For the past decade Asset and Wealth managers have seen tremendous change affect their business model. Fee pressure has led to an all out price war and the move to passive investment has put active managers on the defensive.
There comes Artificial Intelligence, Machine Learning and other data anlytics technologies. All together, AI, Ml and the use of NLP (Natural language processing) are offering the industry efficient solutions on both the need to generate alpha and the need to contain costs.
Natural language processing associated with machine learning is now helping by
- Incorporating a wider range of sources into investment models, retrieving filings, financial reports, press releases, news articles, social media activity
- Analyzing a wider range of unstructured data, i.e. alternative data including, satellite images, credit card data, store circulation data, country risk data
- Facilitating the automation of manual middle and back office tasks by implementing intelligent automation solutions capable of reducing costs associated with repetitive and high volume tasks
- Improving first line of defense supervision, by adding efficiency to real time monitoring and surveillance of suspicious transactions as well as conduct reviews, including chat, email, communications monitoring.
Asset and Wealth Managers Challenges
– Growth of passive investments
– Decrease of investment fees
– Decreasing confidence in the future
Investors have flock toward ETFs and other index funds simply replicating the make up of major indices. The switch from active to passive is so brutal that passive strategies are now being reviewed by the Federal reserve economists. According to the Fed “As of December 2017, U.S. stocks held in passive MFs and ETFs accounted for almost 14 percent of the domestic equity market, up from less than four percent in 2005.”
But for the managers themselves, the major effect of the growth in passive investments have been combined with regulatory pressure. The combination has engineered a dramatic decrease in investment fees.
In Europe and the US a succession of regulators initiatives have been aiming to reduce conflicts of interests, increase transparency and disclose the costs of investments. Some succeeded, other failed but investors preferences changed and asset managers responded by creating clean shares, that exclude fees taken by advisers and platforms for their role in distribution. The industry reacted with an all out price war.
The result is a race to the bottom where asset managers see revenue sink, while costs are rising and the future looks cloudy.
Growth of passive investments and their associated lower fees could cut fees by almost 20% on an asset-weighted basis across the mutual fund industry by 2025.
From Bloomberg
Passive strategies are growing because they are less expensive, “holding the the market” has proven being as efficient if not more than trying to “beat the market”.
Fees have been decreasing for all fund types and will continue to do so. One of the reason for decreasing fees is the greater regulatory focus on the price charged to retail investors for investment products. The trend is true for both traditional funds and alternative investments, including hedge funds.
Source: value walk
Globalization means opportunity and challenges in legacy systems and regulation costs
Source: McKinsey
According to a Deloitte study cited by Bloomberg, costs have outpaced or matched revenue growth during the past four to five years, while aggregate fees declined almost 20%. Industry revenue expanded only 8% from 2015 to 2018, reaching $289 billion. By contrast, assets jumped 20% during that period to $71.8 trillion.
- Treat data as a corporate asset
- Share data seamlessly through the enterprise
- Leverage market insights and customer feedback faster
- Adopt emerging technologies
Enter Artificial Intelligence
Artificial Intelligence and other emerging technologies have been deployed at numerous asset and wealth managers.
In “Artificial Intelligence — the Next frontier for Investment Manager, Consulting firm Deloitte, identifies 10 use cases for AI in investment management.
For Investment Managers AI is becoming a powerful front office tool. Machine Learning is now used to build strategies based on quantitative and non quantitative data.
Text mining is used to recognized keywords that reflect confidence or denote a lack thereof. Companies like Business Intelligence Advisors offer market monitoring services to Institutional Investors.
Prattle is also a player in the field. The start-up has started providing real-time, quantitative analysis of company earnings calls. Prattle describes itself as a provider of automated investment research solutions for asset managers, research analysts and other finance professionals. Their platform includes predictive sentiment data and automated research reports on official corporate communications issued by more than 3,000 companies. They are also specialized in analyzing central banks announcements and predict market movements based on the central bankers language.
Kensho was founded in May 2013 by Nadler, a Harvard trained Ph.D. and acquired by S&P Global in 2018. According to their website clients include BOA, Morgan Stanley, JP Morgan, Goldman Sachs Citi. Kensho’s machine learning systems crawl through data and market-moving information, searching for correlations between world events and their impact on asset prices. The Kensho Global Event Database, powers its machine learning analysis, while Kensho’s Knowledge Graph delivers real-time living graph model of world events. The platform’s information is packaged with search tools and data visualization capability.
