Gould Scholastic Award – Julian Fung, Lasse Fuss, Tommy Ng

Transcending Traditional Service Models with

Disruptive Technology Julian Fung, [email protected], (872) 203-‐4854

Lasse Fuss, [email protected], (816) 872-‐0016

Tommy Ng, [email protected], (660) 998-‐4500

Truman State University

Charles Boughton

[email protected], (660) 785-‐4521

DST Robert L. Gould Scholastic Award [2014 -‐2015]

2

Executive Summary In order to secure the enduring success of the wealth management industry and gain absolute

advantages over e-‐services, financial services companies need to incorporate Big Data technology,

advances in behavioral finance, and alternative services into a holistic service model. With only 24% of

wealth managers prepared for the upcoming challenge due to technological advancement, there seems

to be an urgency to redefine the wealth management industry. In the next two years, financial advisors

expect to increase social networks usage by 40% and mobile and tablet usage by 85%.1 Identifying and

incorporating disruptive technology into a holistic service model is essential for financial advisors to

adjust to the new environment. This paper addresses the future of financial decision-‐making and its

impact on financial services companies.

As the amount of open data increases exponentially, data analytics are becoming a crucial

emerging disruptive technology that can provide competitive differentiation among financial services

firms. Thus, firms need to incorporate Big Data to develop and gain insights into customers, provide

personalized offerings, discover investment opportunities, reduce risk and assist with compliance.

In addition, building on advances in behavioral science, financial advising software has to

incorporate behavioral models to augment client interactions with wealth managers and financial

planners. A holistic service model has to account for unsound client behaviors and aid practitioners in

moderating or adapting to such behavior. At the same time, behavioral nudges are instrumental in

encouraging clients to save and invest.

The growing expectations from investors are poised to reshape the entire industry. Emerging e-‐

services provide investors platforms to seek investment consultation free of charge, track portfolios in

real time, and automate financial decision making based on efficient algorithms. Conventional service

models should incorporate adaptable and innovative financial advising alternatives to serve various

customer needs in order to improve wealth management.

Ultimately, the purpose of wealth management is to create a desirable value to customers. In

order to stay competitive and defend themselves against the growing threat of “robo-‐advising”,

knowing what investors are looking for and embracing technological usage has become compulsory for

financial advisors. Thus, the holistic service model should incorporate Big Data usage, behavioral

finance, and user-‐friendly technology to surpass e-‐services competitors.

1 Crosby, C. Steven, Jensen, Jeremy, Ong, Justin. Navigating to Tomorrow: Serving Clients and Creating Value. PDF file.


3

Capitalizing on Big Data Along with new growth opportunities from the advancement of technology, the financial

services industry faces extraordinary challenges such as sustaining clients’ confidence and meeting their

demands for convenience and higher returns, while restraining escalating operating expenses and

improving productivity. In their effort to overcome these challenges, financial services firms must

leverage their information assets to gain a comprehensive understanding of the various key aspects in

the financial services industry and contribute to better service models. Thus, a holistic service model

needs to incorporate Big Data to gain insights into customers and prospects, discover investment

opportunities, assist with risk and compliance, and provide competitive differentiation. Bill Gerneglia,

COO of CIOZone.com, describes Big Data as “a process of collecting, storing, and analyzing fragments of

information that can be rapidly assembled to identify subtle macro trends or create actionable profiles

that precisely target unique individuals”.2

Customer segmentation is a Big Data use case that can bring great value to financial services

firms. In the industry, customer segmentation is a key tool for sales, promotion, and marketing

campaigns. Firms can implement better marketing plans and strategies for customers if they can group

customers with differing demands into different segments. Firms often segment customers by

demographic information, but with more advanced analytical software, firms can now segment

customers by their behaviors. Firms can use analytical software such as the MapR distribution, an

enterprise-‐grade distributed data platform, to collect and analyze all available customer data. This

includes daily transactions, customer interactions (e.g., social media, call centers), house price index,

and merchant records in real time. Once these data sets are gathered, companies can group customers

into one or more segments based on their needs in terms of products and services, and plan their sales,

promotion and marketing campaigns accordingly.3 With these segmentations, we recommend that firms

take a step further and include these segments in an urgent/important matrix as shown in attachment

A. Using this matrix, firms are able to obtain a clearer view of the importance and urgency of each

segment and prioritize accordingly. If a particular segment is deemed important and urgent, companies

know they must approach this segment first by creating personalized promotions and marketing

2 Gerneglia, Bill. “Finding Value in Open Data Vs Big Data.” myBigDATAview., Blog. 21 Nov. 2014. 3 "Big Data and Apache Hadoop for Financial Services." MapR, Hadoop. n.d. Web. 21 Nov. 2014. <https://www.mapr.com/solutions/industry/big-‐data-‐and-‐apache-‐hadoop-‐financial-‐services>.


4

strategies for the segment. Conversely, firms should spend less time in tackling segments that are

categorized as unimportant and not urgent.

Through technology, emerging online services companies have been able to produce advanced

financial advising algorithms to reduce investment risk and costs, and claim that customers have the

potential to obtain higher returns with these algorithms than they might with a traditional advisor.

While this may be true, Big Data provides more human oversight than automated advisors and handles

market anomalies in a more pragmatic manner. With accurate and up-‐to-‐date customer segmentation,

firms can use Big Data to further understand customers on a micro-‐level, enabling personalized

customer service and product offering. This allows for prediction of new products and services, and

therefore, firms can customize relevant offers based on these predictions to segmented customers.

