Industrial Marketing Management 86 (2020) 144–153

Contents lists available at ScienceDirect

Industrial Marketing Management

journal homepage: www.elsevier.com/locate/indmarman

Research paper

Big data analytics for supply chain relationship in banking T

a,⁎ b c
Jui-Long Hung , Wu He , Jiancheng Shen
a
Department of Educational Technology, Boise State University, 1910 University Drive, Boise, ID 83725-1747, United States of America
b
Department of Information Technology & Decision Sciences, Strome College of Business, Old Dominion University, Constant Hall 2075, Norfolk, VA 23529, United States
of America
c
Taylor University, Upland, IN 46989, United States of America

A R T I C LE I N FO A B S T R A C T

Keywords: This paper reports how a commercial bank in Asia uses big data analytic as a tool to explore the internal B2B
Supply chain finance data to improve supply chain finance and the efficiency of marketing tactics and campaigns. A case study was
B2B analytics conducted by analyzing two types of supply chain relationships: (1) supply chain relationships in the credit
reports; (2) e-wiring transactions among supply chain companies. The results show that big data analytics is very
useful in terms of improving the commercial banks' marketing and risk management performances. The case
study also set a good example for B2B firms seeking to understand how they could leverage big data analytics to
differentiate customer solutions, sustain profitability and generate new business values. Theorical and practical
implications are also discussed.

1. Introduction factoring”, is a set of solutions that optimizes cash flow by allowing

businesses to lengthen their payment terms to their suppliers while
Two main purposes of a marketing campaign are to retain existing providing the option for their large and Small and Medium-sized En-
customers and to acquire new customers. Many businesses are re- terprises (SME) suppliers to get paid early (Bogdan & Sava, 2018).
cognizing the significant role that big data analytics could play in Nowadays, all business activities can be tracked in terms of cash and
growing customer loyalty and in marketing, especially for the banking information flows, which are usually completed electronically among
industry (Hassani, Huang, & Silva, 2018). The major two divisions in different bank accounts, either within the same bank or across different
the banking industry are personal and corporate banking. The former banks. These business activities can be utilized to establish a specific
provides services to individuals, and the latter focuses on corporate company's supply chain, and then can be extended to a larger supply
customers. Many banks systematically track and store large amounts of chain network by including affiliated or upstream/downstream com-
customer data (Ghafari & Ansari, 2018). However, regarding the idea panies along their related supply chains. Such information was mainly
applying big data analytics in marketing, this effort has been mainly limited to risk management like fraud detection (DuHadway,
focused on personal banking (Hassani et al., 2018; He, Wang, & Akula, Carnovale, & Hazen, 2017) or like loan early warning (Wang, 2017).
2017). Since corporate banking is the major revenue source for most Since its supply chain activities reflect a business's operations, banks
banks, their applications of data analytics have been limited to risk should explore other potential applications by analyzing the data. Lilien
management only (Choi, Chan, & Yue, 2017). (2016) found that most of the B2B data have not been analyzed in
The term “supply chain” has been defined as “the integration of key meaningful ways to identify potential corporate customers or to im-
business processes from end user through original suppliers that provides prove business offerings to existing corporate customers along the
products, services, and information that add value for customers and other business's supply chains. That means that banks or financial institutions
stakeholders” (Lambert, Cooper, & Pagh, 1998). That means that each of do not know how to effectively utilize big data analytics to identify
corporations is a node in the supply chain network. As the results of potential corporate loan customers via supply chain relationships or
internationalization, supply chains might stretch across the globe with how to differentiate their product and service offerings for existing
multinational buyers and suppliers. Corporations are under pressure to customers. Thus, this study aims to illustrate how a bank can identify
unlock the working capital trapped in their supply chains. Banks' supply potential customers via analyzing supply chain relationships. Our spe-
chain finance, also known as “supplier finance” or as “reverse cific research questions are:

⁎
Corresponding author.
E-mail addresses: andyhung@boisestate.edu (J.-L. Hung), whe@odu.edu (W. He), jiancheng_shen@taylor.edu (J. Shen).

https://doi.org/10.1016/j.indmarman.2019.11.001
Received 1 July 2018; Received in revised form 9 October 2019; Accepted 4 November 2019
Available online 22 November 2019
0019-8501/ Published by Elsevier Inc.
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

