Top 10 AI Prompts and Use Cases and in the Financial Services Industry in Ukraine

AI is already reshaping Ukraine's financial services -

opening up new prospects

for banks and fintechs as institutions pursue automation, better fraud detection and smarter credit decisions while navigating wartime constraints (see Oschadbank); at the same time Ukraine's recovery plan and limited impact‑capital flows (only about 6% of global impact investment reaches Eastern Europe) mean public‑private action is needed to scale AI safely and attract funding for resilient systems (CSIS analysis on impact investing in Ukraine).

European authorities flag big upside alongside clear risks - bias, hallucination and supplier concentration - so pragmatic governance matters (ECB analysis of AI benefits and risks).

For practitioners and managers looking to turn strategy into capability, practical training such as Nucamp's 15‑week Nucamp AI Essentials for Work bootcamp (15 Weeks) teaches prompt design and applied workflows that finance teams can use right away, especially in less conflict‑affected regions where business is restarting.

Bootcamp	Length	Early bird cost	Registration
AI Essentials for Work	15 Weeks	$3,582 (early bird)	Register for the AI Essentials for Work bootcamp

Methodology: prompts and use cases were chosen to balance immediate operational impact for Ukrainian banks and fintechs with practical safety and scalability: priority went to prompts that boost efficiency and accuracy in routine finance tasks (reporting, forecasting, expense categorization) as highlighted in Glean's prompt library, while also favoring prompts that support rigorous financial reporting and controls from DFIN's checklist approach - so every prompt either shortens a manual workflow or tightens an audit trail.

Selection criteria included: (1) clear business outcome (time saved, error reduction), (2) compatibility with Ukrainian incentives and platforms (e.g., Diia City R&D tax-friendly environment for fintechs), (3) regulatory and verification fit (specifying constraints to reduce hallucination per ICAS/FinQuery guidance), and (4) prompt engineering technique - SPARK and Chain‑of‑Thought methods were used to shape each prompt for repeatable accuracy and safe iteration.

The result is a Top 10 that reads like an execution playbook: deployable prompts for forecasting, AML monitoring, underwriting and back‑office RPA that turn messy ledgers into concise, auditable dashboards - ready to scale where Diia and local tech policy lower the cost of pilots.

For reference, see Glean's prompt examples and F9's SPARK framework for practical prompt structure.

Analyze historical revenue data and predict next quarter's revenue based on current market trends.

Denser-powered automated customer support can supercharge Ukraine's digital banking experience by combining resilient cloud delivery, proven conversational AI and newly opened API channels: banks can route routine queries and payment prompts to a scalable GenAI assistant while escalating exceptions to human teams, reducing branch congestion during spikes and letting teams focus on complex cases.

The idea builds on local precedents - PrivatBank's dramatic cloud migration that preserved services for 20 million customers under fire shows why resilience matters for any always-on support system (PrivatBank cloud migration and continuity); retailers and malls already use chatbots and AI-driven messaging in Ukrainian channels, proving customer comfort with conversational interfaces (Gulliver's AI chatbot and retail AI examples).

With Ukraine's open banking rollout enabling secure API access for third-party services, Monobank-style digital brands and Diia-linked business services (Diia.Business / Start Path) can safely integrate automated support to authenticate, surface account information, and start self‑service flows - freeing staff for the one thing machines can't (judgment on complex, high‑stakes cases).

A vivid metric to watch: a resilient chatbot that cuts average wait times from minutes to seconds during a cash‑run day becomes a tangible measure of financial stability for millions.

“When tensions start, what do citizens usually do? They go withdraw money,” Kaczmarek said.

HSBC's playbook for real‑time fraud detection offers a practical template for Ukrainian banks and fintechs: cloud‑scale ML that links transactions into behavioral networks, continuous learning that adapts to new laundering tactics, and generative summaries that turn noisy alerts into concise investigator-ready narratives.

HSBC's Dynamic Risk Assessment - a partnership built with Google - shows how combining fast scenario modeling and network analysis makes it possible to spot coordinated schemes rather than isolated hits (HSBC Dynamic Risk Assessment AI partnership with Google); independent reporting credits HSBC's system with processing vast volumes and detecting 2–4× more suspicious activity while cutting false positives by ~60%, which in practice shifts compliance work from triage to high‑value investigation (Chief AI Officer report on HSBC AI detecting suspicious activity and reducing false positives).

For Ukraine, the payoff is tangible: faster, clearer alerts mean regulators get better SARs sooner and local teams can prioritize blocking risky flows during stress days - turning an overwhelming inbox into a short list of high‑confidence cases and freeing human experts to follow the story rather than hunt for it.

“The speed itself is mind blowing. You should have seen the faces of some of our guys when they saw the numbers come out in 15 minutes.”

Zest‑style AI models that blend hundreds of traditional and non‑traditional signals can materially sharpen credit decisions in Ukraine - using transaction patterns, digital footprints and even device intelligence to find creditworthy borrowers the old scores miss, while producing a human‑readable rationale for decisions that regulators and customers can accept; academic and industry work shows this approach can lift approvals by roughly 20–30% without raising portfolio risk, a jump that translates into real opportunity for underbanked Ukrainians and SMEs (see Zest AI examples in the AIFT review AI and non-traditional data sources for credit scoring).

Practical deployments should pair powerful ML with dimension‑reduction and scorecard techniques so models collapse hundreds of predictors into one‑to‑three interpretable directions - as recent research on DRA‑CS / MD‑SDR demonstrates - while following FICO's guidance to combine alternative and bureau data and preserve explainability for audits and customer notices (FICO guidance on using alternative data and explainability).