Achieving these benefits requires real-‐time analysis of unstructured data from customer

decisions, purchase frequency and timing, browsing data on financial services and products, social

media activity, and other sources. This will enable customer and market sentiment analysis to learn

customer preferences and sentiments about products or services offered, assess customer sentiment

through the study of converging trends, and identify the current feel or tone of the market.4 For

example, financial services software can use the MapR distribution to analyze and track customer

movements and responses on social media or product review sites. This new insight can help firms

respond to emerging problems in a timely manner and also predict what kind of investments or

retirement plans appeal to individual customers. Western Union, a financial services company, has

adopted Cloudera’s data hub to acquire important insights from initial contact with customers. One

insight revealed by Cloudera’s hub was that many web and mobile customers frequently process

repeated transactions to the same recipient at the same time each month. This data prompted Western

Union to add a “Send Again” button to make the process of repeating payments more convenient for

customers.5 As predictive analytics have not advanced far and may not always provide accurate results,

we suggest that financial advisors combine their expertise in the industry with these predictive tools to

provide appropriate proposals and solutions to clients.

New legal requirements and increasing demand for better internal management support lead

many firms to focus on finance and risk management. Big Data can help with risk management by

enabling a centralized risk data management that can quickly and flexibly address new requirements.

Firms can create real-‐time individual risk profiles for customers based on the ample amount of 4 Kumar, Anjani. “Big Data use cases in financial services.” Infosys., 19 Jul. 2014. Web. 21 Nov. 2014. 5 Saraf, Sanjay. “Western Union Implements Enterprise Data Hub on its Path to Deliver an Omni-‐channel Customer Experience.” Cloudera. n.d. Web. 21 Nov. 2014.


5

unstructured data available. Similar to the micro-‐level customer analysis and personalized product

offerings, Big Data uses customer segments to further analyze customer behavior and spending habits to

increase the accuracy of risk profiles and improve firms’ risk management capabilities. In addition, firms

can draw data on market events from news, reports, social media and other sources to provide further

insight in real-‐time. Firms can also use these data to form predictive credit risk models that can help

prioritize customers and collection activities.6 The data platform should be flexible and adaptable to

various types of analytical software, and be able to process data in real-‐time.7 United Overseas Bank

successfully tested a risk system based on Big Data and managed to reduce the calculation time of its

total-‐bank risk from about eighteen hours to only a few minutes. Thus, banks can carry out stress tests

in real time and react more quickly to new risks in the future.8

With better risk management capabilities, firms can improve fraud detection. Credit card fraud

has become more sophisticated. Today, most credit card thieves avoid making big purchases with credit

cards. Instead, they make many smaller transactions that amount to the same lump sum. For example, it

would be highly suspicious if a large transaction of over $50,000 was made to purchase a diamond ring,

but if a customer made 5,000 ten dollar transactions at various locations, it would be harder to detect

the fraud purchase. However, these frauds can be easily identified with the help of Big Data through

proactive analysis of geolocation, point of sale, authorization and transaction data.9 For example, Big

Data can help identify ATMs that are likely to be targeted by fraudsters.10 In many cases when fraud is

anticipated, the transaction can be blocked even before it takes place.

Zions Bank, a subsidiary of Zions Bancorporation that operates more than 500 offices and 600

ATMs in ten Western U.S. states uses MapR as a critical part of their security architecture. By using

MapR, the bank is able to predict phishing behavior and payments fraud in real-‐time, and minimize their

impact, as well as run more detailed analytics and forensics. Zions Bank has been able to lower storage

and capacity planning costs significantly, as well as increase the speed of their analytics activities.11 By

aggregating all these data, we believe that it may be possible to create a system that assigns every

customer a latent risk score in the near future that will greatly assist in the firms’ risk management. This

score is determined based on past transactions, behaviors, and customer interactions. It indicates the

6 Kumar, Anjani. “Big Data use cases in financial services.” Infosys., 19 Jul. 2014. Web. 21 Nov. 2014. 7 Shamgar, Idor. “5 Big Data Use Cases for Banking and Financial Services – Part 2.” SAP., Blog. 21 Nov. 2014. 8 Huber, Andreas, Hannappel Hauke, Nagode Felix. “Big Data: Potentials from a risk management perspective.” Banking Hub., 01 Jul. 2014. Web. 21 Nov. 2014. 9 “Financial Services.” Datameer. n.d. Web. 21 Nov. 2014. 10 Kumar, Anjani. “Big Data use cases in financial services.” Infosys., 19 Jul. 2014. Web. 21 Nov. 2014. 11 “Combating Financial Fraud with Big Data and Hadoop.” MapR, Hadoop. 18 Dec 2013. Web. 21 Nov. 2014.


6

potential risk a customer possesses and the threat it poses to the firm. With this, financial services firms

can rank their customers from lowest to highest in terms of latent risk, and can put more scrutiny and

attention to customers of high risk.

With the relentless growth of Big Data, financial services firms need to acquire the right talent

and expertise to take charge of the data analytics in their firms. Rising demand for Big Data expertise has

created a severe skill shortage in the field that has pushed the average salary to $55,000 – 31% higher

than the average IT position. According to Financial Times, “Financial service was also the most

commonly cited employer in Big Data advertisements, accounting for about 20% of all positions in the

industry in 2013.”12 With all this demand and competition for data scientists, firms should begin to scout

for relevant expertise to ensure a smoother transition into Big Data.13 Firms should also invest in

professional training and development for current employees to better prepare them for the adoption

of Big Data in their companies.

