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Thursday, January 31,
2019 10.17AM / By Raghav Bharadwaj of Emerj / Image
Credit: Medium
According
to a 2017
report by the Financial Stability Board, artificial intelligence
(AI) and machine learning firms managed assets of over $10 billion in 2017,
with further growth projected in the next five years. The reasons for this are
clear; AI can now present wealth managers with new capabilities to enhance and
further personalize their services, at scale.
The
advent of big data and AI has changed how wealth management professionals go
about their work. What was traditionally a “cookie-cutter” approach in which
clients whose trading preferences would be categorized as “aggressive,
conservative, or balanced” might now be more nuanced with AI. AI has the
potential today to enable wealth managers and financial
advisors to understand client requirements better. AI sentiment
analysis can help with this on social media data, and customer information can
help coax out insights about the current and future financial state of each
client.
Identifying
a major life event in a customer’s life, such as a retirement, might help
advisors offer customized investment
services. AI can be used to make sense of a customer’s social media
interactions, transaction and investment data, and their declared preferences,
to help advisors offer more personalized service to clients while
simultaneously finding the right product-customer opportunities.
While
AI can be used to make sense of a customer’s social signals, it can also be
applied to analyzing a customer’s communication history. AI software can track
and monitor a customer’s preferred communication channel (mail, email, phone
calls, text messages) and what time of the day the customer prefers to receive
communications. AI software might also predict the customer’s preferred
frequency of communications and prompt wealth advisors to initiate any
communications accordingly.
Additionally,
regulatory compliances involved with wealth management or financial advisory
services (such as MiFiD II or GDPR) are evolving. Ensuring that the returns for
clients are maximized while still maintaining regulatory compliance might be
challenging for wealth advisors with thousands of clients. Analyzing thousands
of policy constraints while simultaneously finding the most profitable trading
strategies for clients involves massive amounts of time and human effort spent
in sifting through data.
Wealth
advisors at large firms might see a reduction in costs and time by using AI
software to monitor and track regulatory risk. AI software can today provide
wealth managers insights and recommendations by taking into account a client’s
preferences, financial trading trends, and regulations associated with advisory
services at a speed and scale that cannot be matched by human analysts.
Moreover,
AI systems “learn” to provide more accurate and personalized insights over time
by using feedback from clients and by observing behavioral patterns in client
data. We spoke with Robert
Golladay, Managing Director, Europe at CognitiveScale, which
offers AI software that helps both wealth advisors personalize insights and
identify new opportunities for clients. According to Golladay, AI is being
applied to wealth
management services in two areas today:
Listen
to the full interview with Robert Golladay here
Traditionally,
when approached by a client looking to invest, a wealth manager might have made
phone calls to the larger
financial investment institutions to purchase research and access
information about quantitative trading data. They would provide the wealth
manager with a one-size-fits-all type of investment advisory service. With the
advent of AI, wealth advisors could start to make sense of financial market
signals and customer communication preferences to contextualize and personalize
investment advice.
The
last couple of years have also seen the rise of FinTech
companies as competitors to banks,
with the promise of better customer
service and customer interaction options in wealth management. In
addition, banks and other wealth advisors might also be pressured by a changing
paradigm in customer communication preferences.
It
seems as though the millennial generation-investors have their “antennae
tuned,” so to speak, toward robo-advisors and FinTech. The fact that FinTech
companies allow users to easily access investment ideas or research financial
trends in news and social media through a companion app seems to have resonated
with younger investors.
Robo-advisors
and FinTech companies also seem to be focused on targeting the “low hanging
fruit” clients with lower levels of financial resources. Banks and
institutional investment firms might lose out on potential future clients to
these newer entrants in the market unless their wealth advisors are equipped to
deliver more personalized experiences to customers.
Augmenting
the capabilities of wealth managers with AI tools might allow wealth management
divisions at banks to provide personalized financial advice to clients at
scale.
Banks
and financial institutions might stand to benefit from using AI to effectively
leverage data and deliver insights to human wealth advisors at the right time.
This data might include user preferences, financial product usage, news, media,
transactions, and other relevant data. AI techniques applied to such data hold
the promise to inform investment decisions and provide wealth management
insights that cannot be gained from traditional software.