1. How can big data analytics be leveraged to identify potential cor- than those of personal banking customers (Guo, 2018). For the case
porate customers, and how can business offerings to existing cor- study reported in this study, the total number of banks was increased by
porate customers be improved via supply chain relationships? 5 times due to the financial deregulation in 1990s. Customers can find a
2. What additional lessons can be learned from the aspects of analytics bank branch every 3.7 km2 and an ATM every 1.32 km2 on average
and marketing campaign implementation? (Hung & Luo, 2016). As one corporate customer might have several
corporate loan accounts with different banks, banks have the desire to
We believe that the results of this case study will set a good example establish long-term relationships with corporate customers in order to
for banks or other types of financial institutions seeking to understand gain a moderate or a greater share of the financial market and of the
how they could leverage big data analytics to differentiate customer customers' business (Heinonen, Johnson, & Peterson, 2014). In addi-
solutions and to sustain profitability via supply chain relationships. tion, retaining an active existing corporate customer is much cheaper
This paper is organized as follows. In the next section, the literature than acquiring a new customer (Ennew, Binks, & Chiplin, 2015).
review of related studies is presented. Next, there is a case study with Therefore, most banks have been investing more efforts in maintaining
background introduction, and the data collection procedure and data existing corporate customers than acquiring new customers. The other
analysis are described. This is followed by the discussion section and reason is that corporate customers involve larger transaction amounts.
then by the conclusion of this research. One default loan from a corporate customer is much more serious than
the default of an individual's personal loan. Therefore, for corporate
2. Literature review customers, research efforts have been focused on risk management
(Valverde, Solas, & Fernández, 2016), and include additional manage-
2.1. Big data analytics in banking rial or monitoring mechanisms to decrease information asymmetry
(Cycon, 2018) as well as better credit evaluation to exclude high-risk
As business management becomes more and more complex due to corporations (Beccalli & Poli, 2015). In the era of big data analytics,
internationalization and to new innovations, business administrators advanced analysis methods and the inclusion of non-traditional data
need better support for decision making. That is why data-driven de- (such as non-financial data) have also been adopted, in order to further
cision making is becoming increasingly important (Tiwari, Wee, & improve credit evaluation or dynamic monitoring (Zhang & Pang,
Daryanto, 2017; He, Zhang, Tian, Tao, & Akula, 2019). For companies, 2019). Due to the rapid development of technology, some of the ex-
there are two major trends in big data analytics: (1) current companies isting banks are adopting big data analytics to enhance their competi-
reply on big data analytics to identify new opportunities, to improve tive strength in order to better deal with challenges from FinTech
current products or services, and to optimize internal processes (Kaisler, companies (Trelewicz, 2017). One competitive advantage, for existing
Armour, Espinosa, & Money, 2013); and (2) new companies reply on banks, is that they own a large amount of historical customer data.
big data analytics to develop innovative products and services (Zhang, However, it is questionable whether a bank can convert these data into
Ren, Liu, & Si, 2016). The banking industry can be considered an early actual benefits (Liu, Liu, Xiao, & Eltabakh, 2018). According to re-
adopter in data-driven decision making. Big data is a term that “de- source-based theory (Barney, Ketchen, & Wright, 2011; Slotegraaf,
scribes large volumes of high velocity, complex and variable data that Moorman, & Inman, 2003; Vorhies & Morgan, 2005), understanding
require advanced techniques and technologies to enable the capture, collectable or collected business/consumer related data resources, and
storage, distribution, management, and analysis of the information.” finding out an appropriate way to utilize them, are key ways to attain a
(TechAmerica Foundation's Federal Big Data Commission, 2012). As a competitive advantage (Gupta & George, 2016; Marr, 2015). For ex-
bank stores a large collection of customer's demographics, behavioral, ample, airlines such as Southwest are using insights from big data to
and transactional data, big data analytics have been proven to be very facilitate its dynamic capability to deliver excellent customer service
useful in terms of improving the marketing of commercial banks (Sun, and meet unrecognized customer needs (van Rijmenam, 2013).
Morris, Xu, Zhu, & Xie, 2014), as well as their risk management per- Southwest has used speech analytics to analyze live-recorded interac-
formance (Rahman & Iverson, 2015). However, regarding the applica- tions between customers and southwest personnel to identify customers'
tion of data analytics in marketing, most of the efforts have been limited needs/issues and staff's corresponding responses. The extracted
to personal banking (often called retail banking) only (Hassani et al., knowledge was used for training to better understand various customer
2018). The most common marketing models are (1) customer lifetime demands/issues and improve customer relationship management. An-
value prediction, the predicted monetary value that represents the other example is American Express (Amex). Amex understood that
amount of revenue that a customer will provide the business over the mobile geotargeted advertising requires accurate geolocation data.
lifetime of their relationship (Moro, Cortez, & Rita, 2015); (2) customer Therefore, Amex partnered with Foursquare which is a technology
clustering, a model, used in customer relationship management, that company to collect users' real-time locations via their sharing and
aims to classify customers based on attribute similarity (Ma, Baer, & checking-in behaviors on the social networking site. Amex can achieve
Chakraborty, 2015); and (3) product affinity prediction, the model that the goal of geotargeted advertising by combining Amex's customer
predicts a customer's preferred products or services by analyzing his/ purchase history, other preferences revealed by analysis, and customer's
her historical transactions and profiles (Dash, Pattnaik, & Rath, 2016). geolocations (Deutsche Bank, 2014).
Since corporate banking is the major revenue source for many banks, To enhance a competitive advantage in marketing, a bank can ex-
applying analytics in the corporate banking marketing realm has not plore the following three steps: first, it can understand what has been
yet attracted much attention. Instead, because corporate banking collected in its data warehouse; second, its can extract insights from
usually involves larger transactional amounts per customer when stored data; and finally, it can utilize these insights to enhance dy-
compared with personal banking (e.g. corporate loan vs. personal loan), namic/adaptive capabilities (Erevelles, Fukawa, & Swayne, 2016).
research efforts in corporate banking have been focused on risk man- Nowadays, the ratio of electronical payments, which involves in-
agement. formation and cash flow transactions within the same bank or between
Studies have found that the key success factor of the corporate banks, is rising (Asokan, Janson, Steiner, & Waidner, 2000). Commer-
banking lies in the management of customer relationships (Turnbull & cial banks can identify potential corporate customers by tracking the
Gibbs, 1987). The risk-aversion trend has the following reasons. It is accumulated cashflow transaction data of existing corporate customers.
noted that the corporate banking market is considerably more valuable These transactions reflect a corporation's operations and can be used to
and more complex, since a company usually works with multiple banks support a customer's credibility (Wang & Gao, 2018). It is especially
at the same time. In addition, the relationships between a bank and its important for new SMEs who have just entered the business and have
corporate customers are more frequently and more closely examined not yet shown good numbers on their financial statements. These new

145
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

Fig. 1. The Framework of SCF.

