Published: 6 May, 2024

How Asset Managers Can Mature Their Data Analytics Journeys

Now that every platform has gone digital, grappling with how best to use them (and what to do differently from everyone else) is a difficult process for asset managers. Achieving success with each of these digital means is another task entirely.

We don’t have to look far back to see how digitisation in financial services has seen abstract ideas taken into practical use to drive value. Whether that be for increasing company productivity, making investment decisions in the C-suite or aligning sales and marketing teams, advanced analytics are transforming all manner of processes in new and exciting ways.

Artificial intelligence may have been the buzzword on the lips of all financial experts, essayists and journalists, but now we can make it actionable. It’s not replacing human analysts as we all worried about, but instead makes their reviewing methods quicker and simpler by crunching down huge amounts of data.

In the past, asset managers have been lukewarm about improving their data-driven strategies, choosing to spectate the efforts of other financial services companies rather than get stuck in themselves. Smaller data science teams were considered, as well as experimentation with data from third-party providers and other sources.

But hedge funds, for example, were more notable in pursuing advanced analytics to better their quantitative research and to drive investment decisions, while reinventing distribution models for the end user and upending their own operational expertise.

Now we’re here, where digital analytics tools are better understood and easier to obtain than ever before. Asset management firms first and foremost need to embrace the idea of trial and error using advanced data. That will hopefully lead to the reward of boosted AuM.

Many operational considerations need to take place, but forward planning to incorporate analytics is far more sustainable for a digital-first future. For instance:

Consider your criteria: How can you get the most value from using data analytics? Is it being used to remove your legacy systems, consolidate your apps, or cut down manual labour? Laying out defined company goals can make sure you’re using data to make considered decisions that can transform business practices for the better

Bring your teams together: Tech platforms, when housing all the same data, bring your sales, marketing, compliance, operations and data teams together in agile workflows. With product managers able to understand a platform front to back, inefficiencies in BAU working practices can be addressed too.

Hiring: Many data experts are out there, able to fill the gaps where advanced data can benefit operational and asset-raising methods. Even considering data scientists, CTOs or product managers from outside the financial industry can bring a fresh perspective.

 Besides making the backend functionality more slick, data analytics also helps extend best practices to the end user. Using AI for instance, fund marketers are able to analyse and compartmentalise a huge number of data points in minutes, most notably to understand their investors.

AI can segment historical user behaviours to granular levels, such as their portfolio-building past, or thematic content preference. It helps salespeople to identify key accounts that may be open to cross-sells or upsells.

McKinsey has deduced that sales and marketing teams at asset managers can achieve between 5% and 30% higher revenues from utilising advanced analytics. Whether using behavioural-based segmentation, predictive algorithms, or prospecting and personalising marketing materials based on data-led insights, AI can help benefit these customer-centric tasks to build out a successful GTM funnel that can be improved over time.

Machine learning and natural language processing are also available to improve compliance issues. Wading through transactions to find patterns and anomalous suspicious activity is important for client onboarding and ongoing monitoring. By leaving data analysis tasks to machines, asset managers can more accurately detect any risk factors that could be missed by the human eye while speeding up time for identification by up to 85%.

Not that the machines are working alone. They act as tests to guide final decisions made by human analysts. For investors at a firm, using data sets to outline past trading activity and behavioural attributes can also help eliminate historical biases and better fund performance.

Enhancing customer journeys is key to standing out in a competitive field – doing it in smarter cost-effective ways means taking a few risks. Asset managers have the data available to empower high-value operational and client-facing decisions and should look to AI and advanced analytics to conquer a whole new digital frontier.

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