Leveraging alternate data sources for adviser enablement is a growing trend in the financial industry. Alternate data refers to any data that is not typically found in financial statements or other traditional financial data sources. It can include social media activity, mobile phone usage, web browsing behaviour, and more. By analysing alternate data, advisers can gain insights into their customers’ behaviour, preferences, and financial habits that can help them provide better financial advice.
Plenty has been written about the importance of leveraging existing data that wealth managers hold within systems or routinely come across. Doing so enables the wealth manager or adviser to better service customers in terms of being able to feed data into automated processes and perhaps reuse data once entered. Gathering as much data as possible about the customer also makes for knowledge that is both granular and holistic. The more the adviser knows about the customer, their situation, and preferences, the better tailored the service can be in terms of content and delivery.
According to Deloitte, the increasing maturity of analytics capabilities and advances in data engineering feeds several issues. These include saturated product categories, limited differentiation and increasing focus on passive products, evolving preferences for sales engagement, 360º view of the customer, and optimisation of product-channel mixes.
The study shows that “wealth management organisations need to invest in modernising data architectures to ingest and consume a variety of structured and unstructured data sources, ranging from real-time trading data to social media and sentiment data. This will require technology updates. More importantly, an integrated data model will need to be defined by the business to logically combine and connect external market data with internal CRM, risk, and financial data.”
This is all the more so within the APAC region, where customer expectations are high. Advisers, too, expect to have the tools available to meet and exceed customer expectations and thus increase share of wallet and asset under management (AUM).
Data management and analytics remain at the heart of achieving the advisers’ goal of share of wallet. Analysing data, cleansing it, and normalising it means they can be used in automated processes such as onboarding, saving the adviser valuable time he can spend with customers. Managing data also means performing accurate and advanced portfolio analytics, trying out ‘what-if’ scenarios, and carrying out customer lifecycle planning, such as long-term cash flow planning.
This is more evident when Artificial Intelligence (AI) and Machine Learning (ML) are added into the mix. They can add an extra layer, in that it can provide diagnostic, predictive, and cognitive analytics, analysing structured and unstructured data and building models based on supervised and unsupervised learning.
Doing this adds structure and predictability around things that are not easily quantifiable, such as anticipating what the customer needs, when they might need it, and over what channel. It adds to the holistic view of the customer and enables the adviser to provide what the customer needs before they know they need it!
In particular, the global trend towards democratisation of wealth management services is fast-moving, and customers are demanding digital delivery with personalisation.
AI and ML-enabled tools allow the adviser to do just that, meaning the adviser can come back to the customer quickly, over the right channel, and with something relevant that will add value to the relationship. In addition, the change from the wealth management relationship being largely transactional towards a more service-led proposition has raised the bar even higher in APAC. Wealth managers who want to manage more of a customer’s assets and affairs will have to work hard for it!
Alternate data
But what about when we then add alternate data into that mix? Alternate data is anything that comes from a source that would not ordinarily come into the realm of the wealth manager - it is contextual information that would add to the overall picture the wealth manager has of the customer. Examples include social media, data from conversations with the customer, knowledge around giving and other philanthropic giving, political support, and the like.
The key drivers attributed to market expansion of data include the significant increase in the types of alternative information sources over the past decade. According to Grand View Research, “While web scraping and financial transactions are the most common sources (of alternate data), the emerging sources, including mobile devices, social media, satellites, sensors, Internet of Things (IoT) - enabled devices, and others, are gaining wider popularity. As such, companies are actively expanding their offering by gathering information from all such sources.”
But because alternate data is often unstructured, lacks specific patterns, and is collected very frequently, it is ideally partnered with AI in all its guises. Its job is to make sense of the nonsensical to come up with patterns and predictions.
Thus, combined with AI, ML, and Natural Language Processing (NLP), alternate data is a powerful tool. AI-enabled processing increases information generation and helps to extract hidden patterns.
For wealth management, the potential benefits are huge. Anything the wealth manager can do to boost the adviser’s knowledge of the customer and deepen the relationship is a win – especially in a region where service level expectations are very high.
In particular, the high demand for digital service delivery offers scope to pick up all that data and use it to better hone the service to the customer’s exact needs regarding touchpoints, frequency, timing, content, and more. Unstructured data from voice and text resides in weblogs, call-centre wave files, CRM notes, emails, social media, and web chat. All can be picked up and used for predicting a potential customer defection, sentiment monitoring, and life stage and behavioural predictions.
NLP, in particular, is very useful in making sense of customer sentiment and behavioural analytics on what the customer is looking at, for how long, etc. It gives valuable insight into customer sentiment, interests, concerns, and generally on what the adviser should do next to alleviate concerns or boost and deepen engagement and the relationship to promote stickiness. The idea is that the marketing effort can be refined once you can see how and where someone is interacting.
Portfolio management The same insight can be conferred regarding portfolio management and finding the exact mix of variables that satisfies the regulator, the customer, their risk appetite, investment aims, and cash flow planning.
In particular, and massively relevant in the region where interest levels are high, alternate data combined with AI can give tremendous actionable insight into ESG Investing using scorecard rating investments on ESG parameters.
Indeed, ESG currently has limited parameters to measure and predict against, so using alternate data is a clear win.
Sustainability and environmental issues go far beyond the carbon footprint of a company. There are other intangibles like water management, pollution, ethical practices, social factors, gender, and the investor’s individual preferences and causes.
If you can use alternate data to identify some of these areas and how well, or not, a firm is doing on them, and you can also match them to what the customer wants to see and not see, then you can come up with a match. This is helpful when it comes to those things that are hard to qualify and tend to be identified and assessed qualitatively.
Three live case studies showcasing how alternate data sources are being leveraged for adviser enablement:
• Social media analysis for investment decisions: Hong Kong-based asset management firm Deep Systems uses a combination of NPL and ML to analyse social media and news articles to make investment decisions. The firm analyses data from sources like Twitter, Reddit, and Bloomberg News to identify trends and sentiment around specific stocks, which is used to inform investment decisions.
• Health data for financial planning: AIA, a life insurance company based in Hong Kong, is using health data from wearables and other connected devices to provide more personalised financial planning advice to its customers. By analysing health data, the company can provide insights into a customer’s lifestyle and recommend financial products and services that are tailored to their specific needs.
• Predictive analytics for wealth management: UOB, a Singapore-based bank, is using predictive analytics to offer more personalised wealth management services to its customers. The bank analyses data from sources like social media, market sentiment, and customer behaviour to identify investment opportunities and provide more targeted financial advice.
In summary, we all know that AI and ML are useful, but the better the data is fed, the more valuable the output will be. Wealth managers aware of the power of alternate data empower their AI even further – making for adviser enablement in both the customer service and the portfolio management sense.
This article is from The Wealth Mosaic’s APAC WealthTech Landscape Report 2023. Access the full report here.