SMEs might not be able to pass a bank's credit evaluation, in order to In-transit financing aims to turn in-transit inventory into cash. For
obtain a corporate loan. However, since supply chain finance's credit example, an importer can turn imported oversea goods into working
evaluation focuses on buyers in the supply chain, it is an alternative capital. Once the inventory is loaded on the ship, the exporter sends the
financing source for new SMEs, if they are vendors of one of the bank's bill of lading, commercial invoice and proof of insurance (if exporter
trustworthy corporate customers (Song, Yu, & Lu, 2018). The next paid) to the importer, the importer can forward these documents to SCF
section will provide more detailed information on supply chain finance. providers for monitoring and can draw on his/her available credit
under his/her inventory-in-transit facility at any time.
2.2. Supply chain finance Pre-shipping financing is another common type of SCF aiming to
convert supplier's non-liquid assets (such as raw materials, inventory,
Supply chain finance (SCF, hereafter) is an emerging topic at the account receivable, etc.) into cash (Vousinas, 2019). With the devel-
intersection of logistics, supply chain management and financing opment of the online B2B platform, electronic invoicing, and e-wiring
(Hofmann & Johnson, 2016). Hofmann (2005) defines SCF as “an ap- services, most of the payment processes can be made electronically and
proach for two or more organizations in a supply chain, including external can be tracked by banks or by B2B platforms (Bogdan & Sava, 2018;
service providers, to jointly create value through means of planning, steering, Vousinas, 2019). That means that SCF providers can better track and
and controlling the flow of financial resources on an inter-organizational monitor business operations and can open opportunities to smaller
level”. In the past decades, many companies have implemented SCF as a companies, even to those which cannot provide complete, well-pre-
solution for optimizing cash flow and improving supply chain-wide fi- pared financial statements (Tsai & Peng, 2017).
nancial health and stability. SCF represent a combination of financial
services and technology tools that provide short-term credit to optimize 2.3. The SCF market
working capital for businesses (Lamoureux & Evans, 2011, 2011; Carlo
& Menno, 2014). Fig. 1 shows a framework which lists possible SCF In the past decade, the market of the SCF sector has maintained
services in the business activities (More & Basu, 2013). It involves three strong demands, and this trend has resulted in many successful business
parties in each transaction: the buyer, the supplier, and the financing applications. PrimeRevenue, a supply chain finance platform, has an
institution (i.e. SCF provider). The SCF provider in the Figure can be operations processing volume of $7 billion worth of invoices each
transitional financial institutions or FinTech companies which provide month (Elms, Hassani, & Low, 2017). In addition, leading banks have
SCF services via traditional application documents or online platforms. also partnered with Fintech companies to set up their SCF services. For
The basic types of SCF can be classified into pre-shipment financing, in- instance, HSBC and Santander have formed an alliance with Tradeshift,
transit financing, and post-shipment financing. The post-shipment fi- an invoicing, finance, and procurement network, to connect with over
nancing is also known as “supplier finance” or as “reverse factoring.” 1.5 million buyers and suppliers worldwide (The Economist, 2017).
The suppliers (or sellers) sell their invoices or receivables at a discount Further, multinational corporations are seizing many new business
to SCF providers. In return, these sellers gain faster access to the money opportunities in emerging markets through the SCF platform. Apple,
that they are owed, which can be used as working capital. At the same Colgate, Dell, P&G, Kellogg's, and Siemens are working with FinTech
time, the buyers usually retain a lengthened payment term. The banks companies to increase capital available to their whole supplier eco-
also get the benefit of collecting the money directly from buyers, rather systems (Rogers, Leuschner, & Choi, 2016). A business case study
than counting on the creditworthiness of the supplier. When the buyer conducted by the Aite Group (Camerinelli & Schizas, 2014) reported
has a better credit rating than the supplier, the SCF allows the supplier that 80% of business-to-business transactions were carried out on credit
access to a larger financial capacity at a lower cost, by leveraging the terms, and that trade credit constituted 37% of the total business assets
buyer's stronger credit rating (Schofer & Fowler, 2017). To optimize the in the United Kingdom. The use of receivables as assets through an SCF
financing efficiency of SCF, financial institutions need to gain knowl- platform is an effective way to optimize the management of the working
edge about a corporate buyer's creditability by analyzing its accumu- capital and the liquidity tied up in supply chain processes for colla-
lated cash flows. Therefore, bank prefers to provide post-shipment fi- borating business partners.
nancing to a large manufacturer (e.g. Apple), so they can count on the
manufacturer's creditworthiness, rather than its hundreds of individual 2.4. Research in SCF
suppliers, especially when these suppliers are relatively smaller with
lower credit ratings. The supply chain processes contain three important flows: the

