AI in FinTech: A Research AgendaLongbing Cao

University of Technology Sydney, Australia

Abstract—Smart FinTech has emerged as a new area thatsynthesizes and transforms AI and finance, and broadly datascience, machine learning, economics, etc. Smart FinTech alsotransforms and drives new economic and financial businesses,services and systems, and plays an increasingly important rolein economy, technology and society transformation. This arti-cle presents a highly summarized research overview of smartFinTech, including FinTech businesses and challenges, variousFinTech-associated data and repositories, FinTech-driven busi-ness decision and optimization, areas in smart FinTech, andresearch methods and techniques for smart FinTech.

Keywords—AI, data science, machine learning, FinTech, fi-nance, economics, AI in FinTech, AI in finance

I. INTRODUCTION

In recent years, finance has become increasingly interactivewith the new-generation artificial intelligence (AI) techniquesand data science (DS) [1], [2], [3], [4], [5]. In particular,FinTech (or Fintech, i.e., financial technology in full) [6], [7]is at the epicentre of synthesizing, innovating and transformingfinancial services, economy, technology, media, communica-tion, and society broadly driven by AIDS techniques [8]. Here,AIDS broadly refers to both classic AI areas including logic,planning, knowledge representation, modeling, autonomoussystems, multiagent systems, expert system (ES), decisionsupport system (DSS), simulation, pattern recognition, im-age processing, and natural language processing (NLP) andmodern AI and DS areas such as representation learning,machine learning, optimization, statistical modeling, mathe-matical modeling, data analytics, knowledge discovery, com-plexity science, computational intelligence, event and behavioranalysis, social media/network analysis, and more recentlydeep learning and cognitive computing. In contrast, we broadlyrefer finance to areas including capital market, trading, bank-ing, insurance, leading/loan, investment, wealth management,risk management, marketing, compliance and regulation, pay-ment, contract, auditing, accounting, financial infrastructure,blockchain, financial operations, financial services, financialsecurity, and financial ethics. In addition, economics andfinance (EcoFin for short) are increasingly synergized witheach other and with AIDS.

AIDS is the keystone enabler of the new-generation ofEcoFin and FinTech [9], [10], [11], [12]. The new-generationAIDS is reshaping or even redefining the concepts, objectives,content and tasks of EcoFin and FinTech. AIDS essentially andcomprehensively transforms the way and effect that moderneconomic and financial (economic-financial) businesses oper-ate, transact, interact and collaborate with their participants

∗A comprehensive review on ‘AI in Finance: A Review’ can be providedon request.

(incl. consumers, markets, and regulators) and environments.AIDS nurtures new economic-financial mechanisms, models,products, services, and many tangible and intangible opportu-nities. As a result, AIDS not only strengthens the efficiency,cost-effectiveness, customer experience, risk mitigation, regu-lation, and security of existing economic-financial systems butalso innovates unprecedented and more intelligent, efficient,convenient, personalized, explainable, secure and proactiveeconomic-financial products and services. In this paper, we callthe synthetic product of AIDS and EcoFin as smart FinTech.

Figure 1 presents a four-dimensional, systematic and inter-active landscape of the synthesis between AIDS and EcoFinto form smart FinTech. The landscape connects the mainEcoFin businesses (bottom) to the EcoFin data and reposi-tories (left), the broad-based AIDS techniques (right), and theEcoFin business objectives (top). In the rest of this paper,we will discuss and summarize the main business domains,technical areas, and techniques associated with smart FinTechin terms of the FinTech businesses and challenges, FinTechbusiness objectives, FinTech data, areas in smart FinTech, andAIDS techniques for smart FinTech. We will conclude withopportunities for future smart FinTech developments.

II. FINTECH BUSINESSES AND CHALLENGES

The FinTech business areas that build on AIDS techniquesinvolve almost all aspects of an economic-financial systemand its environment and broadly all EcoFin businesses [1],[12]. Here, we highlight the following major business areasin smart FinTech: (1) economic-financial innovations: e.g.,new mechanisms and products; (2) economic-financial mar-kets: including products and services; (3) economic-financialparticipants: including individual and retail investors, institu-tions and regulators; (4) economic-financial behaviors: e.g.,investor activities and company announcements; (5) economic-financial events: e.g., company merger and financial crisis; (6)economic-financial services: e.g., banking, insurance, lending,financing, and crowdfunding services; (7) economic-financialmechanisms: e.g., market mechanisms, business models, andderivative pricing; (8) economic-financial systems: e.g., acompany’s finance; (9) economic-financial infrastructure: in-cluding fundamental supporting systems and blockchain; (10)economic-financial pricing: including the valuation of under-lying and derivative assets in capital markets; (11) economic-financial trading: including trading, investment, and execution;(12) economic-financial payment: e.g., online, mobile andcontactless payment; (13) economic-financial valuation: e.g.,the estimation of property value, credit and intangible assets;(14) economic-financial marketing: including campaign, andcustomer care; (15) economic-financial relationship manage-ment: e.g., stakeholder relations, and business partnership;

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Fig. 1. A Four-dimensional Research Landscape of the Synthesis between AIDS and EcoFin toward Fostering Smart FinTech. Shapes in different colorsrepresent distinct dimensions in the synthetic landscape. Each dimension initiates its interactions and communications with the three other dimensions through itssame-colored, directed connectors and channels. Each channel carries business, problem, data (incl. information and knowledge), intelligence, and technology,etc. from one end to another. The four dimensions interact with each other to address specific economic-financial business problems of underlying businessesby particular AIDS techniques on the corresponding data.

