AI in FinTech: Wealth Management
As the second part in our “AI in FinTech” series (first piece on ‘Fraud and Risk Controls’, let’s explore the wealth and asset management sectors. The theme of robo-advisors has become common in financial services as companies such as Robinhood and Wealthfront granted access to a wider scope of retail investors. With artificial intelligence, firms have used AI systems to support higher quality advice at a lower cost to the firm, perform routine tasks, and allow wealth managers to focus more time on high-level strategies. AI in FinTech has broadened services across the industry while still maintaining aspects for customization.
For the next generation of improvements, AI is enhancing the trading and advisory capabilities in wealth management, and boosting research and modeling tools in asset management. With a variety of improved tools, wealth managers have the research and insights to make faster and informative decisions for a wider scope of clients.
Financial Coaching and Advising through ai
When it comes to advisory and coaching services, transactional bots are a key component for users in multiple industries due to its wide-scope in application. Commonly referred as ‘digital assistants,’ these bots help individuals and companies manage overall financial health, such as budgeting and savings. The benefits can lead to gains in customer experience and loyalty.
Built with natural language processing (NLP), digital assistants utilize machine learning to process human language. Recommendations are layered on top to help users make decisions on products that are a best fit. Clients can be informed on changes in benefits when it comes to insurance coverage or pension plans.
AI is also being used for customer sentiment analysis in response to news and market events. Reviewing multiple articles from media helps wealth managers understand what clients are reading and how they are reacting to market volatility. Advisors can also identify which customers from their book of business are most at risk.
Algorithmic Trading
As a general concept, trading is based on the analysis of data to make decisions. Machine learning (as a subset within artificial intelligence) is able to process the vast data sets of market data to detect patterns quickly and execute transactions on the spot based on insights.
Human traders are at a disadvantage, especially when it comes to daily-trading based on rapid price movement and time sensitivity. The critical factor is ensuring proper data is being used in training these machine learning models.
With portfolio management before AI, wealth managers depended on themselves to perform extensive evaluations of data sources. Algorithms have taken on this task and analyze every possible trend to optimize portfolios without constant oversight by managers. Automatic adjustments can be made in real-time by AI, offering increased service and support for clients. With portfolios more actively managed and optimized with a data-centric approach, clients don’t have to worry about sudden changes or unresponsive money managers limiting their returns.
improvement in research tools
Within the finance sector, investing time and effort into research is a critical requirement. Machine learning has taken the data to a new level. One of the top examples is with sentiment analysis, which is used in research on various management companies. Data can be assigned a ‘tone’ based on patterns in news, financial ratings, and the performance of a company.
Visual insights in satellite image recognition have also helped researchers have additional data to make factual assessments about customer traffic in retail sectors, logistics in freight shipping, and manufacturing output.
Reviewing reports is enhanced by NLP that makes it easier to extract the most relevant data for an analyst in financial reports, or format data easily to compare companies within the same industry.
augmented Valuation Models
The most innovative enhancements from artificial intelligence are with valuation models, most commonly in banking and investments. These models can perform asset valuation based on current and historical data, and add various weights on the most significant areas. Real estate companies have used valuation models to evaluate sales history and economic factors, and investment firms highlight financial analysis and market data for predicting growth and value.
Beyond general research, wealth managers and clients are demanding quantamental analysis (artificial intelligence that combines quantitative and fundamental analysis). This level of analysis requires vast amounts of historical data being compared to today’s market indicators — structured and unstructured data is given to machine learning and NLP tools to build intelligent financial advice.
Click here for the 1st part in our series: AI in FinTech - Fraud & Risk Controls
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