146
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

material, the information, and the cash flows (Tang & Musa, 2011). applications of big data analytics in personal banking (Jagtiani,
With the development of the online B2B platform, electronic invoicing, Vermilyea, & Wall, 2018; Sun et al., 2014). The literature review shows
and e-wiring services, the ratio of trackable cash flow is rising, as well intensive studies have been focused on the discussion of innovative SCF
(Schoenherr & Pero, 2015). Therefore, there is an increasing amount of services offered by FinTech companies (e.g. Kharif, 2016; Li, 2018; Tsai
effort spent on applying analytics in supply chain research (Waller & & Peng, 2017; Zhou et al., 2016). In addition, studies have been focused
Fawcett, 2013), especially in the risk management. For banks, risk on the risk management in SCF, rather than marketing. Therefore, the
management has been aimed at addressing various issues in B2B purpose of this study aims to describe how a large commercial bank in
transactions. Those issues could be identified and classified as the po- Asia combined multiple data sources to establish and to expand its
tential risks associated with material, cash, and information flows (Tang customers' supply chain network and how it actively used those analytic
& Musa, 2011). Material flow risks arise either from the problems of results for corporate banking marketing. Case study was adopted as the
coordinating supply and demand or from the disruptions to normal research method for this study because it can provide in-depth and
activities (Kleindorfer & Saad, 2005). Cash flow risks vary with the contextual understanding about the phenomenon of the target case. Yin
fluctuations of the cash inflows, outflows, and netflows in each period (2017) defined a case study is “an empirical method that investigates a
of a planning horizon (Tsai, 2008). Information flow risks cover various contemporary phenomenon in depth and within its real-life context, espe-
issues, including information accuracy, information system security and cially when the boundaries between phenomenon and context may not be
disruption, intellectual property, and information outsourcing (Faisal, clearly evident”. The case study research method allows researchers to
Banwet, & Shankar, 2007; Tang & Musa, 2011). focus in-depth on a case or cases. It is commonly used in many social
The other risk management trend is the inclusion of non-traditional science disciplines and the practicing professions such as business, so-
data sources and advanced analytic methods in SCF's credit evaluations cial work and education (Yin, 2017). Many researchers in the business
(Fu & Zhu, 2016), especially in SCF services offered by FinTech com- domain successfully use the case study research method to study real
panies (Li, 2018). Traditionally, banks have long-term relationships business situated issues (Eriksson & Kovalainen, 2015).
with buyers in the SCF, and these corporations have been evaluated and To answer the two research questions proposed in the introduction
monitored periodically before they offer SCF services to their sellers. section, the case study starts with the descriptions of the case back-
However, as FinTech companies which offer SCF services, their target ground, data sources, and processes of the supply chain network con-
customers are SMEs (including sellers and buyers) (Chen, 2016). These struction. A campaign was implemented, based on the supply chain
companies, usually, do not complete detailed financial statements. network, to acquire new customers of corporate loan. The details about
Therefore, these platforms need to rely on additional data and on robust the campaign design, the implementation, and outcomes were reported
analytic methods to improve the quality of credit evaluation (Zoran, in the case study as well. Lessons learned from the case study and im-
Tatjana, & Aleksandra, 2018) by building a robust fraud detection and plications were discussed in the discussion section.
prevention mechanism to tackle possible risks. According to a Deloitte
poll, the use of analytics to mitigate third-party fraud, waste, and abuse 3.1. Case background
risks in supply chains jumped to 35% in 2017, compared to its level of
25.2% in 2014 (Deloitte, 2017). Zainal, Som, and Mohamed (2017) The target bank (ABC Bank, hereafter) is under a financial holding
reviewed existing computer platforms such as spreadsheets, big data, company and is ranked as one of the top 250 banks worldwide. Its
forensic analytics, text analytics, and expert systems to detect and headquarters is in Asia, and it has 190 branches, 34 overseas branches/
prevent digital fraud. They suggested that, to curb fraud, expert systems representative offices, and over 7000 domestic employees. Overall, ABC
are the best option among the alternative tools available. Researchers Bank's strength is in corporate banking, especially in SME loans. Until
have also recommended that the application of a Benford analysis the end of 2018, the bank's active corporate customers numbered about
(Clearly & Thibodeau, 2015) could support forensic analytics, to detect fourteen thousand companies. Here, “active” means that the corporate
supply chain fraud (Kraus & Valverde, 2014; Varma & Khan, 2012). The customer had completed at least one active transaction in the past six
Benford analysis approach identifies abnormally mismatched data in an months. Among the fourteen thousand companies, about 35,000 com-
excel sheet, and helps to locate fraudulent transactions on a dataset of a panies (25%) applied the e-wiring service, which allows corporates to
supply chain network. This could help the commercial banks to detect schedule online payments to corporate accounts. Table 1 shows the
and to prevent financial fraud within a short time span. More research statistics of e-wiring transactions in 2016.
efforts are needed to develop robust fraud detection and prevention
systems, in order to reduce the risk of fraud in supply chain finance. 3.2. Analysis
Some FinTech companies collect front-end data, which includes
transactional data from SMEs' customers and competitors, actual sales, The purpose of the analysis aims to establish and expand the supply
and customers' reviews, for credit evaluation (Kharif, 2016). For ex- chain network relationships of the ABC's corporate customers. The
ample, Alibaba utilized transactional, behavioral, rating, and profile supply chain relationships come from the following sources: (1) af-
data for credit evaluation (Li, 2018). JD Logistics combines a corpor- filiated or upstream/downstream companies on a corporation's credit
ation's financial data and material flows tracked by IoT technology, as report; or (2) e-wiring transactions to upstream or downstream com-
well as payment information, for its credit evaluations (Tsai & Peng, panies tracked by ABC Bank. A corporation has the obligation to reveal
2017). The Ping An Bank chose to collaborate with third-party com- important affiliated companies, major downstream/upstream compa-
panies to obtain additional data like invoices, tax records, and receipts nies, and the list of the members of its board of directors, when it files a
from water and electricity spending, in order to better understand a loan application. Fig. 2 lists the data collection and the data analysis
corporation's operations (Zhou, S, & Yang, 2016).
The literature review shows that banks should look for innovative Table 1
ways to better utilize their B2B customer data, especially in the realm of Statistics of e-wiring in 2016.
marketing. However, due to the unique characteristics of corporate Category Statistics
banking, there are some special considerations in corporate banking
marketing, as compared with the retail industry. Transaction frequencies 175,290
Average number of wiring receivers 9.6
Average number of wiring per receivers 5.9
3. Case study
Average wiring amount per transaction $33,000
Average registered capital amount per receiver $9,110,700
As precision marketing and risk management are two major

147
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

Fig. 2. The Analytic Flow of the Supply Chain Network.

flows. First, an e-wiring network is developed, using all online wiring president, a list of the members of its board of directors, the company's
transactions among companies. The senders of these wiring transactions phone number, the company's address, and its registered capital
are ABC Bank's corporate customers. The analytic flow examines whe- amount. The next step checks whether the members of the board of
ther the receivers are ABC Bank's corporate customers, as well. If the directors are ABC Bank's personal banking customers or if they own
answer is No, then the list will become a partial list of A. If the answer is other companies. If the answer is Yes, then it continues to search
Yes, then the analytic flow checks whether sender's or receiver's credit whether the credit report is available, and the loop will continue until
report is available. If the sender/receiver is ABC Bank's corporate cus- all potential customers have been identified. If the answer is No, then
tomer, and the credit report is available, then affiliated and down- the analytic flow stops. Fig. 3 shows the results of the initial network
stream/upstream companies can be retrieved from the report. These (i.e. the list of potential customers), which contains 225,733 compa-
affiliated and downstream/upstream companies will be appended to nies. Compared with the original 35,000 customers upon opening the e-
the wiring network. At the same time, if affiliated and upstream/ wiring service, the analytic flow expands the network 5.4 times larger.
downstream companies are also ABC Bank's corporate customers, the Fig. 3 can be further used to generate potential customers by ap-
analytic flow will loop back to search for corresponding credit reports. plying filtering conditions. For example, Fig. 4 shows the network of
If not, these companies will be merged into List A. The loop will con- corporations with at least eight annual wiring transactions and over
tinue until all potential customers have been identified via the credit 13,500 USD total wiring amounts. The network contains 1621 current
reports (Lists B and C). corporate customers (green nodes). The other 2563 companies were
List A includes companies identified via various relationships, but non-ABC Bank customers (purple nodes). Both types of companies can
they are not ABC Bank's corporate customers. E-wiring transactions or be potential customers for marketing campaigns. Fig. 4 shows that more
credit reports are required to include a company's Tax ID, which allows than half of the companies were not ABC bank customers. How to select
a search of the government's open data for company's contact in- potential customers with higher responses and higher credit approval
formation. The contact information contains the name pf the company's rates are the major goals of the follow-up analysis.

