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Bikulcius D: Intelligent Finance with
SAP

Intelligent Finance with SAP

AI-Based Automation and Custom Solutions

... Preface ... 17 
 ... Target Audience ... 18
 
 ... Objectives of This Book ... 20
 
 ... How to Read This Book ... 22
 
 ... Closing Thoughts ... 23
 1 ... Introduction to AI with SAP ... 25 
 1.1 ... Types of AI in Your SAP Landscape ... 26
 
 1.2 ... SAP Business AI ... 41
 
 1.3 ... Solutions in SAP BTP ... 47
 
 1.4 ... Third-Party Solutions ... 58
 
 1.5 ... Summary ... 63
 PART I ... Leveraging Standard and Generative AI ... 65 2 ... Accounts Receivable ... 67 
 2.1 ... Master Data ... 68
 
 2.2 ... Customer Invoices ... 79
 
 2.3 ... Payment Advice and Bank Statements ... 89
 
 2.4 ... Dispute Management ... 91
 
 2.5 ... Cash Application ... 94
 
 2.6 ... Cash Forecasting ... 98
 
 2.7 ... Summary ... 99
 3 ... Accounts Payable ... 101 
 3.1 ... Master Data ... 101
 
 3.2 ... Supplier Invoices ... 102
 
 3.3 ... Supplier Downpayment Requests ... 111
 
 3.4 ... GR/IR Reconciliation ... 113
 
 3.5 ... Invoice Automation with OpenText ... 114
 
 3.6 ... Summary ... 118
 4 ... Intelligent Asset Accounting ... 121 
 4.1 ... Master Data ... 122
 
 4.2 ... Asset Acquisitions ... 127
 
 4.3 ... Asset Transfers ... 130
 
 4.4 ... Asset Retirements ... 136
 
 4.5 ... Asset Scrapping ... 142
 
 4.6 ... Summary ... 144
 5 ... General Ledger Accounting ... 147 
 5.1 ... Journal Entries ... 148
 
 5.2 ... AI-Assisted Journal Upload ... 154
 
 5.3 ... Accruals ... 160
 
 5.4 ... Reclassification ... 164
 
 5.5 ... Exchange Rates ... 167
 
 5.6 ... Intelligent Intercompany Matching and Reporting ... 171
 
 5.7 ... Summary ... 177
 6 ... Predictive Accounting ... 179 
 6.1 ... Prediction Ledger ... 179
 
 6.2 ... Sales Order ... 181
 
 6.3 ... Data Quality Management ... 183
 
 6.4 ... Profit Center Reorganization ... 186
 
 6.5 ... Travel and Expense Management ... 193
 
 6.6 ... Summary ... 198
 7 ... Predictive Planning and Analytics ... 201 
 7.1 ... Just Ask ... 202
 
 7.2 ... Smart Insights ... 210
 
 7.3 ... Time Series Forecasting ... 217
 
 7.4 ... Smart Grouping ... 225
 
 7.5 ... Predictive Planning ... 232
 
 7.6 ... Regression and Classification via Smart Predict ... 238
 
 7.7 ... Summary ... 244
 PART II ... Developing Custom AI Solutions ... 247 8 ... Designing, Deploying, and Scaling Custom AI Solutions for Finance ... 249 
 8.1 ... Identify Information Need and Finance Stakeholders ... 250
 
 8.2 ... Assess Out-of-the-Box Functionality ... 256
 
 8.3 ... Define Use Cases ... 259
 
 8.4 ... Prioritize for Impact ... 263
 
 8.5 ... Review Available Tools ... 266
 
 8.6 ... Assess Development Cost ... 278
 
 8.7 ... Define Implementation Steps ... 281
 
 8.8 ... Understand Audit and Compliance Concerns ... 284
 
 8.9 ... Summary ... 288
 9 ... Summarizing and Synthesizing Financial Data ... 291 
 9.1 ... Automated Variance Commentary ... 292
 
