Lecture 4: Flipped — Agentic AI & LLMs in Finance

Group presentations · token allocation · discussion

Prof. Dr. Andre Guettler Director of the Institute
Helmholtzstraße 22, Room 205
andre.guettler@uni-ulm.de
+49 731 50 31 030

Oliver Padmaperuma Doctoral Candidate
Helmholtzstraße 22, Room 203
oliver.padmaperuma@uni-ulm.de
+49 731 50 31 036

4.1 Course objectives

• 4.1 Course objectives
• 4.2 Recap: Agentic AI & LLMs in Finance
• 4.3 Session agenda
• 4.5 Token allocation
• 4.6 Lecturer reflection
• 4.7 Wrap-up & next steps

• Welcome to
• Course Objective
• Course at a glance (1/3)
• Course at a glance (2/3)
• Course at a glance (3/3)
• Assignments / Exams

Welcome to Emerging Technology & Finance

• This is a flipped-classroom Bachelor course: every regular lecture (odd weeks) is followed by a flipped session (even weeks) where all groups present on the same topic. There is no exam to register for — sign up on the course Moodle page by 15 October 2026 so you receive announcements and the token-allocation quiz links.
• Form a group of 4 by the end of Week 1 (Moodle sign-up sheet). Stragglers will be allocated by the lecturers.
• Grading is 100% cumulative across the 6 flipped sessions: each session = 50% peer-allocated tokens + 50% lecturer evaluation. Each group gets 20 fresh tokens every flipped week to allocate to other groups via a Moodle quiz within 5 minutes of the session ending.
• Submission per session: upload your slide PDF to Moodle before each flipped session starts. Ask questions during or right after each session — that is the preferred channel.
• Admin / studies / exam-eligibility questions go to the registrar’s office (Studiensekretariat) at studiensekretariat@uni-ulm.de.
• Course-content questions outside class: email oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de.
• We also recommend the student advisory service.

Course Objective

Scope

We will:

• Survey six emerging-technology modules at the cutting edge of finance: agentic AI · blockchain & DeFi · fintech business models · RegTech & cybersecurity · CBDCs
• Pair every regular lecture with a flipped session in which every group presents their angle on the topic
• Train critical evaluation, presentation, and peer-judgment skills via a transparent token-based peer-grading mechanic
• Place the technologies in a real-world business and regulatory context (PSD2/3, MiCA, EU AI Act, post-quantum standards)

We will NOT:

• Build production-grade fintech systems or trade live capital
• Cover deep technical implementations (we treat code as supplement, not core)
• Run a separate written exam or final-pitch competition — the cumulative flipped-session grade is the entire grade

Approach

Flipped-classroom alternation (12 weeks)

• Odd weeks (W1, W3, W5, W7, W9, W11): regular lecture introducing the topic
• Even weeks (W2, W4, W6, W8, W10, W12): flipped session — all groups present and allocate tokens
• Groups of 4, formed by end of Week 1

Token mechanic (the grading vehicle)

• 20 tokens per group per flipped session
• Each group allocates them to other groups, weighing insight · originality · clarity · critical depth
• Cumulative across 6 sessions = 50% of final grade · lecturer evaluation = 50%

Course at a glance (1/3)

Foundations of Digital Disruption in Financial Services

Week 1

22.10.2026

What is ‘emerging tech in finance’, how did we get here, where is it going

• Three waves of digital disruption in finance
• Today’s actors: incumbents, challengers, Big Tech, infrastructure
• Regulatory backdrop: PSD2, MiCA, EU AI Act
• Why now: structural drivers
• What this course will cover

Flipped — Digital Disruption in Financial Services

Week 2

29.10.2026

Group presentations · token allocation · discussion

• Recap of the foundations lecture
• Group presentations on digital disruption
• Token allocation & next steps

Agentic AI & LLMs in Finance

Week 3

05.11.2026

From LLMs to agents · applications · failure modes · EU AI Act

• LLMs in finance: architecture, training, capabilities
• Agentic AI: from answers to actions
• Applications: RAG, robo-advisors, AML, trading agents
• Failure modes: hallucination, drift, prompt injection
• Governance: EU AI Act and high-risk obligations

Flipped — Agentic AI & LLMs in Finance

Week 4

12.11.2026

Group presentations · token allocation · discussion

• Recap of the agentic AI lecture
• Group presentations on real LLM and agent deployments
• Token allocation & next steps