(16) economic-financial resource management: including man-agement of human, material, informational and intangibleassets; (17) economic-financial operations: e.g., the processesand services for supporting financial innovation, design andproduction; (18) economic-financial compliance and regula-tion: e.g., enforcing operational orders and business rules byauthorities; (19) economic-financial crisis, risk and security:e.g., investment risk, systemic risk, and cybersecurity; and (20)economic-financial ethics: including social and ethical issuesand privacy.

Major FinTech challenges associated with the above Fin-Tech business areas as well as their associated EcoFin busi-nesses, problems, data and objectives in Fig. 1 can be cat-egorized into (1) innovation challenges: e.g., AIDS tech-niques for inventing novel, efficient, intelligent and sustain-able mechanisms, products, services and platforms; (2) busi-ness complexities: such as AIDS techniques for representing,learning and managing the intricate working mechanisms,

structures, interactions, relations, hierarchy, scale, dynamics,anomaly, uncertainty, emergence and exceptions associatedwith a market, a product or a participant; (3) organizationaland operational complexities: such as AIDS techniques forunderstanding and managing the diversity and personalizationof individuals and departmental teams, the departmental andinstitutional coherence and consensus, and the inconsistentand volatile efficiency and performance; (4) human and socialcomplexities: such as AIDS techniques for modeling andmanaging the diversity and inconsistency of participant’s cog-nitive, emotional and technical capabilities and performance,and for enabling effective communications, cooperation andcollaboration within a department and between stakeholders;(5) environmental complexities: such as AIDS techniques formodeling and managing the interactions with contextual andenvironmental factors and systems and their influence on atarget business system and problem; (6) regional and globalchallenges: such as understanding and managing the relations

between an economy entity and its financial systems with therelated regional and global counterparts and stakeholders andtheir influence on a target problems; (7) data complexities:such as extracting, representing, analyzing and managing dataquality issues, misinformation and complicated data charac-teristics, e.g., uncertainty and extreme dimensionality, sparsityand skewness; (8) dynamic complexities: such as modeling,predicting and managing evolving but nonstationary behaviors,events and activities of individual and block markets, products,services and participants; and (9) integrative complexities: e.g.,systematically modeling and managing the various aspectsof the above complexities that are often tightly and looselycoupled with each other in an underlying economic-financialsystem.

III. THE SYNTHETIC AREAS OF SMART FINTECH

Figure 2 summarizes the synergy between major AIDStechniques and finance areas to generate various syntheticsmart FinTech areas. Our discussion focuses on AIDS tech-niques for transforming the following smart FinTech areas[12], [7]: smart banking, smart insurance, smart lending, smartmarketing, smart payment, smart regulation, smart risk, smartsecurity, smart trading, smart wealth, and smart blockchain,in addition to the foundations for smart FinTech. Each ofthese primary areas is further explained in terms of specificproblems and tasks where AIDS techniques can play distinctand significant roles [3].

The foundations of smart FinTech refer to the AIDSareas and techniques to enable smart FinTech. Examplesare actionable financial intelligence; AIDS for financial in-frastructure; analyzing big economic-financial data; analyzingcloud, online and mobile economic-financial services; ana-lyzing environmental, social and governance data; analyz-ing global economic-financial event and impact; analyzinghigh-dimensional, sequential and evolving economic-financialdata; analyzing multisource, multimodal and heterogeneouseconomic-financial data; automated economic-financial sys-tems and services; computational intelligence for EcoFin; datamining and knowledge discovery for EcoFin; deep learningand representation for EcoFin; distributed learning, feder-ated learning and transfer learning for EcoFin; intelligentcross-market economic-financial data processing; intelligenteconomic-financial recommendation; modeling economic in-centives; modeling economic mechanisms and social wel-fare; modeling evolutionary equilibrium of financial markets;modeling economic-financial chaos, uncertainty and change;modeling economic-financial couplings, interactions and de-pendencies; modeling economic-financial information flow;modeling economic-financial information propagation and in-fluence; modeling financial market microstructure; modelingeconomic-financial mechanisms, models, products and ser-vices; modeling economic-financial networking and intercon-nections; modeling economic-financial structures and hierar-chy; modeling natural, online, socioeconomic, political andcultural factors; personalized economic-financial recommen-dation; reinforcement learning for EcoFin; and statistical andmathematical learning for EcoFin.

The smart banking are active, personalized, automated,trusted, robust, secure, risk-averse, and fragility-averse bank-ing businesses and services. AIDS research for enabling smartbanking includes AIDS techniques for detecting, analyzingand managing bank network risk and risk contagion; AIDSfor modeling, detecting and managing banking risk and fraud;AIDS for conducting credit analysis and improving pricing;AIDS for optimizing, validating and risk-managing creditloans; AIDS for creating, mining, securing, risk-managingand optimizing cryptocurrencies; AIDS for creating, securing,evaluating, risk-managing and optimizing digital currencies;AIDS for personalizing, automating, validating, securing, risk-managing and optimizing Internet/online banking; AIDS forenabling, personalizing, automating, securing, risk-managingand optimizing open banking; AIDS for evaluating, securingand risk-managing shadow banking; and AIDS for makingsmarter and more automated, personalized, mobile and engag-ing banking services; etc.

The smart insurance enables insurance businesses and ser-vices that are proactive, tailored, trustful, resilient, secure andrisk-averse. The AIDS research for enabling smart insuranceincludes AIDS for tailoring individual, business and commer-cial insurance products and services; AIDS for insurance frauddetection; AIDS for enabling personalized recommendationof insurance products and services; AIDS for evaluating,analyzing, detecting, managing and optimizing insurance riskand compliance; AIDS for evaluating, automating, detectingand optimizing insurance security; AIDS for creating novelinsurance models, products and services; AIDS for evaluating,automating and improving insurance business operations; andAIDS for integrating, recommending and optimizing multi-policy, multi-product and cross-selling insurance products andservices; etc.