 9.2 ... Executive-Ready Flash Reporting ... 300
 
 9.3 ... Earnings Call Preparation ... 307
 
 9.4 ... Cross-LOB Performance Synthesis ... 317
 
 9.5 ... Summary ... 323
 10 ... Retrieving Financial Data ... 325 
 10.1 ... Natural Language Assistants ... 325
 
 10.2 ... Automated KPI Lookups ... 331
 
 10.3 ... Contract and Transaction Data Retrieval ... 341
 
 10.4 ... Dynamic Drilldowns into Reports ... 349
 
 10.5 ... Summary ... 358
 11 ... Transforming Content ... 359 
 11.1 ... Raw Transaction Logs ... 360
 
 11.2 ... Regulatory Filings ... 368
 
 11.3 ... Multientity Reports ... 378
 
 11.4 ... Historical Data ... 388
 
 11.5 ... Summary ... 396
 12 ... Turning Static Outputs into Dynamic Stakeholder Narratives ... 399 
 12.1 ... Additional Context ... 401
 
 12.2 ... Market Intelligence ... 407
 
 12.3 ... Real-Time KPIs ... 412
 
 12.4 ... Sentiment and Peer Analysis ... 418
 
 12.5 ... Summary ... 424
 13 ... Leveraging Assistants ... 427 
 13.1 ... Financial Analyst Copilots ... 428
 
 13.2 ... Investor Relations Assistants ... 439
 
 13.3 ... Procurement Bots ... 448
 
 13.4 ... Controllership Agents ... 458
 
 13.5 ... Summary ... 467
 14 ... Creating Content ... 469 
 14.1 ... Commentary ... 470
 
 14.2 ... Investor Relations Scripts ... 476
 
 14.3 ... Financial Summaries ... 480
 
 14.4 ... Policies ... 485
 
 14.5 ... Summary ... 489
 PART III ... Looking Ahead for AI ... 491 15 ... Nine Elements of Future ERP AI Automation ... 493 
 15.1 ... The Four Pillars of the Intelligent Enterprise ... 494
 
 15.2 ... The Nine Elements ... 495
 
 15.3 ... Implications for Finance Leaders ... 508
 
 15.4 ... Technology Impacts and Operating Model Shifts ... 510
 
 15.5 ... Common Misconceptions and Failure Patterns ... 512
 
 15.6 ... Putting the Nine Elements into Motion ... 513
 
 15.7 ... From Principles to Roadmap: Building the Automation Portfolio ... 518
 
 15.8 ... Measured Outcomes and Value Realization ... 520
 
 15.9 ... Summary ... 522
 ... The Author ... 525 
 ... The Contributors ... 525
 ... Index ... 527

Demis Hassabis, DeepMind and the Quest for Superintelligence

The Infinity Machine

Edge AI is transforming everyday devices by putting intelligence where it matters most: directly inside the hardware. With on-device inference, a camera can recognize a visitor instantly, a phone can translate speech without streaming audio to the cloud, and a wearable can detect anomalies in real time-fast, private, and reliable even when the network disappears.This book is your practical guide to building exactly those kinds of systems with the Raspberry&nbsp;Pi AI&nbsp;HAT+ and the Hailo-8L accelerator. You'll start with clear foundations: core&nbsp;AI and machine-learning concepts, how neural networks work, and what truly distinguishes Edge&nbsp;AI from cloud AI-plus an honest look at ethical considerations and future impacts.Then it's straight to hands-on physical computing. Step by step, you'll set up Raspberry&nbsp;Pi&nbsp;OS, power and cooling, and develop in Python using the Thonny&nbsp;IDE. You'll learn GPIO basics with lights and servos, mount the AI&nbsp;HAT+ hardware, install and verify the Hailo software stack, and connect the right camera-official modules or USB webcams, even multiple cameras.From your first pipeline to real projects, you'll run person detection, pose estimation, segmentation, and depth estimation, then level up with YOLO object detection: smart alerts, guest counters, and custom extensions. You'll even connect vision to motion by combining gesture recognition with servo-driven mechanisms, including a robotic arm.With troubleshooting tips, hardware essentials, and a practical Python refresher, this book turns Edge&nbsp;AI from buzzword into buildable reality.

