Design and Architecture of A Robo-Advisor Platform..(3/3)

This three part series explores the automated investment management or the “Robo-advisor” (RA) movement. The first post in this series @- http://www.vamsitalkstech.com/?p=2329 – discussed how Wealth Management has been an area largely untouched by automation as far as the front office is concerned. As a result, automated investment vehicles have largely begun changing that trend and they helping create a variety of business models in the industry esp those catering to the Millenial Mass Affluent Segment. The second post @- http://www.vamsitalkstech.com/?p=2418  focused on the overall business model & main functions of a Robo-Advisor (RA). This third and final post covers a generic technology architecture for a RA platform.

Business Requirements for a Robo-Advisor (RA) Platform…

Some of the key business requirements of a RA platform that confer it advantages as compared to the manual/human driven style of investing are:

  • Collect Individual Client Data – RA Platforms need to offer a high degree of customization from the standpoint of an individual investor. This means an ability to provide a preferably mobile and web interface to capture detailed customer financial background, existing investments as well as any historical data regarding customer segments etc.
  • Client Segmentation – Clients are to be segmented  across granular segments as opposed to the traditional asset based methodology (e.g mass affluent, high net worth, ultra high net worth etc).
  • Algorithm Based Investment Allocation – Once the client data is collected,  normalized & segmented –  a variety of algorithms are applied to the data to classify the client’s overall risk profile and an investment portfolio is allocated based on those requirements. Appropriate securities are purchased as we will discuss in the below sections.
  • Portfolio Rebalancing  – The client’s portfolio is rebalanced appropriately depending on life event changes and market movements.
  • Tax Loss Harvesting – Tax-loss harvesting is the mechanism of selling securities that have a loss associated with them. By doing so or by taking  a loss, the idea is that that client can offset taxes on both gains and income. The sold securities are replaced by similar securities by the RA platform thus maintaining the optimal investment mix.
  • A Single View of a Client’s Financial History- From the WM firm’s standpoint, it would be very useful to have a single view capability for a RA client that shows all of their accounts, interactions & preferences in one view.

User Interface Requirements for a Robo-Advisor (RA) Platform…

Once a customer logs in using any of the digital channels supported (e.g. Mobile, eBanking, Phone etc)  – they are presented with a single view of all their accounts. This view has a few critical areas – Summary View (showing an aggregated view of their financial picture), the Transfer View (allowing one to transfer funds across accounts with other providers).

The Summary View lists the below

  • Demographic info: Customer name, address, age
  • Relationships: customer rating influence, connections, associations across client groups
  • Current activity: financial products, account interactions, any burning customer issues, payments missed etc
  • Customer Journey Graph: which products or services they are associated with since the time they became a customer first etc,

Depending on the clients risk tolerance and investment horizon, the weighted allocation of investments across these categories will vary. To illustrate this, a Model Portfolio and an example are shown below.

Algorithms for a Robo-Advisor (RA) Platform…

There are a variety of algorithmic approaches that could be taken to building out an RA platform. However the common feature of all of these is to –

  • Leverage data science & statistical modeling to automatically allocate client wealth across different asset classes (such as domestic/foreign stocks, bonds & real estate related securities) to automatically rebalance portfolio positions based on changing market conditions or client preferences. These investment decisions are also made based on detailed behavioral understanding of a client’s financial journey metrics – Age, Risk Appetite & other related information. 
  • A mixture of different algorithms can be used such as Modern Portfolio Theory (MPT), Capital Asset Pricing Model (CAPM), the Black Litterman Model, the Fama-French etc. These are used to allocate assets as well as to adjust positions based on market movements and conditions.
  • RA platforms also provide 24×7 tracking of market movements to use that to track rebalancing decisions from not just a portfolio standpoint but also from a taxation standpoint.