The smart lending supports lending businesses and servicesthat are efficient, risk-averse, personalized, context-oriented,predictive, robust and secure. AIDS research directions forsmart leading cover problems and tasks such as AIDS foraddressing various aspects and problems of blockchain; AIDSfor automating crowdfunding, e.g., campaign design and strat-egy optimization; AIDS for creating and optimizing distributedledger technologies; AIDS for fundraising, e.g., creating per-sonalized recommendations, efficient fundraising models, andintelligent services; AIDS for analyzing, detecting, managingand mitigating lending risk and security; AIDS for enablingsmart, efficient and low-risk peer-to-peer lending; and AIDSfor creating, validating and optimizing smart contracts; etc.

The smart marketing offers marketing activities and ser-vices that are cost-effective, relevant, proactive, personalized,positive, sequential and context-oriented. AIDS-enabled smartmarketing involves many research areas, such as AIDS forconducting consumer and client sentiment analysis; under-standing consumer and client opinion and intention; AIDS forevaluating and optimizing customer relationship management;AIDS for validating, enhancing, synergizing and optimizingeconometrics; AIDS for evaluating, risk-managing, person-alizing and optimizing stakeholder relationship; AIDS formaking, evaluating and optimizing financial recommendations;and AIDS for enabling, automating, evaluating, securing, risk-

Fig. 2. The Synthesis between AI/DS Techniques and Financial Businesses to Form Smart FinTech. The upper fin refers to broad AI and DS areas andtechniques, the lower fin consists of major financial areas, and the ridge includes various synthetic areas of smart FinTech.

managing and optimizing supply chain finance; etc.The smart payment includes online, mobile, wifi and

contactless payment methods and services that are secure,risk-averse, fast and convenient. Smart payment becomesincreasing important in digital society and economy. AIDS-enabled smart payment research include areas such as AIDSfor validating, automating, securing and risk-managing con-tactless payment; AIDS for enabling, securing, risk-managingand optimizing e-payment; AIDS for automating, securing,risk-managing and optimizing Internet and online payment;AIDS for automating, securing, risk-managing and optimizingmobile payment; AIDS for modeling, detecting, analyzing andmitigating payment risk; and AIDS for validating, detecting,analyzing and mitigating payment security; etc.

The smart regulation supports automated or human-machine-cooperative, risk-sensitive, proactive, systematic, dy-namic and evidence-based regulation and compliance opera-tions, governance and risk control. AIDS-empowered smartregulation consists of many research topics, such as AIDSfor enhancing corporate finance transparency; AIDS for eval-uating and optimizing corporate governance and regulation;AIDS for making and optimizing cross-market regulation;AIDS for creating and optimizing digital currency regulation;AIDS for detecting, analyzing, risk-managing and managingfinancial crime; AIDS for verifying, automating, securing andoptimizing financial digital authentication; AIDS for enabling,validating, verifying, detecting, risk-managing and optimizingfinancial digital identification; AIDS for quantifying, validat-ing, monitoring, detecting, analyzing and mitigating financialfragility, crisis and stability; AIDS for recognizing, detecting,analyzing and mitigating financial fraud; AIDS for enablingsmarter and more efficient and personalized financial market

regulation, design and policy implication; AIDS for automat-ing, evaluating and optimizing financial operations; AIDS forquantifying, evaluating and managing information asymme-try and transparency; AIDS for enabling, automating andoptimizing international regulation; AIDS for validating andimproving market legitimacy; AIDS for quantifying, verifying,detecting, analyzing and improving market social justice; andAIDS for quantifying, evaluating, automating and improvingmarketplace trust and coordination; etc.

The smart risk and security offers whole-of-time, businessand customer risk and security management, which are pre-dictive, systematic, evidence-based, and dynamic. AIDS canplay critical roles with many research tasks customizable forspecific economic-financial businesses, products and services.Typical AIDS-enabled smart risk and security tasks includeAIDS for automating, evaluating and optimizing anti-moneylaundering; AIDS for monitoring, detecting, categorizing, fac-torizing, predicting and intervening client financial security,retail investor risk, financial systematic risk, financial institu-tional risk, financial network risk, and cross-market risk; AIDSfor detecting and quantifying financial risk factors and predict-ing and evaluating financial risk factors and areas; AIDS forquantifying, characterizing, evaluating, predicting and improv-ing consumer confidence and sentiment, market reputation andtrust; AIDS for quantifying, analyzing, detecting, predictingand mitigating financial crisis and crisis contagion; AIDSfor categorizing, monitoring, detecting, analyzing, predicting,evaluating and managing financial event, market movement,change, exception and emergence and their consequencesand impact; AIDS for modeling, detecting, evaluating andmanaging financial system security, financial network security,and financial instrument security; AIDS for characterizing,

quantifying, analyzing, evaluating, predicting and managingfinancial system vulnerability; etc.

The smart trading supplies predictive, active, dynamic,risk-averse, anti-fragility, and high-utility trading strategiesand services. Smart trading involves a very broad and widelyexplored area. The AIDS-driven smart trading involves manyresearch topics, e.g., AIDS for making and optimizing al-gorithmic trading, arbitrage trading, high frequency (cross-market) trading, and institutional trading; AIDS for model-ing, automating and optimizing digital asset pricing; AIDSfor enabling smart e-commerce; AIDS for enabling smart,efficient, personalized and secure Internet finance; AIDS fordesigning, automating and optimizing novel, secure, smart andpersonalized financial mechanisms, financial models, financialproducts, and financial services; AIDS for conducting portfolioanalytics and enabling smart portfolio management; AIDSfor predictive trading and strategic trading; AIDS for design-ing, automating and optimizing trading strategies, algorithmsand platforms; AIDS for enabling and optimizing sociallyresponsible investment; AIDS for modeling, evaluating andoptimizing trading complementarity and substitutionary; andAIDS for characterizing and improving trading incentive andcampaign; etc.