BankSight offers a platform dedicated to customer insights. By aggregating customers activity in a single dashboard, the platform gives the advisors a view of the clients relationships, all key financial accounts of the client, out of the ordinary inflows and outflows, call reports, past appointments. The platform helps deliver personalized AI-driven recommendations to financial advisors clients.
Amenity Analytics develops cloud-based text analytics solutions using natural language processing (NLP) and machine learning. Their Amenity Viewer analyzes every earnings call transcript in order to spot outliers, identify insights, and understand key drivers. Amenity has created an “Amenity Score” to assess relative sentiment of individual transcripts and trends over time. The company has recently developed a Deception model trained to parse every word of every sentence for the underlying financial meaning but also to identify linguistic patterns that Amenity Analytics has determined as being consistent with deceptive language. The text AI identifies linguistic trends called “Events” under the key driver, Deception. These Deception Events may indicate evasive language, attempting to spin a negative, tension with the analyst community, etc.
Dataminr for Finance alerted clients to an explosion at one of the largest gas hubs in the world before the story hit local and major news networks. The ensuing shutdown of the Baumgarten facility sent gas futures soaring. The company sells real time alerts based on social media feeds and was able to alert clients when the king of Saudi Arabia died, more than four hours before oil prices started to rise.
There are also specialized funds like Rebellion research.
Rebellion research developing and using AI which uses quantitative analysis to pick investments. They have developed 2 strategies. The Rebellion’s Artificial Intelligence-based Global Equity strategy has been managing money for clients since January 1st, 2007 and the absolute Return Strategy a 0 beta, low volatility strategy comprised of 15–20 ETFs. Both funds charge 1% Annually
Algorithms are also able to build non visible or hidden relationships between data points that seem un correlated.
One of the most talk about story is the rise of AI Hedge Funds. According to Preqin they are already outperforming hedge funds. One of the recent change is the move from Pre -AI models where Quantitative Analysts needed to regularly re enter parameters in models to account for market changes to “Pure AI” models where the algorithms evolve and change by itself depending on market conditions. Preqin Research notes that funds like “some recently launched firms that incorporate this refined version of AI modeling include Cerebellum Capital, Taaffeite Capital Management and Numerai.”
For Wealth Managers AI is driving Personalized Advice and Next Best Action
As wealth management customers are looking at holistic advice, they care at personalization. When we open Netflix, the hows we have access to are being recommended to us based on our preferences. This is the future of Wealth and Asset Management.
First the challenge is to gather the relevant data from customers while protecting trust, respecting privacy and regulations. Using clients survey and alternative data firms can now propose the Next Best Action to their clients. Next Best Action offers the ability to marrow down investment choices, take in account life changing events and factor for potential political and market risk. According to a 2017 article in the Harvard Business Review, Morgan Stanley has implemented an algorithm capable of helping Financial Advisors to better serve clients.
Building a Customer Data Centric Organization
One of the major challenge of implementing AI solutions like the Next best Action is the structure of the data organization.
- Most organizations do not have a Chief Data Officer. Today data is stored in different systems based on where the systems in the value chain.
- Compliance, Know- Your-Client, on-boarding data is stored separately from transactions data, communications data, advisors notes, list of interactions, life events and clients preferences.
- Being able to organize, clean, and structure every data points around clients and build a customer data centric organization is a rela challenge.
- Systems are various and walled. Legacy systems rarely communicate with each other and congregating the data in an organized manner to be productively analyzed by AI models is hard to do.
The industry is also grappling with a dearth of talents. An industry viewed as conservative and hierarchical has hard time competing with fintech start ups.
Solutions:
Managed services — Outsourcing is now moving from Back office to Front office and now includes managing of trading desks, business development, marketing, tax and compliance
Staff augmentation — In a cost pressured environment, staff augmentation provides solutions to manage ad hoc projects, like regulation related projects, technology management, middle office services or operations.
In Summary, AWM companies face similar challenges as other financial services providers.
Revenues are declining as fees are decreasing. Regulatory pressure is increasing adding geographical complexity, region based reporting, technology investments, push for transparency and removal of conflict of interests.
To adapt AWM firms need to embrace digital initiatives with a focus on front to back integrations and partnerships with technology providers. While moving to digital it will be essential to correctly assess and prevent cybersecurity risks and the need for additional talent and competencies.
The path to growth will be both organic, by chasing alpha using alternative data and artificial intelligence as well as inorganic through acquisitions and joint ventures.
Credit: BecomingHuman By: Melvin Manchau