Blockchain, Crypto, DeFi & Tokenisation

Week 5

19.11.2026

From distributed ledgers to MiCA-regulated markets

• Blockchain primer: ledgers, consensus, smart contracts
• Crypto markets: BTC, ETH, stablecoins
• DeFi primitives: AMMs, lending, derivatives
• Tokenisation of real-world assets
• MiCA framework and EU enforcement

Course at a glance (2/3)

Flipped — Blockchain, Crypto, DeFi & Tokenisation

Week 6

26.11.2026

Group presentations · token allocation · discussion

• Recap of the blockchain & DeFi lecture
• Group presentations on real protocols and deployments
• Token allocation & next steps

Fintech Business Models

Week 7

03.12.2026

Neobanks, embedded finance, BNPL, Open Banking, Big Tech in finance

• Neobanks: N26, Revolut, Monzo, Chime
• Embedded finance & BaaS
• BNPL: Klarna, Affirm, regulatory pushback
• Open Banking & PSD2 outcomes
• Big Tech in finance

Flipped — Fintech Business Models

Week 8

10.12.2026

Neobanks, embedded finance, BNPL · group presentations · token allocation

• Recap of the fintech business-models lecture
• Group presentations on real companies and unit economics
• Token allocation & next steps

RegTech, Cybersecurity & Privacy-Preserving Compute

Week 9

17.12.2026

Industrialising compliance · cyber-threat landscape · ZKPs, MPC, federated learning · post-quantum

• RegTech overview: industrialising compliance
• KYC/AML automation in production
• Cybersecurity threats in finance
• Privacy-preserving compute: ZKPs, MPC, federated learning
• Post-quantum cryptography & the migration

Flipped — RegTech, Cybersecurity & Privacy-Preserving Compute

Week 10

07.01.2027

Group presentations · token allocation · discussion

• Recap of the RegTech & security lecture
• Group presentations on vendors, incidents, and emerging tech
• Token allocation & next steps

Course at a glance (3/3)

CBDCs & the Future of Money

Week 11

14.01.2027

Wholesale vs retail design · Digital Euro · e-CNY · programmable money

• What’s a CBDC: wholesale vs retail
• The Digital Euro state of play
• China’s e-CNY and small-country implementations
• Programmable money: feature, threat, or both
• Stablecoins as private money: tension with CBDCs

Flipped — CBDCs & the Future of Money

Week 12

21.01.2027

Final session · group presentations · token allocation · course wrap-up

• Recap of the CBDCs lecture
• Group presentations on real CBDC projects
• Token allocation, final standings, and course retrospective

Assignments / Exams

Flipped-Classroom Presentation Series 100% of your grade

Six in-class group presentations across six emerging-tech topics, graded cumulatively. Each session: 50% peer-allocated tokens + 50% lecturer evaluation.

Group of up to 4.

Submit by emailing oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de. Subject pattern: Emerging Technology & Finance_assignment-1-flipped-classroom-presentations_surname1_surname2_…

21 January 2027

4.2 Recap: Agentic AI & LLMs in Finance

• 4.1 Course objectives
• 4.2 Recap: Agentic AI & LLMs in Finance
• 4.3 Session agenda
• 4.5 Token allocation
• 4.6 Lecturer reflection
• 4.7 Wrap-up & next steps

• What we covered last week
• Open questions to dig into today

What we covered last week

• LLMs in finance — the architecture (transformers), training (RLHF), capabilities (summarisation, extraction, reasoning), and the family of finance-specific variants (Chen, Yang, and Liu 2023).
• Agentic AI — moving from “answer a question” to “execute a multi-step task with tool use, memory, and planning.” Why this changes the deployment economics dramatically (Agrawal, Gans, and Goldfarb 2022).
• Concrete applications — RAG over filings and research, robo-advisors, AML/KYC screening, customer-support agents, and the most-claimed but least-deployed: autonomous trading.
• Governance & EU AI Act — risk-based regulation, “high-risk” categories, and the 2026 obligations finance firms face (European Parliament and Council 2024).
• Failure modes — hallucination, prompt injection, distribution shift, model-error catastrophe at scale (Acemoglu and Restrepo 2020).

The pivot last week was from “AI as a feature” to “AI as a worker.” Agentic systems open a different cost curve: they can do work that previously required headcount, but they also fail in ways no human would. Your job tonight is to find a specific deployment where this trade-off is observable — where you can point to what it replaced, how it failed, and who is now responsible when it does.

