For years, industry experts have predicted that artificial intelligence (AI) would have a profound impact on the investment industry. Investment managers, on the other hand, have been skeptical of the hype in the absence of substantial use cases. Fast forward a few years and AI has not only arrived but is already having a transformative impact on virtually every facet of investment accounting and middle-office operations.
Today’s investment managers need the agility and scalability to adapt quickly to new regulatory, accounting and reporting requirements, not to mention unforeseen market shocks and accelerated trading volume. Operational costs remain critical as regulations, fee pressures and other risks evolve.
As a result, outsourcing and process automation are more important than ever. Different variants of AI, such as machine learning (ML), are already being implemented to streamline and accelerate typical investment processes, many of which are still highly manual.
Artificial intelligence and machine learning are often used interchangeably which begs the question, do you really know the difference? While closely related, ML is a subset of the broader category of AI and differs in several ways, including scope and applications.
What is Artificial Intelligence?
AI is a broad term, which refers to the use of technologies that can mimic cognitive abilities and go beyond human intelligence, such as understanding and responding to language, analyzing data or making recommendations.
AI isn’t one system in itself. Think of it as an umbrella set of technologies implemented in a system to enable it to reason, learn, and act to solve a complex problem. AI is at the heart of something you likely use every day—your smartphone. Devices with voice assistants such as Apple’s Siri are powered by AI.[i]
What is Machine Learning?
Machine learning is a subset of AI that enables a system to learn and improve from experience. ML uses algorithms to analyze large amounts of data, learn from the insights, and make informed decisions. ML algorithms get better and better over time as they are “trained” by being exposed to more data. After running algorithms on data, machine learning models are created, which are capable of performing complex tasks. The more data used, the better the model will get.[ii]
The Benefits of Using AI and ML Together
While artificial intelligence encompasses the idea that a machine can mimic human intelligence, machine learning does not. This is where the scope of the two differ. Machine learning teaches a machine to perform a specific task and by identifying patterns provide accurate results. Used together, AI and ML allow for the analysis of more data sources, accelerate data processing and reduce human error. This leads to faster decision making, operational efficiency and reduced costs.
Real-World Applications for Machine Learning
Insurance investment teams have generally been conservative when it comes to adopting advanced technology. Still, talent shortages compounded by ever-more complex portfolios and regulatory requirements are creating pressures in the market that advanced technology can alleviate.
In our "4 AI Technologies, 10 Benefits for Insurance Investment Operations" guide we explore the ways investment managers can apply existing, real-world AI solutions to critical functions such as matching and reconciliation as well as accounting. The resulting cost savings are quite astounding.
Download the "4 AI Technologies, 10 Benefits for Insurance Investment Operations" guide to dive into the 10 ways AI and its subsets like machine learning are transforming investment operations.
[i] https://ai.engineering.columbia.edu/ai-vs-machine-learning/
[ii] https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning
