📌 Algorithmic advisor - AI for personalized wealth management

AI case studies | How AI is transforming the world?

Mar 15, 2024

New email: Wild Intelligence, March 15th, “Investing shouldn't be scary: Let AI be your financial Superhero!”

Hey there, Yael!

Ditch your broker, embrace your Robo-Advisor: AI takes the wheel

The financial services industry is already experiencing the benefits of AI/ML. The deployment of automated chatbots in customer service and the use of AI in bank lending for underwriting are just a few examples of how AI is transforming the industry.

However, daily wealth management processes have remained unchanged since the 1990s, which presents AI opportunities to provide insights that are impossible with traditional methods.

AI can automate repetitive tasks, freeing wealth managers to focus on higher-value activities like relationship building and strategic planning. They can stay ahead of the competition, provide superior services to their clients, and increase their bottom line.

However, some constraints must be addressed to transform daily processes and workflows and provide personalized services that meet clients' changing needs:

How do platforms ensure the security and privacy of client financial data?
What risk management frameworks does AI incorporate to account for market volatility and unforeseen events?
Who in the organization leads to address the potential for emotional bias influencing algorithmic investment decisions?

What is important?

Algorithmic advisors showcase the power of AI in democratizing wealth management. As the technology matures, we can expect a future where AI personalizes investment strategies, manages risk with greater precision, and seamlessly collaborates with human advisors to provide a comprehensive financial experience.

But when it comes to choosing between human and algorithmic advisors, the role of responsibility-sharing hits a barrier:

“Algorithms are increasingly employed to provide highly accurate advice and recommendations across domains, yet in many cases, people tend to prefer human advisors. Studies to date have focused mainly on the advisor’s perceived competence and the outcome of the advice as determinants of advice takers’ willingness to accept advice from human and algorithmic advisors and to arbitrate between them1.”

The challenge

Traditional wealth management often involves high fees and may not effectively address individual financial goals. New AI-powered solutions sought to democratize wealth management by introducing algorithmic advisors:

Client data analysis: The advisor gathers and analyzes a client's financial data, including income, expenses, investments, and risk tolerance.
Algorithmic portfolio construction: Machine learning algorithms then construct personalized investment portfolios based on modern portfolio theory and the client's unique profile.
Automated rebalancing: The advisor monitors market conditions and automatically rebalances the portfolio to maintain the desired asset allocation.

The results

Increased accessibility: The Algorithmic advisors offer a low-cost wealth management solution, making it accessible to a broader range of investors.
Improved performance: Studies suggest robo-advisors, on average, outperform human advisors in terms of risk-adjusted returns.
Enhanced convenience: The AI-powered platforms allow for 24/7 account access and automated portfolio management, freeing up clients' time.

The future of algorithmic advisors

Advanced risk management: AI will incorporate more sophisticated risk models for emotional biases and black swan events.
Holistic financial planning: Integration with financial planning tools will enable advisors to consider retirement goals, estate planning, and tax implications.
Human-AI collaboration: Financial advisors will leverage AI for portfolio construction and risk management while focusing on personalized client communication and holistic financial guidance.

An extended view

Drawing inspiration from the dynamic intersection of artificial intelligence and human potential, Klopp delves into how AI can intimately understand our unique strengths, preferences, and interests.

The things to know

The global robo-advisor market is projected to reach $1.4 trillion in assets under management by 2025 on Statista.
Over 50% of millennials already use or consider using a robo-advisor on Magnify Money
A study suggests robo-advisors outperform human advisors by an average of 0.74% annually on Smart assets

Explore more

The adoption of robo-advisory among millennials in the 21st century: trust, usability and knowledge perception

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🚀 Unbundling AI

Workbook: Building a sustainable data-driven organization, part 1

Yael Rozencwajg

Jan 1

Workbook: Building a sustainable data-driven organization, part 1

Hello, This workbook is based on conversations with AI startup founders and engineers. We present it as an evolving planner to help founders and leaders integrate the central notions of building a sustainable data-driven organization. There are many ways to build business solutions and tools with AI, including open-source models, LLMs, training models, fine-tuning, or using hosted APIs, all from scratch. But we also see a surge of interest in data-driven business acquisitions among large companies. It entails buying an existing solution, not starting it from zero, with the potential for more autonomy and ownership.

Read full story

📌 AI case studies

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