Overall, Big Data is of great value to the financial services industry. Financial services firms need

to invest in data analytics through research and development, training, and other possible ways to

prepare themselves for the Big Data tidal wave. Firms also need to identify and define business

capabilities through improved insights achieved through Big Data, and develop a holistic service model

for execution. While Big Data is pertinent to the transformation of the industry, behavioral finance is yet

another crucial aspect that must be integrated into the holistic service model.

Incorporating Behavioral Finance Behavioral economic research has spent many years in the “ivory tower” before developing into

a more mainstream topic. Acknowledging that investors do not always make rational decisions

benefitting their own interests is an essential aspect of financial decision-‐making and needs to be

reflected in a holistic service model. Oftentimes, financial advisors would like to address these

behavioral issues but lack diagnostics. 14 Thus, a holistic service model needs to incorporate behavioral

aspects to augment client interactions with wealth managers and financial planners.

Most financial advisors use a standard asset allocation program in which they first administer a

risk-‐tolerance questionnaire, discuss clients’ financial goals and constraints, and then follow the output

12 Warrell, Helen. “Demand for Big Data and skills shortages drive wages boom. “ Financial Times., 30 Oct. 2014. Web. 21 Nov. 2014. 13 Ibid 14 How Industry Experts Are Making Sense of Behavioral Economics. FinancialPlanning, Feb. 2013. Web. 28 September 2014


7

of a mean-‐variance optimization – a quantitative tool to make allocations by considering the trade-‐off

between risk and return. This procedure works well for most institutional investors, but individuals often

want to modify their asset allocation plan in response to short-‐term market fluctuations and dramatic

news that negatively impact long-‐term investment or retirement plans. Table 1 lists typical behavioral

irrationalities causing unsound client behavior.

Behavioral Bias Description Loss aversion The tendency to feel pain of losses more than the pleasure of gains. Anchoring and adjustments

The tendency to believe that current market levels are “right” by unevenly weighting recent experiences.

Selective memory

The tendency to recall only events consistent with one’s understanding of the past.

Availability bias The tendency to rely on immediate examples that come to a person's mind when thinking of a certain topic.

Overconfidence The tendency to overestimate one’s skill and experience in investing. Present-‐bias The tendency to favor rewards today instead waiting till tomorrow. Regret The tendency to feel deep disappointment for having made incorrect decisions. Table 1: Behavioral irrationalities impacting financial decision-‐making 15

To avoid spending valuable time on modifying investment and retirement plans later on,

financial planners and advisors have to quickly moderate or adapt to unsound client behavior. Pompian

(CFA, CFP) and Longo (Ph.D., CFA) rely on Kahneman’s “best practical allocation” model to suggest an

asset allocation that suits clients’ natural psychological preferences and opposes the traditional model

of maximizing expected returns for a pre-‐determined level of risk.16 Pompian and Longo recommend

that advisors moderate cognitive biases, such as selective memory and present bias, and adapt to

emotional biases such as loss aversion and regret. Advisors should also moderate behavior if their

client’s wealth is low since biases and irrational behavior can jeopardize financial security. Overall,

advisors have to weigh these biases for a “best practical allocation” as shown on the biaxial model of

adapting and moderating in Attachment B. Currently, most mean variance outputs only allow a +/-‐ 10%

deviation from suggested allocations.17 Financial software should not only allow adjustments for

unsound behavior at the discretion of practitioners, but also incorporate behavioral models to provide

guidance to practitioners. For example, a client plans to retire with the goal to not outlive his assets and

is afraid of losing money since he still remembers the Financial Crisis and the Dot Com bubble, indicating

selective memory and loss aversion. The client is also prone to anchoring and adjustments since he

15 Longo, John M., and Miachel M.Pompian. The Future of Wealth Management: Incorporating Behavioral Finance into Your Practice. Dartmouth U, n.d. PDF file. 26 October 2014. 16 Ibid 17 Longo, John M., and Miachel M.Pompian. The Future of Wealth Management: Incorporating Behavioral Finance into Your Practice. Dartmouth U, n.d. PDF file. 26 October 2014.


8

believes current market levels are “right.” Adapting to these biases would lead to a portfolio with mostly

bonds, jeopardizing the client’s financial security. Since these biases are principally cognitive, an advisor

would moderate his client’s behavior by mixing stocks into the portfolio and administering an investor

education program, explaining the risk of outliving one’s assets.

The key to incorporating behavioral models into asset allocation lies in evaluating clients’

behavior as deeply and objectively as possible. Standard risk-‐tolerance questionnaires do not fulfill this

purpose and most financial advisors lack training and only subjectively evaluate clients’ behavior. Thus,

indicative tests have to be developed that analyze clients’ behavioral biases and also allow input from

advisor’s firsthand experience. Designing these tests requires extensive research and the help of

behavioral scientists. One example is Merrill Lynch’s “Investment Personality Assessment” which is

mostly administered to its ultra-‐high net-‐worth clients to determine their “mindset towards risk,

preferred investment approach, and purpose.”18 Developing tests that automatically code for emotional

and cognitive biases and incorporating these results into asset allocation programs will facilitate the

work of financial advisors. At the same time, financial advisors have to become skilled in using

behavioral cues to deduce their customers’ risk tolerance and investment objective, which will also help

fend off the growing competition of online advising and wealth management robots. For example,

despite agreeing verbally, customers’ physical reactions such as nervous hand movements, an agitated

voice, sweat, and other signs can inform advisors that clients are not comfortable with their investment

plans. These attitudes may remain hidden unless advisors are trained to recognize non-‐verbal feedback,

which reflects the importance of face-‐to-‐face interactions with clients.

