book

Hands-On Artificial Intelligence for Banking

Name: Hands-On Artificial Intelligence for Banking
ISBN: 9781788830782

by Jeffrey Ng, Subhash Shah

July 2020

Beginner

240 pages

5h 47m

English

Packt Publishing

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Title Page
Copyright and Credits
Hands-On Artificial Intelligence for Banking
About Packt
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Contributors
About the authorsAbout the reviewersPackt is searching for authors like you
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
Section 1: Quick Review of AI in the Finance Industry
The Importance of AI in Banking
What is AI?How does a machine learn?Software requirements for the implementation of AINeural networks and deep learningHardware requirements for the implementation of AIGraphics processing unitsSolid-state drivesModeling approach—CRISP-DMUnderstanding the banking sectorThe size of banking relative to the world's economiesCustomers in bankingImportance of accessible bankingOpen source software and dataWhy do we need AI if a good banker can do the job?Applications of AI in bankingImpact of AI on a bank's profitabilitySummary
Section 2: Machine Learning Algorithms and Hands-on Examples
Time Series Analysis
Understanding time series analysisM2M communicationThe role of M2M communication in commercial bankingThe basic concepts of financial bankingThe functions of financial markets – spot and future pricingChoosing between a physical delivery and cash settlementOptions to hedge price riskAI modeling techniquesIntroducing the time series model – ARIMAIntroducing neural networks – the secret sauce for accurately predicting demandBackpropagationNeural network architectureUsing epochs for neural network trainingScalingSamplingDemand forecasting using time series analysisDownloading the dataPreprocessing the dataModel fitting the dataProcuring commodities using neural networks on KerasData flowPreprocessing the data (in the SQL database)Importing libraries and defining variablesReading in dataPreprocessing the data (in Python)Training and validating the modelTesting the modelVisualizing the test resultGenerating the model for productionSummary
Using Features and Reinforcement Learning to Automate Bank Financing
Breaking down the functions of a bankMajor risk typesAsset liability managementInterest rate calculationCredit ratingAI modeling techniquesMonte Carlo simulationThe logistic regression modelDecision treesNeural networksReinforcement learningDeep learningMetrics of model performanceMetric 1 – ROC curveMetric 2 – confusion matrixMetric 3 – classification reportBuilding a bankruptcy risk prediction modelObtaining the dataBuilding the modelFunding a loan using reinforcement learningUnderstanding the stakeholdersArriving at the solutionSummary

Mechanizing Capital Market Decisions
Understanding the vision of investment bankingPerformance of investment banking-based businessesBasic concepts of the finance domainFinancial statementsReal-time financial reportingTheories for optimizing the best structure of the firmWhat decisions need to be made?Financial theories on capital structureTotal factor productivity to measure project valuesThe cash flow pattern of a projectForecasting financial statement itemsAI modeling techniquesLinear optimizationThe linear regression modelFinding the optimal capital structureImplementation stepsDownloading the data and loading it into the modelPreparing the parameters and modelsProjectionsCalculating the weighted average cost of capitalConstraints used in optimizationProviding a financial performance forecast using macroeconomic scenariosImplementation stepsSummary
Predicting the Future of Investment Bankers
Basics of investment bankingThe job of investment bankers in IPOsStock classification – styleInvestor classificationMergers and acquisitionsApplication of AI in M&AFiling obligations of listing companiesUnderstanding data technologiesClustering modelsAuto syndication for new issuesSolving the problemBuilding similarity modelsBuilding the investor clustering modelBuilding the stock-clustering modelIdentifying acquirers and targetsSummary
Automated Portfolio Management Using Treynor-Black Model and ResNet
Financial conceptsAlpha and beta returns in the capital asset pricing modelRealized and unrealized investment returnsInvestment policy statementsAsset classPlayers in the investment industryBenchmark – the baseline of comparisonInvestors are return-seekingTrend following fundUsing technical analysis as a means to generate alphaTrading decisions – strategyUnderstanding the Markowitz mean-variance modelExploring the Treynor-Black modelIntroducing ResNet – the convolutional neural network for pattern recognitionPooling layerReLU activation layerSoftmaxPortfolio construction using the Treynor-Black modelSolutionDownloading price data on an asset in scopeCalculating the risk-free rate and defining the marketCalculating the alpha, beta, and variance of error of each asset typeCalculating the optimal portfolio allocationPredicting the trend of a securitySolutionLoading, converting, and storing dataSetting up the neural networkLoading the data to the neural network for trainingSaving and fine-tuning the neural networkLoading the runtime data and running through the neural networkGenerating a trading strategy from the result and performing performance analysisSummary
Sensing Market Sentiment for Algorithmic Marketing at Sell Side
Understanding sentiment analysisSensing market requirements using sentiment analysisSolution and stepsDownloading the data from TwitterConverting the downloaded tweets into recordsPerforming sentiment analysisComparing the daily sentiment against the daily priceNetwork building and analysis using Neo4jSolutionUsing PDFMiner to extract text from a PDFEntity extractionsUsing NetworkX to store the network structureUsing Neo4j for graph visualization and queryingSummary
Building Personal Wealth Advisers with Bank APIs
Managing customer's digital dataThe Open Bank ProjectSmart devices – using APIs with Flask and MongoDB as storageUnderstanding IPSBehavioral Analyzer – expenditure analyzersExposing AI services as APIsPerforming document layout analysisSteps for document layout analysisUsing Gensim for topic modelingVector dimensions of Word2vecCash flow projection using the Open Bank APISteps involvedRegistering to use Open Bank APICreating and downloading demo dataCreating a NoSQL database to store the data locallySetting up the API for forecastingUsing invoice entity recognition to track daily expensesSteps involvedSummary
Mass Customization of Client Lifetime Wealth
Financial concepts of wealth instrumentsSources of wealth: asset, income, and giftedCustomer life cycleEnsemble learningKnowledge retrieval via graph databasesPredict customer responsesSolutionBuilding a chatbot to service customers 24/7Knowledge management using NLP and graphsPractical implementationCross-checking and further requesting missing informationExtracting the answerSample script of interactionsSummary
Real-World Considerations
Summary of techniques coveredAI modeling techniquesImpact on banking professionals, regulators, and governmentImplications for banking professionalsImplications for regulatorsImplications for governmentHow to come up with features and acquire the domain knowledgeIT production considerations in connection with AI deploymentWhere to look for more use casesWhich areas require more practical research?Summary
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Overview

In "Hands-On Artificial Intelligence for Banking," you will learn how to leverage artificial intelligence techniques to revolutionize banking operations. This book guides you through practical examples to automate tasks, improve decision-making, and enhance client interaction in the financial industry by integrating machine learning capabilities.

What this Book will help me do

Develop intelligent banking applications using Python and machine learning libraries.
Automate investment banking tasks like portfolio management using AI models.
Analyze and predict financial data with tools like NLP and time series analysis.
Build personalized client solutions, including automated wealth advisors.
Understand the application of AI across different banking sectors and its real-world implications.

Author(s)

Jeffrey Ng and Subhash Shah are seasoned professionals with extensive experience in applying technology to banking and finance. Jeffrey is known for his expertise in AI-based automation, while Subhash focuses on the interplay between technology and financial services. Together, they bring a practical, hands-on approach to teaching AI applications for banking.

Who is it for?

This book is perfect for machine learning engineers, data engineers, and data scientists aiming to apply AI to financial data and applications. It also caters to aspiring fintech developers, investment professionals, and entrepreneurs curious about leveraging AI for advanced banking solutions. Ideal readers should have a foundational understanding of finance and Python programming.

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ISBN: 9781788830782

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