Step 1 – Collect Knowledge Resources And Essential Skillsets
Robo advisory is a specialised business. To build one, the critical path from conception to implementation begins with acquiring the requisite knowledge and skillsets. It is also about building a dynamic team of people who can work seamlessly together to solve challenging technological issues.
Our white label robo-advisors use a goal-based approach to investing. Users are encouraged to set financial goals, which the advisor helps them identify, optimise in terms of monetary allocations, and remain on track towards. The artificial intelligence (AI) research base that enables our advisors to provide such services involves deep learning and machine learning. We use various algorithms and models to fulfill a variety of functions – e.g. the apriori algorithm for data synthesis, unsupervised machine learning algorithms for goal prediction and suggestion, and artificial neural networks for determining the optimal monetary amount needed to achieve a given goal.
However, this technology does not build itself. We have organised a research and development team fit for the job and continue to recruit top talent with a knack for innovating fintech creatively. The skillsets of our personnel include UI/UX design, software engineering, backend/frontend development, dataset analysis, and automated investment and AI research.
Step 2 – Identify Target Market
Whether you are contemplating building your robo-advisor or shopping the market for the right white label solution for your needs, it is important to understand your target market. Who are your customers? What are their investment preferences? Pain points? Demographics? Are there patterns to their investment behaviour? Your answers to these questions should form the basis for the type of solution you end up acquiring.
Our target market includes B2B and B2B2C clients. Our products are created for delivery to two major market segments, namely, high-net-worth individuals and affluent and retail investors. For the high-net-worth individual segment, the need we are addressing surrounds the working efficiency of relationship managers, a problem area that we knew fintech had the potential to solve. Within the affluent and retail investor segment, we saw the need for a goal-based investment approach for our clients’ customers, an approach flexible enough to appeal to the sensibilities and imaginative capacity of such investors.
It was important for us to differentiate our technology across segments, as the needs of, say, a wealth manager’s private clients are not going to be the same as the needs of a commercial bank’s retail customers. In other words, there is no “one size fits all” solution and our market research confirmed this.
Step 3 – Determine Desired Product Features
Once you understand your customers and their needs, it is time to identify the kind of product features and capabilities you would like your robo-advisor to have. This is the step where you “give the people what they want,” as it were.
The features we have developed for our robo-advisors are in view of two general goals: creating a natural or intuitive investing experience and delivering a pleasing aesthetical experience. In support of such goals, our feature list is extensive and growing annually. Below is a small sampling:
Walk of Life: allows a user to view their life in financial terms, projects a user’s income over time, and shows incrementally how they will reach their stated financial goals.
People Like Me: suggests financial goals to users based on the goals being worked toward by other users of a similar demographic and geographic proximity.
Health Check: alerts users to keep their goals realistic and achievable and notifies them if their goals become unattainable given their financial situation at the time.
Risk Questionnaire: determines a user’s risk tolerance for purposes of making appropriate portfolio recommendations and placing their portfolio accurately along the efficient frontier.
Part 4 – Identify Solutions Providers
Finding a suitable partner can be daunting. Not only should they have demonstrated product knowledge, technical ability, and unique differentiators, but they should also have a good industry reputation, a strong commitment to ethical standards of excellence, and a win-win approach to doing business. These latter considerations form the basis for trust, an essential ingredient for mutually beneficial long-term partnerships.
In imagining ourselves as a solutions provider, we thought about those qualities we would want if we were our clients. The first is to be comprehensive – e.g. we deliver on six core product dimensions, namely, UI branding templates, customised methodologies for portfolio allocation and rebalancing, backend system integrations for execution and custody, responsive HTML5 websites optimised across devices, machine learning algorithms that update automatically from user data and public statistics, and a browser-based dashboard for easy administrative management.
The second is to be customer-centric. We decided at conception to customise all of our white-label solutions to the exact specifications of our customers. In other words, we do not provide “off-the-shelf” products. A customised approach requires us to consistently innovate improvements based on the shifting needs of the market and continuously learn from our clients about their priorities.
The third is to manage expectations about the experience of working with us to minimise uncertainty and build trust. Our approach – Consult -> Design -> Build -> Execute -> Custodise – follows an intuitive process that results in the delivery of robo-advisors that fulfill all customer requirements and expectations.
Part 5 – Build, Integrate, And Launch Product
Once the groundwork is laid, the actual work of building and launching a robo-advisor is nuanced and meticulous. It requires rounds of iterations and phases of product adjustment and testing. It involves integrating new technology with existing systems and ensuring the transition to robo-advisory doesn’t interrupt current processes. However, granting proper due diligence, the provision of robo-advisory services to your customers is close at hand.
Our robo-advisors are productive; they perform goal/risk assessment, asset modelling, portfolio allocation, risk/return simulation, onboarding, KYC and AML, account and goal funding, portfolio rebalancing, cash and fee management, order management, performance monitoring, and reporting and auditing. Building an end-to-end solution and optimising it for integration with our clients’ backend systems is a challenge, but one we feel is necessary to undertake to really draw out the potential of what robo-advisory has to offer.
Our timeline for delivering a white label solution takes between 9-12 weeks in total. We begin with relationship building and discussions about desired product functionality and specifications. We then commence the building and integration phase – involving everything from UX/UI adaptation and algorithm development to platform parameterisation and integration. We wrap up with deployment, which includes user acceptance testing and go-live training for relevant client personnel.
To meet our deadlines and our quality standards, we use an agile project management model that allows us to iterate through the process of development. The role of iteration is important because it discovers and resolves issues incrementally and integrates customer feedback as it is received after each product release.
Call To Action:
If you are interested in learning more about our solutions or approaches to building robo-advisors, please do get in touch. We would love to hear from you.