AI in Asset Management: A Conversation with The Archer Technology Team

Ready or not, artificial intelligence has arrived in asset management.

There have been countless articles written about the seismic implications this revolutionary technology will have on the future of nearly every industry. But for many growing asset managers, it can be difficult to understand exactly how AI will impact their business and whether or not the time is right to invest.

At Archer, we have hands-on experience using AI to streamline investment operations. We’ve been following AI in the asset management industry for years and and aim to help our clients better understand these new technologies and their potential impact to our industry.

Whenever a new technology emerges, there are benefits and risks to being an early adopter. To help educate our clients on how this technology is being used today, we sat down with two of Archer's technology experts, Albert Chu and Peter Ryczek, to discuss what they’re seeing when it comes to AI in asset management.

AI has leapt into the public awareness over the past year or so, creating opportunities and challenges. What do you see as the promise and the danger of using AI in our industry?

ALBERT: The promise of AI and generative AI (GenAI) is that it will be the next leap in productivity and efficiency for all industries. Here are a few ways:

  • Improving operational efficiencies by automating and optimizing business processes via report writing and code development and assisting with regulatory compliance.
  • Assisting customers and employees by providing personalized recommendations and suggestions for problems.
  • Providing insight into new opportunities by discovering trends, including customer and market patterns.
  • Generating growth areas such as new models, investment strategies, marketing campaigns, and training materials.

GenAI tools however are still relatively young. It is important to understand the underlying data sources and vet the information provided.

PETER: The impact of AI on the financial industry is substantial. It can enhance efficiency, streamline processes, and provide more accurate predictions. For instance, AI algorithms can analyze vast amounts of financial data much faster than humans, helping in making better investment decisions and managing risks.

However, danger can lie in the potential for over-reliance. If we let AI make all the decisions without human oversight, there's a risk of blindly trusting the algorithms, which may not account for unforeseen events or outliers. Plus, there's always the concern about data security and privacy.

Finding the right balance between the benefits of AI and the need for human judgment is crucial to ensure a harmonious integration into the financial world.

Investment operations is a core feature of Archer's services and you both mentioned that AI can be used to increase efficiency in operations. What are the ways that Archer is currently using AI?

ALBERT: Traditional AI is the solving of specific tasks with predefined rules. When patterns are found, the patterns can be turned into automated processes by codifying the manual steps. Automation reduces errors. Common errors are also patterns, and they can be eliminated by adding in rule-based checks. GenAI extends AI by creating new content that shares characteristics of the datasets it was developed from.

At Archer, we excel at processing many accounts at scale through automation. For instance, our Order Management System utilizes blocking and pro-ration functions to reduce the number of times a scenario needs to be executed. Our system also leverages AI to smooth the process for new account setup, systematic withdrawals, and tax selling. In addition, it allows us to identify repetitive errors for certain transactions or user behaviors, and either stop or warn about the errors. The ultimate benefit is that we have high operational productivity.

What do you see as some other opportunities for AI to be used in the Financial Services industry?

PETER: I see a multitude of opportunities for AI in the financial services industry. As I mentioned earlier, the key to maximizing AI’s potential benefits is implementing it responsibly and ethically.

Here are a few examples that showcase the versatility of AI in transforming various facets of the financial services industry, from customer interactions to back-end operations:

  • Fraud Detection: AI can spot unusual patterns in transactions or behaviors, helping to identify and prevent fraudulent activities.
  • Risk Management: AI can assess and predict risks more accurately, aiding in portfolio management, insurance underwriting, and lending decisions.
  • Algorithmic Trading: AI algorithms can analyze market trends and execute trades at speeds impossible for humans, optimizing investment strategies.
  • Regulatory Compliance: AI can assist in ensuring compliance with ever-evolving regulations by automating processes and monitoring transactions for suspicious activities.
  • Data Security: AI can bolster cybersecurity by identifying and mitigating potential threats, ensuring the safety of sensitive financial information.
  • Operational Efficiency: Automation through AI can streamline routine tasks, reducing operational costs and increasing overall efficiency.
  • Predictive Analytics: AI models can analyze historical data to predict market trends, customer behaviors and economic shifts, aiding in strategic decision-making.

ALBERT: In the capital markets, traders use AI to identify and act on market trends. Investment management shops acting on those same trends create products and marketing materials customized to their target customers. Another interesting GenAI tool is IBM’s watsonx Code Assistant for Z which reads legacy COBOL code prevalent on many financial system mainframes and converts it to structured Java.

