Create your very own robo-intern battalion

On January 20th 2017, independent consultant Andrea Sutter lost yet another project to a larger consulting firm. She was baffled. Her skills and experience perfectly suited the client’s requirements. Her pitch was flawless. She was sure that her proposal was much more cost-effective than that of her larger competitor. Still, after the effort, all that she got was a polite rejection note and maybe some goodwill for a future attempt. The reason for the loss: a “lack of analytical breadth”.

A lack of confidence in one-person operations

The project was a market entry study focused on China’s FMCG industry – her specialty. However, it required a large-scale analysis of competition, pricing and product offerings in seven Chinese cities. While she had already delivered over a dozen studies on the topic for other corporations, the client’s executives challenged her capacity as a one-woman show to research a landscape of around 3,500 products with enough detail. They were probably right.

Independent consultants often face this challenge when developing business or executing projects. They can have the right practical training, often honed during years at top-tier management consulting firms. They can present an outstanding value-for-money proposition. However, when they are independent, they are also alone. They miss the wealth of information available to peers at bigger firms and the access to junior analysts to support research and content development work.

Enhancing the independent consultant’s capabilities

That is precisely where artificial intelligence (AI) and automation technologies can help. Independent consultants can benefit from them to gain productivity and analytical muscle, compensating the small scale of their operations. What is important is for them to correctly frame AI use cases within their business. For example, after the setback outlined above, Andrea recognized three ways in which she could leverage these emerging technologies in similar projects.

First, she realized that she could have applied an AI-based data mining system to automate the collection and classification of online information on FMCG products. Prices, user reviews, product characteristics, technical specifications and images of thousands of products can be automatically saved to an Excel spreadsheet for further analysis. This saves the independent consultant from having to pay for access to specialized databases or hire additional team members to support data collection. Andrea calculated that, for a portfolio of close to 3,500 products, the total time required to integrate the information would be reduced by at least a factor of 10. Furthermore, once the system was set up, human intervention in the data collection process would be minimal.

Second, beyond standard spreadsheet analysis, Andrea understood she could have used machine learning to analyze pricing ranges, consumer behavior and competitor clusters in the dataset. For example, classification and sentiment analysis algorithms could have been used to link product characteristics to positive customer sentiment, spot unmet needs and sources of negative sentiment, and establish sensitivity to pricing based on consumer experience. All variables in the product portfolio could have been classified according to their influence on each other, allowing Andrea to deliver hard evidence on key purchase factors and propose new product profiles with high market potential.

Third, Andrea could have deployed an automated PPT slide design system to generate both detailed profiles for each of the products reviewed and overview analyses for each of the clusters recognized. The system would have allowed fast development of customized reports for different product management and marketing teams within the client organization. With minimal manual work, she would have been able to facilitate market entry planning at both the strategic and the tactical level.

Building your robo-intern battalion

Given Andrea’s example, how can independent consultants brainstorm AI use cases to increase their productivity? A good starting point is to identify what data collection, data analysis and data reporting tasks might normally be assigned to an intern. That is, what work requires human level cognitive skills but might be lengthy or repetitive? Then consider AI as a way to build a battalion of robo-interns to tackle the challenge. Thinking this way is the first step for independent consultants to greatly expand their project delivery capacities and gain new business. Hopefully it will also make the tasks assigned to talented interns more interesting.

Digital Wealth Management

The basic conditions for the management of private assets have changed drastically in recent years. This is due to a number of factors, including:

  • Products: The increasing importance of passively managed and therefore low-margin funds, which should reach a market share of around 30% by 2020 (EY Global ETF Research 2017)
  • Profitability: The decrease in profitability since 2009, from 37 to 23 basis points of assets under management (BCG Global Wealth Report 2017)
  • Regulatory: The increase in transparency requirements – e.g. regarding costs in the EU through Mifid II
  • Digitization: The rapid growth of assets managed by FinTechs and robo-advisers – e.g. to over $150 billion in the US (TechFluence, 2017)

When these dimensions are compared internationally, there are significant differences. The US, for example, is much more advanced than Germany especially regarding the adoption of digital solutions However, in Germany, the importance of robo-advisers will also massively increase in the next few years and, according to Oliver Wyman, the assets managed by them will increase to $42 billion by 2021.

