Financial institutions today face rising costs, margin pressures and fierce competition even as customer expectations for superior, personalized experiences continue to soar. Legacy technologies and manual processes further challenge agility and efficiency.
A potent solution helping leading global wealth managers successfully address these challenges is AI-powered conversational interfaces, commonly known as chatbots. Let‘s analyze the 6 most functional applications of these chatbots in wealth management today, along with real-world examples and data-driven insights into their exponential future evolution.
1. Streamlined Onboarding to Accelerate Client Acquisition
Opening a new investment account can be tedious for customers, requiring extensive paperwork, identity verification and understanding risk appetites. Constant bottlenecks in onboarding also hinder acquisition for financial institutions.
This is where AI-enabled chatbots help tremendously. Using Optical Character Recognition (OCR), they can securely ingest documents and data needed from clients to eliminate manual efforts. Advanced Natural Language Processing (NLP) further helps them classify clients into segments, route them appropriately to relationship managers if needed and drastically reduce onboarding bottlenecks.
And by having friendly, jargon-free conversations with leads, chatbots can also educate customers on available investment products, tools and processes to drive faster account openings. The largest bank in India, SBI, employed chatbots on their YONO app to reduce costs by 60% while boosting productivity manyfold during client onboarding. SBI‘s Chairman expects 70% of retail banking transactions moving to their chatbot channel by 2025.
2. 24/7 Customer Support and Engagement
Today‘s consumers expect instant, hyper-personalized services and support across channels any time. Yet answering thousands of diverse customer queries daily while optimizing call center costs poses a huge challenge.
This is where chatbots, available 24/7 across websites, apps and messaging platforms prove invaluable for financial institutions. For instance, robo-advisory firm Betterment‘s chatbot leverages conversational AI to resolve 70% of customer service questions right away without human assistance. Award-winning startup Trueface also relies on computer vision and NLP to authenticate users and enable self-service.
Upstox‘s WhatsApp chatbot can address 80% of its Indian customer questions on the fly through AI-driven information retrieval from an extensive, constantly updated knowledge base. For the remaining 20% of complex queries, it intelligently routes conversations to appropriate human agents.
Tata Mutual Fund also reported a 30% jump in customer engagement along with over 50% in cost savings after deploying a chatbot handling most queries across their digital properties while referring intricate investment planning questions to experts.
The Future of Engagement
As AI and chatbots better understand sentiment, context and meaning, expect ever-more natural and intelligent conversations with customers seeking financial advice or information in 2024. Gartner predicts that by 2025, 70% of white-collar workers will interact with conversational AI daily as the interfaces keep improving. Most leaders also expect augmented and virtual reality interfaces eventually complementing text and voice conversations.
3. Democratized Access to Financial Advisory at Scale
Retail investors today often struggle to find trustworthy, affordable financial and investment advisory aligned to personal goals, constraints and risk appetite. Wealth managers simultaneously wrestle with bandwidth limitations in servicing client portfolios spread across geographies.
AI-driven chatbots promise to bridge this gap by democratizing access to professional advisory. These "robo-advisors" first gauge a client‘s unique investment objectives, financial situation and risk tolerance through friendly advisory chats. Then they generate suitable portfolio guidance or directions leveraging quantitative algorithms combined with the latest market research and data.
For instance, leading robo-advisor Betterment runs an AI-powered interface providing investment advice, portfolio management and retirement planning guidance to over 800,000 customers globally – that too at fractional costs compared to traditional wealth managers.
Female-focused robo-advisor Ellevest relies on similar algorithms and conversational interfaces to generate custom goal-based investment recommendations suited to women‘s specific financial journey patterns and requirements. Their solutions have become hugely popular even amidst broader industry skepticism of robo-advisors‘ capabilities vis-a-vis human financial experts.
The massive addressable global wealth management industry, estimated by McKinsey at over $200 trillion in assets under management, signals vast growth potential for such AI-powered, low-cost automated advisory services as the technology keeps maturing.
| Global Assets under Management | $212 trillion |
| Expected AUM Growth (2025) | +9% CAGR |
| Addressable Market for Robo Advisors | $15-20 trillion |
Source: McKinsey, Gartner, AIMultiple Research
The Future of Automated Advisory
While critics argue purely system-driven advisory still lacks the emotional intelligence and trust offered by human wealth managers, robo-advisors continue to rapidly evolve:
- Incorporating quantified soft factors like confidence, sentiment and personality in advisory algorithms
- Managing not just investments by overall wealth including real estate, insurance, taxes and retirement planning
- Hybrid models with both automated and human components – the best of both worlds
As the technology matures, automated wealth management chatbots integrated with customer life events and market changes promise to make professional advisory easily accessible across even underserved retail investor segments globally. Democratization at scale seems inevitable.
