Assisting investment management with AI in finance
Element AI Element AI
March 12 4 min

Assisting investment management with AI in finance

In their recent report, professional services giant Deloitte called AI “the next frontier for investment management firms.” And they’re right: cutting-edge AI in finance is growing. We’re seeing more and more businesses adopt AI to make better use of their data and improve operations.

This blog will explain how AI in finance can help portfolio managers achieve lower implementation costs by recommending trade volumes that minimize market impact and price risk. Such software can also provide information and suggestions on length of trade schedules, daily trade flow-in and flow-out, and volume of individual stocks — while continuously optimizing trade schedules based on dynamic market information and user decisions.

How AI in finance is reshaping the industry

The adoption of AI in finance is empowering businesses to do things they couldn’t conceive of before. From enhancing the capabilities of their workforce with process automation, to reshaping their operating models with AI powered credit scoring and delivering new levels of value to customers with fraud detection systems — AI is facilitating the development of many exciting new possibilities within the industry.

In an industry where book-keeping and records are pivotal, AI can be implemented into processes that involve auditing financial transactions and filtering large quantities of data. Alongside data analysis and management, AI is used in the financial sector in the following capacities:

  • Risk assessment
  • Fraud detection
  • Personal financial management
  • Advisory services

A specific area of the finance sector that is benefiting from AI is investment management. In the next section, we’ll explore this further.

Investment management and AI

The field of investment management faces numerous challenges:

  • Unpredictable capital market returns
  • Limited organic growth
  • Internal or market forces on their margins
  • Time-consuming manual processes
  • Regulatory issues
  • Spiraling costs

In efficient trading decisions are especially costly. These costs are amplified in circumstances of limited liquidity and large volumes. This is an inviting problem for recent advances in AI. While asset management firms have been at the forefront of AI innovations, the ongoing dynamism of markets demand ongoing innovation.

Portfolio Managers today are especially sensitive to implementation costs, and strive to lower them as they are facing pressure from multiple fronts. The headline trend here is that revenue growth for established asset management companies is expected to stall (with global CAGR projected at 1%). This is partly due to the effect of new entrants providing passive strategies. Additionally, weakening industrial growth in developed markets is driving a global shift in wealth generation toward emerging markets.

When deployed and implemented successfully, AI can improve the efficiency of widescale investment management operations and help advisors to holistically understand the preferences of investors in real time. AI also helps asset management firms to better understand big data and manage risks so that they can make informed decisions, strengthen compliance, and refocus employees’ time on higher value tasks.

Of course, it takes a degree of time and expertise to get AI up and running in your business. Finding a trusted partner to help you plan an AI business and data strategy that builds on the unique strengths of your business is crucial.

Improved processes and reduced costs with Element AI’s Trade Flow Scheduler

Designed to help institutional investors, our EAI Trade Flow Scheduler analyses market conditions and data to make recommendations to portfolio managers on when trade should be made, alongside suggestions on inflows and outflows to have the least impact on the market. The business benefits of our cutting-edge AI solution include:

Risk avoidance

By recommending trade volumes that minimize market impact and price risk, the Trade Flow Scheduler helps portfolio managers achieve lower implementation costs. The tool also offers an adjustable risk aversion parameter, so that portfolio managers can express their view of the market and adapt the pace of trading accordingly.

Simplified workflows

The software also enables businesses to easily configure user workflows with customized benchmarks and a pre-built UI or API to integrate with internal and third-party vendor systems. Essentially, the Trade Flow Scheduler helps your team conduct tasks more efficiently and create workflows that achieve the highest impact.

Scalability and optimization

The Trade Flow Scheduler offers scalability. Through leveraging machine learning to provide recommendations across a range of constituents, businesses can benefit from assistance in smart decision making and avoid overgeneralized decisions. Users can easily make use of multiple forecasting engines, including the latest deep learning breakthroughs. They can also enjoy continuous optimization — based on dynamic information and the most recent data.

Here at Element AI, our AI enablement tools save you time and resources. We’ll work with you to implement governance, train your people, and assemble data and technology to scale to your full ambition and realize your business goals.