As we all know, the financial services industry faces lots of new and changing regulations. Developers are using AI to understand these regulatory changes and assist them in the making of necessary code changes which can be cross checked to a code repository. Banks are using GenAI to find and summarize information in documents and contracts, which accelerates creation of documents, reports pitch books and client presentations. They are also deploying AI chatbots and virtual assistants to help answer customer questions or to direct inquiries to the appropriate departments.

With AI becoming increasingly accessible, it feels like we are on the cusp of some incredible changes. What are the risks and benefits of being an earlier adopter of AI? A late adopter?

PETER: Here's how I would break it down:




  • Competitive Advantage: Early adopters can gain a significant edge over competitors by leveraging AI for improved efficiency, better decision-making, and innovative products and services.
  • Innovation Leadership: Being at the forefront of AI adoption positions a company as an industry leader, attracting talent, partners and customers.
  • Learning Curve: Early adopters have the opportunity to learn and iterate quickly, understanding the nuances of AI implementation and refining strategies.
  • Uncertain ROI: The initial investment in AI technology can be high, and the return on investment may not be immediately apparent or guaranteed.
  • Technical Challenges: Early AI solutions might be prone to bugs or technical issues, requiring dedicated resources for troubleshooting and refinement.
  • Regulatory Uncertainty: Early adopters might face challenges in navigating evolving regulations related to AI, potentially leading to compliance issues.


  • Mature Technology: Late adopters can benefit from more mature and stable AI technologies with proven track records, reducing the likelihood of early-stage issues.
  • Cost Savings: Prices for AI technologies tend to decrease over time. Late adopters may benefit from more affordable solutions and a clearer understanding of the technology's value.
  • Learn from Others: Late adopters can learn from the experiences, successes and failures of early adopters, allowing for a more informed and strategic implementation.
  • Competitive Disadvantage: Falling behind in AI adoption can result in a loss of competitiveness, as rivals may already be reaping the benefits of enhanced productivity and innovation.
  • Talent Acquisition: Skilled professionals in the AI field might be in high demand, making it challenging for late adopters to secure the necessary expertise.
  • Missed Opportunities: Late adopters may miss out on early opportunities to explore new markets, meet customer demands or address industry challenges using AI.

In essence, the decision to be an early or late adopter of AI involves careful consideration of industry dynamics, organizational readiness and risk tolerance. Striking the right balance is key to harnessing the transformative power of AI while mitigating potential pitfalls.

ALBERT: The benefits of being an early adopter are recognizing the power of AI/GenAI and immediately reaping the productivity and operational efficiency benefits. However, an early adopter must also understand the risks and limitations of GenAI. There may be embedded bias in the underlying data. Data privacy, cybersecurity, and intellectual property are also key concerns. Indiscriminate and careless use has gotten many early adopters in trouble, so all output still needs to be carefully vetted.

The AI Era

With revolutionary technologies pushing the industry forward, asset managers should weigh the benefits and risks of implementing AI into their current operations and product development. Even if your business is not ready to make a significant investment in AI, it’s important to pay attention to the technology and consider the different ways it can help your business grow. Generating alpha, analyzing data, and driving back-office efficiencies are just the beginning. No matter what stage your business is in, reach out to us today to find out more about the ways AI can help your business now and in the future.

Download a PDF version of this article here.

Our Technology Contributors

Albert Chu, Senior Vice President, Technology
Albert Chu, joined Archer in 2001. With experience leading management consulting projects at Andersen Consulting and as the Technology Director at Smith Barney Capital Management, Albert has managed a spectrum of large projects focused on integrated business knowledge, performance measurement, web integration, distributed processing tech. At Archer, he serves as an enterprise architect, where he combines both strategic and technical knowledge needed to design, build and scale solutions to meet the needs of Archer’s clients.

Peter Ryczek, Senior Vice President, Technology
Peter has over 20 years of experience in development, integration, and implementation of high-performance software and computer systems for research and financial institutions. Throughout his career, Peter has been involved in conversion of legacy systems, leading the implementation of one of the first high performance IBM SP parallel UNIX systems. Peter was part of the supercomputer division at the Lawrence Livermore National Laboratory, where he was involved in software development, configuration, security, and performance tuning of ASCI Blue and White systems. Peter has been with Archer since 2001, where he focuses on developing and evolving Archer's award-winning platform to meet the needs of our growing client base.

Get Started with Archer

Ready to accelerate your growth? Speak to a member of our business development team to see how Archer can help you hit your targets — faster.

Contact Us

Our website uses cookies. By using this site, you agree to its use of cookies.