Building AI-Powered Robo-Advisors for Investment: A Comprehensive Development Guide

Understanding robo-advisors and investment principles

In short, robo-advisors are online platforms providing you with automated financial planning and investment services. They are algorithm-driven and require little to no human supervision. 

It goes like this: You fill out an online survey about your financial situation, risk tolerance, and future goals. Then, you get advice from the platform and it automatically invests your buck for you. Well, for your clients. Or, your client’s clients, in case you provide software for advisory firms. In both cases, getting a robo-advisor is now equal to getting up to speed in the wealth management and investing market. From $186.9B in 2017, the value of assets under robo-advisor management shot up to $2,761B in 2023 and is projected to reach $4,664B in the next four years.

A competitive automated investment advisor packs users with a few-click account setup, accurate and detailed goal planning, automated portfolio management, and easy-to-use account services. Other essentials include robust security, an intuitive user interface, and low fees.

Let’s zoom in on the investment principles that guide the functioning of robo-advisors.

• Robo-advisors emphasize diversifying investments across various asset classes, be it stocks or real estate. Diversification helps spread risk and reduces the impact of poor performance in one asset class.
  
  
• Questionnaires or profiling let digital advice platforms assess each investor's risk tolerance. They then allocate assets based on the investor's willingness and ability to tolerate risk.
  
  
• Robo-advisors focus on investors' financial goals, such as retirement, buying a home, or funding education. They tailor investment strategies to align with these goals, considering factors like time horizon and required returns.
  
  
• Minimizing fees and expenses is another feature of automated investment platforms. This cost-efficient approach helps investors keep more of their returns.
  
  
• Some robo-advisors employ tax-loss harvesting strategies to minimize tax liabilities. They sell losing investments to offset gains, reducing the overall tax impact on the portfolio.
  
  
• Automatic reinvesting of dividends and interest income maximizes the power of compounding. This ensures that idle cash is put to work in the portfolio.
  
  
• Robo-advisors consider the liquidity needs of investors. They allocate a portion of the portfolio to liquid assets like cash or short-term bonds to cover short-term expenses and emergencies.
  
  
• Finally, some robo-advisors will polish up your investment strategy by customizing your portfolio. Investors can specify preferences, exclusions, or specific asset classes they want to include or avoid.

Data collection and preprocessing

Selecting data sources

Say, you’ve decided what features your robo-advisor should rock. Next, your team should identify relevant financial data sources. Typically, you aim for a mix of asset prices, economic indicators, and user profiles, which flow in through user input, external APIs, and other channels.

Custodian accounts will help the robo-advisor get information about the funding sources of the clients. Their personal details like age, social security amount, current salary, target financial goals, and future planned contributions the customers will fill in themselves. To make the process a click-and-go business, provide clickable options. For instance, for financial goals, insert a dropdown menu with options like future retirement, education, large purchases, and parameters of those goals — when and how much funds the customer will need for those goals.

To aggregate and timely process the relevant data from custodians, make sure your solution supports a rich number of data source formats. These can vary from CSV files in different formats to API sources like MX. If you choose a paid source, make sure to check several options to find the best pricing/billing option. Some charge per API call, per specific fine-tuned request (API response can be in JSON, CSV, etc.), or offer a file-based delivery model (oriented towards massive data delivery).

By the way, there are free APIs to get stock data (e.g. security position prices), like Twelve Data and Alpha Vantage, which save a good part of your development budget without undermining your robo-advisor’s security.

Model portfolios are another option to add to your platform’s feature galore. These are investment templates your customers can use, with a certain risk profile and set returns. Your team can either program the robo-advisor to create new custom asset mixes or use pre-designed upgradeable model portfolios like the ones that Charles Schwab or Goldman Sachs provides.

Data cleaning and preparation

Whether you rely on Monte Carlo simulation (more on it below) or machine learning, you’ll need to keep the data flowing in nice and clean. To achieve that, you need to pick the right data integration method: ETL or ELT.