Current allocation models do not only need revision in terms of emotional and cognitive biases,

but also need to consider the definitions of risk and return. Independent of the investing objective,

returns are usually perceived as “potential happiness.” Often, financial advisors and planners serve as

life planners who are ultimately concerned about their client’s comfort and happiness.19 Thus, shifting

the focus from pure return maximization to incorporating comfort and potential happiness may help

financial planners, behavioral tests, and allocation programs determine what is most important to

clients. With the rise of various online competitors offering low-‐cost advising and wealth management

alternatives, it is evermore important for advisors to offer financial advice in the context of lifestyle,

future plans, and personality traits. Since computer algorithms lack the ability to find underlying motives

18 How Industry Experts Are Making Sense of Behavioral Economics. FinancialPlanning, Feb. 2013. Web. 28 September 2014 19 Tomlinson Joseph. Behavioral Finance—Implications for Investment Planning. Joe Tomlinson, n.d. PDF file. 26 October 2014.


9

and life goals of customers, financial advisors have to build their service model around understanding

the customer and offering individualized services.

Various studies have shown that personal control rather than income predicts people’s

happiness.20 Moreover, most people experience happiness in relation to the fortunes of others. Service

models that incorporate such behavioral aspects can build an even deeper relationship between

advisors and clients. Similarly, risk should be considered “potential regret”. Thus, advisors essentially

maximize happiness with as little regret as possible. 21 Greg Davies, managing director and head of

behavioral finance and investment philosophy at Barclays, defines risk as the “anxiety-‐adjusted” return,

taking into account the “anxiety, discomfort, and stress” a client endures.22 Based on individual client

profiles, financial software can assist advisors by evaluating potential investments in terms of

experienced risk for each client. For instance “potential regret” could be a composite measure of

volatility, intrinsic risk, and news coverage of an asset, which is then automatically evaluated based on

personality tests.

Behavioral models are not only important in asset allocation models but can also help in the

retirement savings crisis by using behavioral nudges to encourage clients to save and invest. According

to the Center for Retirement at Boston College, “the fraction of workers at risk of having inadequate

funds to maintain their lifestyle through retirement has increased from approximately 31% to 53% from

1983 to 2010.”23 Such statistics may alarm financial planners whose goal is to assure their clients of a

secure retirement.

Financial advising software needs to incorporate social proof and visualization while promoting

seamless change to ensure secure retirement for clients. Social proof refers to human’s biological

predisposition to imitate behavior. It is an evolutionary adaptation promoting survival over thousands of

generations. Financial planners have been using dramatic messages such as “61% of workers report less

than $25,000 in retirement savings to convince people to save and invest.” However, such messages

may inform people that having a shortfall is a normal behavior and beguile them into thinking that there

is no need to act. In fact, people with only $50,000 would feel great about themselves. An effective

application of social proof should use injunctive norms showing success, not descriptive norms of

20 Nettle, Daniel. Happiness: The Science behind Your Smile. Oxford, UK: Oxford UP, 2005. Google Books. Web. 1 Jan. 2015. 21 Benartzi, Shlomo, and Richard H. Thaler. "Behavioral Economics and the Retirement Savings Crisis." Science 339 (2013): 1152-‐153. Web. 27 Oct. 2014. 22 How Industry Experts Are Making Sense of Behavioral Economics. FinancialPlanning, Feb. 2013. Web. 28 September 2014 23 Ibid


10

common failure. Thus, financial planners can encourage financial planning by telling prospective clients

“the average successful retiree had an account balance of $750,000.”24 Moreover, constantly growing

databases with numerous client metrics allow financial planners to use social proof for individual clients

based on their demographics. At the same time, financial advisors need to take advantage of technology

that allows clients to visualize themselves during retirement. Chip and Dan Heath’s prominent model

considers the relation between an elephant and its rider an analogy to internal decision-‐making: The

rider is rational and tries to steer the elephant; however, the elephant, driven by emotions, is more

powerful and can overrule the rider. Thus, to accomplish behavioral change, messages have to impact

people’s emotions and provide actionable goals. 25 Clients who imagine their future selves vividly,

including their problems and needs, are better prepared for retirement and more motived to save.26

Hershfield conducted a study with computer-‐generated digital representation of people as they age.

Seeing an avatar of themselves in the future significantly increased people’s willingness to save for

retirement.27 Joseph Coughlin, the director of MIT's AgeLab, further explains the importance of

visualization: “While consumers are acutely concerned about ‘their numbers’, they are far more likely to

understand and engage in discussion around products that are connected to concrete expenses rather

than an ambiguous goal of ‘secure retirement’”.28 To prevent decision paralysis, technology has to aid in

creating vivid and concrete forecasts of living circumstances during retirement, including expected and

unexpected expenses.

The most crucial step toward secure retirement is establishing an automatic investment

behavior. Since people are loss averse and often unwilling to give up money today to invest for

retirement, behavioral economists developed a savings plan called “Save More Tomorrow”. Employees

commit to increasing their savings rate as they receive pay raises. Since the increase in savings rate is

only a proportion of the pay raise, there is no decrease in discretionary income. 29 At the first company

which implemented this plan, participants almost quadrupled their saving rate from 3.5% to 13.6% in

24 Kitces, Michael. "Using Social Proof To Help Clients Make Better Financial Planning Decisions | Kitces.com." Kitces.com: Advancing Knowledge in Financial Planning. 30 Oct. 2013. Web. 13 Dec. 2014. 25 Heath, Chip, and Dan Heath. Switch: How to Change Things When Change Is Hard New York: Broadway, 2010. Print. 26 How Industry Experts Are Making Sense of Behavioral Economics. FinancialPlanning, Feb. 2013. Web. 28 September 2014 27 Benartzi, Shlomo. Behavioral Finance in Action. Allianz Global Investors, Mar. 2011. PDF file. 26 October 2014. 28 How Industry Experts Are Making Sense of Behavioral Economics. FinancialPlanning, Feb. 2013. Web. 28 September 2014 29 Benartzi, Shlomo, and Richard H. Thaler. "Behavioral Economics and the Retirement Savings Crisis." Science 339 (2013): 1152-‐153. Web. 27 Oct. 2014.