The smart wealth becomes increasing topical, with suchexpected businesses and services for safe, secure, personal-ized, anti-fragility, and automated wealth management. AIDS-supported smart wealth involves many emergent researchareas, including AIDS for making, evaluating and optimizingdata monetization; AIDS for valuating, analyzing, evaluating,optimizing and managing digital assets, Internet wealth, publicwelfare, institutional welfare, and superannuation; AIDS forenabling, automating, risk-managing and optimizing digitalfinancial advising; AIDS for designing and optimizing novel,personalized, secure and healthy financing mechanisms; AIDSfor enabling, automating, evaluating and optimizing roboadvis-ing; and AIDS for supporting secure, personalized, low-riskand sustainable venture capital management; etc.

The smart blockchain enables efficient, secure, risk-tolerant, automated or semi-automated, dynamic, and high-performing blockchain infrastructures, computing and ser-vices. Both classic and advanced AIDS techniques can playcritical roles in enabling smart blockchains. Examples are pre-dicting the price movement of cryptocurrencies, constructingrisk-averse cryptocurrency trading strategies and portfolios,detecting risk and assuring smart contracts, efficient miningof bitcoins in distributed systems, supporting proactive andsystemic blockchain governance and regulation, and detectingrisk, intrusion and fragility in blockchain systems, behaviorsand activities, etc.

IV. AI TECHNIQUES FOR SMART FINTECH

The AIDS techniques required to achieve the aforemen-tioned smart FinTech objectives and EcoFin businesses arevery broad and diversified. Each technique may play distinctroles in addressing respective EcoFin business challenges anddata complexities. Here, we group the main AIDS techniquesfor smart FinTech into the following technical families [13],

[14], [9], [15], [16], [17], [18], [19], [20], [3], [21], [22],[23], [24], [25]: (a) Mathematical and statistical modeling,(b)Complex system methods,(c) Classic analysis and learningmethods,(d) Computational intelligence methods,(e) ModernAIDS methods,(f) Hybrid AIDS methods.Table I summarizesthese AIDS techniques and their applications for smart Fin-Tech.

A. Mathematical and Statistical ModelingThe family of mathematical and statistical modeling lays

the foundation for characterizing, formulating and modelingeconomic-financial systems and their working mechanisms,problems and solutions. Accordingly, here, we discuss fourareas that are widely used for analyzing economic-financialsystems and enable smart FinTech.

Numerical methods are used to build quantitative rep-resentation and analysis of economic-financial systems andproblems, e.g., for value-at-risk (VaR), option valuation, optionpricing, portfolio simulation, portfolio optimisation, hedging,and capital budgeting. Typical methods include linear andnonlinear equations, least squares problems, interpolation,optimization, binomial and trinomial methods, finite differencemethods, dependency modeling, financial simulation, Monte-Carlo simulation, random number generators, and econometricmodels such as term structure modelling and regression.

Time-series and signal analysis describe, characterize,analyze and forecast the temporal movement, its transition,regression, trend and change by treating economic-financialindicators as time-series and signals. Time-series analysis andsignal processing are widely used in EcoFin. For example, thesignals of a security price and its derivative price and marketindex and their relations can be analyzed by multivariateregression and modeling high-frequency trading signals jointlyby time-domain sequence analysis and frequency-domainwavelet. Typical methods include state space modeling, lin-ear time-series analysis, nonlinear time-series analysis, time-frequency and time-scale analysis, spectral analysis, Kalmanfilter, fractional time-series analysis, long-memory time-seriesanalysis, seasonal time-series analysis, transfer function mod-els, multiscale analysis, multivariate analysis, stationary anal-ysis, nonstationary analysis, and outlier analysis.

Statistical learning methods measure, estimate and learnthe uncertainty, randomness, risk, pricing, rating, performanceor dependency of economic-financial systems, products andproblems in terms of probabilistic theories. Examples areestimating the pricing of options, forecasting the VaR andperformance of a portfolio, modeling the sequential tradingbehaviors of a group of investors or a firm by coupledhidden Markov models, modeling the high-dimensional de-pendency between multiple time series by a copula method,and measuring the probabilistic relations between financialindicators as a Bayesian network. Typical methods include ran-dom walk models, efficient portfolio theories, factor models,Black-Sholes models, Monte-Carlo methods, Delta-Gammaapproximation, Levy processes, stochastic volatility models,copula methods, filtering methods e.g. particle filters, and non-parametric methods such as Bayesian networks and Markovnetworks.

AIDS areas AIDS methods AIDS techniques Examples of smart FinTech applications

Mathematicalmodeling

Numericalmethods

Linear and nonlinear equations, least square problem,finite difference methods, dependence modeling, Monte-Carlo simulation

Valuation, pricing, portfolio simulation, portfolio optimization, capitalbudgeting, hedging, price movement prediction, risk modeling, andtrend forecasting.

Time-series andsignal analysis

State space modeling, time-series analysis, spectralanalysis, long-memory time-series analysis, nonstation-ary analysis

Price prediction, trend forecasting, market movement prediction, IPOprediction, equity-derivative correlation analysis, change detection, fi-nancial crisis analysis, trading strategy discovery, and cross-marketanalysis.

Statisticallearningmethods

Random walk models, factor models, stochastic volatil-ity models, copula methods, nonparametric methods

Market trend forecasting, pricing, valuation, price estimation, VaR fore-casting, financial variable dependency modeling, portfolio performanceestimate, and cross-market analysis.