4. Hyper-Personalized Alerts and Notifications
In wealth management, getting notifications for events like stock price triggers, account activity, research updates or market swings can hugely impact customer engagement and trust. But only if these notifications are:
a) Contextual to current holdings and accounts
b) Personalized to expressed interests and preferences
c) Timely for immediate value
Fortunately, advances in AI now enable chatbots to deliver such hyper-personalized alerts and communication across client segments, mapped to explicit preferences and modeled implicit behavior.
For instance, leading brokerage and robo-advisor Wealthfront spotlights how users receive investment ideas fitting expressed interests while market alerts are timed perfectly to enable actions. Further, clients can opt into notifications on upcoming IPOs or asset categories of interest via their chatbot.
India‘s top discount brokerage Upstox also highlights how its chatbot lets users set personalized alerts for price changes or events related to stocks in their portfolio or watchlists. Investors also receive quick trade confirmations and monthly statement notifications to their preference, building trust and recall.
The Future of Hyper-Personalized Communication
As chatbots get better at contextual recommendation systems through neural networks, CX experts expect even greater personalization in future:
- Tailored insights into market developments based on client portfolio, risk profile and past behaviour
- Proactive warnings on portfolio risks aligned to macro forces like interest rate changes
- Notifications timed for client readiness based on activity patterns, mood and channel affinity
Ultimately, ambitiously futuristic as it seems today, wealth management chatbots promise to evolve into full-blown personal assistants converging advisory, execution, alerts and performance tracking into a single personalized dashboard optimizing returns while minimizing risks in line with client priorities.
5. Scaled Investor Education to Improve Financial Literacy
Extensive global research shows that improving financial literacy amongst investors directly improves the efficiency of capital allocation and asset management at individual and systemic levels.
However, continually educating dispersed retail investors at scale remains operationally challenging for wealth management institutions like mutual funds or brokerages.
This is where AI-enabled chatbots prove highly effective. For instance, Tata Mutual Fund, one of India‘s largest asset managers, deployed a chatbot to handle most common investor queries across digital properties while also systematically driving education on topics like mutual funds, SIP investments, portfolio allocation strategies or managing market volatility risks using relatable stories and humor via natural conversational interfaces.
The conversational AI-based interface proved highly effective – measurable improvements in understanding and engagement were witnessed. Over 50% of questions asked by investors were resolved directly by the bot, reducing human workload while boosting productivity. Operational costs also lowered by 30% after deployment.
Similar encouraging outcomes indicating chatbots‘ immense potential for scalable investor education have been witnessed by leading firms like PayTM Money, Upstox, INDMoney and more who target improved financial literacy across India‘s underserved retail base.
The Future of Investor Education at Scale
As NLP and generative AI continue advancing exponentially, the future possibilities are even more compelling – chatbots will likely integrate education modules seamlessly into advisory and engagement conversations. Further, modules could be hyper-personalized based on knowledge levels across audience micro-segments mapped through analytics.
Educating millions while optimizing outcomes is set to become far more achievable than ever before once AI fully delivers on its promise. And firms able to leverage these interfaces smartly could disproportionately attract and retain customers in the years ahead.
6. Driving Intelligent Process Automation to Optimize Efficiency
Despite growing digitization, wealth management still relies extensively on legacy enterprise software and manual processes built up over decades. This affects operational agility and productivity while increasing costs and compliance risks.
Here AI-driven process automation through chatbots promises tremendous improvements:
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Continuous workflows spanning front to back processes can be tracked by bots using contextual signals to minimize breaks
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Documents, forms and data required can be automatically ingested from clients using NLP and OCR to eliminate manual efforts
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Applicable downstream systems can be triggered via API integrations to ensure seamless handovers once bot has completed process steps
Furthermore, prescribed control points can be integrated across processes. Checks against missing signatures, oversights threatening compliance or emergent risks using algorithms promise improved governance.
And by taking over high-volume repetitive tasks across customer onboarding, transactions, portfolio management and more, operational costs can be optimized even as humans focus on more complex process issues and innovation.