11

less than 4 years. Today, more than 50% of larger employers in the U.S. offer the program.30 Innovative

technology can help financial planners to capitalize on “Save More Tomorrow,” by applying the concept

to investing. “Invest More Tomorrow” serves as an action framework that overcomes investor paralysis

and procrastination since clients pre-‐commit to have pay-‐raises transfer into

retirement/college/nursing/etc. funds. Advances in financial software can facilitate this process by

allowing communication and potentially even integration with corporate payroll and ERP systems.

Besides establishing an automatic investment behavior, we believe advisors have to increasingly

target college graduates. Immediately after graduation, most college graduates experience a sudden

spike in disposable income, allowing them to invest excess funds and benefit from compound interest

due to their young age. This not only combats the retirement crisis but also ensures extraordinary gains

for clients by avoiding the cost of delaying investments as illustrated in Attachment C. In order to appeal

to the younger generation, we believe advisors have to make themselves more available and fight the

stigma of being a service for the wealthy and elderly. Even though generation Y wants to be

independent and handle their finances themselves, financial advisors are more qualified to help them

plan their future. Thus, advisors need to rebrand themselves and highlight how their convenient,

individualized, and experienced services can help recent college graduates. To do so, financial advisors

may start with educating college students about financial planning, investing, and retirement. Even

though college students are educated in their respective discipline, many lack financial literacy.31 Thus,

financial educational programs that truly aim at helping students can be an excellent starting point for

advisors to introduce their services and how they can help recent graduates.

Overall, incorporating behavioral aspects into a holistic service model helps financial advisors to

retain and attract customers, while differentiating themselves from online advising robots.

Simultaneously, advisors benefit from better understanding their clients’ needs and having more money

available to invest so their clients are more likely to achieve secure retirement.

Alternative Financial Services The financial services industry is undergoing a rapid stage of flux. The old saying that ‘nothing

endures but change’ describes pertinently the impact of disruptive technology on wealth management.

The shortening time horizon in transactions and advances of efficient technology allow new service

models to emerge, serving the needs of the industry. In fact, CNN listed the top 15 financial apps and

30 Benartzi, Shlomo. Behavioral Finance in Action. Allianz Global Investors, Mar. 2011. PDF file. 26 October 2014. 31 Bidwell, Allie. "Closing the Financial Literacy Gap to Combat Student Debt." US News. U.S.News & World Report, 3 Oct. 2013. Web. 1 Jan. 2015.


12

sites with most having customized portfolios, free advising services, mobile platforms accessibility, and

real time trading in 2014.32 Disruptive technology prompts deliberations on how consumers will seek

financial advice, where technology advancement will lead the industry, and how financial advising

should best adapt to the new environment.

In order to acquire new customers, online competitors have already taken several steps to

incorporate technologies into new service models. For instance, new service models offer additional

features such as automated risk assessments using Big Data.33 Computerized programs then match

individual risk tolerance with corresponding ETFs. Such service models appeal to various demographics

and aim to provide superior services, such as high-‐speed trading, mobile accessibility, and diversifiable

portfolios without forgoing profits. Conventional service models should target multiple demographics by

offering multiple instruments and services. We believe models should not only be built around a time

horizon, risk tolerance, and income levels, but also address the needs of different genders, generations,

and ethnic groups.

Traditionally, the absence of taking transactional fees into consideration has been a downside to

various finance theories, such as the efficient market hypothesis and the option-‐pricing model. LOYAL3

and Robinhood are online platforms for fee-‐free investing. This empowers investors to trade freely

without concern for the underlying fees behind each transaction. The downside of these sites is that

they do not offer real time trading or sufficient investing platforms, such as providing trades only on

apps. In general, the advantage of fee-‐free investing will become less significant, since transaction and

service fees are slowly diminishing in the foreseeable future. New service models should not only aim to

profit from service charges but rather build on a comprehensive view of clients’ wealth. In addition,

financial companies are also conducting services in a more personal manner. The terms wealth

management, financial claim, and client relationship management aim to grow a closer relationship with

consumers to replace traditional terms such as saving and borrowing.34 As consumers have more control

over their accounts, their influences on how to allocate assets, and manage risk and return increases.

Hence, service models should incorporate the dynamics of consumer behavior to accommodate the new

environment as well as to serve individual needs.

Technology has revolutionized the traditional practices of investing and led to a new stage of

wealth management. Financial advisors from investment companies have to learn to provide

32 "Save with Every Purchase." CNNMoney. Cable News Network, n.d. Web. 12 Dec. 2014. 33 "Betterment vs. Wealthfront -‐ How Do These Robo Advisors Compare?"Investor Junkie. N.p., 28 July 2014. Web. 34 Charles S. Sanford, Jr. "Financial Markets in 2020." Proceedings. 1994.