AI Projects for the Raspberry Pi with the AI HAT+

Edge AI Made Practical

An unsparing challenge to conventional thinking about data regulation, showing that privacy laws choke off beneficial technologies without protecting society and individuals from harm. Ask anyone about the downsides of the digital economy, and one of the first concerns that comes to mind will be privacy. Indeed, activists and politicians increasingly talk about data privacy as though it were a fundamental human right. In this boldly contrarian rejoinder, Omri Ben-Shahar argues that many privacy laws are worse than useless. They do nothing to address the serious social problems of our data-soaked world-think, for instance, of algorithmic discrimination, concentrated platform power, or social media's impact on mental health. Meanwhile, promising digital technologies are held back by privacy regulations. Tracking devices that provide feedback on dangerous driving could cut fatal car crashes by 30 percent, yet they are prohibited in California. Facial recognition technology has already been used to rescue thousands of victims of human trafficking, yet it is largely banned in the EU. And machine-learning analysis of electronic health records would be a boon to biomedical research, but privacy rules often stand in the way. Fear of Data offers a new paradigm for the age of AI: We should think about data not in terms of individual rights but as a shared concern, like the environment. Technology's pollution of our digital commons can be remediated only through collective action. And just as we regulate factory emissions without shutting down factories altogether, so we can regulate data technologies without giving up all the good they have to offer. Drawing on the latest research and vivid real-world cases, Ben-Shahar helps us navigate the cost-benefit trade-offs that will enable a future of data-enriched flourishing.

How Privacy Panic Led Tech Regulation Astray—and How to Fix It

Fear of Data

Zum Werk Künstliche Intelligenz (KI) ist der Oberbegriff für eine Reihe von Technologien, welche - mit im Detail unterschiedlichen Ansätzen -auf Grundlage großer Datenmengen in der Lage sind, Muster zu erkennen und auf dieser Basis Entscheidungen zu treffen. Das Rechtshandbuch führt zunächst in technische Grundlagen ein und erörtert im Anschluss verschiedene praxisrelevante Rechtsfragen bei der Entwicklung und dem Einsatz von KI, wie etwa zivilrechtliche Haftung, Produktsicherheit, strafrechtliche Verantwortung, Vertragsrecht, Immaterialgüterrecht, Datenschutz, Gesellschaftsrecht, Verbraucherschutz, Arbeitsrecht, Insolvenzrecht, gerichtliche und außergerichtliche Streitbeilegung sowie anwaltliches Berufsrecht. Enthalten sind zudem Praxishilfen für häufig auftretende Sachverhalte. Vorteile auf einen Blick<ul><li>technische Hintergründe verständlich erläutert</li><li>übersichtliche Darstellung der praxisrelevanten Rechtsfragen</li><li>Praxishilfen</li></ul> Zur Neuauflage Die umfassend überarbeitete Neuauflage trägt der rasanten technischen und rechtlichen Entwicklung Rechnung. Es werden nicht nur die Besonderheiten großer Sprachmodelle (Large Language Models, LLMs) und generativer KI vertieft dargestellt, sondern auch die Vorgaben aus der kontrovers diskutierten KI-VO ("AI Act") berücksichtigt. Zielgruppe Für Anwaltschaft, Unternehmensjuristinnen und -juristen, Richterschaft, Referenteninnen und Referenten oder Sachbearbeitung in Behörden, Rechtspolitikerinnen und -politiker.