Model Portfolios…

  1. Equity  

             A) US Domestic Stock – Large Cap, Medium Cap , Small Cap, Dividend Stocks 

             B) Foreign Stock – Emerging Markets, Developed Markets

       2. Fixed Income

             A) Developed Market Bonds 

             B) US Bonds

             C) International Bonds

             D) Emerging Markets Bonds

      3. Other 

             A) Real Estate  

             B) Currencies

             C) Gold and Precious Metals

             D) Commodities

       4. Cash

Sample Portfolios – for an aggressive investor…

  1. Equity  – 85%

             A) US Domestic Stock (50%) – Large Cap – 30%, Medium Cap – 10% , Small Cap – 10%, Dividend Stocks – 0%

             B) Foreign Stock – (35%) –  Emerging Markets – 18%, Developed Markets – 17% 

       2. Fixed Income – 5%

             A) Developed Market Bonds  – 2%

             B) US Bonds – 1%

             C) International Bonds – 1%

             D) Emerging Markets Bonds – 1%

      3. Other – 5%

             A) Real Estate  – 3%

             B) Currencies – 0%

             C) Gold and Precious Metals – 0%

             D) Commodities – 2%

       4. Cash – 5%

Technology Requirements for a Robo-Advisor (RA) Platform…

An intelligent RA platform has a few core technology requirements (based on the above business requirements).

  1. A Single Data Repository – A shared data repository called a Data Lake is created, that can capture every bit of client data (explained in more detail below) as well as external data. The RA datalake provides more visibility into all data to a variety of different stakeholders. Wealth Advisors access processed data to view client accounts etc. Clients can access their own detailed positions,account balances etc. The Risk group accesses this shared data lake to processes more position, execution and balance data.  Data Scientists (or Quants) who develop models for the RA platform also access this data to perform analysis on fresh data (from the current workday) or on historical data. All historical data is available for at least five years—much longer than before. Moreover, the Hadoop platform enables ingest of data across a range of systems despite their having disparate data definitions and infrastructures. All the data that pertains to trade decisions and lifecycle needs to be made resident in a general enterprise storage pool that is run on the HDFS (Hadoop Distributed Filesystem) or similar Cloud based filesystem. This repository is augmented by incremental feeds with intra-day trading activity data that will be streamed in using technologies like Sqoop, Kafka and Storm.
  2. Customer Data Collection – Existing Financial Data across the below categories is collected & aggregated into the data lake. This data ranges from Customer Data, Reference Data, Market Data & other Client communications. All of this data, can be ingested using a API or pulled into the lake from a relational system using connectors supplied in the RA Data Platform. Examples of data collected include – Customer’s existing Brokerage accounts, Customer’s Savings Accounts, Behavioral Finance Suveys and Questionnaires etc etc. The RA Data Lake stores all internal & external data.
  3. Algorithms – The core of the RA Platform are data science algos. Whatever algorithms are used – a few critical workflows are common to them. The first is Asset Allocation is to take the customers input in the “ADVICE” tab for each type of account and to tailor the portfolio based on the input. The others include Portfolio Rebalancing and Tax Loss Harvesting.
  4. The RA platform should be able to store market data across years both from a macro and from an individual portfolio standpoint so that several key risk measures such as volatility (e.g. position risk, any residual risk and market risk), Beta, and R-Squared – can be calculated at multiple levels.  This for individual securities, a specified index, and for the client portfolio as a whole.

