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Data
science
and
AI
in
FinTech:
An
overview
Longbing Cao · Qiang
Yang · Philip S. Yu
Received: date / Accepted: date
Abstract Financial technology (FinTech) has been playing an increasingly critical role in driving modern
economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely
inspired and empowered by data science and new-generation AI and (DSAI) techniques. Smart FinTech synthe-
sizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and
personalized economic and financial businesses, services and systems. The research on data science and AI in
FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech,
WealthTech, PayTech, RiskTech, cryptocurrencies, and blockchain, and the DSAI techniques including complex
system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep
learning, federated learning, privacy-preserving processing, augmentation, optimization, and system intelligence
enhancement. Here, we present a highly dense research overview of smart financial businesses and their challenges,
the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and some research directions of smart
FinTech futures to the DSAI communities.
Keywords Financial technology · FinTech · Financial service · Smart FinTech · Finance · Economics ·
Blockchain · Data science · AI · Intelligent systems · Machine learning · Deep learning · Federated learning ·
Privacy-preserving · Modeling · Mathematics · Statistics · Ethics
1
Introduction
In recent years, finance has become increasingly interac-
tive with new-generation data science and artificial in-
telligence (DSAI) techniques
[2, 15, 7, 27, 48, 49].
In
par-
ticular, FinTech (or Fintech) [13, 44] is at the epicen-
tre of synthesizing, innovating and transforming finan-
cial services, economy, technology, media, communica-
tion, and society broadly driven by DSAI techniques [8].
Here, DSAI broadly refers to (1) classic AI areas in-
cluding logic, planning, knowledge representation, mod-
eling, autonomous systems, multiagent systems, com-
plexity science, expert system (ES), decision support
system (DSS), optimization, simulation, pattern recog-
nition, image processing, and natural language process-
ing (NLP); (2) advanced DSAI areas including intelli-
gent interactions and conversations, intelligent identifi-
cation and authentication, privacy-preserving process-
ing [19], advanced representation learning, advanced
analytics
and
learning,
knowledge
discovery,
computa-
tional intelligence, event and behavior analysis, social
media/network analysis; and (3) more recent advances
including deep learning [22], automated interactions,
learning and responses, transfer learning [47], federated
learning [57, 56], human-centered computing, and brain
and cognitive computing [42]. Other fundamental areas
such as statistical modeling and mathematical modeling
also play a critical role in enabling FinTech. In contrast,
our reference to finance includes areas such as capital
market, trading, banking, insurance, leading/loan, in-
vestment, wealth management, risk management, mar-
keting, compliance and regulation, payment, contract,
auditing, accounting, and digital currencies and their
supporting infrastructure (including blockchain [45]),
operations, services, management, security, and ethics.
Economics and finance (EcoFin) are increasingly syn-
ergized with FinTech and DSAI.
DSAI is the keystone enabler of the new genera-
tion of EcoFin and FinTech [15, 28, 35, 39]. The new-
generation DSAI is reshaping or even redefining the
Noname
manuscript
No.
(will be inserted by the editor)
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2
concepts, objectives, content and tasks of EcoFin and
FinTech. DSAI essentially and comprehensively trans-
forms the way that modern economic and financial
(economic-financial) businesses operate, transact, inter-
act and collaborate with their participants (incl. con-
sumers, markets, and regulators) and environments.
DSAI nurtures new economic-financial mechanisms,
models, products, services, and many tangible and in-
tangible opportunities. As a result, DSAI not only
strengthens the efficiency, cost-effectiveness, customer
experience, risk mitigation, regulation, and security
of existing economic-financial systems, it also inno-
vates unprecedented and more intelligent, efficient, con-
venient, personalized, explainable, secure and proac-
tive economic-financial products and services. The syn-
thetic product of DSAI and EcoFin forms the new
area of smart FinTech, as shown in Fig. 2 in [8],
which presents a four-dimensional, systematic and in-
teractive landscape of the synthesis between DSAI and
EcoFin in forming smart FinTech. The landscape con-
nects the main EcoFin businesses to the EcoFin data
and repositories, the broad-based DSAI techniques, and
the EcoFin business objectives. Accordingly, the fam-
ily of FinTech has expanded from BankingTech, Trade-
Tech, LendTech, InsurTech, WealthTech and PayTech
to RiskTech, etc.
In the rest of this paper, we discuss and summa-
rize the main business domains and their challenges,
the ecosystems of smart FinTech which substantially
expand the above FinTech family, and the DSAI tech-
niques driving smart EcoFin businesses and FinTech. A
brief introduction to this selected topic on data science
and AI in FinTech is then given, followed by discussion
on future directions.
2
FinTech
businesses
and
challenges
Below, we briefly summarize the main businesses and
challenges in smart FinTech
1
. Typical applications of
smart FinTech include Internet banking, mobile pay-
ments, online shopping, peer-to-peer leading, online
crowdfunding projects, cryptocurrency, cross-market
portfolio management, and global supply chain man-
agement.
FinTech business areas. Building on DSAI tech-
niques, these areas involve almost all aspects of an
economic-financial system and its environment and
broadly all EcoFin businesses [8, 9, 2, 39]. Here, we high-
light the following major business areas in smart Fin-
Tech:
1
Interested readers can refer to a comprehensive review on
AI in finance in [8, 9].