Rechtshandbuch Künstliche Intelligenz

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Deep Learning Crash Course

Die Bekämpfung der Geldwäsche stellt die Kreditinstitute vor große Herausforderungen. Kreditinstitute können derzeit neben den "klassischen" Verfahren wie der Implementierung von "unflexiblen" IT-basierten AML-Lösungen (Anti-Money-Laundering) nur mit immer größeren Personalressourcen auf die Herausforderungen reagieren. Das Forschungsziel der vorliegenden Arbeit ist es, zu untersuchen, ob der Einsatz der künstlichen Intelligenz (Artificial Intelligence) bei der Bekämpfung der Geldwäsche wirkungsvoll unterstützen kann. Dazu wird die folgende Forschungsfrage gestellt: "Inwieweit können Kreditinstitute durch den Einsatz von KI-Technologien auffällige Muster und Hinweise auf Geldwäsche effizienter als mit bestehenden IT-Systemen entdecken?" Um die Forschungsfrage zu beantworten, wird eine qualitative Forschungsmethode gewählt. Dabei wird die vorhandene akademische Literatur zum Einsatz künstlicher Intelligenz bei der Bekämpfung der Geldwäsche untersucht und werden qualitative Interviews mit Branchenexperten als Instrument zur Datenerhebung mit Branchenexperten durchgeführt. Als Resultat zeigt sich, dass die Lücke zwischen den aktuellen AML-Methoden und der hochmodernen künstlichen Intelligenz (KI) geschlossen werden kann. Durch die neuen Trends und Richtungen in der KI-Technologie ist es möglich, skalierbare AML-Lösungen zu entwickeln.

Qualitative Analyse und Betrachtung

Einsatz von Artificial Intelligence zur Bekämpfung der Geldwäsche in Kreditinstituten

Dynamic Modeling and Econometrics in Economics and Finance

Practical Guide to Machine Learning in Financial Applications

Non-Linearity in Financial Modeling - Vol 3

In today’s AI-driven business environment, the ability to translate data into sound decisions is increasingly important. This textbook introduces the foundations and practical applications of business analytics, machine learning, and artificial intelligence. Readers learn how to explore and visualize business data, build and evaluate predictive models, and apply analytical methods to support business decisions.The book progresses from core concepts such as data visualization, optimization, model evaluation, supervised learning, and unsupervised learning to advanced topics in deep learning, computer vision, generative AI, text mining, natural language processing, and large language models. It also addresses the limitations and ethical challenges of AI. Step-by-step explanations, business examples, and Python exercises help readers connect analytical concepts with real-world applications.Designed for students and professionals with little or no prior experience in programming or machine learning, the book develops skills progressively from basic concepts to advanced methods. It prepares readers to select, implement, interpret, and evaluate AI-based analytical approaches for a wide range of business problems.Hyunhwan “Aiden” Lee
 is Assistant Professor of Marketing at California State University, Long Beach, USA. His research focuses on marketing analytics, brand and content analytics, and the application of artificial intelligence to business and marketing problems. His work uses machine learning, natural language processing, computer vision, location-based data analysis, large-scale data analysis, and mathematical modeling.
 Reo Song
 &nbsp;is Professor of Marketing and Director of the M.S. in Marketing Analytics program at California State University, Long Beach. His research focuses on machine learning, AI, business analytics, and digital marketing, with publications in leading journals including&nbsp;
 Journal of Marketing
 &nbsp;and&nbsp;
 Production and Operations Management
 . He has advised organizations on AI and machine learning applications, delivers frequent invited talks on AI and business analytics, and contributes regularly to newspaper columns on AI and digital transformation.

An Advanced Guide to Data- Driven Decision Making

Business Analytics and Artificial Intelligence

This book is a comprehensive guide designed to equip engineers, data scientists, and AI practitioners with the principles, tools, and strategies needed to ensure reliability, performance, and accountability in Large Language Models (LLMs). 
The book begins by laying the groundwork with the foundations of observability, introducing LLMs, their significance in modern AI, and the critical role observability plays in maintaining robust systems. It then explores SRE principles, service level objectives, and incident response, while distinguishing the unique observability challenges that arise in AI and ML systems. Building on this foundation, the book dives into measuring performance, from defining SLOs tailored for LLMs to monitoring computational and token-level metrics. Readers gain practical insights into structured logging, debugging, and distributed tracing methods that provide visibility into complex LLM workflows. Scaling challenges are addressed through strategies for cross-model observability, autoscaling, latency reduction, and fault-tolerant infrastructure design. The book further explores chaos engineering, guiding readers through resilience testing in LLMs and the automation of chaos experiments in CI/CD pipelines. Finally, it highlights monitoring, retraining, and ethical considerations in AI observability, including governance, privacy, and accountability.