roboadvisor_design_arch

                      Illustration: Architecture of a Robo-Advisor (RA) Platform 

The overall logical flow of data in the system –

  • Information sources are depicted at the left. These encompass a variety of institutional, system and human actors potentially sending thousands of real time messages per hour or by sending over batch feeds.
  • A highly scalable messaging system to help bring these feeds into the RA Platform architecture as well as normalize them and send them in for further processing. Apache Kafka is a good choice for this tier. Realtime data is published by a range of systems over Kafka queues. Each of the transactions could potentially include 100s of attributes that can be analyzed in real time to detect business patterns.  We leverage Kafka integration with Apache Storm to read one value at a time and perform some kind of storage like persist the data into a HBase cluster.In a modern data architecture built on Apache Hadoop, Kafka ( a fast, scalable and durable message broker) works in combination with Storm, HBase (and Spark) for real-time analysis and rendering of streaming data. 
  • Trade data is thus streamed into the platform (on a T+1 basis), which thus ingests, collects, transforms and analyzes core information in real time. The analysis can be both simple and complex event processing & based on pre-existing rules that can be defined in a rules engine, which is invoked with Apache Storm. A Complex Event Processing (CEP) tier can process these feeds at scale to understand relationships among them; where the relationships among these events are defined by business owners in a non technical or by developers in a technical language. Apache Storm integrates with Kafka to process incoming data. 
  • For Real time or Batch Analytics, Apache HBase provides near real-time, random read and write access to tables (or ‘maps’) storing billions of rows and millions of columns. In this case once we store this rapidly and continuously growing dataset from the information producers, we are able  to do perform super fast lookup for analytics irrespective of the data size.
  • Data that has analytic relevance and needs to be kept for offline or batch processing can be stored using the Hadoop Distributed Filesystem (HDFS) or an equivalent filesystem such as Amazon S3 or EMC Isilon or Red Hat Gluster. The idea to deploy Hadoop oriented workloads (MapReduce, or, Machine Learning) directly on the data layer. This is done to perform analytics on small, medium or massive data volumes over a period of time. Historical data can be fed into Machine Learning models created above and commingled with streaming data as discussed in step 1.
  • Horizontal scale-out (read Cloud based IaaS) is preferred as a deployment approach as this helps the architecture scale linearly as the loads placed on the system increase over time. This approach enables the Market Surveillance engine to distribute the load dynamically across a cluster of cloud based servers based on trade data volumes.
  • It is recommended to take an incremental approach to building the RA platform, once all data resides in a general enterprise storage pool and makes the data accessible to many analytical workloads including Trade Surveillance, Risk, Compliance, etc. A shared data repository across multiple lines of business provides more visibility into all intra-day trading activities. Data can be also fed into downstream systems in a seamless manner using technologies like SQOOP, Kafka and Storm. The results of the processing and queries can be exported in various data formats, a simple CSV/txt format or more optimized binary formats, json formats, or you can plug in custom SERDE for custom formats. Additionally, with HIVE or HBASE, data within HDFS can be queried via standard SQL using JDBC or ODBC. The results will be in the form of standard relational DB data types (e.g. String, Date, Numeric, Boolean). Finally, REST APIs in HDP natively support both JSON and XML output by default.
  • Operational data across a bunch of asset classes, risk types and geographies is thus available to investment analysts during the entire trading window when markets are still open, enabling them to reduce risk of that day’s trading activities. The specific advantages to this approach are two-fold: Existing architectures typically are only able to hold a limited set of asset classes within a given system. This means that the data is only assembled for risk processing at the end of the day. In addition, historical data is often not available in sufficient detail. Hadoop accelerates a firm’s speed-to-analytics and also extends its data retention timeline
  • Apache Atlas is used to provide Data Governance capabilities in the platform that use both prescriptive and forensic models, which are enriched by a given businesses data taxonomy and metadata.  This allows for tagging of trade data  between the different businesses data views, which is a key requirement for good data governance and reporting. Atlas also provides audit trail management as data is processed in a pipeline in the lake
  • Another important capability that Big Data/Hadoop can provide is the establishment and adoption of a Lightweight Entity ID service – which aids dramatically in the holistic viewing & audit tracking of trades. The service will consist of entity assignment for both institutional and individual traders. The goal here is to get each target institution to propagate the Entity ID back into their trade booking and execution systems, then transaction data will flow into the lake with this ID attached providing a way to do Client 360.
  • Output data elements can be written out to HDFS, and managed by HBase. From here, reports and visualizations can easily be constructed. One can optionally layer in search and/or workflow engines to present the right data to the right business user at the right time.  

Conclusion…

As one can see clearly, though automated investing methods are still in early stages of maturity – they hold out a tremendous amount of promise. As they are unmistakably the next big trend in the WM industry industry players should begin developing such capabilities.