13

information digitally and rapidly. PwC’s research forecasts expenditure on mobile, tablet, and social

networks will nearly double to promote interaction digitally with clients to help achieve their goals

within the minimum time frame. Currently, 47% of communication between financial advisors and

clients is carried digitally through emails, text messages, and social networks as shown in Attachment

D.35 In addition, wealth management robots promote computer programming to evaluate most of the

risk assessments. This enables the new generation to look for wealth models that are convenient and

fast progression, a succinct and accurate approach. To outperform online service models, retain existing

clients, and attract new the generation, a lifetime model helps plan for clients’ future expenses such as

education, marriage and retirement. This model will consist of a comprehensive personal wealth

account that includes personal assets, such as housing, cars, savings, etc.36 Owners of wealth account

will be able to optimize their credit margins, manage their wealth, allocate funds for upcoming events

such as vacations and weddings. For instance, if clients indicate an early interest in financing a house or

moving into a new place, wealth accounts will provide quick evaluations on how much money clients are

going to need. Automated models then start allocating funds periodically to ensure sufficient funds will

be available to finance clients’ expenses. To visualize such transformation, clients may indicate a

preference of traveling at the end of the year on their accounts. By doing so, a subaccount will be

generated to start taking off partial returns from clients’ portfolios. At the end of the year, an account

indicated as “vacation” will be ready to use for clients. Clients neither have to make any changes for

their investments nor worry about market fluctuations if additional funding is needed in the future. This

also ensures funds will continue generating profits instead of sitting aside in checking accounts until

usage for future purposes. Transcending wealth management is essential such that advisors are able to

develop a lifetime relationship with clients, not only managing their wealth, but also assisting them to

plan for their future expenses and allocate funds according to any extenuating circumstances.

Unlike traditional advising that depends primarily on financial advisors, investors now rely on

inputs and collective thinking from peers whether they are choosing wealth advisors or purchasing

financial instruments.37 For instance, wars, oil price fluctuations, currency risk, and many global affairs

become growing concerns for investors. New service models should be able to provide instant and

professional customer service, such as instant messaging or chat options if clients so desire. Global

events can often trigger disastrous effects in markets. Advisors should be able to reassure clients in real-‐ 35 Crosby, C. Steven, Jensen, Jeremy, Ong, Justin. Navigating to Tomorrow: Serving Clients and Creating Value. PDF file. 36 Charles S. Sanford, Jr. "Financial Markets in 2020." Proceedings. 1994. 37 Venkateswaran, S., & Vaed, K. (2013). The future of wealth management services. FT.Com,


14

time and prevent them from making rash decisions. This provides financial advisors with an edge over

self-‐managed and algorithm-‐based online advisors. Although investors are drifting away from traditional

financial practices through phone calls and brokers, they continue to seek improved and more precise

financial advice.38 In fact, societal change is inclined to strengthen the bond between clients and

advisors. While companies are seeking new technology and predicting upcoming changes of the market,

they should not forget the goal of accomplishing outstanding relationships with clients.

Self-‐managed portfolios are a rising threat to financial advisors. Online applications allow

investors to monitor the market remotely and devise their own investment strategies to obtain higher

returns. Websites such as Macroaxis, Investopedia, Wikinvest, and other open source intelligences

provide services free of charge, analyses, and user friendly platforms to access information about the

markets. Although they do not provide outstanding services and analyses that firms like Morningstar

and Bloomberg do, technology allows individuals access to financial advice and the ability to share them

with others in a more accessible and affordable manner. Hence, the comparative advantages for wealth

management firms have to be substantial to offset the cost of seeking financial advice. In fact, sites such

as ‘Seeking Alpha’ provide analytical services and additional insights from industry experts such that

investors can obtain an overview of companies’ performance and strategies.39 However, unreliable

information from uncertified experts can result in confusion and inaccuracy. Investors have to spend

time researching on their own to gather useful data. Many consider the process to be lengthy and time

consuming. In spite of the shortcomings, consumers are now able to choose among various alternatives

and platforms to pursue independent financial advice and manage their portfolio themselves.

Wealth management is moving to a more complex model to serve a wider range of consumer

demographics from age, income, geographical data, gender, and behavior. According to Movenbank,

42% of mass affluent clients will belong to generation Y by 2020.40 To serve and capture the attention of

generation Y, it is essential to accommodate their needs to seek the best alternatives. One of the best

approaches is to identify their interests. In particular, Generation Y is viewed as technologically aware

with desires for higher return and lower risk. The retention of clients becomes a challenge as the new

generation constantly seeks new opportunities such as online services with independent advising and

investment offerings.41

38 Ibid 39 "About Seeking Alpha." Seeking Alpha. N.p., n.d. Web. 30 Nov. 2014. 40 Armstrong, David. "The Advisor of the Future." The Advisor of the Future. N.p., n.d. Web. 19 Jan. 2015. 41 Crosby, C. Steven, Jensen, Jeremy, Ong, Justin. Navigating to Tomorrow: Serving Clients and Creating Value. PDF file.


