5 Practical Ways AI Can Support Your Asset Management Firm
Introducing new technology into the workplace is key to keeping an organization up to date and capable of adapting to a constantly changing business world. Asset management is a prime example of an industry where artificial intelligence (AI) and machine learning can aid in the quality and efficiency of work. AI can assist with specific processes like RFP processing, trip planning, and fraud detection, supplying a more complex solution than just a yes/no answer. Because a computer can process vast amounts of data much more quickly than a human being can, the use of AI can greatly increase productivity in the area of asset management.
Why AI and Machine Learning?
AI has proven to be a very effective resource for companies, not only increasing the efficiency of work produced, but also the quality. The term AI includes any software designed to work in a similar way to the human brain. Machine learning, a kind of AI, describes a program that alters itself based on information fed into it to train it, and the ensuing feedback from the humans that built it and those who use it. Asset management revolves around making important investment decisions for clients. In order to do this, in-depth research is required and the use of powerful analytical tools is prevalent. AI software paired with machine learning capabilities can help to speed up the research process while providing reliable investment suggestions. This ability, when applied to the workplace, can significantly cut down on operating expenses and allow people to be more focused on making the best possible, informed suggestions for their clients.
Furthermore, consider the learning curve associated with AI. While a human sorting and analyzing data can remember on average 10% of what they read, a machine can remember 100% and hold it in memory for future reference, giving it the ability to crosscheck information, locate patterns in information, and select important data from any point in time within seconds. The ability for a machine to analyze more data, as well as researching it faster is what gives it so many practical applications in the asset management industry.
To find tasks suited to the strengths of AI and machine learning, look to the time consuming and repetitive processes within your field which require a great deal of concentration and analysis.
Evaluating Investment Opportunities
Evaluating opportunities is one of those tasks which consumes a significant part of an asset manager’s day. The process of sifting through massive amounts of data and comparing hundreds of data sources with each other is a complex task for a human, but with the implementation of AI in this area, this task can be done in a fraction of the time, with greater accuracy, and with no bias. AI would be able to deliver the best possible results and even be able to project the success rate/potential opportunity for each of the proposed options. By using AI in this way, asset managers can focus more of their time on the opportunities which present a high probability for success, and less time sifting through data which most of the time will not be looked at twice.
In the field of asset management, opportunities are presented as Request for Proposal (RFP). Since AI can use sets of unstructured and structured data, the simple act of scanning your RFP will allow a machine to read it and learn anything necessary to produce your desired answer. This removes the time-consuming data submission process. An AI application would even be able to tell you the opportunity associated with a specific RFP. As more RFP’s are processed the data lake grows which essentially means that your machine is becoming smarter and smarter the more it is used. Finally, the machine is able to recommend adjustments to proposals based on previous performance and human input, which will help your organization find greater success in the long run.
Trip planning for managers and salespeople is a logistical mess. When on trips to score sales, people are often time bound and have to decide which few people they will meet out of the many options they are presented with. It is human tendency to choose the people who would be the most fun or easiest to meet with, rather than those who pose the best opportunities for an organization. This is a bias which is hard to alleviate as long as humans are making these decisions. By implementing AI into this decision making process, bias is eliminated and every last factor can be considered and compared before a decision is made.
Of course, when it comes down to it, the final choice must be made by the person who will be taking the trip and the meetings, but by allowing software to make trip-planning suggestions, managers and salespeople can take more time to prepare quality presentations for the people with whom they are going to meet, which ultimately increases the chance of a positive outcome for the organization.
Fraud detection is one key operation which already benefits from the use of machine learning. A machine’s ability to track and compare countless data points while instantly comparing the data to previous transactions on an account is incomparable to the efficiency of a human doing the same job. This assists with investor servicing by detecting and reporting unusual activity on an account. An example of this would be AI flagging in real time a transaction of $10 million on an account usually withdrawing less than $100,000.
Thanks to its versatility, AI can also provide new opportunities for risk management. With the ability to quickly and effectively analyze nontraditional data such as images, product reviews, website activity, geolocation data, satellite imagery, and social posts while instantly making connections between data points and mapping out relevant trends, companies can more easily oversee risk management and trading activities.
Implementing AI and ML technology into asset management will help provide clients with faster and more accurate services. By eliminating time consuming tasks and allowing software programs to make well-informed, data-based suggestions, your organization can increase efficiency, productivity, and satisfaction. The processes listed above are by no means the only areas where AI might apply, either. AI is bound by nothing but our willingness to implement it and develop it to suit our needs. By putting aside the tedious work for intelligent machines to do, growth-minded people can push themselves to release their full potential and help expand the fields in which they work every day.