Next Gen Wealth Management Architecture..(3/3)

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.

The Four Key Business Tenets – 

How well a WM firm harness technology determines it’s overall competitive advantage. When advisors can get seamless access to a variety of data, it can help them in manifold ways. For example it helps them make better decisions for their clients as well as make productive use of the day by having the right client data at their fingertips with the push of a button or by means of an intuitive user interface. Similarly, greater access to their portfolios gives clients an more engaging and unified experience.

So, to recap the four strategic goals that WM firms need to operate towards – 

1. Increase Client Loyalty by Digitizing Client Interactions –  WM Clients who use services like Uber, Zillow, Amazon etc in their daily lives are now very vocal in demanding a seamless experience across all of the WM services using digital channels.  The vast majority of  WM applications still lag the innovation cycle, are archaic & are still separately managed. The net issue with this is that the client is faced with distinct user experiences ranging from client onboarding to servicing to transaction management. There is a crying need for IT infrastructure modernization ranging across the industry from Cloud Computing to Big Data to microservices to agile cultures promoting techniques such as a DevOps approach to building out these architectures. Such applications need to provide anticipatory or predictive capabilities at scale while understand the specific customers lifestyles, financial needs & behavioral preferences. 
2. Generate Optimal Client Experiences –  In most existing WM systems, siloed functions have led to brittle data architectures operating on custom built legacy applications. This problem is firstly compounded by inflexible core banking systems and secondly exacerbated by a gross lack of standardization in application stacks underlying ancillary capabilities. These factors inhibit deployment flexibility across a range of platforms thus leading to extremely high IT costs and technical debut. The consequence is that these inhibit client facing applications from using data in a manner that constantly & positively impacts the client experience. There is clearly a need to provide an integrated digital experience across a global customer base. And then to offer more intelligent functions based on existing data assets. Current players do possess a huge first mover advantage as they offer highly established financial products across their large (and largely loyal & sticky) customer bases, a wide networks of physical locations, rich troves of data that pertain to customer accounts & demographic information. However, it is not enough to just possess the data. They must be able to drive change through legacy thinking and infrastructures as things change around the entire industry as it struggles to adapt to a major new segment – the millenials – who increasingly use mobile devices and demand more contextual services as well as a seamless and highly analytic driven & unified banking experience – akin to what they commonly experience via the internet – at web properties like Facebook, Amazon, Google or Yahoo etc
3. Automate Back & Mid Office Processes Across the WM Value Chain – The needs to forge a closer banker/client experience is not just driving demand around data silos & streams themselves but also forcing players to move away from paper based models to more of a seamless, digital & highly automated model to rework a ton of existing back & front office processes – which is the weakest link in the chain. These processes range from risk data aggregation, supranational compliance (AML,KYC, CRS & FATCA), financial reporting across a range of global regions & Cyber Security. Can the Data architectures & the IT systems  that leverage them be created in such a way that they permit agility while constantly learning & optimizing their behaviors across national regulations, InfoSec & compliance requirements? Can every piece of actionable data be aggregated,secured, transformed and reported on in such a way that it’s quality across the entire lifecycle is guaranteed? 
4. Tune existing business models based on client tastes and feedback – While Automation 1.0 focuses on digitizing processes, rules & workflow as stated above; Automation 2.0 implies strong predictive modeling capabilities working at large scale – systems that constantly learn and optimize products & services based on client needs & preferences. The clear ongoing theme in the WM space is constant innovation. Firms need to ask themselves if they are offering the right products that cater to an increasingly affluent yet dynamic clientele. This is the area where firms need to show that they can compete with the FinTechs (Wealthfront, Nutmeg, Fodor Bank et al) to attract younger customers.

Now that we have covered the business bases, what foundational technology choices comprise the satisfaction of the above? Lets examine that first at a higher level and then in more detail.

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 & 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 & deployment
• The Architecture shall support a high degree of data agility and data intelligence. The end goal being that that every customer click, discussion & 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 & omni-channel transactions while working across 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 & data elements based on changing business requirements 
• The Architecture shall support standardization across application stacks, toolsets for development & 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 or an 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 to 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 

Lets examine each of the tiers starting from the lowest –

Infrastructure Tier –

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. These capabilities include –

IaaS (infrastructure-as-a-service) to provision compute, network & storage, PaaS (platform-as-a-service) to develop applications & exposing their business services as  SaaS (software-as-a-service) via APIs.

Choosing Cloud based infrastructure – whether that is secure public cloud  (Amazon AWS or Microsoft Azure) or an internal private cloud (OpenStack etc) or even a hybrid approach is a safe and sound bet for WM applications. Business innovation and transformation are best enabled by a cloud based infrastructure.

Data Tier – 

While banking data tiers are usually composed of different technologies like RDBMS, EDW (Enterprise Data Warehouses), CMS (Content Management Systems) & Big Data etc. My recommendation for the target state is largely dominated by a Big Data Platform powered by Hadoop. Given the focus of the digital Wealth Manager to leverage algorithmic asset management and providing 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.  