It might not come as a surprise to wealth managers that understanding the preferences of a client, their goals, and their current financial state are essential for providing the most accurate investment advice. Additionally, this data might enable personalization, thereby allowing wealth advisory service providers to differentiate their offerings.
Golladay
says that AI can help wealth advisors make use of market trend information and
data about clients or their investment preferences. He adds that AI software
can also identify patterns in the data and understand the context in the data.
For
instance, AI software might be capable of scouring through all the news media
from the Financial Times or Bloomberg and automatically prompt wealth advisors
with information about which of their clients might be at risk due to a
particular market event, such as new financial regulation.
Golladay
says there are usually five features to “augmented intelligence” software that
can help wealth advisors personalize their services to each client:
In
essence, wealth advisors might use AI software to create a “Profile-of-One” for
each client. Through the application of machine learning and natural
language processing (NLP) techniques, AI solutions might be able to
generate these profiles for each client. The profile might include the client’s
declared investment leanings and preferred characteristics identified from
customer behavior data, such as transactional information on where and when the
client spent their money.
Golladay
explains with an example:
If
Disney’s stock prices closed at below a 20th percentile relative to trading
history in the last three months, AI software might firstly, recognize this
event and secondly, recognize that the client has a preference for Disney and
automatically pull up historical records for similar scenarios that happened in
the past.
The
software might find that this has happened three times in the past and had you
acted within 7 days of the triggering of that insight, and then held the stock
for 180 days, your median result will be a 27% return. You currently hold
shares of NBC Universal Parks and held Disney in the past.
The
software might then prompt the client with information on how that the tech
company’s stock
prices closed at similar prices three times in the past and suggest that
holding the stocks for a month might lead to some median returns (in the form
of a percentage). Golladay states that although data is being collected by
wealth management firms, contextualizing insights from the data to each user
might be the biggest value-add from AI software.
With
wealth managers coming under pressure to improve their customer-facing
processes due to the advent of FinTech, understanding who to market their
products to, in what language or through which channel (email, calls, messages
and so on) might be critical. The fact that wealth management institutions
usually have hundreds or thousands of clients makes this a highly challenging
task.
Golladay
gives an example of a wealth advisory firm which also has an asset management
division:
Let’s
say the asset management division created a new ‘organic mutual fund’ with
carbon-neutral companies that are environmentally conscious and ‘green’. AI
software can recognize that the financial product here has the attributes
‘organic’ or ‘green.’
The
software can then scour through the advisor’s client base to find clients with
a certain level of investable income, whose lifestyles indicate the label
‘organic.’ Lastly the software might prompt the wealth advisor to offer the
fund to the prospective client at a particular time of day through the
customer’s preferred channel of communication.
What
business leaders in the wealth management space might need to note here is that
information about the product and client lists can be combined to create
query-able databases. Wealth advisory professionals can then search and
discover the right product-customer fit.
For
instance, AI software might “learn” to recognize over time events that lead to
interactions with the client, such as a portfolio change recommendation, and
automatically send communications to the client after similar future events.
Golladay points out that there may be several opportunities for AI software focused on self-directed investors. He adds that this might be especially true of wealth management AI software that provides these individual investors with the data and information resources measurable with those institutional investors.
AI
software for wealth management might help businesses and individual wealth
advisors better leverage data, such as customer social media interactions and
investment preferences. Wealth advisors can gain insights about clients’
financial leanings, their level of aversion to risk, their current financial
situations, and their intended future financial goals.
Wealth
advisors at institutional investment services firms might find that using AI
software might also help improve their customer engagement levels. By looking
at historical data about customer interactions, the software can prompt
advisors at the right time and recommend the channel of communication.
As
Golladay puts it:
In the
next two decades, roughly 30 trillion in financial and nonfinancial assets will
pass on from baby boomers to their children. The winners in the wealth
management space are probably going to use AI to understand how a person wants
to be communicated with and marketed to.
In
terms of real-world business benefits, AI systems might help wealth managers
and financial advisors in the following ways:
This article was sponsored by CognitiveScale, and was written, edited,
and published in alignment with transparent Emerj sponsored content guidelines.
About The Author
Raghav
Bharadwaj serves as Content Lead at Emerj,
covering our major industry areas and conducting research. Raghav has a
personal interest in robotics, and previously worked for research firms like
Frost & Sullivan and Infiniti Research.
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