ELT (extract, load, transform) is the more recent one. It does basic data transformations, like data validation, through data warehousing. Under the more traditional ETL (extract, transform, load), the data is extracted to a separate processing server, where it gets cleaned and organized before being transferred to the target system.

Data can be transformed in numerous ways. Below, I share the techniques that will help you comb and style it perfectly.

Data cleaning

• Identify and handle missing data points. You can do that through imputation (replacing missing values with statistical measures like mean or median) or removing rows or columns with missing data.
• Detect and deal with outliers that can skew analysis or modeling results. You can cap, truncate, or transform extreme values.
  
  

Data transformation

• Scale numerical features to a common range (e.g., 0 to 1) to ensure they have equal importance in modeling. Methods include Min-Max scaling and Z-score standardization.
  
• Convert categorical data (like asset classes or investment types) into numerical values through techniques such as one-hot encoding or label encoding.
• Create new features or modify existing ones to capture relevant information. For instance, calculate rolling averages or create interaction terms between features.
• Apply logarithmic transformations to variables with skewed distributions. This will make them more normally distributed.

Data reduction

• Reduce the number of features while preserving as much information as possible. Techniques like Principal Component Analysis (PCA) can be applied to high-dimensional data.
  
  
• Select the most relevant features based on their importance in predicting the target variable. Use mutual information, chi-squared tests, or feature importance from tree-based models.
  
  

Data splitting

Split the dataset into training, validation, and test sets. The training set is used to train the machine learning model, the validation set is used to tune hyperparameters, and the test set is used to evaluate model performance.

Time series data handling (if applicable)

If dealing with time series data (e.g., stock prices), consider techniques like lagging, rolling windows, or resampling to align the data for analysis.

Handling skewed data (if applicable)

For imbalanced datasets, consider techniques like stratified sampling to ensure that each class is adequately represented in the training and test sets.

Data normalization (if applicable)

Normalizing returns can be important to compare assets with different price levels. Calculate returns based on price data to normalize.

Data validation

Perform data validation checks to ensure data consistency and integrity, such as checking for duplicate records or data entry errors.

These data preprocessing techniques are essential for ensuring that the data used in a robo-advisor is clean, relevant, and well-structured for accurate analysis and model training.

Implementing machine learning models

ML algorithms are at the core of automating investment recommendations and portfolio management. The latest advisory software may have hundreds of portfolio options built up, fit for user profiles of all stripes. To make the number less scary, below are the most common algorithms with the respective use cases.

However, there’s another option that feels good in the cutting-edge software. The Monte Carlo simulation method sparked in the mind of Stanislaw Ulam, a scientist involved in the Oppenheimer-led Manhattan Project. It’s the investment world’s classical method that uses random sampling of assets (usually securities) to model price variations in the future years and produce an array of possible results for achieving your customers’ financial goals. 

You can combine the Monte Carlo method for financial planning with ML algorithms for an enhanced result. For instance, Monte Carlo simulations are excellent in risk assessment and management: by simulating thousands to millions of possible future scenarios, they provide a comprehensive view of the range of potential outcomes. ML algorithms, on the other hand, can sieve historical market data to identify patterns and correlations that make traditional models' blind spots, which helps in understanding how different factors contribute to risk.

Creating the recommendation engine

High personalization can earn your robo-advisor a better market position. That’s a good enough reason to develop a recommendation engine that suggests tailored investment strategies to users. Based on financial plan simulations, you can add to your robo-advisor recommendations like the following:

• Increase funding recommendation
• Change the model
• Decrease goals spending
• Change the goal dates

To ensure above-average accuracy, incorporate techniques such as collaborative filtering and content-based recommendation. The latter uses the customer’s historical preferences to find the best investment options. It focuses on matching the features of content (investments) with a user's profile and preferences. Collaborative filtering, as the name suggests, matches users with investment options based on the preferences and behaviors of other users with similar investment profiles.

Find out how to build a recommendation engine, with each step visualized, below.

Portfolio management automation

Investment portfolios are dynamic: they swing on the changing market conditions, and user preferences are unlikely to stay the same for years, too. So, you need to put in place techniques and algorithms that automatically adjust investment portfolios based on the given variables.