Random meth-ods

Random sampling, random walk models, random forest,stochastic theory, fuzzy set theory, quantum mechanics

Abnormal behavior analysis, outlier detection, market event analysis,market movement modeling, influence transition analysis, associatedaccount analysis, crowdsourcing modeling, and marketing modeling.

Complexsystems

Complexity sci-ence

Systems theory, complex adaptive systems, chaos the-ory, random fractal theory

System complexity modeling, market simulation, market mechanismdesign, globalization analysis, crisis contagion, and market informationflow.

Game theory Zero-sum game, differential game, combinatorial game,evolutionary game, Bayesian game

Interaction modeling, policy simulation and optimization, regional con-flict modeling, mechanism testing, coalition formation, and cryptocur-rency mechanism testing.

Agent-basedmodeling

Multiagent systems, belief-desire-intention model, reac-tive model, swarm intelligence, reinforcement learning

Testing economic hypotheses, simulating policies, supply/chain rela-tion modeling and optimization, cooperation analysis, self-organizationmodeling, portfolio optimization, and reinforcement learning.

Networkscience

Linkage analysis, graph methods, power law, smallworlds, contagion theory

Modeling entity movement, community formation, interactions and link-age, influence and contagion propagation; pool manipulation analysis,and analyzing investor relations.

Classicanalytics andlearning

Pattern miningmethods

Frequent itemset mining, sequence analysis, combinedpattern mining, high-utility pattern mining, tree pattern,network pattern, knot pattern, interactive pattern

Trading behavior analysis, abnormal trading analysis, outlier detection,investor relation analysis, customer profiling, high-utility trading patternanalysis, and cross-market trading behavior analysis.

Kernel learningmethods

Vector space kernel, tree kernel, support vector machine,spectral kernel, Fisher kernel, nonlinear kernel, multi-kernel methods

Price and market movement prediction, cross-market time series analy-sis, financial crisis analysis, crowdfunding estimate, market dependencymodeling, and customer profiling and classification.

Event and be-havior analysis

Sequence analysis, Markov chain process, high-impactbehavior, high-utility behavior, nonoccurring behavioranalysis

Financial event analysis, investor behavior analysis, price co-movementprediction, abnormal behavior analysis, market exception and changeanalysis, market event detection, and group behavior analysis.

Document anal-ysis and NLP

Language models, case-based reasoning, statistical lan-guage model, Bayesian model, latent Dirichlet alloca-tion, Transformer, BERT

Financial event analysis, investor sentiment analysis, company valua-tion, financial review and auditing analysis, misinformation and rumoranalysis, automated question-answering, and keyword searching.

Model-basedmethods

Probabilistic graphical model, Bayesian networks,expectation-maximization model, clustering, classifica-tion, deep neural models

Hypothesis testing, customer clustering, price prediction, trend fore-casting, index modeling, event analysis, fraud detection, movementforecasting, valuation, and risk scoring and prediction.

Social mediaanalysis

Topic modeling, sentiment analysis, emotional analysis,influence analysis, linkage analysis, interaction learning

Social influence analysis, investment linkage analysis, associated ac-count analysis, sentiment analysis, influence modeling, market and pricemovement, detecting manipulation and insider trading, and understand-ing company branding and development.

Computationalintelligencemethods

Neural comput-ing methods

Wavelet neural network, genetic neural network, recur-rent neural network, deep neural network

Macroeconomic and microeconomic factor correlation analysis, val-uation and pricing modeling, relation analysis, sequence modeling,portfolio optimization, and trend and movement prediction.

Evolutionarycomputingmethods

Ant algorithm, genetic programming, self-organizingmap, artificial immune system, swarm intelligence,neural-genetic algorithm

New product simulation, financial objective optimization, portfoliooptimization, marketing strategy optimization, price and policy testing,market risk analysis, and market performance optimization.

Fuzzy set meth-ods

Fuzzy set theory, fuzzy logic, fuzzy neural network,genetic fuzzy logic

Modeling market momentum, financial solvency analysis, risk andcapital cost modeling, and market uncertainty modeling.

Modern AIDSmethods

Representationlearning

Probabilistic model, graph network, network embed-ding, tree model, neural embedding

Representation of stocks, assets, capital markets, portfolios, financialevents, behaviors, and financial reports.

Short and infor-mal text analy-sis

Conceptualization, term/tag/phrase similarity learning,dependency parsing, word embedding, deep neural mod-els

Text-based trend forecasting of price, market, sentiment and reputation,question/answering modeling and recommendation.

Optimizationmethods

Nonlinear, stochastic and dynamic programming, infor-mation theory, Bayesian optimization

Optimizing policies, portfolios, trading strategies, VaR, and marketperformance, cost-benefit optimization.

Reinforcementlearning

Bellman Equation, actor-critic model, Markov dynamicprogress, deep Q-network, adversarial reinforcementlearning

Simulating and optimizing supply/demand of new assets and services,optimizing portfolios and trading strategies, option valuation optimiza-tion.

Deep learningmethods

Convolutional neural network, attention network, gen-erative adversarial network, autoencoder, deep Bayesiannetwork

Market modeling, behavior modeling, trading modeling, risk analysis,price and movement prediction, financial event modeling, cross-marketanalysis

Hybrid AIDSmethods

Parallel ensem-ble

Evolutionary neural models, deep Bayesian model, cop-ula graph neural network, combining complexity sci-ence and game theory

Price and market movement forecasting, multi-aspect risk analysis,macro/microeconomic factor analysis, financial event detection, cus-tomer profiling, blockchain and cryptocurrency modeling.

Sequential andhierarchical hy-bridization

Time-series analysis plus classification,macro/microeconomic dependency modeling, deepsequential modeling-based event detection

Financial review-based fraud detection, macroeconomic influence onmarket movement, social media impact on price movement, epidemicevolution, and impact on index.