148
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

Fig. 3. Results of initial network.

3.3. Campaign implementation companies with or without wiring transactions; (3) inbound or
outbound wiring transactions with the core companies only.
Because the corporate loan is the most profitable product in cor- • Exclude companies whose tax IDs or contact information cannot be
porate finance, the first campaign aimed to identify potential corporate located.
loan customers. However, if the potential customer does not need a
corporate loan, the account official will introduce other corporate fi- The first round of implementation selected 4800 companies. Table 3
nancial services. The campaign list was generated with the following lists the distributions of these 4800 companies, which were assigned to
filtering conditions: account officers at all domestic branches. Because corporate finance
usually requires a longer time to interact with customers, to prepare
• Identify companies with at least 50 million in USD loan amounts as application materials, and to review and approve applications, the
the core companies. Limit the search scope to the companies which implementation period lasted nine months, during 2017. Table 2 lists
had supply chain relationships with these core companies. (Most of the numbers of potential companies and their relationships with the
these core companies are exchange-listed companies, and this con- core companies. Based on Table 2, it is clear that most of the potential
dition promotes target potential companies' credits and enhances companies had only one relationship with the core companies: (1)
the credit approval rate.) companies with an affiliate relationship only (34.4%), (2) companies
• Possible supply chain relationships include: (1) affiliated companies with an upstream/downstream relationship only (12.7%), or (3) com-
with or without wiring transactions; (2) upstream/downstream panies with an e-wiring transaction relationship only (50.3%).

Fig. 4. Network after applying filtering conditions.

149
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

Table 2 existing banks, one advantage is the large number of existing customers
The distribution of potential companies. and the generated historical data. The case study reported here de-
Affiliated Upstream/downstream Wiring Percentage monstrates how a bank utilizes the stored data resources for customer
relationship relationship relationship acquisition (Gupta & George, 2016). The discussion will focus on two
research questions: (1) How can big data analytics be leveraged to
Y Y Y 0.1%
identify potential corporate customers and to improve business offer-
N 0.6%
N Y 1.1%
ings to existing corporate customers via supply chain relationships? (2)
N 34.4% What additional lessons can be learned from the aspects of analytics
N Y Y 0.9% and marketing campaign implementation?
N 12.7%
N Y 50.3%
5.1. The unique characteristics of corporate customer marketing

4. Results Compared with personal banking, customer relationship manage-

ment in the corporate banking is more complex and time-consuming. In
Table 3 shows the campaign results by checking the response and this case study, we can observe the following unique characteristics of
the approval rates. Traditionally, account officers were used to check corporate banking:
into the yellow pages or the list of companies within their branch's
region to identify potential customers. They were also required to input •A longer implementation period and a lower response rate:
their contact histories with these potential customers into the salesforce Compared with personal banking marketing, corporate banking
system. The baseline response rate is the number of applied companies marketing requires a longer implementation period, because each
divided by the total number of contacted companies. The baseline ap- customer needs multiple visits, longer discussions, and more appli-
proval rate is the number of approved applications divided by the cation materials. Because the process involves more complex con-
number of submitted applications. Both are the indictors to evaluate the siderations at both ends (bank and corporate), the response rate is
effectiveness of this campaign. Based on Table 3's results, it is clear that usually lower than that for personal banking marketing.
both the response and the approval rate were significantly higher than • Precise marketing with risk-aversion consideration: Risk manage-
the baseline rates. ABC Bank approved almost all of the applications in ment is the core of the banking industry. The case study shows that
the campaign. potential customers increased by 4.4 times (compared with the
Fig. 5 shows the results of a decision tree analysis which analyzed number of existing customers) via assorted relationship connections.
what kind of companies responded to the campaign and applied for a In the corporate marketing, risk is an indispensable factor which
corporate loan (since it was the major target product of this campaign). should be considered when generating the list of customers for
The results show the average rate of response to the corporate loan was marketing. Otherwise, even a corporation which might be interested
2.17% (104 companies). The response rates were higher if these po- in a corporate loan might not be approved after the bank's credit
tential customers already had a corporate account at ABC Bank. If their evaluation.
experience with ABC was longer than 4.5 years, then the response rate • Supplementary information to boost the response rate: The sum-
was 1.96%. However, if their experience with ABC was shorter than marized relationship information regarding the target company and
4.5 years, the response rate increased to 15.35%. The condition, plus at its relationships with the core and with other corporate customers
least one wiring transaction, increased the response rate to 20.66%. It was provided to the corporate banking officer. That allowed the
decreased to 9.35% for companies without any wiring transactions. officer to design a strategic plan and to customize services before
For companies with a longer experience with ABC Bank (longer than visiting the target company. Based on the results, this action also
4.5 years), the average response rate was 1.96%. The response rate boosted the response rate.
increased to 9.52% for companies with at least 3.5 of ABC Bank's cor- • A growing supply chain network: When new customers are con-
porate product holdings. The response rate decreased to 1.71% if the verted, they also bring new relationships with new potential cus-
product holding was less than 3.5. For companies that were only listed tomers. That means that the supply chain network keeps growing, as
on the core companies' credit reports (i.e., with zero corporate product more marketing efforts are spent in this approach.
holdings), the response rate was zero. Finally, for companies with
wiring transactions, but which had not been ABC Bank's customers 5.2. The type of customers with a higher response rate
before the campaign began, the response rate was 1.25% (still higher
than the baseline). The relationship network was generated by analyzing the following
three supply chain relationships: (1) affiliated companies, (2) upstream
5. Discussion and downstream companies, and (3) e-wiring transactions. When the
scope is limited to companies with any one of above relationships with
Traditional banks and Fintech companies might choose to collabo- the core companies, the results show that both the response and the
rate or compete with each other (Hung & Luo, 2016). Traditional banks approval rates are significantly higher than those in the past. The core
look for new technologies to maintain their competitive strength, when companies had long-term relationships with ABC Bank, so when ac-
they are facing challenges from FinTech companies (Hung & Luo, count officers contacted these potential customers, both ABC Bank and
2016). FinTech companies might have creative ideas to design and potential customers had at least a common connection (i.e., the core
develop innovative products and services. However, the challenge is companies). Therefore, the success rate was significantly higher. In
how to attract customers to use their innovative services. However, for addition, because these potential customers showed stable relationships