How Robo-Advisors work..(2/3)

Millennials want “finance at their fingertips”..they want to be able to email and text the financial advisors and talk to them on a real-time basis,” – Greg Fleming, Ex-Morgan Stanley
The first post in this series on Robo-advisors touched on the fact that Wealth Management has been an area largely untouched by automation as far as the front office is concerned. Automated investment vehicles have largely begun changing that trend and they helping create a variety of business models in the industry. This three part series explored the automated “Robo-advisor” movement in the first post.This second post will focus on the overall business model & main functions of a Robo-advisor.
Introduction
FinTechs led by Wealthfront and Betterment have pioneered the somewhat revolutionary concept of Robo-advisors. To define the term – a Robo-advisor is an algorithm based automated investment advisor that can provide a range of Wealth Management services tailored to a variety of life situations.
Robo-advisors offer completely automated financial planning services. We have seen how the engine of the Wealth Management business is new customer acquisition. The industry is focused on acquiring the millennial or post millennial HNWI (High Net Worth Investor) generation. The technology friendliness of this group ensures that are the primary target market for automated investment advice. Not just the millenials, anyone who is comfortable with using technology and wants lower cost services can benefit from automated investment planning. However,  leaders in the space such as – Wealthfront & Betterment – have disclosed that their average investor age is around 35 years. [1]
Robo-advisors provide algorithm-based portfolio management methods around investment creation, provide automatic portfolio rebalancing and value added services like tax-loss harvesting as we will see. The chief investment vehicle of choice seems to be low-cost, passive exchange-traded funds (ETFs).

What are the main WM business models

Currently there are a few different business models that are being adopted by WM firms.

  1. Full service online Robo-advisor that is a 100% automated without any human element
  2. Hybrid Robo-advisor model being pioneered by firms like Vanguard & Charles Schwab
  3. Pure online advisor that is primarily human in nature

What do Robo-advisors typically do?

The Robo-advisor can be optionally augmented & supervised by a human adviser. At the moment, owing to the popularity of Robo-advisors among the younger high networth investors (HNWI), a range of established players like Vanguard, Charles Schwab as well as a number of FinTech start-ups have developed these automated online investment tools or have acquired FinTech’s in this space.e.g Blackrock. The Robo-advisor is typically offered as  a free service (below a certain minumum) and typically invests in low cost ETFs.  built using digital techniques – such as data science & Big Data.

Robo_Process

                                  Illustration: Essential functions of a Robo-advisor

The major business areas & client offerings in the Wealth & Asset Management space have been covered in the first post in this series at http://www.vamsitalkstech.com/?p=2329

Automated advisors only cover a subset of all of the above at the moment. The major usecases are as below –

  1. Determine individual Client profiles & preferences—e.g. For a given client profile- determine financial goals, expectations of investment return, diversification etc
  2. Identify appropriate financial products that can be offered either as pre-packaged portfolios or custom investments based on the client profile identified in the first step
  3. Establish correct Investment Mix for the client’s profile – these can included but are not ,limited to equities, bonds, ETFs & other securities in the firm’s portfolios . For instance, placing  tax-inefficient assets in retirement accounts like IRA’s as well as  tax efficient municipal bonds in taxable accounts etc.
  4. Using a algorithmic approach, choose the appropriate securities for each client account
  5. Continuously monitor the portfolio & transactions within it to tune performance , lower transaction costs, tax impacts etc based on how the markets are doing. Also ensure that a client’s preferences are being incorporated so that appropriate diversification and risk mitigation is being performed
  6. Provide value added services like Tax loss harvesting to ensure that the client is taking tax benefits into account as they rebalance portfolios or accrue dividends.
  7. Finally ,ensure the best user experience by handling a whole range of financial services – trading, account administration, loans,bill pay, cash transfers, tax reporting, statements in one intuitive user interface.

000-graph

                             Illustration: Betterment user interface. Source – Joe Jansen

To illustrate these concepts in action, leaders like Wealthfront & Betterment are increasingly adding features where  highly relevant, data-driven advice is being provided based on existing data as well as aggregated data from other providers. Wealthfront now provides recommendations on diversification, taxes and fees that are personalized not only to the specific investments in client’s account, but also tailored to their specific financial profile and risk tolerance. For instance, is enough cash being set aside in the emergency fund ? Is a customer holding too much stock in your employer? [1]

The final post will look at a technology & architectural approach to building out a Robo-advisor. We will also discuss best practices from a WM & industry standpoint in the context of Robo-advisors.