Open questions to dig into today

• When does an LLM cross from “smart autocomplete” to “agentic enough to matter”? Is BloombergGPT agentic? Is J.P. Morgan’s IndexGPT?
• Hallucination in finance: insurmountable barrier or solvable engineering problem?
• Robo-advisors have existed for 15 years — why is their AUM still small relative to incumbent wealth managers?
• What does the EU AI Act actually require of a bank deploying an LLM in 2026?

Don’t accept vendor claims at face value tonight. A bank says it “uses AI”; a vendor says its model is “production-grade.” Ask: what does the model do that was previously done by whom, and what happens when it gets it wrong?

4.3 Session agenda

• 4.1 Course objectives
• 4.2 Recap: Agentic AI & LLMs in Finance
• 4.3 Session agenda
• 4.5 Token allocation
• 4.6 Lecturer reflection
• 4.7 Wrap-up & next steps

• Today’s flow
• Group presentation order
• What groups present today
• Group 1 — A deployed LLM in finance
• Group 2 — Robo-advisor analysis
• Group 3 — AI trading agent
• Group 4 — AML/KYC automation
• Group 5 — EU AI Act × finance
• Group 6 — Catastrophic AI failure scenario

Today’s flow

• 14:00 — Welcome & recap (5 min)
• 14:05 — Group presentations (6 min + 2 min Q&A each)
• 15:00 — Lecturer reflection (10 min)
• 15:10 — Token allocation (5 min on Moodle)
• 15:15 — Wrap-up & prep for next regular lecture (5 min)

Same format as Week 2. Time-box is firm.

Group presentation order

• Angle: A specific deployed LLM in finance (BloombergGPT · FinGPT · IndexGPT)
• Slot: 14:05–14:13

• Angle: A robo-advisor (Betterment · Wealthfront · Scalable Capital) — model and unit economics
• Slot: 14:13–14:21

• Angle: An AI trading agent — real or hype?
• Slot: 14:21–14:29

• Angle: AML/KYC automation case (ComplyAdvantage · Chainalysis · J.P. Morgan)
• Slot: 14:29–14:37

• Angle: EU AI Act × finance — what changes by 2026?
• Slot: 14:37–14:45

• Angle: Catastrophic-risk angle — what’s the worst plausible AI failure in finance?
• Slot: 14:45–14:53

What groups present today

Presentation brief

6 min + 2 min Q&A. Each presentation must include:

1. One specific deployment (named company, named model, named product).
2. What it actually does — the input, the output, the human-in-the-loop role.
3. The failure mode you found — not the brochure version.
4. 1–2 discussion prompts.

4.4 Group presentations

Group 1 — A deployed LLM in finance

• Candidates: BloombergGPT (proprietary, 50B params), FinGPT (open-source), J.P. Morgan IndexGPT, Goldman Sachs’ “GS-AI Platform.”
• Bring: what task it does, what evaluation benchmark it’s measured on, and what a human used to do that it now does.

Live-fill slide.

Group 2 — Robo-advisor analysis

• Candidates: Betterment, Wealthfront, Scalable Capital, Nutmeg, Vanguard PAS.
• Bring: AUM, customer-acquisition cost, fee structure, and the structural reason their AUM is still much smaller than incumbents’.

Live-fill slide.

Group 3 — AI trading agent

• Candidates: Renaissance / Two Sigma claims, retail “AI trading” products, “agentic” hedge-fund pitches.
• Bring: separate the claim from the evidence. If the evidence is opaque, that’s the presentation.

Live-fill slide.

Group 4 — AML/KYC automation

• Candidates: ComplyAdvantage, Chainalysis Reactor, Feedzai, J.P. Morgan’s COIN, HSBC’s AI screening.
• Bring: a measured false-positive / false-negative trade-off, and what happens when the AI flags wrongly.

Live-fill slide.

Group 5 — EU AI Act × finance

• Bring: the high-risk category definitions, which finance-AI use cases land there, and the concrete obligations (documentation, human oversight, post-market monitoring) by what date.

Live-fill slide.

Group 6 — Catastrophic AI failure scenario

• Candidates: a plausible flash-crash-by-LLM scenario; a coordinated prompt-injection at scale; an AML model that systematically under-flags one customer segment.
• Bring: the mechanism, the realistic blast radius, and what regulator / market-structure change would prevent it.