Table 3
Results of campaign response and approval rates.
Financial Services Baseline response rate Baseline approval rate Campaign response rate Campaign approval rate

Corporate loan 0.98% 48.1% 2.17% 89.4%

Other financial services 2.67% 73.6% 3.75% 100%

150
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

Fig. 5. Companies which responded to the corporate loan in the campaign.

with these core companies, the loan approval rate was also significantly 5.4. Other possible applications
higher than the natural baseline. Unlike banks that have more complete
customer profiles, historical wiring records, and account activities, The relationship network can have other possible applications. For
most of the FinTech platforms track transactional data only (Song et al., example, in the constructed network, there were 117 companies which
2018). That means that banks own more detailed data in the credit had wiring transactions with more than 100 companies. In addition, 95
evaluation and can develop the Supply Chain Network for other ap- out of the 117 companies had at least 10 million USD in registered
plications (Sawers, 2017). capital. ABC Bank is contacting these companies for more advanced
Based on the results, relatively new existing customers (those with SCF. Because the process is sophisticated, and involves companies' ERP
less than 4.5 year) with wiring transaction relationships showed the systems, the campaign (supple chain + sale chain finance) is still on-
highest response rate. The second highest group was relatively older going.
existing customers (those with more than 4.5 years) with more than 3.5
product holdings (no corporate loans). The third highest group was
relatively new existing customers without a wiring transaction re- 5.5. Implications
lationship. Therefore, for ABC Bank, attracting companies to open a
checking or a saving account as a starting point is crucial, as these The discussion presented in this paper provides several insights for
companies can start using the e-wiring service. On the other hand, older both theory and practice. In terms of theoretical implications, the study
companies might have been contacted by the account officers, so the provides a concrete real-world case to support the resource-based
success rate is lower unless these companies had already made use of theory in the Big Data Era, which advocates that big data analytics
several of ABC's corporate banking services. In summary, the wiring should be considered by banks as key resources in attaining a compe-
transaction relationship promoted the success rate, especially when titive advantage (Gupta & George, 2016). Since Big Data is a new source
they potential loan customers were already existing ABC Bank custo- of capital in today's marketplace and is also a great source of idea
mers. In addition, an increase in the number of relationships with the generation for product development, customer service, and so on, or-
core companies, or with the bank, showed a higher response rate, as ganizations that do not develop the resources and capabilities to ef-
well. fectively use Big Data will have a hard time to survive the Big Data
An intensive literature search has shown successful stories about the revolution (Erevelles et al., 2016). Due to the unique characteristics of
Fintech companies in SCF (Fenwick, McCahery, & Vermeulen, 2017; corporate banking, marketing considerations are different from per-
Song et al., 2018). Our study reveals how traditional banks can respond sonal banking. Because corporate loan must be approved via credit
to challenges via analytics. Most banks should have more corporate evaluation, the selection of potential customers should take risk into
customers and historical customer data than Fintech companies. The consideration to raise the approval rate. In addition, the campaign re-
key becomes whether banks can convert data into revenue. sponse rate indicates the importance of customer relationship man-
agement in corporate banking. As marketing research in banking in-
dustry has been focused on personal banking, more research efforts are
5.3. True and false supply chain relationship desired to focus on the B2B marketing.
In term of practical implications, banking firms can learn from our
From the aspect of analysis, wiring transactions cannot always be findings to improve their finance services by 1) providing convenient
regarded as a supply chain relationship. For example, a shipping com- B2B e-wiring service and attracting potential customers in the supply
pany might show many inbound wiring transactions, since it provides chain to open a checking or a saving account. It is a good start point to
shipping services to the core company. Therefore, it cannot be regarded manage potential corporate customers and these corporates can start to
as a potential customer, due to its not having a supply chain relation- accumulate credits.; by 2) enhancing interactions with existing custo-
ship. However, if the wiring transactions occur among companies mers to strengthen relationships, as customers with higher numbers of
within the shipping industry, then these transactions can be regarded as product holdings also showing higher response rates; and by (3) better
supply chain relationships. Because the strength of wiring transactions utilizing B2B data to generate more corporate banking applications.
was calculated by the wiring frequencies and the wiring amounts, non-
supply chain transactions should be excluded from the computation.