In conclusion, this book provides a holistic roadmap to building reliable, transparent, and future-ready LLM systems.

What you will learn:
How to design observability pipelines for LLMs, including token-level logging, prompt tracing, and 



latency analysis.
Techniques for applying chaos engineering principles to test LLM robustness under stress and



failure scenarios.
Methods for building SLOs, SLAs, and dashboards tailored to inference quality and model



reliability.
Strategies for monitoring hallucinations, drift, bias, and ethical failures in real-time.




Who this book is for:
This book is for AI infrastructure engineers, SREs, machine learning platform teams, and applied AI practitioners deploying or maintaining LLM-based applications.

Site Reliability and Chaos Engineering for AI at Scale

Observability for Large Language Models

The primary objective of this work is to develop a behavioral basis for an AI algorithm to assist managers of finance, production, and marketing divisions of firms in imperfect markets. Production, sales revenue, external finance, and networth (market value of the fixed assets) have the pivotal role as objectives. However, all of them cannot be maximized simultaneously due to the interdependence among them. The managers tend to pursue the most valuable objective using the most productive strategy at each point of time. This was utilized as a basis for the AI algorithm. Non-linearities in the switches in objectives and strategies are a short run phenomenon while swaps will be preferred if firms have a long run advantage. Such observed phenomena were also incorporated in the specification of the AI algorithms. The important results are as follows. (i) In most industries the shortage of demand is not a constraint. Instead, the ability of firms to increase their strategic supply is dominant. (ii) Most firms pursue strategic supply based on the capital stock available. This indicates a preference for external finance. (iii) In the short run firms utilize working capital finance and selling costs to ensure that the sales revenue achieved is commensurate with the strategic supply. (iv) Thus, the postulate that expected demand determines the strategic supply as well as the profit maximization postulate for the short run do not appear as the priorities.

Towards an AI Algorithm for Multiple Objectives in Imperfect Markets

'Michael Auerbach has one of the most original minds I've ever encountered . . . This is a dazzling and entertaining book . . . But it is also an urgent warning' PATRICK RADDEN KEEFE, author of Empire of Pain A brilliant exploration of the phenomenon of prediction markets that asks: what happens when democracy becomes another commodity? Prediction markets are sweeping the world, fundamentally changing the nature of gambling - and the very ways in which we attempt to divine the future. Posed as binary 'Yes/No' questions about future events, prediction markets allow any difference of opinion - whether on pop culture or politics, wars or assassinations - to be financialised. A regulatory minefield with moral implications that divide nations, governments and bettors, they have proved as effective at accurately predicting the timing of missile strikes as they have at calling the outcomes of elections.But are these markets really a new phenomenon? And what does it mean when those tasked with safeguarding democracy hold financial stakes in its volatility?From Renaissance Rome, where merchants wagered on papal conclaves, through nineteenth-century London's wartime coffee house gossip, to the Pentagon's ill-fated terrorism futures exchange and blockchain-based betting on Polymarket today, The Big Bet tells the gripping five-hundred-year story of a permissive regime that no one designed and no one voted for.Drawing on deep expertise, Michael Auerbach examines how these markets are changing our world, and what they mean for the future where democracy becomes just another commodity to be priced and traded. Because prediction markets are not an anomaly - they are the logical endpoint of a society that has applied market capitalism to every aspect of life. And in a world where everything is a wager and nothing is off limits, knowing where the chips will fall matters more than ever.

Prediction Markets and the Future of Democracy

The Big Bet

How Enterprises Build, Govern, and Operationalize AI

Trust at Scale

Intelligent Finance with SAP

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