References:

  1. Wealthfront Blog – “Introducing the new Dashboard”

Big Data Driven Disruption – The Robo-Advisor..(1/3)

Wealth Management is the highest growth businesses for any medium to large financial institution. It also is the highest customer touch segment of banking and is fostered on long term (read extremely lucrative advisory) relationships. This three part series explores the automated “Robo-advisor” movement in the first post. We will cover the business background and some definitions . The second post will focus on the overall business model & main functions of a Robo-advisor. The final post will look at a technology & architectural approach to building out a Robo-advisor. We will also discuss best practices from a WM & industry standpoint in the context of Robo-advisors.

roboadvisor

(Image Credit – Forbes)

The term ‘Wealth Management‘ broadly refers to an aggregation of financial services that are typically bespoke and offered to highly affluent clients.  These include financial advisory,  personal investment management, financial advisory, and planning disciplines directly for the benefit of high-net-worth (HNWI) clients.  This term can refer to a wide range of possible functions and business models.

A wealth manager is a specialized financial advisor who helps a client construct an entire investment portfolio and advises on how to prepare for present and future financial needs. The investment portion of wealth management normally entails both asset allocation of a whole portfolio as well as the selection of individual investments. The planning function of wealth management often incorporates tax planning around the investment portfolio as well as estate planning for individuals as well as family estates.

The ability to sign up wealthy individuals & families; then retaining them over the years by offer those engaging, bespoke & contextual services will largely provide growth in the Wealth Management industry in 2016 and beyond.

However,  WM as an industry sector has lagged other areas within banking from a technology & digitization standpoint. Multiple business forces ranging from increased regulatory & compliance demands, digital demands & expectations from younger, technology savvy customers and new Age FinTechs have led to firms slowly begin a makeover process. Let us examine these trends in more detail. 

Business Trends Driving the need for Robo/Automated Investment Advisors –

These trends  are a combination of industry reality as well as changing preferences on behalf of the HNWI clientele –

  1. Growth in the Wealth Management business largely depends on the ability to sign up new clients. Previously WM shops would not be interested in signinup up clients with less than a certain value of investable assets (typical threshold being $ 1 million). However the need to on-ramp these folks onto a long term relationship means being able to offer lower cost automated business models that better fit their mindsets
  2. The mentality of younger clientele has also evolved over the years. These clients are technologically savvy, they largely have a DIY (Do It Yourself) mindset and their digital needs are largely being missed by the wealth management community. This rising segment demands digital services that are highly automated & 24/7 in nature without needing to pay the premium charged by a human advisor
  3. Regulatory, cost pressures are rising which are leading to commodification of services
  4. Innovative automation and usage techniques of data assets among new entrants aka the FinTechs are leading to the rise of automated advisory services thus challenging incumbent firms. At traditional brokerage firms like  Morgan Stanley, Bank of America Corp. and Wells Fargo & Co. about 46,000 human advisers were employed as of 2016. The challenge for these incumbent firms will be to develop such automated investing tools as well as offer more self-service channels for customers [2]
  5. A need to offer aggregated & holistic financial services tailored to the behavioral needs of the HNWI investors on an individual basis

So where is the biggest trend in this disruption? It is undoubtedly, the Robo-advisor.

Introducing the Automated Advisor (affectionately called the Robo-advisor) –

FinTechs led by Wealthfront and Betterment have pioneered the somewhat revolutionary concept of Robo-advisors. To define the term – a Robo-advisor is an algorithm based automated investment advisor that can provide a range of Wealth Management services described below. The Robo-advisor can be optionally augmented & supervised by a human adviser. At the moment, owing to the popularity of Robo-advisors among the younger high networth investors (HNWI), a range of established players like Vanguard, Charles Schwab as well as a number of FinTech start-ups have developed these automated online investment tools or have acquired FinTech’s in this space.e.g Blackrock. The Robo-advisor is built using digital techniques – such as data science & Big Data – as we will explore in the next post.