Cross-disciplinaryhybridization

Deep multi-time series analysis, copula deep models,autoregression deep model, behavioral economics andfinance

Psychological factor and irrational market behavior analysis, behavioralfinance, misinformation and mispricing on market inefficiency, financialethics modeling, interpretable financial modeling.

Behavioral eco-nomics and fi-nance

Prospect theory, nudge theory, natural experiment, ex-perimental economics, behavior informatics, intentionlearning, next-best action modeling

Modeling investment rationality, irrational behaviors, mispricing, marketefficiency, mental activities, decision-making process, intention, emo-tion, and cognitive activities.

TABLE IAIDS TECHNIQUES AND THEIR REPRESENTATIVE APPLICATIONS FOR SMART FINTECH.

Random methods characterize, model, simulate and an-alyze an economic-financial problem in terms of theory ofrandomness and uncertainty. For example, one can modelthe evolution of abnormal and exceptional market behaviors,global events, and black swan events by random methods. Typ-ical random methods include random sampling, random walkmodels, random forest, stochastic theory, fuzzy set theory, andquantum mechanics.

B. Theories of Complex Systems

The theories of complex systems have been widely usedin classic AI-driven EcoFin research and applications. Theyare powerful for understanding, simulating and analyzing theworking mechanisms, system characteristics and complexities,and emergence and consequences of economic and financialsystems and problems. We introduce four of such methodsbelow.

Complexity science methods model an economic-financialsystem (e.g., a bitcoin market) as a complex system. Then,complexity science can understand its intrinsic and intricateworking mechanisms and system complexities, global econ-omy and its evolution, inter-regional and inter-country rela-tion analysis, migration, crisis contagion, conflict modeling,international trading and information flow. Combined withsociology and systems theory, typical methods include theoriesof complexities, systems theory, emergence, self-organization,complex adaptive systems, ABM, chaos theory, and randomfractal theory.

Game theory methods build mathematical and logic mod-els to design, characterize, simulate and analyze the inter-actions, conflict, cooperation, communication, coalition, un-certainty, behavioral relations, social norm, reputation, trust,Nash equilibrium, and consensus-building mechanisms andprocesses in complex economic and financial systems. Theycan be used to model the conflict between political systems,estimate rational and irrational threats in regional conflict, andmodel the market mechanisms of blockchain and cryptocur-rencies by continuous game theories, to name a few. Typicalmethods include zero-sum games, continuous games, differen-tial games, combinatorial games, evolutionary games, stochas-tic games, Bayesian games, strategic-form games, extensive-form games, and the communication, bargaining, cooperation,coalition in collective and cooperative games.

Agent-based modeling and simulation model and simulatethe working mechanisms, dynamic formation processes, in-ternal interactions between entities (i.e., agents), and learningand evolutionary processes of economic and financial systems.ABM builds multiagent systems to simulate an economic-financial problem as a complex system, and incorporatessuch mechanisms as perception, interaction, policy selection,rules, reinforcement learning and optimization to simulate theworking mechanisms in such systems. ABM has been widelyexplored in economics and finance.

Network science characterizes, models, analyzes and pre-dicts the directed and undirected connections and interac-tions, entity movement, community formation and influencepropagation and contagion in economic-financial systems by

modeling such systems as complex networks. Typical methodsinclude network linkage analysis, graph methods, scale-freeand power law, small worlds, influence diffusion and contagiontheories and tools. Such methods can be used to model groupinvestor interactions, relations and communications as a di-rected network, e.g., by Poisson factorization-based Bayesianmodels or modeling the interactions between market partici-pants in an economic market.

C. Classic Analytics and Learning Methods

Classic analytics and learning methods have played criticalroles in making EcoFin intelligent. This can be achieved byanalyzing economic-financial data, discovering the patterns,clusters, classes, trends and outliers in economic-financialsystems. We categorize such methods into the following sixmethods.

Pattern mining methods model and discover patterns andpatternable behaviors, structures and relations in economic-financial systems. Examples are identifying arbitrage tradingbehaviors by mining frequent and high-utility cross-market in-vestment strategies, discovering high-frequency trading strate-gies, and analyzing financial and social relations betweeninvestors and firms. Typical methods include discovering fre-quent patterns, sequential patterns, graph patterns, networkpatterns, tree patterns, knot patterns, interactive patterns, andcombined patterns.

Kernel learning methods describe, represent and analyzethe distributions and numerical relations between economic-financial indicators by individual or multiple (linear or non-linear) kernel functions and their couplings. Typical methodsinclude linear and nonlinear kernels, vector (space) kernels,tree kernels, sequence kernels, support vector machines, Fisherkernels, spectrum kernels and multi-kernel learning. Suchmethods can characterize the inter-dependency between micro-market variables such as security price and volume andbetween macro-economic indicators such as GDP values,exchange rates, and gold prices by multi-distribution kernelfunctions.

Event and behavior analysis characterize the occurrences,driving forces, evolution and consequences of events, ac-tivities and behaviors undertaken by or on an economic-financial object, and detect and predict abnormal, unexpectedand changing events and behaviors. Examples are detect-ing exceptional co-movement between an underlying equity,derivative products, and the equity company’s announcementreleasing activities by coupling learning and coupled behavioranalysis of their activity sequences. Typical methods includinghistorical event analysis, sequence analysis, Markov chainprocess, nonoccurring behavior analysis, high-impact behavioranalysis, high-utility behavior analysis, and group behavioranalysis.