Live-fill slide.

4.5 Token allocation

• 4.1 Course objectives
• 4.2 Recap: Agentic AI & LLMs in Finance
• 4.3 Session agenda
• 4.5 Token allocation
• 4.6 Lecturer reflection
• 4.7 Wrap-up & next steps

• How tokens work
• Allocation rubric
• Tonight’s leaderboard

How tokens work

• 20 fresh tokens per group per session.
• Distribute among other groups — no self-allocation. Total = 20.
• Moodle quiz opens for 5 minutes after the last presentation.

Same rules every flipped week. The 5-minute window is firm.

Allocation rubric

• Insight — taught you something new?
• Originality — fresh angle or the obvious one?
• Clarity — could you follow the argument?
• Critical depth — honest appraisal or sales pitch?

Tonight especially, reward groups that pushed back on a vendor’s claim — that’s the rarest and most valuable skill in this topic.

Tonight’s leaderboard

Group	Tokens this week	Cumulative
Group 1	—	—
Group 2	—	—
Group 3	—	—
Group 4	—	—
Group 5	—	—
Group 6	—	—

• Filled in after the Moodle quiz closes.
• “Cumulative” carries forward from Week 2.

4.6 Lecturer reflection

• 4.1 Course objectives
• 4.2 Recap: Agentic AI & LLMs in Finance
• 4.3 Session agenda
• 4.5 Token allocation
• 4.6 Lecturer reflection
• 4.7 Wrap-up & next steps

• Standout insights & common gaps
• Concepts to revisit next regular lecture

Standout insights & common gaps

• Standouts (live-fill): 2–3 groups whose evidence on a real deployment was particularly strong.
• Common gaps (live-fill): typically — (a) confused “uses AI” with “is agentic”; (b) failed to surface a measurable failure mode; (c) conflated robo-advisor with autonomous trader.

The single most common failure mode in Week-4 presentations across institutions is taking marketing language at face value. If a vendor says “our agent autonomously executes trades,” ask who pre-authorised the trade and at what time. The answers are almost always heavily human-in-the-loop.

Concepts to revisit next regular lecture

• The transition from agentic-AI (off-chain, opaque models) to on-chain, transparent code execution is the topic of Lecture 5 — smart contracts as a different kind of “agent.”
• Trust + auditability — AI offers neither natively; blockchain offers transparent state but no semantic guarantee. Where do they meet?

This bridges to Lecture 5 directly: agentic AI promises action with weak transparency; smart contracts promise transparency with weak semantic flexibility. The intersection is where the most interesting failure modes live.

4.7 Wrap-up & next steps

• 4.1 Course objectives
• 4.2 Recap: Agentic AI & LLMs in Finance
• 4.3 Session agenda
• 4.5 Token allocation
• 4.6 Lecturer reflection
• 4.7 Wrap-up & next steps

• Course at a glance (1/3)
• Course at a glance (2/3)
• Course at a glance (3/3)
• Prepare for next regular lecture
• See you next time
• References

Course at a glance (1/3)

Foundations of Digital Disruption in Financial Services

Week 1

22.10.2026

What is ‘emerging tech in finance’, how did we get here, where is it going

• Three waves of digital disruption in finance
• Today’s actors: incumbents, challengers, Big Tech, infrastructure
• Regulatory backdrop: PSD2, MiCA, EU AI Act
• Why now: structural drivers
• What this course will cover

Flipped — Digital Disruption in Financial Services

Week 2

29.10.2026

Group presentations · token allocation · discussion

• Recap of the foundations lecture
• Group presentations on digital disruption
• Token allocation & next steps

Agentic AI & LLMs in Finance

Week 3

05.11.2026

From LLMs to agents · applications · failure modes · EU AI Act

• LLMs in finance: architecture, training, capabilities
• Agentic AI: from answers to actions
• Applications: RAG, robo-advisors, AML, trading agents
• Failure modes: hallucination, drift, prompt injection
• Governance: EU AI Act and high-risk obligations

Flipped — Agentic AI & LLMs in Finance

Week 4

12.11.2026

Group presentations · token allocation · discussion

• Recap of the agentic AI lecture
• Group presentations on real LLM and agent deployments
• Token allocation & next steps