151
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

5.6. Conclusion Fu, Y. G., & Zhu, J. M. (2016). Network supplier credit evaluation model based on big
data. Journal of Central University of Finance & Economics, 8.
Ghafari, H., & Ansari, S. (2018). Effect of five key factors on the implementation of core
Big data analytics has been widely adopted in personal banking banking system. International Journal of Scientific Research and Management, 6(07).
marketing, especially in customer segmentation and profiling, in pro- Guo, Y. S. (2018). Implementing relationship banking strategies and techniques and
duct affinity prediction, and in customer attrition prediction. This study improving customer value. Finance and Market, 2(2).
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics cap-
shows a potential application of big data analytics by analyzing three ability. Information & Management, 53(8), 1049–1064.
types of supply chain relationships to identify potential corporate cus- Hassani, H., Huang, X., & Silva, E. (2018). Digitalisation and big data mining in banking.
tomers. Results indicate that the approach can significantly enhance a Big Data and Cognitive Computing, 2(3), 18.
He, W., Wang, F., & Akula, V. (2017). Managing extracted knowledge from big social data
customer's response rate to the marketing campaign and can improve for business decision making. Journal of Knowledge Management, 21(2), 275–294.
the approval rate. The relationship analysis can, then, be extended to He, W., Zhang, W., Tian, X., Tao, R., & Akula, V. (2019). Identifying customer knowledge
more sophisticated applications, such as supply chain financing and on social media through data analytics. Journal of Enterprise Information Management,
32(1), 152–169.
supply chain risk detection.
Heinonen, D. K., Johnson, D. S., & Peterson, M. (2014). Consumer financial anxiety.
International Journal of Bank Marketing, 32(6), 515–533.
5.7. Limitation and future research Hofmann, E. (2005). Supply chain finance: some conceptual insights. Beiträge Zu
Beschaffung Und Logistik, 203–214.
Hofmann, E., & Johnson, M. (2016). Supply chain finance - some conceptual thoughts
This study shows a potential application of big data analytics in reloaded. International Journal of Physical Distribution & Logistics Management,
identifying potential corporate customers. The authors compared what 46(4), 1–8.
types of customers had higher response rates and proposed our inter- Hung, J. L., & Luo, B. (2016). FinTech in Taiwan: a case study of a bank’s strategic
planning for an investment in a FinTech company. Financial Innovation, 2(1),
pretation. However, we cannot prove our assumption by interviewing Retrieved from https://jfin-swufe.springeropen.com/articles/10.1186/s40854-016-
account officers and customers. The relationship network identified 0037-6.
many potential customers and applied filtering conditions to generate Jagtiani, J., Vermilyea, T., & Wall, L. D. (2018). Forthcoming banking perspectives. The
roles of big data and machine learning in Bank supervision. Retrieved from https://
the campaign list. Future research might focus on other filtering ap- papers.ssrn.com/sol3/papers.cfm?abstract_id=3221309.
proaches, for better outcomes. Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). Big data: issues and chal-
lenges moving forward. 46th Hawaii International Conference on System Sciences,
995–1004.
References Kharif, O. (2016). No credit history? (No Problem. Lenders are looking at your phone
data. Bloomberg. com).
Asokan, N., Janson, P., Steiner, M., & Waidner, M. (2000). State of the art in electronic Kleindorfer, P. R., & Saad, G. H. (2005). Managing disruption risks in supply chains.
payment systems. Advances in Computers, 53, 425–449. Production and Operations Management, 14(1), 53–68.
Barney, J., Ketchen, D., & Wright, M. (2011). The future of resource based theory: Kraus, C., & Valverde, R. (2014). A data warehouse design for the detection of fraud in the
Revitalization or decline? Journal of Management, 37(5), 1299–1315. supply chain by using the Benford’s law. American Journal of Applied Sciences, 11(9),
Beccalli, E., & Poli, F. (2015). Bank Risk Governance and Regulation. 1507–1518.
Bogdan, M., & Sava, A. (2018). Supply chain finance–a solution to improve business ef- Lambert, D. M., Cooper, M. C., & Pagh, J. D. (1998). Supply chain management: im-
ficiency. Acta Technica Napocensis, 61(4). plementation issues and research opportunities. The International Journal of Logistics
Camerinelli, E., & Schizas, E. (2014). A study of the business case for supply chain finance. Management, 9(2), 1–20.
Aite Group The Association of Chartered Certified Accountants. Lamoureux, J. F., & Evans, T. A. (2011). Supply chain finance: a new means to support the
Carlo, D. M., & Menno, D. B. (2014). Cross-border supply-chain finance: An important competitiveness and resilience of global value chains.SSRN Electronic Journal
offering in transaction banking. Journal of Payments Strategy & Systems, 7(4), Available at SSRN 2179944. https://ssrn.com/abstract=2179944 or https://doi.org/
304–318. 10.2139/ssrn.2179944
Chen, L. (2016). From fintech to finlife: the case of fintech development in china. China Li, R. (2018). E-commerce supply chain internet financing research. Rural Finance
Economic Journal, 9(3), 225–239. Research, 1(006), 30–34.
Choi, T., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for Lilien, G. L. (2016). The B2B knowledge gap. International Journal of Research in
business operations and risk management. IEEE Transactions on Cybernetics, 47(1), Marketing, 33(3), 543–556.
81–92. Liu, Y., Liu, H., Xiao, D., & Eltabakh, M. Y. (2018). Adaptive correlation exploitation in
Clearly, R., & Thibodeau, J. C. (2015). Applying digital analysis using Benford’s law to big data query optimization. The International Journal on Very Large Data Bases, 27(6),
detect fraud: the dangers of type I errors. A Journal of Practice and Theory. 24(1), 873–898.
77–81. Ma, J., Baer, D., & Chakraborty, G. (2015). Statistical evaluation of the doughnut clustering
Cycon, L. (2018). Not all that glitters is gold: on the value and utilization of banks’ private method for product affinity segmentation. Retrieved from https://business.okstate.edu/
information. Financial Intermediation and Unconventional Monetary Policy, 1. site-files/docs/analytics/3150-2015.pdf.
Dash, P., Pattnaik, S., & Rath, B. (2016). Knowledge discovery in databases (KDD) as tools Marr, B. (2015). Big data: Using SMART big data, analytics and metrics to make better de-
for developing customer relationship management as external uncertain environ- cisions and improve performance. John Wiley & Sons.
ment: a case study with reference to state bank of India. Indian Journal of Science & More, D., & Basu, P. (2013). Challenges of supply chain finance: A detailed study and a
Technology, 9(4). hierarchical model based on the experiences of an Indian firm. Business Process
Deloitte (2017). Deloitte poll finds use of supply chain fraud analytics on the rise. Management Journal, 19(4), 624–647.
Retrieved June 10, 2018 from https://www2.deloitte.com/us/en/pages/about- Moro, S., Cortez, P., & Rita, P. (2015). Using customer lifetime value and neural networks
deloitte/articles/press-releases/deloitte-finds-supply-chain-fraud-analytics-use- to improve the prediction of bank deposit subscription in telemarketing campaigns.
increase.html. Neural Computing and Applications, 26(1), 131–139.
Deutsche Bank (2014). Big data: How it can become a differentiator. Retrieved from Rahman, N., & Iverson, S. (2015). Big data business intelligence in bank risk analysis.
https://cib.db.com/docs_new/GTB_Big_Data_Whitepaper_(DB0324)_v2.pdf. International Journal of Business Intelligence Research, 6(2).
DuHadway, S., Carnovale, S., & Hazen, B. (2017). Understanding risk management for van Rijmenam, M. (2013). Southwest airlines uses big data to deliver excellent customer
intentional supply chain disruptions: risk detection, risk mitigation, and risk re- service. Retrieved on February 1, 2019 from https://datafloq.com/read/southwest-
covery. Annals of Operations Research, 1(20). airlines-uses-big-data-deliver-excellent/371.
Elms, D. K., Hassani, A., & Low, P. (2017). The intangible economy: How services shape Rogers, D., Leuschner, R., & Choi, T. Y. (2016). The rise of FinTech in supply chains.
global production and consumption. Cambridge University Press. Retrieved June 10, 2018 from https://hbr.org/2016/06/the-rise-of-fintech-in-supply-
Ennew, C. T., Binks, M. R., & Chiplin, B. (2015). Customer satisfaction and customer chains.
retention: An examination of small businesses and their banks in the UK. Proceedings Sawers, A. (2017). Banks versus FinTechs in SCF: competing and collaborating. SCF Briefing.
of the 1994 Academy of Marketing Science (AMS) annual conference (pp. 188–192). Retrieved from http://www.scfbriefing.com/banks-versus-fintechs-in-scf-battling-or-
Cham: Springer. collaborating.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big data consumer analytics and the Schoenherr, T., & Pero, C. S. (2015). Data science, predictive analytics, and big data in
transformation of marketing. Journal of Business Research, 69(2), 897–904. supply chain management: current state and future potential. Journal of business lo-
Eriksson, P., & Kovalainen, A. (2015). Qualitative methods in business research: a practical gistics, 36(1), 120–132.
guide to social research. London, England: SAGE Publications. Schofer, J., & Fowler, J. (2017). Understanding supply chain. Finance. Retrieved from
Faisal, M. N., Banwet, D. K., & Shankar, R. (2007). Information risks management in https://www.pwc.com.au/publications/pdf/supply-chain-finance-jul17.pdf.
supply chains: an assessment and mitigation framework. Journal of Enterprise Slotegraaf, R., Moorman, C., & Inman, J. (2003). The role of firm resources in returns to
Information Management, 20(6), 677–699. market deployment. Journal of Marketing Research, 40(3), 295–309.
Fenwick, M., McCahery, J. A., & Vermeulen, E. P. (2017). Fintech and the financing of Song, H., Yu, K., & Lu, Q. (2018). Financial service providers and banks’ role in helping
entrepreneurs: from crowdfunding to marketplace lending. SSRN Electronic Journal, SMEs to access finance. International Journal of Physical Distribution & Logistics
1–53. Tilburg law and economics center, Retrieved from https://papers.ssrn.com/ Management, 48(1), 69–92.
sol3/papers.cfm?abstract_id=2967891. Sun, N., Morris, J. G., Xu, J., Zhu, X., & Xie, M. (2014). iCARE: a framework for big data-

