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.

                            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.