MyPrivateBanking Blog
Daily Comments on the World of Wealth Management

Archive for June, 2017

No WeChat? No Chance to Win HNWIs’ Hearts in China

Friday, June 23rd, 2017

Today, China is home to the second highest number of HNWIs in the APAC region and its economy is expected to overtake that of the U.S. in the coming years - some even expect this to happen as early as in 2018. The huge growth of China’s FinTech industry, a major source of new wealth it is, too, is the third pillar of China becoming one of the most attractive and promising wealth management markets in the world.

asia-wealth

This is why we focus on the Chinese wealth management market in our new report on Digital Wealth Management in Asia 2017. The second emerging market that we look at in detail in this report, is India with similar growth patterns and opportunities for private banks.

Besides presenting comprehensive research findings on the Chinese wealth market and FinTech space, our report provides an in-depth view on existing digital offerings of the ten largest wealth managers in China. Our key finding is that WeChat is essential for wealth managers’ digital strategy if they want to succeed in this unique digital ecosystem. WeChat is reported to occupy 35 percent of the time spent on mobile phones in China, which makes the social messenger an inevitable platform not only for retailers. Digital financial services are deeply rooted into Chinese people’s daily lives - thanks to tech giants Alibaba and Tencent who laid the foundation for a financial market that is unique and highly digitized.

While we could observe some international wealth managers making the mistake to link their corporate Facebook and YouTube channels on their Chinese websites, we also came across some best practices in how to engage with HNW clients on WeChat. For example, Noah Holdings Limited, a domestic private bank, sets itself apart through a broad range of WeChat features such as the possibility to check account balances, book an appointment with an advisor or read speeches from well-known investors. Additionally, Noah’s clients are provided with articles and information about recent events.

This is only one facet of the level of digital service Chinese HNWIs expect from their wealth manager. Hence, private banks wo aim at entering the Chinese market, or want to strengthening their market position, inevitably need to roll out a digital strategy that is tailored to these unique requirements.

Grab your copy of our latest research report to get a detailed picture of the wealth markets in China and India. Additionally, you will be offered an overview of current dynamics in the APAC region as a whole plus insightful glimpses into cultural differences between the Asian regions and FinTech developments in Thailand and Indonesia.

 

Why BigData projects often fail (and how to make sure yours isn’t one of them)

Thursday, June 1st, 2017

(By Onawa Lacewell, Analyst)

Big Data projects in the financial sector (and other sectors) often produce underwhelming results. It may be tempting to conclude that Big Data is just a passing fad and that wealth managers need not concern themselves with Big Data solutions. However, MyPrivateBanking’s new report on Big Data in Wealth Management thinks this is the wrong conclusion to draw from the current lackluster performance of Big Data projects. Instead, we argue that poor implementation planning that fails to take a needs-based approach is to blame for the inferior performance of Big Data projects to date. We suggest that by reversing the standard implementation process wealth managers can help ensure that Big Data projects succeed.

Why is Big Data more than just hype?

Big Data may seem like just a passing fad or a buzzword. Nevertheless, Big Data is here to stay and, from everything we’ve seen in the past five years, it seems highly likely that we are just now at the dawn of Big Data. “Big Data” simply refers to the increase volume, variety, velocity and veracity of data flooding into today’s businesses every day.

“It took from the dawn of civilization to the year 2003 for the world to generate 1.8 zettabytes (10 to the 12th gigabytes) of data. In 2011, it took two days on average to generate the same amount of data.”

(icrunchdata)

In today’s highly digital world, almost everything we do generates data: location data, social media activity, search engine metrics, and banking transactions. Additionally, as the Internet of Things brings more and more of our activity online this volume of data is only set to grow-from smart houses that provide insights into how you live to smart cars that, for example, allow insurance companies to closely monitor your driving habits. Businesses can now get nuanced, granular, and highly accurate data about their customers-both existing and potential. This is, of course, the lure of Big Data. For financial actors, the potential of Big Data is quite high. Banks can see when, where, and how their clients spend money. Wealth Managers can understand more about the behavioral profiles of their U/HNWIs. All financial actors can accurately and easily automate manual data entry processes both freeing up time for personnel to work on other tasks and reducing the possibility of data entry errors.

Why Big Data projects fail

The purpose of Big Data is clear: to help drive innovation and profits for businesses, to apply a fact-based business strategy, and to uncover insights to help increase organizational efficiency and improve client relations. Why, then, do Big Data projects have such a reputation for underperforming? MyPrivateBanking argues that much of the failure of Big Data is not because the data is somehow less useful than imagined but rather that the way many firms approach implementation sets these projects up for ultimate failure. The standard implementation process often starts with shopping around for a Big Data vendor-perhaps one that promises an all-encompassing Big Data solution that will use the firm’s internal and external data along with unstructured data (like social media commentary or search engine metrics) to modernize the entire digital ecosystem. Then, once the vendor’s solution is in place, the firm realizes that they actually don’t need all the data that they are collecting. Or, that there isn’t inhouse data science talent that can really get the most out of this new wealth of data analytically. Or, possibly, the firm realizes that the organizational siloing is standing in the way of using the new data.

A Needs-Based approach to Big Data

We argue that in order to get the most out of Big Data, and to ensure that Big Data projects are really successful, wealth managers and other financial providers should reverse the standard implementation process. Instead of focusing on the end solution, or trying to modernize the entire digital ecosystem with a general and comprehensive Big Data project, firms should instead take a needs-based approach to Big Data. The steps of this approach are rather simple, but this simple change in approach to the implementation process can make all the difference when it comes to whether your project will be successful or not.

1.     Identify the exact need (objective) of the project

This is a key step and should not be undertaken quickly-determine explicit needs, or objectives, where your firm needs a Big Data solution.

2.     Determine the type of data that best address this need

Do you need structured data? Unstructured data? A mix between the two? Determining which type of data addresses your need will help determine what type of data solution you require.

3.     Evaluate whether this data already exists within the organization

A lot of organizations think that Big Data means external data-using Facebook data, for example. However, Big Data can also mean internal data. Taking a deep look at the types of data your organization or firm already has may reveal that you don’t need external data at all-and this will determine what type of third party solution you need to shop for.

4.     Determine success metrics and expected ROI

Determining the success of a Big Data project can sometimes be difficult. Therefore, it is crucial that measures of profitability be part of the pre-planning discussion and strategy meetings.

5.     Shop around for a vendor who offers a solution that fits closely to the need

There are many different vendors offering everything from comprehensive Big Data solutions to narrowly targeted ones. Seeking a vendor that fits to your specific organizational need will help ensure that the resulting implementation plan will be a success.

6.     Determine the correct infrastructure and implementation plan to fulfill this need

Only after every other step in the needs-based chain is fulfilled should a firm or organization determine the type of infrastructure necessary for a Big Data project. The need should always drive the infrastructure-not the other way around.

By approaching Big Data from a needs-based plan, wealth managers and other financial providers stand a better chance that the resulting Big Data project will be successful. For practical information about how to take a needs-based approach to Big Data see our latest report. Here you will find practical tools that will help with determining success metrics, charting the implementation path, engaging in pre-planning and more.

 
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