of clients
• Data-driven client prospecting and retention
• Predictive algos to improve sales productivity
• Personalized digital marketing

This optimization added 5-30% higher revenues, the authors found, as well as freed up 15% of existing sales force capacity.

2) Investment management production: Advanced analytics have added a “meaningful improvement in performance,” the authors note. The reasons:

• Debiased investment decisions
• New sources of alpha through alternative data
• Automated “big data” ingestion for research
• Improved trade execution algorithms

The new sources of research is especially key when stitching them together “into predictive models that improve decision making,” the authors state. These sources include social media data, crowd sourcing estimates, online platforms that connect investors to corporates (sidestepping brokers as a source of access), using AI “across broader and deeper sets of data,” natural language techniques that help produce research summaries, and technology that consolidates information from independent research providers.

3) Asset administration in middle and back office: Advanced analytics applied to middle and back office administration has lowered costs by 10-30%, according to the authors. This is due to:

• Increased process automation of time-consuming tasks
• Automated trade surveillance, thus improving risk management and cutting surveillance activity time by 55-85%
• Decreased costs for data management
• Increased administrative efficiency

Common Thread

Finally, the authors found shared characteristics of asset managers who “have extracted meaningful value from data and advanced analytics.” These include:

• Ruthlessly prioritize based on business value
• Recognize that analytics is a team sport, and works best when led by small, agile teams with end-to-end responsibility
• Focus on the “last mile” adoption, that is, the question of how end users will actually engage with analytics is addressed at the beginning of the process.
• Adopt a “minimum viable product” mentality: the firms use a “test and learn — and fail — quickly” method as they work the process. That is, “firms learn more from playing the game than from standing on the sidelines,” the authors state.
• Invest in next-level data and analytics talent, therefore realize they need a “vibrant community and strong talent plan (eg., career paths, robust professional development).”
• Create an integrated target-state vision for data and analytics, that is, “create self-sustaining data and analytics engine that drives measurable business value.”

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