Document analysis and NLP recognize, identify, ex-tract, summarize, and classify sentiments, opinions, financialevents, and entities. They can support topic-oriented modeling,sentiment-oriented analysis, searching, counting and compar-ison of rule-violated, suspicious, malicious, fragile, decep-tive and risky statements, claims, announcements, opinions,

sentiments or entities within and between economic-financialdocuments, reports, and news. Examples are identifying andcomparing problematic descriptions, revenue and budget statis-tics, misleading or misclassified reporting and review com-ments across monthly financial review reports by Transformerand BERT-derived neural models. Typical methods includelanguage models, named entity analysis, case-based reasoning,sequence labeling, statistical language models, context-freeparsing, logical and dependency semantics, distributional anddistributed semantics, topic modeling such as latent Dirichletallocation, word embedding, Bayesian networks, and neuralmodels such as Transformer and BERT models.

Model-based methods characterize, represent and analyzeeconomic and financial problems in terms of given hypothesesand models. Examples are modeling trading behaviors, mar-ket movements, institutional trading, price and index trend,and the influence of trader’s sentiment on the dynamics offinancial indicators. Typical models include numerical com-putation models such as kernel functions, statistical modelssuch as probabilistic graphical models and Bayesian networks,expectation-maximization models, the models for clustering,classification and semi-supervised learning, deep reinforce-ment learning (DRL) models, and deep neural models.

Social media analysis characterizes, detects, classifies,clusters and predicts linkages, interactions, feedback, senti-ment and networking behaviors between entities of economic-financial systems. Such methods model economic-financialsystems as social networks by involving networking data fromrelevant social media, mobile apps, and instant messagingplatforms to characterize, quantify and trace the sentiment andopinion and their influence on a security price movement ora market index movement by short text analysis and networkanalysis. Typical methods include topic modeling, sentimentanalysis, emotional analysis, influence analysis, and modelinglinkage and interactions.

D. Computational Intelligence Methods

Computational intelligence methods model the workingmechanisms of economic-financial systems, investment analy-sis, economic forecasting, portfolio analysis, and inflation pre-diction, etc. Such methods are inspired by natural, biologicaland evolutionary systems, and categorized into the followingthree classes.

Neural computing methods model the relations, struc-tures, sequential movements, transitions and influence betweeneconomic-financial variables by neural networks (NN), inparticular, deep neural networks (DNN) nowadays. Examplesare modeling the coupled dependencies and dynamics betweena stock price movement and macroeconomic variables such aspetrol and gold prices by recurrent neural networks (RNN).Typical methods include various artificial neural networks,wavelet neural networks, genetic neural networks, fuzzy neuralnetworks, and DNN.

Evolutionary computing methods characterize, simulate,analyze and optimize the working mechanisms, evolution,interactions, variances, performance and risk of economic-financial systems inspired by biological and evolutionary

models. Examples are simulating and optimizing the devel-opment and evolution of a newly released financial productin a market by a particle swarm organization (PSO) model,and estimating and optimizing security price, market index,exchange rate and inflation rate, and credit profiling. Typicalmethods include ant algorithms, self-organizing map, geneticcomputing/programming, artificial immune systems, gene ex-pression programming, particle swarm optimization, swarmintelligence, and neural-genetic algorithms.

Fuzzy set methods characterize the relationships, distribu-tions and structures of economic-financial systems in termsof fuzzy set and fuzzy system theories. Examples are model-ing market momentum, capital cost, risk, financial solvency,financial structures, and relations between costs on profit andbetween financial structures and capital costs. Typical methodsinclude fuzzy set theories, fuzzy systems, fuzzy logic, fuzzyneural networks, and genetic fuzzy logic models.

E. Modern AIDS MethodsHere, we discuss several categories of modern AIDS meth-

ods, which have been focused for the economic-financialproblem-solving in the recent decade or so. They are represen-tation learning, short and informal text analysis, optimization,reinforcement learning, and deep learning methods.

Representation learning methods describe, characterizeand model the intrinsic processes, interactions, relations, struc-tures, distributions and characteristics of economic-financialsystems, products or problems. Examples include establish-ing a probabilistic, mathematical, graph-based, network-based,tree-like or neural representation of the assets, participants,and role interactions in a derivative market, and representingstocks by involving tick-by-tick data and external news andsocial media data about the stocks. Typical methods includeprobabilistic models, graph networks, network embedding, treemodels, neural embedding models, and adversarial learningmethods.

Short and informal text analysis collects, extracts, recog-nizes, analyzes and classifies short text in social media, SMSand instant messaging systems about economic-financial insti-tutions, products, services, trends, news or participants. Suchinformation is alternative to core data available in marketsand economic-financial institutions, but consists of importantmessages. It can be used for various purposes, e.g., predictingthe trend and movements of price, index and exchange rate,extracting and representing keywords, phrases and expres-sions in a social and virtual community about manipulatinga commodity price, and predicting the market sentiment,reputation and confidence on a product or service. Typicalmethods include explicit short text understanding such as con-ceptualization for segmentation and labeling, term/tag/phrasesimilarity learning, dependency parsing, syntax structure anal-ysis, short text classification, query and recommendation; andimplicit short-text analysis, e.g., word/phrase/sentence em-bedding, contextual embedding, short-text conversation (e.g.,chatbot), question/answering by neural models such as longshort-term memory, attentive RNN, neural models with multi-head attention, encoder-decoder, and variational autoencoder,Transformer, etc.

Optimization methods model economic-financial problemsas optimization problems or apply optimization methods tocharacterize and analyze optimal economic-financial solutions.Examples include optimizing portfolio design and strategiesand return on investment w.r.t. relevant micro- and macro-levelfinancial indicators, optimizing the VaR of a cross-market port-folio with transaction cost, and optimizing algorithmic trad-ing strategies. Typical methods include linear programming,nonlinear programming, quadratic programming, stochasticprogramming, dynamic programming, multi-objective evolu-tionary computing, swarm intelligence, information theory,and Bayesian optimization.

