Portfolio analysis reports are central to how your clients judge their investments and your competence. Hence, it is evident that this report should sum up every facet of your efforts and present a wholesome picture to your client.
The intricate part isn’t understanding why a portfolio analysis report is crucial for you but understanding how to capitalize on it, how to build your client’s confidence, and how to steer them on a path of unwavering trust in your services and you.
An ideal portfolio analysis report helps your client in understanding his investments in the context of the whole portfolio. Such a report should include his profits, losses, and risks associated. The key is to tailor the report to best explain the client’s portfolio position in context to the rest of the industry.
However, it is not so easy to personalize reports for each client. Sharing personalized insights and recommendations with each retail customer is financially impractical given the magnitude of effort involved versus the returns. Thus, such premium services are mostly exclusive to HNI clients. Manually inferring and compiling each client’s unique details is also both time consuming and resource-intensive.
Now, the question is, do you have any technology that can generate these reports and make them as intuitive and personalized as possible?
The answer is: Natural Language Generation (NLG). NLG is a powerful technology that can convert numerical data into meaningful, easy-to-understand insights. The data that you would need to study for days to create meaningful reports, an NLG-powered platform can use the same data and generate personalized, fact-based, insightful portfolio analysis within seconds.
Let’s discuss how NLG can be utilized to personalize portfolio analysis reports :
1. Portfolio Snapshot
Much like an overview or executive summary of the report, a portfolio snapshot sums up the most useful and relevant insights into your report, right at the top. Using NLG, you can create a succinct brief of the entire report in a few bullet points which gives your clients a clear understanding of their investment scenario.
2. Asset Allocation
The asset allocation section depicts how each of the assets are performing and what the client stands to lose or gain if he/she switches to a particular asset with other competitive options. With Natural language generation, you can provide easy-to-grasp actionable statements which not only tell you what has happened but also discloses why it has happened and what needs to be done further with predictive analytics.
3. Segment and Sector Allocation
In this section, the client can see narrative-based recommendations about his potential investments in various segments (large-cap, mid-cap, small-cap). These recommendations help the client understand various aspects of the asset performance in sectors like real estate, stocks, etc., and how investing in either of these will impact the current portfolio. Using NLG to convert the readily available numbers for these sectors takes off the majority of the load off of the portfolio manager.
4. Detailed Equity Recommendations
In the detailed equity recommendations section, the client can find all the major and minor details related to suggested equities and investment options. Numerical data and graphs only provide a limited understanding. However, using Natural language generation to assess and generate insights and trends about the client's investment opportunity, can provide meaningful & comprehensive reports without as much labor.
Interactive Dashboards for Real-time Investment Management
Natural Language Generation has recently become a mainstream technology utilized in AI report writing. Phrazor, an augmented analytics tool is one of the most sophisticated platforms catering to a major chunk of the demand right now.
Further, Phrazor has a proven capacity to generate dynamic dashboards that the clients can log in at any time to evaluate and restructure their portfolio without any human assistance, at a click of a button. These dashboards provide filters and switch options to choose from various assets, compare real-time performance of the existing and potential investments, etc. Such NLG powered features corroborate the portfolio advisor’s choices and build lasting trust between the client and the manager.
Romil Shah is an AI enthusiast with considerable experience across varied technology domains, primarily Natural Language Generation (NLG), and Blockchain technology. He is passionate about technological innovation and more importantly its real-world applications. His work within the field has brought about constructive conversations and explorations around AI and its extended use in businesses.