What service models can Robo-advisors satisfy –

Full service Wealth Management firms broadly provide services in the following core areas which Robo-advisors can slowly begin supplementing –

  1. Investment Advisory – Helping a client construct an investment portfolio that helps her/him prepare for life changes based on their respective risk appetites & time horizons. The financial instruments invested in range from the mundane – equities, bonds etc to the arcane – hedging derivatives etc
  2. Retirement Planning – Retirement planning is a obvious function of a client’s personal financial journey & one that lends itself to automation. From a HNWI standpoint, there is a need to provide complex retirement services while balancing taxes, income needs & estate prevention etc. Robo-advisors are able to bring in market trends and movements of securities to ensure that client’s retirement holdings are not  skewed toward particular sectors of the marke.
  3. Estate Planning Services – A key function of wealth management is to help clients pass on their assets via inheritance. The Robo-advisor can assist a human wealth managers helps construct wills that leverage trusts and suggest suitable forms of insurance etc to help facilitate a smooth process of estate planning
  4. Tax Planning – Robo-advisors can help clients manage their wealth in such a manner that tax impacts are reduced from a taxation (e.g IRS in the US) perspective. As the pools of wealth increase, even small rates of taxation can have a magnified impact either way. The ability to achieve the right mix of investments from a tax perspective is a key capability and one that can be automated to a high degree
  5. Insurance Management – A Robo-advisor can help suggest and manage  the kinds of insurance purchased by their HNWI clients so that the appropriate hedging services could be put in place based on the client’s specific investment mix & exposures
  6. Institutional Investments– Institutional Robo-advisors can provide investment services to investors like pension funds, hedge funds etc while automating them a variety of backoffice functions

Currently most Robo-advisors limit themselves to providing the first function only i.e portfolio management (i.e. allocating investments among asset classes) without addressing issues such as estate and retirement planning and cash-flow management, which are also the domain of financial planning.[1]

Expect this to change as the technology rapidly matures in the years to come with advances in cognitive computing that will enable . At one of the earliest Robo-advisors, Betterment,  as of early 2016 – more than half of their $3.3 billion of assets under management comes from people with more than $100,000 at the firm. Another early starter, Wealthfront estimated more than a third of its almost $3 billion in assets in accounts requiring at least $100,000. Schwab, one of the first established investment firms to produce an automated product, attracted $5.3 billion to its offering in its first nine months.[2]

Robo

Robo-advisory business models

Currently there are a few different business models that are being adopted by firms.

  1. Full service online Robo-advisor that is a 100% automated without any human element
  2. Hybrid Robo-advisor model being pioneered by firms like Vanguard & Charles Schwab
  3. Pure online advisor that is primarily human in nature

Conclusion –

As one can see clearly, automated investing methods are still in early stages of maturity. However, they are unmistakably the next big trend in the WM industry and one that players should begin developing capabilities around. According to AT.Kearney, by 2020, Roboadvisors will manage around $2.2 trillion in global HNWI assets.[2]

The next post in this three part series will focus on the pivotal role of Big Data in creating a Robo-advisor. We will discuss system requirements & propose a reference architecture. 

References

  1. Wikipedia – https://en.wikipedia.org/wiki/Robo-advisor
  2. Bloomberg – “The Rich are already using Roboadvisors and that scares the banks..”

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.

NextGen_WM

                            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.

WM_Arch

                   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.

Big Data Drives Disruption In Wealth Management..(2/3)

The first post in this three part series brought to the fore critical strategic trends in the Wealth & Asset Management (WM) space – the most lucrative portion of Banking. This second post will describe an innovation framework for a forward looking WM institution.We will do this by means of concrete & granular use cases across the entire WM business lifecycle. The final post will cover technology architecture and business strategy recommendations for WM CXO’s.

Introduction:

The ability to sign up wealthy individuals & families; then retaining them over the years by offer those engaging, bespoke & contextual services will largely provide growth in the Wealth Management industry in 2016 and beyond. However,  WM as an industry sector has lagged other areas within banking from a technology & digitization standpoint.Multiple business forces ranging from increased regulatory & compliance demands, technology savvy customers and new Age FinTechs have led to firms slowly begin a makeover process.

So all of this begs the question – what do WM need to do to grow their client base and ultimately revenues? I contend that there are four strategic goals that firms need to operate across – 

  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.