Blockchain, Crypto, DeFi & Tokenisation

Week 5

19.11.2026

From distributed ledgers to MiCA-regulated markets

• Blockchain primer: ledgers, consensus, smart contracts
• Crypto markets: BTC, ETH, stablecoins
• DeFi primitives: AMMs, lending, derivatives
• Tokenisation of real-world assets
• MiCA framework and EU enforcement

Course at a glance (2/3)

Flipped — Blockchain, Crypto, DeFi & Tokenisation

Week 6

26.11.2026

Group presentations · token allocation · discussion

• Recap of the blockchain & DeFi lecture
• Group presentations on real protocols and deployments
• Token allocation & next steps

Fintech Business Models

Week 7

03.12.2026

Neobanks, embedded finance, BNPL, Open Banking, Big Tech in finance

• Neobanks: N26, Revolut, Monzo, Chime
• Embedded finance & BaaS
• BNPL: Klarna, Affirm, regulatory pushback
• Open Banking & PSD2 outcomes
• Big Tech in finance

Flipped — Fintech Business Models

Week 8

10.12.2026

Neobanks, embedded finance, BNPL · group presentations · token allocation

• Recap of the fintech business-models lecture
• Group presentations on real companies and unit economics
• Token allocation & next steps

RegTech, Cybersecurity & Privacy-Preserving Compute

Week 9

17.12.2026

Industrialising compliance · cyber-threat landscape · ZKPs, MPC, federated learning · post-quantum

• RegTech overview: industrialising compliance
• KYC/AML automation in production
• Cybersecurity threats in finance
• Privacy-preserving compute: ZKPs, MPC, federated learning
• Post-quantum cryptography & the migration

Flipped — RegTech, Cybersecurity & Privacy-Preserving Compute

Week 10

07.01.2027

Group presentations · token allocation · discussion

• Recap of the RegTech & security lecture
• Group presentations on vendors, incidents, and emerging tech
• Token allocation & next steps

Course at a glance (3/3)

CBDCs & the Future of Money

Week 11

14.01.2027

Wholesale vs retail design · Digital Euro · e-CNY · programmable money

• What’s a CBDC: wholesale vs retail
• The Digital Euro state of play
• China’s e-CNY and small-country implementations
• Programmable money: feature, threat, or both
• Stablecoins as private money: tension with CBDCs

Flipped — CBDCs & the Future of Money

Week 12

21.01.2027

Final session · group presentations · token allocation · course wrap-up

• Recap of the CBDCs lecture
• Group presentations on real CBDC projects
• Token allocation, final standings, and course retrospective

Prepare for next regular lecture

1. Skim the original Bitcoin whitepaper (Nakamoto 2008) and the Ethereum whitepaper (Buterin 2014) (sections 1–3 of each).
2. Track one DeFi event this week: a hack, a launch, a regulatory action, a tokenisation deal.
3. Group discussion Sunday: pick a Week-6 angle on Blockchain / DeFi / Tokenisation.

Lecture 5 covers a lot of ground; pre-reading the whitepapers (they are short) lifts the lecture by an order of magnitude.

See you next time

Reminder

• Token allocation: Moodle quiz open now — 5 minutes.
• Slide PDFs from all groups archived under Week 4.
• Lecture 5 (next Thursday): Blockchain, Crypto, DeFi & Tokenisation — what’s actually being built, what’s smoke.

References

Acemoglu, Daron, and Pascual Restrepo. 2020. “The Wrong Kind of AI? Artificial Intelligence and the Future of Labour Demand.” Cambridge Journal of Regions, Economy and Society 13 (1): 25–35. https://doi.org/10.1093/cjres/rsz022.

Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2022. Prediction Machines: The Simple Economics of Artificial Intelligence. Updated. Boston, MA: Harvard Business Review Press.

Buterin, Vitalik. 2014. “A Next-Generation Smart Contract and Decentralized Application Platform.” https://ethereum.org/en/whitepaper/.

Chen, Hongyang, Hongyang Yang, and Xiao-Yang Liu. 2023. “FinGPT: Open-Source Financial Large Language Models.” arXiv preprint arXiv:2306.06031. https://arxiv.org/abs/2306.06031.

European Parliament and Council. 2024. “Regulation (EU) 2024/1689 Laying down Harmonised Rules on Artificial Intelligence (AI Act).” Official Journal of the European Union, L 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj.

Nakamoto, Satoshi. 2008. “Bitcoin: A Peer-to-Peer Electronic Cash System.” https://bitcoin.org/bitcoin.pdf.