Rebalancing and tax optimization are key in supporting optimal portfolio performance. Rebalancing realigns the asset allocation within an investment portfolio to ensure that it adheres to the predefined target asset allocation. Usually, the time to press the “rebalance” button (if you have no advisor or robo-advisor to do it for you) is when market fluctuations cause the actual allocation to drift away from the desired allocation. As for tax optimization, it helps users pay just as much taxes as needed, with no extra spending or headache from complex calculations.

Again, you can build rebalancing or tax optimization functionality from scratch or integrate solutions like Smartleaf.

Oh, and let’s go meta: you can automate the management of the management process itself. For instance, by adding a special type of recommendation in the financial planner, you can set the regular “Change the portfolio model” notification which will be issued on specific dates. If the user accepts it, the financial advisor will need to initiate rebalancing of the portfolio according to the newly chosen model.

User interface and experience

Wrap-ups of the robo-advisor should be as well-thought-out as its core. To let users enjoy your solution, you need to design and develop an intuitive user interface. All the cool features you add are good for nothing if your UX is confusing and challenging to use. 

What UI/UX to develop depends on your target audience, whether it’s B2C or B2B (professional advisors). Each will need different configurations or tuning and visualization detailing level. For instance, client-oriented charts should visualize the progression of the financial plan and the composition of the portfolio (asset allocation) in a simple, interactive way, without heavy wealth management jargon.

Important: The simulation process for complex visualizations may take a bit longer (from 10 to 30 seconds) than a normal web request would (1-3 seconds). So, you’ll need some kind of async solution like websockets or polling to get the simulation result. 

Another great idea to improve user experience is to have a scheduled job that would execute the plan and check results on an everyday basis since we need to track the plan progress (alert the client when it stagnates) and detect when we need to offer the model change (reallocation). 

Testing and validation

You’ll need to check if your robo-advisor is good to go before it enters the market. 

• Deploy testing methodologies to ensure the accuracy and reliability of investment predictions and portfolio adjustments.
• Use historical data to validate the robo-advisor's performance against real-world market trends.
• The past wealth will allow you to match the modeling predictions and actual financial planning results.
  
  

Security and compliance considerations

When you’re in Fintech, you must implement robust risk assessment procedures, organize employee training, and plan out incident response. Also, you have to make sure your employee and vendor contracts focus enough on security. Other essential considerations include regular updates and patches, data backup and recovery mechanisms, continuous monitoring, and compliance with financial regulations and data privacy laws.

As for the solution itself, users must be able to see only their own data while advisors have to only see the data of the clients they manage. In the same manner, admins are allowed to access the data of all of the firm’s clients. Access control should be implemented for clients, advisors, and admins. Company-wise, implement access controls to ensure only authorized individuals can access sensitive data and systems. 

Future enhancements and optimization

The future of your solution starts with its initial architecture. By choosing microservices, you will ensure that your robo-advisor is scalable and secure. Such architecture will allow your team to make changes to each component of the system without risking to butch the user experience.

Or, you can purchase a white-label solution. This way, you’ll only need to build customer-oriented front ends. The downside is the potential incompatibility of such a system with your integration of choice, which can affect customization potential.

Building your robo-advisor on integrations can speed up your time to market. However, this way you might compromise the solution’s scalability and personalization. If you need the opinion of seasoned development experts, you can schedule a call here.

Polishing it up

Now you know the key steps to developing your own robo-advisor and can let more people know what financial freedom is. Guided by the key investment principles, together with your team, you’ll select the right data sources and ensure proper data processing.

Carefully selected algorithms will drive portfolio management and tailor the recommendations for your customers enjoying every step of their financial journey with you. By testing every bit of the technology, you’ll ensure user experience will stay excellent and only become better. 

Feel free to ask me your questions to ensure that your robo-advisor will turn out the picture-perfect you imagine. Good luck!

Special thanks to INSART’s Eugene Pekhulia, BE Teach Lead, and Oleksii Petinov, Java/Scala Developer, for contributing their expertise to this article.