The previous two posts have covered the business & strategic need for Wealth Management IT applications to reimagine themselves to support their clients. How is this to be accomplished and what does a candidate architectural design pattern look like? What are the key enterprise wide IT concerns? This third & final post (3/3) tackles these questions. An additional following post will return our focus to the business end when we focus on strategic recommendations to industry CXO’s.
Ten Key Overall System Architecture Tenets
The design and architecture of a solution as large and complex as a WM enterprise is a multidimensional challenge. The below illustration catalogs the four foundational capabilities of such a system: Omnichannel, Mobile Native Experiences, Massive Data processing capabilities, Cloud Computing, and Predictive Analytics – all operating at scale.
Illustration – Top Level Architectural Components
Here are some of the key global design characteristics for a common architecture framework:
- The Architecture shall support automated application delivery, configuration management, and deployment.
- The Architecture shall support a high degree of data agility and data intelligence. The end goal being that that every customer click, discussion, and preference shall drive an analytics infused interaction between the advisor and the client.
- The Architecture shall support algorithmic capabilities that enable the creation of new services like automated (or Robo) advisors.
- The Architecture shall support a very high degree of scale across many numbers of users, interactions, and omni-channel transactions while working across a global infrastructure.
- The Architecture shall support deployment across cost efficient platforms like a public or private cloud. In short, the design of the application shall not constrain the available deployment options – which may vary because of cost considerations. The infrastructure options supported shall range from virtual machines to docker based containers – whether running on a public cloud, private cloud, or in a hybrid cloud.
- The Architecture shall support small, incremental changes to business services and data elements based on changing business requirements.
- The Architecture shall support standardization across application stacks and toolsets for development and data technology to a high degree.
- The Architecture shall support the creation of a user interface that is highly visual and feature rich from a content standpoint when accessed across any device.
- The Architecture shall support an API based model to invoke any interaction – by a client, advisor, or a business partner.
- The Architecture shall support the development and deployment of an application that encourages a DevOps-based approach.
- The Architecture shall support the easy creation of scalable business processes that natively emit business metrics from the time they’re instantiated and throughout their lifecycle.
Given the above list of requirements – the application architecture that is a “best fit” is shown below.
Illustration – Target State Architecture for Digital Wealth Management
Cloud Computing across it’s three main delivery models (IaaS, PaaS & SaaS) is largely a mainstream endeavor in financial services and no longer an esoteric adventure only for brave innovators. A range of institutions are either deploying or testing cloud-based solutions that span the full range of cloud delivery models. Business innovation and transformation are best enabled by a cloud based infrastructure.
While banking data tiers are usually composed of different technologies like RDBMS, EDW (Enterprise Data Warehouses), CMS (Content Management Systems) & Big Data etc.Given the focus of a Digital Wealth Manager has in leveraging algorithmic asset management & predictive analytics to create tailored & managed portfolios for their clients – Hadoop is a natural fit as it is fast emerging as the platform of choice for analytic applications.
The overall goals of the services tier are to help design, develop, modify and deploy business components in such a way that overall WM application delivery follows a continuous delivery/deployment (CI/CD) paradigm.
A highly scalable, open source & industry leading platform as a service (PaaS) is recommended as the way of building out and hosting this tier. Microservices have moved from the webscale world to fast becoming the standard for building mission critical applications in many industries.
Predictive Analytics and Business Process Tier
Techniques like Machine Learning, Data Science & AI feed into core business processes thus improving them. For instance, Machine Learning techniques support the creation of self improving algorithms which get better with data thus making accurate business predictions. Thus, the overarching goal of the analytics tier should be to support a higher degree of automation by working with the business process and the services tier.
User Experience Tier
The UX (User Experience) tier fronts humans – client. advisor, regulator, management and other business users across all touchpoints. An API tier is provided for partner applications and other non-human actors to interact with business service tier.
The UX tier has the following global responsibilities :
- Provide a consistent user experience across all channels (mobile, eBanking, tablet, etc.) in a way that is a seamless and non-siloded view. The implication is that clients should be able to begin a business transaction in channel A and be able to continue them in channel B.
- Understand client personas and integrate with the business & predictive analytic tier
- Provide advanced visualization (wireframes, process control, social media collaboration)
Putting it All Together
- WM Applications that are omnichannel, truly digital, and thus highly engaging have been proven to drive higher rates of customer interaction.
- Higher and more long-lived customer interactions (across channels) drive increased product uptake and increased revenue per client while constantly producing more valuable data.
- Increased and relevant data volumes in turn help improve the predictive capabilities of customer models as they can be constantly harnessed to drive higher insight and visibility into a range of areas: client tastes, product fit, business strategy.
- These in turn provide valuable insights to drive improvements in products and services.
- Rinse and Repeat – constantly optimize and learn on the go.