152
J.-L. Hung, et al. Industrial Marketing Management 86 (2020) 144–153

based banking customer analytics. IBM Journal of Research and Development, 58(5/6), challenges ahead. In W. Tate, L. Bals, & L. Ellram (Eds.). Supply Chain Finance: Risk
41–49. Management, Resilience and Supplier Management (pp. 63–95). New York: Kogan Page.
Tang, O., & Musa, S. N. (2011). Identifying risk issues and research advancements in Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a
supply chain risk management. International Journal of Production Economics, 133(1), revolution that will transform supply chain design and management. Journal of
25–34. Business Logistics, 34(2), 77–84.
TechAmerica Foundation's Federal Big Data Commission (2012). Demystifying big data: A Wang, J. (2017). Current status and risk evaluation of supply chain finance business in
practical guide to transforming the business of Government. Retrieved from http:// commercial banks. Revista de la Facultad de Ingeniería, 32, 106–115.
www.techamerica.org/Docs/fileManager.cfm?f=techamerica-bigdatareport-final. Wang, Y., & Gao, X. H. (2018). China's commercial bank credit business Research on
pdf. challenges and development countermeasures in the age of big data. 2018
The Economist (2017). Technology is revolutionsing supply-chain finance. Retrieved June 10, International Conference on Economics, Business, Management and Corporate Social
2018 from https://www.economist.com/finance-and-economics/2017/10/12/ Responsibility. https://doi.org/10.2991/ebmcsr-18.2018.22 (EBMCSR 2018).
technology-is-revolutionising-supply-chain-finance. Yin, R. K. (2017). Case study research and applications: Design and methods. Sage publica-
Trelewicz, J. Q. (2017). Big data and big money: The role of data in the financial sector. IT tions.
Professional, 19(3), 8–10. Zainal, R., Som, A. M., & Mohamed, N. (2017). A review on computer technology ap-
Tsai, C. Y. (2008). On supply chain cash flow risks. Decision Support Systems, 44(4), plications in fraud detection and prevention. Management and Accounting Review,
1031–1042. 16(2), 91–100.
Tsai, C. H., & Peng, K. J. (2017). The Fintech revolution and financial regulation : The Zhang, M., & Pang, L. J. (2019). Review of domestic application research of big data
case of online supply chain financing. Asian Journal of Law and Society, 4, 109–132. mining technology-SVM in credit risk evaluation. In 3rd international seminar on
Turnbull, P. W., & Gibbs, M. L. (1987). Marketing banking services to corporate custo- education innovation and economic management (SEIEM 2018). Atlantis Press.
mers: the importance of relationships. International Journal of Banking Marketing, 5(1), Zhang, Y. F., Ren, S., Liu, Y., & Si, S. B. (2016). A big data analytics architecture for
19–26. cleaner manufacturing and maintenance processes of complex products. Journal of
Valverde, S. C., Solas, P. J. C., & Fernández, F. R. (2016). Efficiency and New Bank Cleaner Production, 142(2), 626–641.
Business Models. Liquidity Risk. Zhou, J., S, X., & Yang, J. (2016). Supply chain financing operation combined with cloud
Varma, T. N., & Khan, D. A. (2012). Fraud detection in supply chain using Benford dis- service research. Internet Finance. 5(6), 14–19.
tribution. International Journal of Research in Management, 5(2), 90–96. Zoran, T., Tatjana, B., & Aleksandra, Z. (2018). Alternative financing sources supporting
Vousinas, G. (2019). Supply chain finance: definition, modern aspects and research SMEs growth. SocioBrains, 230–236.

153