A Reference Architecture for The Open Banking Standard..

This is the second in a series of four posts on the Open Banking Standard (OBS) in the UK. This second post will briefly look at the strategic drivers for banks while proposing an architectural style or approach for incumbents to drive change in their platforms to achieve OBS Compliance. We will examine the overall data layer implications in the next post. The final post will look at key strategic levers and possible business models that the standard could help banks to drive innovation towards. 

Introduction…

The Open Banking Standard will steward the development of layers of guidelines (API interoperability standards, data security & privacy and governance) which primarily deal with data sharing in banking. The belief is that this regulation will ultimately spur open competition and unlock innovation. For years, the industry has grappled with fundamental platform issues that are native to every domain of banking. Some of these include systems are siloe-d by function, platforms that are inflexible in responding to rapidly changing market conditions & consumer tastes. Bank IT is perceived by the business to be glacially slow in responding to their needs.

The Open Banking Standard (OBS) represents a vast opportunity for banking organizations in multiple ways. First off, Bank IT has the luxury of using the regulatory mandate to slowly re-architect hitherto inflexible and siloe-d business systems. Secondly, doing so will enable Banks to significantly monetize their vast data resources in several key business areas.  

This will need to change with the introduction of Open Banking Standard. Banks that do not change will not be able to derive and sustain a competitive advantage. PSD2 Compliance (Payment Systems Directive – 2) – which will be mandated by the EU is one of the first layers in the OBS. Further layers will include API standards definitions for business processes (e.g View Account, Transfer Funds, Chargebacks, Dispute Handling etc). 

The OBWG (Open Banking Working Group) standards include the following key constituencies & their requirements [1] – 

 1. Customers: defined as account holders & businesses who agree to sharing their data & any publishers who share open datasets 

2. Data attribute providers: defined as banks & other financial services providers whose customers produce data as part of daily banking activities 

3. Third parties: Interested developers, financial services startups aka FinTechs, and any organisations (e.g  Retail Merchants) who can leverage the data to provide new views & products  

It naturally follows from the above, that the key technical requirements of the framework will include:

1. A set of Data elements, API definitions and Security Standards to provide both data security and a set of access restrictions 

2. A Governance model, a body which will develop & oversee the standards 

3. Developer resources, which will enable third parties to discover, educate and experiment.

The Four Strategic Drivers in the Open Bank Standard …

Clearly the more intelligently a firm harness technology (in pursuit of OBS compliance goals) will determine it’s overall competitive advantage.  This important to note since a range of players across the value chain (the above Third Parties as designated by the standard) can now obtain seamless access to a variety of data. Once obtained the data can help the Third Parties reimagine it in manifold ways. For example they can help consumers make better personal financial decisions for their clients at the expense of the Banks owning the data. For instance, FinTechs have generally been able to make more productive use of client data. They do this by providing clients with intuitive access to cross asset data, tailoring algorithms based on behavioral characteristics  and by providing clients with a more engaging and unified experience.

So, the four strategic business goals that OBS compliant architectures need to solve in the long run – 

1. Digitize The Customer Journey –  Bank 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 their banking ervices using digital channels.  The vast majority of Bank applications still lag the innovation cycle, are archaic & are 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. Such applications need to provide anticipatory or predictive capabilities at scale while understand the specific customers lifestyles, financial needs & behavioral preferences. 
2. Provide Improved Access to Personal Financial Management & Improved Lending Processes  –  Provide consumers with a single aggregated picture of all their accounts. Also improve lending systems by providing more efficient access to loans by incorporating a large amount of contextual data in the process.
3. Automate Back & Mid Office Processes Across Lending, Risk, Compliance & Fraud – 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. 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 the initial build out of the core architecture may seem to focus on digitizing interactions and exposing data via APIs. What follows fast is strong predictive modeling capabilities working at large scale where systems need to constantly learn and optimize their interactions, responsiveness & services based on client needs & preferences. 