Eyevinn Technology highlighted how wealth management process automation using conversational interfaces lowered costs by over 35% across document collection, verification and archiving processes while improving quality and speed performance six-fold.
The exponentaily growing datasets that feed AI promise more breakthroughs in intelligent decisioning and automation. Over the next decade, rapid acceleration in leveraging chatbots for optimizing efficiencies seems inevitable. Leaders reimagining processes and integrating analytics with conversational AI will surely pull ahead.
Industry Impact from AI-Powered Chatbots – 2025 and Beyond
As detailed above, wealth management chatbots are already proving highly valuable across priorities like client acquisition, 24/7 sales and support, advisory democratization, hyper-personalization, scalable education and driving automation.
But given the nascency of underlying technologies like NLP, computer vision and generative AI, the exponential growth runway seems almost limitless in the coming decade. Let‘s analyze 5 projected outcomes:
1. 80% Interactions Happening via AI
By 2025 itself, Gartner predicts that 70% of enterprise interactions will happen via AI, with 85% incorporating it by 2030. For customer/investor interactions at financial institutions specifically, PWC estimates >80% adoption of chatbots given round the clock availability and extreme personalization capabilities.
Meticulous user experience design and governance will remain vital though for managing risks as AI handles sensitive data.
2. Over $15 Billion in Global Cost Savings
Operational automation using AI promises tremendous cost optimizations for global wealth managers still reliant on legacy systems, manual processes and high regulatory overheads.
Accenture estimates over $7 billion in cost savings in capital markets just from AI-enabled employee productivity improvements and automation by 2025. Expanding across functions, McKinsey sees up to $15 to 20 billion in global savings likely from AI adoption across wealth management. New customer acquisition costs could also drop by 15 to 25%, directly benefiting competitiveness and growth.
3. Democratized Wealth Advisory Reaching Billions
Robo-advisors leveraging AI promise to expand personalized wealth management to potentially billions who were underserved historically given the operational challenges in profitably servicing mass retail segments.
As algorithmic advisory matures, robo-solutions could end up managing 15 to 20% of total global assets under management based on projections – that translates to a staggering $30 to 40 trillion market being democratized. Betterment, Wealthfront, Ellevest, SigFig, Nutmeg and more firms seem poised to capitalize.
4. Hyper-Personalization at Scale
As AI chatbots get better at predictive analytics and Natural Language Generation (NLG), they could deliver personalized communication and advisory for every client mapped to expressed needs and modelled behavioral patterns.
Imagine proactive warnings if market risks emerge in line with your portfolio. Or timely IPO allocation reminders for aspirational stocks you wished to invest in. Such hyper-personalization at scale for millions seems achievable over 5-10 years. Leading human advisors may struggle to match AI capabilities here.
5. $1 Trillion+ Market for AI Solutions
Given the massively disruptive potential of AI across wealth management, internal AI Solutions teams will likely struggle with skills gaps or data integration complexities. This promises a vast addressable market for enterprise AI vendors.
Per an analysis by Ocean Tomo, the growth outlook for external AI solutions and services tapped by wealth management firms could be over $1 trillion by 2030. Finding the right partners will surely emerge amongst the highest priorities through the decade ahead.
Key Takeaways for Financial Institutions
As illustrated across multiple dimensions above, AI-powered conversational interfaces promise tremendous value for global wealth management firms struggling with legacy constraints. However, to capitalize fully as chatbot adoption accelerates, 4 aspects require priority:
1. Reimagining Client Journeys:
Institutions must re-analyze all customer and operational journeys through the possibilities lens provided by exponentially advancing NLP/NLG, computer vision and generative AI.
2. Change Management Investments:
As AI redefines processes and interactions, substantial investments in reskilling talent and driving adoption are vital to manage risks and extract full value.
3. Discovering AI-Powered Innovations:
Wealth managers must proactively engage pureplay AI startups and vendors through accelerators or sandpits to discover new solutions meeting key unmet needs.
4. Developing Partnership Ecosystems:
As internal teams often lack specialist AI skills or data advantages, forging long term, trust-based partnerships with external vendors and advisors can provide sustainable competitive advantage
The possibilities for AI in wealth management indeed seem limitless, but realizing the full potential requires strategy, conviction and vision given existential pressures and black swan disruption risks. Those who can execute deftly will own the future. Are you leading disruption or awaiting it?