15

The automated portfolio solutions commonly known as robots catch plenty of attention lately

due to their emergence in the financial services industry. A recent study from Oxford University

estimated that robots will replace 60% of financial advisors in the future. 42 The conventional practices

of setting high expectations and providing lengthy reports have become obsolete. Robo-‐advisors such as

Wealthfront first examine investors’ risk-‐tolerance and then categorize them into one of ten possible

portfolio models. These models consist of inexpensive ETFs which come from various asset classes. An

algorithm then allocates assets between taxable and non-‐taxable accounts to maximize returns. Another

algorithm tracks the error of each component against comparable indices and makes adjustments if

necessary. Similarly, FutureAdvisor links to their clients’ 401(k) and taxable investment accounts. Clients’

portfolio holdings are compared to numerous investment options, and FutureAdvisor’s algorithm then

suggests specific recommendations of index funds and other asset classes. This service is currently free

of charge and poses a significant threat to advisors’ traditional service model.43 Understanding clients’

advising and investment alternatives is essential to foster long-‐term relationships between clients and

advisors. Financial advisors help clients to set realistic goals, and pinpoint useful information from a pool

of data. Developing outstanding customer service is key to the everlasting success for advisors that

could not easily be replaced by automated robots.44

While various functionalities of online resources continue to emerge, it is crucial for financial

advisors to understand them and improve upon them based on what they are currently missing. The

science of wealth management has been diverted into a passive movement due to the changing

environment. Wealth management should continue to take an active measure in order to develop a

more sophisticated service model. Subsequently, financial advisors should recognize the use of

technology and learn how to provide adequate financial advice to investors with new ways of

communication through technology. Technology has enabled the dynamics of the financial world. At the

same time, having the knowledge of financial instruments is no longer enough for financial firms to

prove their success. Despite the emphasis on technology and detaching the focus of face-‐to-‐face

interactions, client relationship management remains crucial for success.

42 Carlson, Ben. "How Financial Advisors Can Fend Off the Robots -‐ A Wealth of Common Sense." A Wealth of Common Sense. N.p., 04 Apr. 2014. Web. 22 Jan. 2015. 43 Veres, Bob. "The Most Underappreciated Threat to the Advisory Business." The Most Underappreciated Threat to the Advisory Business. N.p., n.d. Web. 22 Jan. 2015. 44 Ibid


16

Conclusion: The Holistic Service Model Big Data, behavioral finance, and technology usage should be integrated into a holistic service

model, which still maintains personal and face-‐to-‐face client interactions. Big Data technology allows

firms to gain insights into their customers and prospects, discover investment opportunities, and assist

with risk management and compliance. New service models incorporating Big Data will be able to meet

and transcend customers’ ever-‐changing demands and overcome potential threats created by self-‐

managed services and robo-‐advisors.

Behavioral models assess unsound client behavior and aid practitioners in moderating or

adapting to such behavior. By addressing cognitive and emotional biases and redefining risk and return

in terms of behavioral aspects, the new service model increases the degree of individualization and goes

beyond purely quantitative measures mainly offered by wealth management robots. As another

essential part of the holistic service model, behavioral science also helps encourage clients to save and

invest.

Technology helps identify future competitors and recognize changes in the competitive

environment. New developments such as wealth management robots and the rapid growth of

generation Y clientele need to be addressed with urgency in order for traditional firms to preserve their

dominance in the industry. In general, advisors should use technology to reduce cost, bolster the bond

with customers, and incorporate successful aspects of e-‐services. The new service model should be able

to adapt easily to the new environment in order to serve individual needs.

Incorporating Big Data, behavioral insight, and technology into a holistic service model

augments services and client interactions of wealth managers and financial planners, allowing them to

build long-‐term relationships with clients that trump online wealth management tools. At the same

time, the holistic service model provides wealth managers and financial planners with a competitive

edge over emerging e-‐services that often lack resources to provide a credible, customized, and holistic

service model.


17

Attachements A: Urgent/Important Matrix45

There are four quadrants to the urgent/important matrix. Customer segments can then be ranked from

highest to lowest in terms of significance. If a customer segment has high importance and high urgency,

firms should act on that segment before all other segments. Then, if a customer segment is placed in the

high urgency and low importance or vice versa, they should be addressed next. Lastly, the segments

with low urgency and importance can either be ignored or acted upon last if needed.

45 Eisenhower, Dwight D. “Eisenhower Matrix.” University of California. 31 Jan. 2015.


18

B: Model for Adapting and Moderating Biases46

C: Cost of Delaying Investing47

Investor A starts investing at age 25 and is investing $5,000 each year. Investor B is doing the same but

starts 10 years later. If both investors earn 6% interests each year and take out their money at age 65,

Investor A will have accumulated 49% more in savings due to compound interest.

46 Longo, John M., and Miachel M.Pompian. The Future of Wealth Management: Incorporating Behavioral Finance into Your Practice. Dartmouth U, n.d. PDF file. 26 October 2014. 47 "The Power of Compound Interest." -‐Why You Should Start It Early. HBSC Bank USA. Web. 19 Jan. 2015.


19

D: Prospect development of wealth management48

PwC conducted a survey in 2013 to forecast the upcoming challenges and changes in private banking

and wealth management industry. As predicted by financial advisors, operations in wealth management

will grow more personally and digitally in the next two years. In order to stay competitive and build

stronger bonds with clients, expenditure will focus on improving and outsourcing new functions to serve

and strengthen new service models. The next survey shows how financial advisors perceive companies’

current position. Achieving an adaptable and efficient process and technology platform is one of the

priorities of wealth management industry. For instance, new service models should incorporate the use

of smartphones and tablets, real time trading, and accessible financial advice and services.

48 Crosby, C. Steven, Jensen, Jeremy, Ong, Justin. Navigating to Tomorrow: Serving Clients and Creating Value. PDF file.


20


21

Bibliography Armstrong, David. "The Advisor of the Future." The Advisor of the Future. N.p., n.d. Web. 19 Jan. 2015.

"About Seeking Alpha." Seeking Alpha. N.p., n.d. Web. 30 Nov. 2014.