–
economic-financial innovations: e.g., new mecha-
nisms and products;
–
economic-financial markets: including products and
services;
–
economic-financial participants: including individ-
ual and retail investors, institutions and regulators;
–
economic-financial behaviors: e.g., investor activi-
ties and company announcements;
–
economic-financial events: e.g., company mergers
and financial crises;
–
economic-financial services: e.g., banking, insur-
ance, lending, financing, and crowdfunding services;
–
economic-financial mechanisms: e.g., market mech-
anisms, business models, and derivative pricing;
–
economic-financial systems: e.g., a company’s fi-
nance;
–
economic-financial infrastructure: including funda-
mental support systems and blockchain;
–
economic-financial pricing: including the valuation
of underlying and derivative assets in capital mar-
kets;
–
economic-financial trading: including trading, in-
vestment, and execution;
–
economic-financial payment: e.g., online, mobile and
contactless payment;
–
economic-financial valuation: e.g., the estimation of
property values, credit and intangible assets;
–
economic-financial marketing: including campaign,
and customer care;
–
economic-financial relationship management: e.g.,
stakeholder relations, and business partnerships;
–
economic-financial resource management: including
the management of human, material, informational
and intangible assets;
–
economic-financial operations: e.g., the processes
and services for supporting financial innovation, de-
sign and production;
–
economic-financial compliance and regulation: e.g.,
the enforcement of operational orders and business
rules by the authorities;
–
economic-financial crisis, risk and security: e.g., in-
vestment risk, systemic risk, and cybersecurity; and
–
economic-financial ethics: including social and ethi-
cal issues and privacy.
FinTech challenges. Major challenges associated
with the above FinTech business areas as well as their
associated EcoFin businesses, problems, data and ob-
jectives can be categorized into:
–
innovation challenges: e.g., DSAI techniques for in-
venting novel, efficient, intelligent and sustainable
mechanisms, products, services and platforms;
–
business complexities: such as DSAI techniques for
representing, learning and managing the intricate
![](https://csdnimg.cn/release/download_crawler_static/89204324/bg3.jpg)
3
working mechanisms, structures, interactions, rela-
tions, hierarchy, scale, dynamics, anomaly, uncer-
tainty, emergence and exceptions associated with a
market, a product or a participant;
–
organizational and operational complexities: such
as DSAI techniques for understanding and manag-
ing the diversity and personalization of individuals
and departmental teams, departmental and insti-
tutional coherence and consensus, and inconsistent
and volatile efficiency and performance;
–
human and social complexities: such as DSAI tech-
niques for modeling and managing the diversity
and inconsistency of a participant’s cognitive, emo-
tional and technical capabilities and performance,
and for enabling effective communications, cooper-
ation and collaboration within a department and
between stakeholders;
–
environmental complexities: such as DSAI tech-
niques for modeling and managing the interactions
with contextual and environmental factors and sys-
tems and their influence on a target business system
and problem;
–
regional and global challenges: such as understand-
ing and managing the relations between an economy
entity and its financial systems with the related re-
gional and global counterparts and stakeholders and
their influence on target problems;
–
data complexities: such as extracting, representing,
analyzing and managing data quality issues, mis-
information and complicated data characteristics,
e.g., uncertainty and extreme dimensionality, spar-
sity and skewness;
–
dynamic complexities: such as modeling, predicting
and managing evolving but nonstationary behav-
iors, events and activities of individual and block
markets, products, services and participants; and
–
integrative complexities: e.g., systematically model-
ing and managing the various aspects of the above
complexities that are often tightly and loosely cou-
pled with each other in an underlying economic-
financial system.
3
The smart FinTech ecosystem
FinTech is evolving to be a large family, compris-
ing various areas including BankingTech, TradeTech,
LendTech, InsurTech, WealthTech, PayTech, and Risk-
Tech. The smart FinTech ecosystem is a multi-
dimensional synergy between EcoFin business objec-
tives, business areas, data and resources, and enabling
techniques, as shown in Fig. 2 in [8] which presents a
four-dimensional landscape of the smart FinTech and
the synthesis between these aspects. In general, the
Fig. 1 The Smart FinTech ecosystem.
ecosystems of smart FinTech can be viewed in terms
of their (1) comprehensive FinTech-driven businesses
and areas, and (2) processes, functions and activities.
On one hand, FinTech-driven businesses and areas are
overwhelming and evolving. Fig. 1 overviews the smart
FinTech ecosystems and categorizes them as follows:
smart banking, smart insurance, smart lending, smart
trading, smart assets and wealth management, smart
payments, smart credit and risk management, smart
Internet finance, smart marketing and services, smart
currencies and blockchain, smart compliance and reg-
ulation, smart ethics and security, and smart innova-
tions. Each of these primary areas is further defined
by their respective businesses and supporting (DSAI)
techniques [9, 27, 39, 44]. Below, we briefly illustrate how
DSAI techniques can assist in these smart FinTech ar-
eas.
Smart banking offers active, personalized, auto-
mated, trusted, robust, secure, risk-averse and fragility-
averse banking businesses and services, including retail
banking, open banking, mobile banking, and business
banking. The DSAI research for enabling smart bank-
ing includes DSAI techniques for detecting, analyzing
and managing a bank’s network risk and risk conta-
gion; modeling, detecting and managing banking risk
and fraud; conducting credit analysis and improving
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