Having set the stage for the capabilities that need to be added or augmented, let us examine what the WM firm of the future can look like.

WM_NewAge

                            Illustration – Technology Driven Wealth Management

Improve the Client Experience

The ability of the clients to view their holistic portfolio, banking,bill pay data & advisor interactions in one intuitive user interface is a must have. All this information needs to be available across multiple channels of banking & across all accounts the client owns with multiple FIs (Financial Institutions). Further, pulling in data from relevant social media properties like Twitter, Facebook etc can help clients gauge the popularity of certain products across their networks thus helping them make targeted, real-time, decisions that increase market share. Easy access to investment advice, portfolio analytics and DIY (Do it Yourself) “what if” scenarios based on the client’s investment profile, past financial behavior & family commitments are highly desirable and encourage client loyalty.

Help the Advisor –

On the other side of the coin, most  WM advisors lack a comprehensive view of their customers. This is due to legacy IT reasons due to which their interactions with clients across multiple channels takes up a lot of their work time but also results in limited collaboration within the bank when servicing client needs.

Other “must have” capabilities –

  • Predicting Customer Attrition & Churn across both a single client as well as over a n advisor’s entire client base
  • Portfolio Rebalancing & risk modeling across multiple dimensions
  • Single View of Customer Segments across multiple product offerings
  • Basket Analysis based on criteria like investment preferences, asset allocation etc – i.e “what products are typically purchased in tandem”
  • Run in place analytics on customer lifetime value (CLV) and yield per customer
  • Suggest Next Best Action for a given client and across a pool of managed clients
  • Provide multiple levels of dashboards ranging from the Descriptive (Business Intelligence) to the Prescriptive (business simulation as well as optimization)

Digitize Business Processes –

Since a high degree of WM technology still lives in the legacy age, it should not be a surprise that a lot of backend processes result in client dissatisfaction as well as an inability to provide lean & efficient operations. Strategic investments in Business Process Management (BPM) systems, Big Data architectures & processing techniques, Digital Signature systems & augmenting tactical document management systems can result in a high degree of digitization. This leads to seamless business interoperability, efficient client operations and an ability to turn around compliance information quickly & more efficiently over to regulatory authorities.

Invest In Technology to Drive the Business –

Strategic deployment of technology assets will be the differentiator in the WM business going forward. The technology investments that WM firms need to make are in three broad areas – Big Data & Predictive Analytics, Cloud Computing & in a DevOps based approach to building out these capabilities.

Big Data & Hadoop provide the foundation to an intelligent approach to unifying data (ingesting, mining & linking micro feeds with existing core banking data) and then fostering  a deep analytical approach based on predictive analytics and machine learning.

So what kind of new age business capabilities can WM firms build on a Big Data & Advanced Analytics based foundation?

  • New Client Acquisition by creating client profiles and helping develop targeted leads across a population of individuals
  • Instrument and understand Risk at multiple levels (customer churn, client risk etc) in real time
  • Advanced Portfolio Analytics
  • Performance Management Metrics for the business across client segments, advisors and specific geographies
  • Better Client Advice based on portfolio optimization which takes client life journey details into account as opposed to static age based rebalancing
  • Promoting client’s ability to self service their accounts thus reducing load on advisors for mundane tasks
  • The biggest (and perhaps the most famous) capability is providing Robo Advisor functionality with advanced visualization capabilities. One of the goals here is to compete with Fintechs which are automating their customer account servicing using an automated approach.
  • Help with Compliance and other reporting functions

Big Data and Hadoop seems to be emerging as the platform of choice for many reasons – ability to handle any kind of data at scale, cost, techniques like deep learning need a lot of computing power which Hadoop can provide via paralleization, integration with SAS/Python and R. A high degree of data preprocessing could be done via Advanced MapReduce techniques.Finally, additive to all of this is an agile infrastructure based on cloud computing principles which calls out for a microservice based approach to building out software architectures, mobile platforms that accelerate customers abilities to bank from anywhere. DevOps dictates an increased focus on automation from a business process to software system delivery  and encourages a culture that encourages risk taking & a “fail fast” approach.

The final post in this series will cover a high level technology architecture and then specific recommendations to WM CXO’s.