Asani Sarkar & Kai Li, 2002. "Should U.S. investors hold foreign stocks?," Current Issues in Economics and

Finance, Federal Reserve Bank of New York, vol. 8(Mar).

Benartzi, Shlomo, and Richard H. Thaler. "Behavioral Economics and the Retirement Savings Crisis."

Science 339 (2013): 1152-‐153. Web. 27 Oct. 2014.

Benartzi, Shlomo. Behavioral Finance in Action. Allianz Global Investors, Mar. 2011. PDF file. 26 October

2014.

"Behavioral Finance: Key Concepts -‐ Prospect Theory." Investopedia. 4 Feb. 2007. Web. 13 Dec. 2014.

<http://www.investopedia.com/university/behavioral_finance/behavioral11.asp>.

"Betterment vs. Wealthfront -‐ How Do These Robo Advisors Compare?"Investor Junkie. N.p., 28 July

2014. Web.

"Big Data and Apache Hadoop for Financial Services." MapR, Hadoop. n.d. Web. 21 Nov. 2014.

<https://www.mapr.com/solutions/industry/big-‐data-‐and-‐apache-‐hadoop-‐financial-‐services>.

Bidwell, Allie. "Closing the Financial Literacy Gap to Combat Student Debt." US News. U.S.News & World

Report, 3 Oct. 2013. Web. 1 Jan. 2015.

Carlson, Ben. "How Financial Advisors Can Fend Off the Robots -‐ A Wealth of Common Sense." A Wealth

of Common Sense. N.p., 04 Apr. 2014. Web. 22 Jan. 2015.

Charles S. Sanford, Jr. "Financial Markets in 2020." Proceedings. 1994.

“Combating Financial Fraud with Big Data and Hadoop.” MapR, Hadoop. 18 Dec 2013. Web. 21 Nov.

2014.

Connors, Shawn, Courbe, Julien, Varsha, Waishampayan. “Where have you been all my life? How the

financial services industry can unlock the value in Big Data.” PwC., 1 Oct. 2013. Web. 21 Nov.

2014.

Crosby, C. Steven, Jensen, Jeremy, Ong, Justin. Navigating to Tomorrow: Serving Clients and Creating

Value. PDF file.

"Deriving Business Insight from Big Data in Banking." IBM. n.d. Web. 21 Nov. 2014. <http://www-‐

01.ibm.com/software/data/bigdata/industry-‐banking.html>.

“Financial Services.” Datameer. n.d. Web. 21 Nov. 2014.

Gerneglia, Bill. “Finding Value in Open Data Vs Big Data.” myBigDATAview., Blog. 21 Nov. 2014.


22

Heath, Chip, and Dan Heath. <i>Switch: How to Change Things When Change Is Hard</i>. New York:

Broadway, 2010. Print.

How Industry Experts Are Making Sense of Behavioral Economics. FinancialPlanning, Feb. 2013. Web. 28

September 2014

Huber, Andreas, Hannappel Hauke, Nagode Felix. “Big Data: Potentials from a risk management

perspective.” Banking Hub., 01 Jul. 2014. Web. 21 Nov. 2014.

Kahneman, Daniel, and Amos Tversky, (1979). “Prospect Theory: An Analysis of Decision under Risk.”

Econometrica 47.2 (1979): 263-‐291. Web. 27 Oct. 2014.

Kitces, Michael. "Using Social Proof To Help Clients Make Better Financial Planning Decisions |

Kitces.com." Kitces.com: Advancing Knowledge in Financial Planning. 30 Oct. 2013. Web. 13 Dec.

2014.

Kumar, Anjani. “Big Data use cases in financial services.” Infosys., 19 Jul. 2014. Web. 21 Nov. 2014.

Longo, John M., and Miachel M.Pompian. The Future of Wealth Management: Incorporating Behavioral

Finance into Your Practice. Dartmouth U, n.d. PDF file. 26 October 2014.

Nettle, Daniel. Happiness: The Science behind Your Smile. Oxford, UK: Oxford UP, 2005. Google Books.

Web. 1 Jan. 2015.

"Save with Every Purchase." CNNMoney. Cable News Network, n.d. Web. 12 Dec. 2014.

Saraf, Sanjay. “Western Union Implements Enterprise Data Hub on its Path to Deliver an Omni-‐channel

Customer Experience.” Cloudera. n.d. Web. 21 Nov. 2014.

Shamgar, Idor. “5 Big Data Use Cases for Banking and Financial Services – Part 2.” SAP., Blog. 21 Nov.

2014.

Thaler, R. H. "Mental Accounting and Consumer Choice." Marketing Science 27.1 (2008): 15-‐25. Web. 13

Nov. 2014.

"The Power of Compound Interest." -‐Why You Should Start It Early. HBSC Bank USA. Web. 19 Jan. 2015.

Tomlinson Joseph. Behavioral Finance—Implications for Investment Planning. Joe Tomlinson, n.d. PDF

file. 26 October 2014.

Venkateswaran, S., & Vaed, K. (2013). The future of wealth management services. FT.Com,

Veres, Bob. "The Most Underappreciated Threat to the Advisory Business." The Most Underappreciated

Threat to the Advisory Business. N.p., n.d. Web. 22 Jan. 2015.

Wagle, Likhit. “What’s Driving Financial Services? Think Big Data. ” Forbes., 31 Jul. 2013. Web. 21 Nov.

2014.


23

Warrell, Helen. “Demand for Big Data and skills shortages drive wages boom. “ Financial Times., 30 Oct.

2014. Web. 21 Nov. 2014.

Documents

Gould Scholastic Award – Julian Fung, Lasse Fuss, Tommy Ng