Financial services in general and Wealth Management specifically deal with manifold data types ranging from Customer Account data, Transaction Data, Wire Data, Trade Data, Customer Relationship Management (CRM), General Ledger and other systems supporting core banking functions. When one factors in social media feeds, mobile clients & other non traditional data types, the challenge is   

The reasons for choosing  Hadoop as the dominant technology in the data tier are the below – 

1. Hadoop’s ability to ingest and work with all the above kinds of data & more (using the schema on read method) has been proven at massive scale. Operational data stores are being built on Hadoop at a fraction of the cost & effort involved with older types of data technology (RDBMS & EDW)
2. The ability to perform multiple types of processing on a given data set. This processing varies across batch, streaming, in memory and realtime which greatly opens up the ability to create, test & deploy closed loop analytics quicker than ever before
3. The DAS (Direct Attached Storage) model that Hadoop provides fits neatly in with the horizontal scale out model that the services, UX and business process tier leverage. This keeps cuts Capital Expenditure  to a bare minimum.
4. The ability to retain data for long periods of time thus providing WM applications with predictive models that can reason on historical data
5. Hadoop provides the ability to run a massive volumes of models in a very short amount of time helps with modeling automation
6. Due to it’s parallel processing nature, Hadoop can run calculations (pricing, risk, portfolio, reporting etc) in minutes versus the hours it took using older technology
7. Hadoop has to work with existing data investments and to augment them with data ingestion & transformation leaving EDW’s to perform complex analytics that they excel at – a huge bonus.

Services Tier –

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.Given that WM Platforms are some of the most complex financial applications out there, this also has the ancillary benefit of leaving different teams – digital channels, client on boarding, bill pay, transaction management & mid/back office teams to develop and update their components largely independent of other teams. Thus a large monolithic WM enterprise platform is decomposed into its constituent services which are loosely coupled and each is focused on one independent & autonomous business task only. The word ‘task’ here referring to a business capability that has tangible business value.

A highly scalable, open source & industry leading platform as a service (PaaS) like Red Hat’s OpenShift 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. Leveraging a PaaS such as OpenShift provides a way to help cut the “technical debt” that has plagued both developers and IT Ops. OpenShift provides the right level of abstraction to encapsulate microservices via it’s native support for Docker Containers. This also has the concomitant advantage of standardizing application stacks, streamlining deployment pipelines thus leading the charge to a DevOps style of building applications. 

Further I recommend that service designer take the approach that their micro services can be deployed in a SaaS application format going forward – which usually implies taking an API based approach.

Now, the services tier has the following global responsibilities – 

1. Promote a SOA style of application development
2. Support component endpoint invocation via standards based REST APIs
3. Promote a cloud, OS & ,development language agnostic style of application development
4. Promote Horizontal scaling and resilience

Predictive Analytics & Business Process Tier – 

Though segments of the banking industry have historically been early adopters of analytics, the wealth management space has largely been a laggard. However, the large datasets that are prevalent in WM as well as the need to drive customer interaction & journeys, risk & compliance reporting, detecting fraud etc calls for a strategic relook at this space. 

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. Predictive Analytics can be leveraged across the value chain of WM – ranging from new customer acquisition to customer journey to the back office. More recently these techniques have found increased rates of adoption with enterprise concerns from cyber security to telemetry data processing.

Though most large banks do have pockets of BPM implementations that are adding or beginning to add significant business value, an enterprise-wide re-look at the core revenue-producing activities is called for, as is a deeper examination of driving competitive advantage. BPM now has evolved into more than just pure process management. Meanwhile, other disciplines have been added to BPM — which has now become an umbrella term. These include business rules management, event processing, and business resource planning.

WM firms are fertile ground for business process automation, since most managers across their various lines of business are simply a collection of core and differentiated processes. Examples are private banking (with processes including onboarding customers, collecting deposits, conducting business via multiple channels, and compliance with regulatory mandates such as KYC and AML); investment banking (including straight-through-processing, trading platforms, prime brokerage, and compliance with regulation); payment services; and portfolio management (including modeling model portfolio positions and providing complete transparency across the end-to-end life cycle). The key takeaway is that driving automation can result not just in better business visibility and accountability on behalf of various actors. It can also drive revenue and contribute significantly to the bottom line.

A business process system should allow an IT analyst or customer or advisor to convey their business process by describing the steps that need to be executed in order to achieve the goal (and explain the order of those steps, typically using a flow chart). This greatly improves the visibility of business logic, resulting in higher-level and domain-specific representations (tailored to finance) that can be understood by business users and should be easier to monitor by management. Again , leveraging a PaaS such as OpenShift in conjunction with an industry leading open source BPMS (Business Process Management System) such as JBOSS BPMS provides an integrated BPM capability that can create cloud ready and horizontally scalable business processes.

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  – 

1. 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 where it makes business sense.
2. Understand client personas and integrate with the business & predictive analytic tier in such a way that the UX is deeply integrated with the overall information architecture
3. Provide advanced visualization (wireframes, process control, social media collaboration) and cross partner authentication & single sign on
4. The UX shall also be designed is such a manner that it’s design, development & ongoing enhancement follow an agile & DevOps method.

Putting it all together- 

How do all of the above foundational technologies (Big Data, UX,Cloud, BPM & Predictive Analytics) help encourage a virtuous cycle?

1. WM Applications that are omnichannel, truly digital and thus highly engaging  have been proven to drive higher rates of customer interaction
2. Higher and more long-lived  customer interactions (across channels) drives increased product uptake & increased revenue per client while constantly producing more valuable data
3. Increased & relevant data volumes in turn help improve predictive capabilities of customer models as they can constantly be harnessed to drive higher insight and visibility into a range of areas – client tastes, product fit & business strategy
4. These in turn provide valuable insights to drive improvements in products & services
5. Rinse and Repeat – constantly optimize and learn on the go

This cycle needs to be accelerated helping the creation of a learning organization which can outlast competition by means of a culture of unafraid experimentation and innovation.

Summary

New Age technology platforms designed around the four key business needs  (Client experience, Advisor productivity, a highly Automated backoffice & a culture of constant innovation)  will create immense operational efficiency, better business models, increased relevance and ultimately drive revenues. These will separate the visionaries, leaders from the laggards in the years to come.