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TL;DR

• Mark Lezanic, a Florida-based wealth manager, integrates AI to help high-net-worth individuals manage complex portfolios and tax strategies.
• AI investment tools are transforming wealth management, with 80% of retail investors expected to use them by 2028.
• Machine Learning (ML) analyzes large financial datasets to optimize asset allocation and predict market movements.
• Natural Language Processing (NLP) connects AI insights to market news, client goals, and real-time sentiment data.
• Robotic Process Automation (RPA) automates routine tasks like reporting and data reconciliation, boosting efficiency.
• Generative AI enhances client communication by creating tailored proposals and portfolio insights.
• Real-world AI examples include MarketPsych (sentiment tracking), Salesforce Financial Cloud (client profiling), and SARGE by EY (compliance automation).

Mark Lezanic, president of a private wealth management firm in Pompano?Beach, Florida, draws on decades of experience helping high?net?worth individuals build diversified portfolios spanning private equity, hedge funds, and real estate. Renowned for solving complex international tax challenges, he also advises on estate planning, business succession, bespoke lending for yachts and jets, and strategic philanthropy through foundations and donor?advised funds. A former collegiate basketball coach, Mark pairs analytical rigor with mentorship skills to deliver long?term growth, prudent risk management, and enduring financial legacies for his clients.

AI-driven investment tools are rapidly expanding across the wealth management industry. A 2025 Deloitte report forecasts that AI investment tools will be the primary conduit for retail investors’ decision-making by 2027, with usage reaching 80 percent by 2028. A key question is whether AI systems can maintain high levels of trust to support successful and long-term advisory relationships.

Many financial professionals argue that AI systems can and will maintain trust since they possess many ways of performing tasks that previously relied on human intelligence. Machine learning (ML) algorithms analyze vast amounts of historical financial data, detecting past patterns and providing fine-tuned risk factors and market movements predictions. By adjusting risk parameters or the basket of assets included in ML analysis, professionals gain insight into optimal client holdings for the short, medium, and long term.

Natural language processing (NLP) helps ensure that ML output links to news cycles, sentiment readings (investor perceptions of how markets are doing), client communications, and stated objectives. This process uses unstructured financial information from market commentary and client conversations, leveraging chatbots to deliver actionable recommendations in rapidly evolving markets.

Wealth management operations focused on compliance and accountability also encompass repetitive, rule-based tasks, such as data entry, report generation, and account reconciliation. Robotic process automation (RPA) enables the automation of such functions, saving expenses, expediting work, and allowing workers to focus on high-touch elements, such as client interactions and strategic planning.

Generative (gen) AI helps bundle the various elements of financial decision-making and reporting in a polished, client-facing deliverable. Advisors use generative AI to craft personalized investment proposals and thought-provoking client communications, providing coherent and relevant market analysis.

Real-world examples of AI in financial analytics and wealth management abound. An example is the London Stock Exchange Group’s financial analytics service, MarketPsych. Powered by an NLP engine, the platform captures millions of social media posts, news articles, and blog posts in real time, generating market-relevant information. Its specific uses include monitoring company and industry mentions and capturing sentiment shifts as they occur. MarketPsych defines emotional indicators such as uncertainty and optimism associated with stocks, markets, commodities, and companies. MarketPsych adopters include banks and investment funds, which use the platform to map market trends and allocate assets accordingly.

On the customer relationship management (CRM) end, Salesforce has developed a Financial Services Cloud for wealth and asset management. The Einstein AI-powered platform features advanced customer analytics that enable advisors to deliver portfolio recommendations tailored to specific customer needs easily. Client summaries provide granular detail on investors’ financial status, risk tolerance, and objectives.

The cloud-based platform SARGE, developed by Ernst & Young, automates compliance with the entire spectrum of financial activities. Powered by NLP and ML algorithms, the AI system automatically extracts investment guidelines from governing contracts. Such contracts define the legal framework used to establish and enforce subsequent agreements. The platform helps ensure that all transactions and allocations comply with the law, eliminating much of the manual work required for compliance monitoring.

AI can influence market decisions on multiple levels with these and other platforms in play.

Frequently Asked Questions

How is AI changing wealth management?

AI automates investment analysis, predicts market trends, enhances client communication, and streamlines compliance, making wealth management faster and more personalized.

What is Mark Lezanic’s role in AI wealth management?

Mark Lezanic leverages AI to provide sophisticated wealth strategies, including private equity, real estate, and succession planning for affluent clients.

What is Machine Learning’s role in investment decisions?

ML analyzes past financial data to forecast risks, optimize asset allocation, and improve portfolio performance.

How does Natural Language Processing (NLP) support financial advisors?

NLP helps interpret market sentiment, integrates news updates, and delivers real-time recommendations aligned with client objectives.

What are some real-world AI tools used in finance?

Examples include MarketPsych (sentiment analysis), Salesforce Financial Services Cloud (CRM and client insights), and EY’s SARGE (compliance automation).

How does AI impact compliance in wealth management?

AI automates guideline monitoring, ensures regulatory adherence, and reduces manual compliance errors, improving operational efficiency.