Tech In the Asset Management Toolchain

Tech is increasingly shaping the asset management industry with the shift towards highly automated and intelligent processes. Tech-enabled market intelligence platforms are becoming more prevalent, providing asset managers with real-time insights and predictive analytics to make informed decisions. The market for tech, more precisely AI, in asset management is expected to grow at a 24.4% CAGR through 2030, indicating tech’s significant impact and potential in this sector.

Liquidity Group has been building tech-powered asset management products for years. That the rest of the industry is finally catching up is exciting, and further exploration is important to bring tech into every corner of the financial world.

Financial services professionals are increasingly adopting tech tools and technologies to enhance their asset management capabilities. These technologies bring improvements in data analysis, risk assessment, and investment strategies, allowing professionals to make more accurate and timely decisions.

In industrial asset management, for example, AI plays a crucial role in streamlining operations, improving efficiency, and maintaining optimal asset conditions. Tech-powered predictive maintenance and asset monitoring systems help to identify potential issues before they occur, reducing downtime and improving overall asset performance.

Tech As A Driver of Change

Technology, including machine learning and artificial intelligence, has the potential to have a significant impact on the investment industry. One potential impact is its ability to automate passive management, allowing for more efficient portfolio management and lower costs for investors. AI can also help identify valuation dislocations by processing large amounts of data and identifying potential mispricings in the market. Additionally, AI can create new thematic fund products by using algorithmic strategies to target specific investment themes or trends.

For example, Liquidity has created a system for automatic company reporting that considers cash-in-hand, future earnings, and risk potential. These tools reduce the risk of new investments to nearly zero, allowing analysts to look only at the cream of the financial crop. 

These tech tools can move the mundane work once given to highly-paid analysts into more automatic systems. While the systems build portfolios, answer questions, and analyze data, the humans in charge can focus on relationship-building, expansion, and strategy. This is the core of the move toward tech-enabled asset management.

From predictive analytics and personalized recommendations to automation and autonomous systems, the tech advantage is becoming increasingly apparent in today's business landscape. Tech lets humans move faster and more efficiently, allows for more freedom, and increases productivity. Just as tech changed manufacturing and product design, tech will change finance is ways that are unimaginable today.

Here are some technica solutions poised to change the asset management landscape. 

Machine-Learning-Enabled Analysts Create Customization at Scale

Machine-learning-enabled analysts have revolutionized the investment industry by using AI algorithms to provide personalized investment advice to a broad range of investors. These automated platforms, like Liquidity Group’s own systems, analyze the company's financial information and runway to generate tailored investment strategies, making AI-based asset management more accessible and cost-effective.

The rise of these analysts has led to a significant increase in revenue, with a projected 15X growth between 2017 and 2023. Asset management firms can tap into this potential by scaling their services to reach underserved customer segments, such as young professionals and individuals with lower investment capital. By leveraging robo-analysts' scalable solutions, firms can cater to a wider client base and enhance their overall profitability.‍

Quantamental Insights Mix Human Experience With Technology

Quantamental insights - insights simultaneously based on quantitative and fundamental analysis -  integrate machine learning and AI with human knowledge and experience to inform investment decisions. This approach involves using AI to mine and analyze large volumes of data from various sources, such as financial reports and market trends. Machine learning algorithms can then identify patterns and signals that human analysts may miss. At the same time, expert commentary and anecdotal insights are used to provide the unique human perspective necessary for making informed investment decisions.

The process of incorporating tech into the investment decision process allows for the efficient analysis of vast amounts of data, leading to more informed and timely investment decisions. By combining AI-driven insights with human expertise, asset managers can gain a competitive edge in identifying investment opportunities and assessing market risks. This approach offers the benefits of leveraging technology for data analysis while maintaining the valuable human perspective in investment decision-making. As a result, asset managers can make informed decisions that reflect both quantitative analysis and qualitative insights, without sacrificing the unique human perspective. Overall, quantamental insights represent a significant advancement in the asset management industry, combining the strengths of AI and human expertise for more effective investment strategies.‍

Risk Management and Fraud Detection

AI is increasingly being used in the financial industry for risk management and fraud detection. AI algorithms are capable of analyzing large volumes of data to detect irregular trading patterns, market disruptions, and fraudulent behavior in real time. This proactive approach helps maintain market integrity and investor confidence by identifying potential risks and anomalies before they escalate.

AI is also used to scan transactions and communications for suspicious activities, reducing the risk of misconduct. By continuously tracking and analyzing data, AI can identify patterns that may indicate fraudulent behavior, such as unauthorized transactions or unusual communication patterns. This level of vigilance helps financial institutions stay ahead of potential threats and maintain a higher level of security.‍

The Road Ahead for Tech Adoption

Financial institutions can adopt tech like generative AI responsibly by first considering the risks and benefits of the technology. They must understand that while genAI has the potential to automate tasks, generate insights, and improve investment decisions in asset management, there are also risks associated with data privacy, bias in algorithms, and potential job displacement.

To incorporate tech into their organizations in a strategic manner, financial institutions should prioritize responsible adoption by implementing ethical guidelines, robust data security protocols, and transparent decision-making processes. They should also invest in ongoing training and education for employees to understand and work effectively with genAI systems.

In asset management, tech can be used to automate repetitive tasks, such as data analysis and reporting, allowing financial professionals to focus on more complex, value-added activities. GenAI can also generate insights from large datasets, identify patterns and trends, and improve investment decisions by providing more accurate predictions and risk assessments.

Collaborations between financial professionals and data scientists are essential in this evolving landscape. Firms must adapt their talent strategy and teaming models to incorporate tech, creating cross-functional teams that can leverage the power of technology to inform decision-making processes.

The tools we are creating today will define the financial industry of the future. Understanding and appreciating their importance is vital to growth, productivity, and success. 

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