The potential of alternative data
The flipside of using better data tools is the ability to harness more source data. Machine learning, a form of AI that teaches itself to spot patterns, is used in many sectors to extract value from unstructured data sources such as internet searches, social media chat, satellite images, shipping data, online sales and credit approvals.
Utilities firms illustrate one useful way to combine alternative data with customer information. They use these insights to predict when customers are about to switch, and to approach them with timely, targeted offers. Investment firms could do the same, offering alternative fee arrangements or other incentives to boost retention.
Smart data analysis is growing in health care too, with some providers combining patient data from wearable devices with geographic data to predict local demand for medication. In a similar way, wealth managers could use online chat and client location data to identify key events – such as a university graduation – that present an opportunity to offer clients some tailored, valuable support.
Investment firms can also learn from the consumer products industry, which is highly experienced in mining and analyzing customer data. EY’s FutureConsumer.Now. research predicts that personal data and preferences, interpreted by AI, will increasingly allow consumers to shape every aspect of their lives, creating better versions of themselves. AI will help consumers optimize everything from what they eat and how they feel, to how they spend their social time and progress their careers. It will even help them manage their personal relationships. Investment firms could capitalize by using AI to harness client data to play a larger role in what, how and when their clients invest.
Identifying future opportunities
A third opportunity that could flow from new data tools and sets relates to another vital aspect of client experience – investment performance.
Leading firms are already harnessing AI and alternative data for investment research, and specialist firms are even reported to be using neural networks for asset allocation decisions.
But once again, it may be worth looking to other sectors for fresh thinking. One example could come from pharma companies, which are studying huge volumes of patient and research data to try and identify future opportunities for drug development, or to redeploy existing drugs. Oil and gas companies are also using AI to help predict potential problems, and some hope AI could help them locate and exploit new resources.
As the availability of data, processing and analysis grows, firms could similarly identify long-term investment opportunities and ideas. This insight could help firms make the kind of early stage investments that were once limited to venture capitalists – a compelling attraction for institutional and high-net-worth clients.
My suggestion for investment firms is to start thinking differently about data and looking beyond their peers for inspiration. With the rise of industry convergence, firms that explore strategies outside of their traditional ecosystem may be in a position to get more value from data.