The Key System Architecture Tenets…

The design and architecture of a solution as large and complex as a reference architecture for Open Banking is a multidimensional challenge and it will vary at every institution based on their existing investments, vendor products & overall culture. 

The OBS calls out the following areas of data as being in scope – Customer transaction data, customer reference data, aggregated data and sensitive commercial data. A thorough review of the OBWSG standard leads one to suggest a logical reference architecture as called out below.

Based on all the above, the Open Bank Architecture shall – 

• Support an API based model to invoke any business process or data elements based on appropriate security  by a third party . E.g client or an advisor or a business partner
• Support the development and deployment of an application that encourages a DevOps based approach
• Support the easy creation of scalable business processes (e.g. client on boarding, KYC, Payment dispute check etc) that natively emit business metrics from the time they’re instantiated to throughout their lifecycle
• Support automated application delivery, configuration management & deployment
• 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 Bank and the client
• Support algorithmic capabilities that enable the creation of new services like automated (or Robo) advisors
• Support a very high degree of scale across many numbers of users, interactions & omni-channel transactions while working across global infrastructure
• 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
• Support small, incremental changes to business services & data elements based on changing business requirements 
• Support standardization across application stacks, toolsets for development & data technology to a high degree
• Shall support the creation of a user interface that is highly visual and feature rich from a content standpoint when accessed across any device

Reference Architecture…

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.

Given the above list of requirements – the application architecture that is a “best fit” is shown below.

                   Illustration – Candidate Reference Architecture for the Open Bank Standard

Lets examine each of the tiers starting from the lowest –

Infrastructure Layer…

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 these applications. Business innovation and transformation are best enabled by a cloud based infrastructure – whether public or private.

Data Layer…

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 OBSWG target state is largely dominated by a Big Data Platform powered by Hadoop technology. The vast majority of initial applications recommended by the OBSWG call out for predictive analytics to create tailored Customer Journeys. Big Data is a natural fit as it is fast emerging as the platform of choice for analytic applications.

Financial services firms 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 not just one of data volumes but also variety and the need to draw conclusions from fast moving data streams by commingling them with years of historical data.

The reasons for choosing Big Data 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 Layer…

The overall goals of the OBSWG 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) is recommended as the way of building out and hosting banking business applications at this layer.  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 adopt such an approach that the applications are microservices native. This implies a deployment approach similar to a SaaS model where capabilities can be exposed via  APIs.

Now, the services tier has the following global responsibilities – 

1. Promote a Microservices/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 Layer…

Though segments of the banking industry have historically been early adopters of analytics, areas being targeted by the OBSWG – Retail lines of business &Payments have generally been laggards. However, the large datasets that are prevalent in Open Bank Standard world 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 the Open Bank Standard – 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.

Another area is improved automation via light weight business process management (BPM). 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.

Financial Services firms general 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.

API & UX Layer…

The API & UX (User Experience) tier fronts humans – clients. business partners, regulators, internal management and other business users across omnichannel touchpoints. A standards based API tier is provided for partner applications and other non-human actors to interact with business service tier. Once the OBSWG defines the exact protocols, data standards & formats – this should be straightforward to implement.

The API/UX tier has the following global responsibilities  – 

1. Provide a seamless experience across all channels (mobile, eBanking, tablet etc) in a way that is a continuous and non-siloed. 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 API is loosely yet logically integrated with the overall information architecture
3. Provide advanced visualization (wireframes, process control, social media collaboration) and cross partner authentication & single sign on
4. Both the API & UX shall also be designed is such a manner that their design, development & ongoing enhancement lends themselves to an Agile & DevOps methodology.

It can all come together…

In most existing Banking 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 capabilities like customer journey, improved lending & fraud detection. 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

Summary

Platforms designed technology platforms designed around the four key business needs   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. The Open Bank Standard will be a catalyst in this immense disruption. 

REFERENCES…

[1] The Open Banking Standard –
https://theodi.org/open-banking-standard