You’d better believe it – the value of assets managed under Robo Advisory and Wealth Management is predicted to reach nearly $3.7 trillion by 2020.

Robo Advisory epitomises the successful implementation of big data technology and machine learning (ML) applications in the 21st century. Defined as low-cost automated investment services, Robo Advisory is flourishing. This is because such technology (combined with Big Data and mathematics) serves the needs of Millennials requiring accessible, dependable investment services that cater to their unique requirements.

In the third instalment of Journeys in Data Science, I want to touch on the deployment of technologies in data-driven services, particularly using the Robo Advisory and Wealth Management in Financial Services (FS) use case.

Points covered in this blog are:

• Scene Setting: Use Cases in FS and the Value of Artificial Intelligence
• How Robo Advisory and Wealth Management Works and Key Players
• Challenges in Robo Advisory and Wealth Management, and Luxoft’s Solutions

Scene Setting

According to IDC, worldwide revenues in big data and business analytics will reach $150.8 billion dollars in 2017. The corresponding spend on cognitive and artificial intelligence (AI) is expected to reach $12.5 bilion by the end of this financial year.

At Luxoft, we are seeing the two heavily regulated markets of banking and securities investment services driving much of this spend. Collectively, these two industries likely account for a significant proportion of worldwide spending on cognitive services and AI. Depending on what you read, it’s anywhere between 20-25%.

Use Cases

Organisational spend on AI and data science is focused on one of, or both the following objectives:

1. Creating and enhancing customer relations to improve products and services, maintaining customer loyalty
2. Making data-driven decisions to improve and streamline operations to reduce costs and increase efficiency
The most prevalent technologies from this spend are highlighted in the table below:


Complimentary to the investment in technologies, analytics is divided between ML and business intelligence. These two sides of the analytical coin are observable in all industries. As organisations seek new ways to bring in business by utilizing predictive analytics, many still use reporting tools to manage their day-to-day operations. Observe this in the infographic below:


Analytics Heat Map in Banking Gartner 2017.

Robo Advisory and Wealth Management

History and Key Players

Pioneered by Betterment and Wealthfront circa 2008, other companies like Personal Capital, Vanguard, etc. have either acquired or developed Robo Advisory platforms by spending millions. These online platforms help create and manage client portfolios quickly and inexpensively. There are more than 100 Robo Advisors across the globe, with the number increasing every year.

List of Robo Advisor/ Wealth Management companies and their AUM (Assets under management)

How does it work?

For a new investor, a Robo Advisor will guide you through steps to determine your investment goals and risk preferences. Then, your money is invested in a diversified portfolio across a variety of assets and industries from many different countries. The objective is to spread the risk of your investment while increasing your chances of a higher return. As assets pay out dividends, they are then re-invested, increasing in-market time and promoting opportunities for growth.

The Robo Advisor is continuously learning from historical data based on your choices, the performance of your assets (sometimes anticipating market trends using ML), as well as comparing you to individuals with similar risk preferences. As certain assets perform differently from others, the Robo Advisor automatically rebalances your portfolio to maintain your risk preferences.

Currently, rebalancing Robo Advisory portfolios isn’t done by any new mathematics or ML algorithms. Most are managed by using Modern Portfolio Theory. Moreover, ML comes into play when forecasting market trends from financial records, news, social media, etc. – often with minimal human intervention. Self-learning platforms have yet to become a reality, as training algorithms using complex and high-volume time series data regarding clients, markets, geography, transactions, accounts etc. is a time-consuming process. (However, a company called Novofina is claiming to make some headway in this area.)

At Luxoft, we are adding a greater dimension to business analysis by utilising ML to gain detailed customer understanding via risk profiling and scoring (underpinned by analytics from social media), spending habits and financial history. As previously mentioned, analysing and training with complex datasets requires serious computational and algorithmic clout. Naturally, deep learning is finding its purpose here.

Technological Challenges:

Creating and implementing a Robo Advisory/Wealth Management platform is a considerable technological investment, and overcoming related problems such as protecting client data, maintaining compliance and satisfying regulatory requirements is difficult.

Wealth management companies also struggle with stranded silos of data that need to be collated and aggregated. Combine this with legacy infrastructure, usually with no integration between front and back office applications, and you’ll understand why it’s difficult to maintain consistency. Check out the graphic below:


Wealth Management Enterprisewide Platform:


With continuously changing regulations and information security challenges, it is imperative for banks to use 21st century technologies that simplify processes in order to increase transparency with regulatory bodies. At Luxoft, the experience from working with our clients shows us that seamless integration of technologies for front, middle and back offices promotes greater efficiency, creating a fully integrated end-to-end solution.

One solution is a cloud-based approach that streamlines data aggregation, cleansing and processing which profoundly influences the implementation of analytical methods later down the line. This option scalability of products and services, but many organisations are concerned with privacy and security issues that may arise when someone else holds their data.

Thus, the question is – to build or to buy? High-profile players in the Robo Advisory game, such as Betterment and Wealthify, are white labelling their products or services. Dedicated hosts such as Black Diamond by Advent, Comarch and Addepar are flourishing as they exploit open-source technologies with proprietary flavouring to deliver their unique selling point to clients. My recommendation is to ensure you get support from an organisation with experience in the relevant data-intensive and technologically complex areas, and at Luxoft we would be happy to help.


The world has reached a convergence point in cloud computing technology, data aggregation and analytics. Robo Advisory is a new implementation of an old idea. However, customers don’t care about platforms; what matters is the quality of services, the overall experience and the reliability and security of a system they have bought into. If you want to chat about how to navigate your business through the pitfalls of developing your own Robo Advisory/Wealth Management platform, or simply to share your stories with me do get in touch by either clicking here or by commenting on this blog.

And in our final entry, we’ll touch on algorithms…stay tuned!
Maya Dillon
A Data Scientist with a passion for AI and Space. She is focused on developing strategic initiatives of businesses – implementing both new technologies and scientific methodologies. She is an experienced and highly acclaimed public speaker not to mention a science and technology evangelist. She is passionate about creating and developing new initiatives incorporating cutting-edge technologies in AI, Machine Learning and Data Science.