Reinforcement learning methods model an economic-financial system as a reinforcement learning problem byoptimizing actions, policies and rewards. Examples includemodeling the portfolio management of derivative products tooptimize the trading objectives, forecasting the price and trendof foreign currencies, modeling property market supply anddemand, optimizing order routing in foreign exchange andfeatures markets, and optimizing trading agent actions, tradingbehaviors, option valuation, and trading and asset manage-ment. Typical methods include classic reinforcement learningsuch as on policy learning, e.g., Bellman Equation, on-policyactor-critic reinforcement learning and Markov dynamic pro-cess reinforcement learning, and off policy learning e.g., Q-learning and deterministic policy gradient. Recent focuses havebeen on deep reforcement learning (DRL) for EcoFin problemmodeling, such as deep Q-network (DQN), double DQN, ε-greedy policy Q-learning, ε-greedy policy double Q-learningand weighted Q-learning, deep deterministic policy gradient,hierarchical reinforcement learning, recurrent reinforcementlearning by integrating RNN with Q-learning, and adversariallearning in reinforcement learning.

Deep learning methods represent financial variables andtheir hidden relations and structures in economic-financial sys-tems by multi-layer abstractions and hidden representations.Deep learning can be used for stock representation, assetrepresentation, portfolio representation; modeling sequentialtrading behaviors and strategies; modeling sequential trading;predicting risk, profit and return of a portfolio; analyzing thecouplings, interactions and influence between macroeconomicand microeconomic variables; detecting abnormal movementsand financial activities in markets and banking; discoveringincompliant behaviors and events in financial reports; un-derstanding customer sentiment and feedback for marketing;supporting smart crowdfunding and crowdsourcing; modelingthe influence of external events and news on market move-ment; and analyzing and predicting the likelihood of financialcrisis, vulnerability, and cross-market influence. Typical deepmodels including convolutional neural networks (CNN), RNN,attention networks, memory networks, generative adversarialnetworks (GAN), encoder-decoder, autoencoder, graph neuralnetworks (GNN), deep Bayesian networks and neural lan-guage models (e.g., Transformer) can be used for the aboveeconomic-financial purposes.

F. Hybrid AIDS Methods

Hybrid methods and ensemble methods model economic-financial systems and problems by multiple complementaryAIDS techniques through their hybridization, ensembling orsynthesis. Such approaches are typically used to address (a)single yet complex economic-financial problems that cannotbe well handled by single models; and (b) mixed problemsthat require multiple methods. Accordingly, there are two maindirections of synergy. On one hand, the aforementioned AIDStechniques may be selectively integrated in parallel, sequentialor hierarchical ways to enhance their capacity and ability.

Parallel hybridization, also called ensemble, integratesmultiple same or different models to address the underlyingproblems. Examples include combining evolutionary comput-ing and neural computing to generate evolutionary neuralmodels to simulate and analyze a capital market; integratingcomplexity science and game theory to model and simulateblockchain and cryptocurrencies; training a deep Bayesiannetwork composed of Bayesian dependency learning and hier-archical networks; and learning a copula GNN handling bothdependency and connectivity.

Sequential and hierarchical hybridization integrates mul-tiple methods sequentially or hierarchically to address underly-ing problems. Examples are to model stock price relations bytime series analysis and then predict the abnormal movementby a decision tree classifier; model the micro/macro-economicvariable dependency by a copula method and then forecast theVaR by a time series model; hierarchically represent marketvariables as input and then model the trading behaviors in amarket by a RNN before the prediction of market movement.Parallel, sequential and hierarchical hybridization have beenwidely explored in the relevant communities.

Cross-disciplinary hybridization integrates economic the-ories and financial theories with AIDS methods, which hasbeen booming in recent years. Examples include migratingmultivariate time series to deep sequential models, combiningcopula methods with deep neural models, integrating autore-gression with DNN, learning high-utility behavior sequences,training actionable trading agents and strategies for learningtrading history and satisfying portfolio optimization objectives(e.g., VaR). This direction presents a more genuine yet chal-lenging approach for addressing significant economic-financialcomplexities.

Behavioral economics and finance is a typical area thatintegrates multiple disciplines of thinking, theories and meth-ods (e.g., psychology, neuroscience, applied behavior analysis,and computational intelligence) to understand, interpret andmodel the less rational to irrational investment, mispricingbehaviors, market inefficiency and market hiddenness thatdrive market decisions and evolution. It migrates from thehypothetical rationality, bounded rationality and efficiency andtransparency in classic economics and finance (i.e., System 1)to the less rational and irrational human choices, market ineffi-ciency (System 2) in real-life human decision-making. Typicalbehavioral economics and finance methods include prospecttheory, nudge theory, natural experiments, and experimen-tal economics. More advanced directions are to incorporate

AIDS-driven proactive, personalized, sequential, contextual,online and dynamic modeling, analytics, simulation, learningand optimization into behavioral economics and finance tounderstand, analyze and interpret human cognitive emotion,mental activities and intention, thinking, decision-making andaction-taking in markets and financial businesses.

V. CONCLUSIONS

This review briefs a comprehensive picture and promis-ing potential of AI-enabled smart FinTech. The new gen-eration of AI, in particular data science, machine learningand deep learning, drives the era of data and intelligence-driven economy and finance, where tremendous opportunitiesemerge in transforming economic-financial theories, researchand practice and advancing AI for tackling real-life significanteconomic-financial challenges and complexities and deliveringactionable intelligence-driven economy and finance.

VI. ACKNOWLEDGMENTS

This work is supported in part by Australian ResearchCouncil Discovery Grant (DP190101079) and ARC FutureFellow grant (FT190100734).

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