The following article outlines developments in Germany. However, the key messages regarding the disruption scenario and future business models can also be transferred to other countries.

What are the implications of the above developments on wealth management – especially if robo-advisers expand their focus from smaller assets to the larger asset volumes of private banking clients? In addition to the high level of convenience that robo-advisers offer, they can provide clients with significantly lower costs than traditional banks. In Germany, while the total cost of traditional private banking (including the costs of depots, trading, portfolio management and products) is around 2% of the value of assets under management per year, the cost of robo-advisers is 0.5–1%.

The significantly higher cost of private banking, which is having a massive impact on returns – especially in the current low-interest-rate environment in Europe – is justified by traditional private banking providers by two aspects:

  1. The active management of clients’ portfolios and the claimed generation of an excess return
  2. The scope and quality of the personal advice they offer

On the one hand, one must question the value of active management in the context of private wealth management given the empiric evidence and lack of scale. On the other hand, personal and high-quality advice cannot justify the significant price differences seen between traditional private banking and robo-advisers. Rather, the significant cost differences result from the complexity of the products and, above all, a lack of standardization and digitization of support and back-office processes. The continuation of trends seen in recent years leads to a disruption scenario in private wealth management.

The resulting disruption scenario is not only promoted by technological change and market developments, but, above all, is accelerated by changes in customer requirements – in particular, by the “generation of heirs”. This generation has a significantly higher online affinity with user behavior and price transparency. Due to the large volumes of money that are inherited across generations – according to estimates for Germany, €2.1 trillion will be inherited between 2015 and 2024 – this change will have a significant impact on the industry.

The resulting changes do not affect banks alone. These also affect all those involved in the value chain, especially asset managers and stock exchanges. We assume that the relevant investors in Germany have liquid assets of €1 trillion. As already described, the total costs of traditional asset management generally amount to at least 2% of the investment volume or, correspondingly, €20 billion per year. In the future, with the improvement of efficiencies, it will be possible to offer private wealth management with a differentiated level of personal advice- profitably for 1% – i.e. there is a potential for disruption of €10 billion per year. In the disruption scenario, two €10 billion-questions will have to be answered: 1) how fast will the efficiency potential of €10 billion per year be lifted; and 2) who earns the remaining €10 billion?

However, the future will not belong solely to robo-advisers. Rather, these developments pave the way for digital wealth management as a profitable business model for existing players. This means the rise of private wealth management for investors with liquid assets of €10 thousand to €1 million, which uses a rule-based investment approach relying on  ETFs to increase efficiency potential and combines this in a hybrid model with comprehensive online and offline advice. The combination of scaled investment processes with personal advice, which efficiently combines online and offline formats, will be the decisive success factor here.

Despite these opportunities for banks and financial services providers, who are adapting quickly to the new conditions, the upcoming disruption will initially lead to massive changes in the market. Two aspects will have to be addressed for the successful rise of digital wealth management. On the one hand, the exploitation of the efficiency potential through the extensive standardization of portfolio management, the digitization of processes and the seamless integration of online and offline customer interaction is important. The second aspect, which is critical to customer loyalty and business model profitability, is a new form of customer focus that breaks away from liquid assets and focuses on the investor and their family’s advisory needs.

Examples of concrete project approaches for banks with traditional retail businesses include:

  • Cost-efficient mapping of smaller asset volumes, which are typically not profitable, by using robo-advisers, possibly as a white label solution
  • Changing from an active to a standardized passive investment approach to increase efficiency
  • Expanding customer advice on all asset components – i.e. liquid and illiquid assets – based on digitized support processes

The key to the success of a digital wealth management strategy and the implementation of these measures will be a comprehensive transformation process that accompanies the introduction and use of new technologies and prepares employees for new roles and tasks within customer interaction. The development and direction of this transformation will be the core task of senior management in preparation for the new world of digital wealth management.