The payoff in Ukraine is concrete: faster, fairer approvals for small firms and freelancers, plus decision rationales that let loan officers, compliance teams and customers follow the why behind every score - turning black‑box boosts into operational trust.

“SEON significantly enhanced our fraud prevention efficiency, freeing up time and resources for better policies, procedures and rules.”

For the National Bank of Ukraine (NBU) the priority is clear: move AML monitoring from slow, batch-based triage to continuous, machine‑assisted detection so investigators get richer case files in time to meet filing windows and sanctions pressures; machine‑learned scores and network graphs can surface linked accounts and cross‑rail activity (cards, wallets, crypto) and feed concise, investigator‑ready summaries that make SAR drafting faster and more defensible (Sardine real-time AI and machine learning for transaction monitoring).

Practical guides on ML for AML underscore the same playbook - use models to reduce false positives, tighten KYC risk scoring, and create a human‑in‑the‑loop feedback loop so systems improve as SARs are closed and reviewed (Global Investigations Review guide to using AI and machine learning in AML programmes).

Operationally, local teams should pair these engines with clear narrative standards and attachments so a flagged case arrives with a one‑paragraph lede plus evidentiary tables; training and simple templates from SAR specialists help ensure SARs filed under the 30/90/120‑day rules are both timely and actionable for law enforcement (Alessa tips for writing Suspicious Activity Report (SAR) narratives), turning an overflowing compliance inbox into a short, high‑value watchlist investigators can act on.

A: No, not really. You know, just enough to be able to capture all of those particular points that I was talking about, where, you know, you summarize in the first paragraph, what is going on, to grab their attention.

Automated underwriting in Ukraine becomes practical when intelligent document processing stitches OCR, Named Entity Recognition (NER) and simple business rules into a single pipeline that turns Diia e-docs and submitted paperwork into machine‑readable, auditable data - think invoices, bank statements and ID fields parsed to JSON so decision engines and credit scorecards can run without manual typing.

NER is the workhorse here: modern pipelines detect names, dates, amounts and organization identifiers and then classify them into loan application fields (see a practical NER primer for technique and best practices Practical Named Entity Recognition (NER) Guide - 2025).

Complementing NER, robust OCR and form‑extraction models extract tables and line items from messy scans, producing standardized outputs that plug directly into underwriting rules and anti‑fraud checks (Form Data Extraction: OCR to Deep Learning Guide).

For Ukrainian banks and fintechs, the tightest wins come from hybrid approaches - rule lists plus transformer models and local language tuning - decreasing manual review while preserving clear evidence trails for auditors and regulators (examples and implementation patterns are summarized in industry NER and IDP guides like Koncile NER Implementation Guide).

The result: fewer paper queues, faster credit decisions, and a single, searchable dossier per applicant instead of a filing cabinet of uncertainty.

An Aladdin‑style portfolio engine for Ukrainian markets would be less about exotic derivatives and more about fast, pragmatic fusion of market, macro and wartime signals: ingest government‑bond order books (government bonds made up roughly 93% of trades in 2023), FX and NBU policy shifts, commodity flows and sanctions data, then boil that into instrument‑level risk, scenario re‑pricing and actionable allocation guidance for managers and sovereign investors - so a desk can see in seconds how a shock to grain logistics or a surge in missile strikes that disrupt infrastructure changes hedging needs and liquidity buffers.

With reconstruction set to draw large capital inflows and Ukraine's competitive, well‑educated tech talent, a local Aladdin variant can also embed compliance checks and Diia‑city‑friendly R&D workflows to lower pilot costs and speed regulator engagement (see the U.S. State Department investment climate review and a practical guide to Diia City incentives).

The practical payoff is vivid: a dashboard that turns an overwhelming pile of fragmented market notices into a three‑line “what to do” for traders and risk teams - reducing decision lag from hours to minutes when it matters most.

Feature	Why it matters
Government bonds ≈93% of trades (2023)	Portfolio engines must prioritize sovereign/liquidity risk
Limited market liquidity & few instruments	Stress testing and scenario analysis become primary risk tools
Large reconstruction inflows expected	Scalable analytics attract foreign and institutional investors

Combining Ukrainian Railways' operational signals - freight schedules, restored bridge reopenings and regional carriage capacity - with parcel and postal network data offers a low‑hanging, high‑impact route to branch liquidity forecasting: when a repaired bridge or a newly delivered set of flatbed wagons restores a freight corridor, banks can expect local cash demand and merchant float to shift within days, not weeks, so models that fuse rail timetables with pickup/drop volumes and point‑of‑sale flows let treasury teams pre‑stage cash, optimize ATM replenishment, and reduce emergency courier runs; this is exactly the sort of intelligent automation payoff that RPA + ML projects deliver in Ukrainian finance back‑offices, shortening reconciliation cycles and turning logistic signals into operational action (World Bank - Restoring and Transforming Ukrainian Railways for a Better Future (2025)) while keeping pilot costs predictable when paired with pragmatic automation playbooks (Nucamp AI Essentials for Work bootcamp - Intelligent automation for Ukrainian financial services (Registration)).

The vivid test: forecasted ATM demand that shifts correctly the day a repaired track reopens - a small model win that prevents long withdrawal lines and signals stability to customers.

“At the time when everything stopped, when the airlines halted flights to Ukraine, Ukrainian Railways remained the key lifeline, not just for passenger movement, but also for cargo and our economy.”

A Regulatory Compliance Assistant that links Diia-based KYC into NBU filings can turn compliance from a paperwork bottleneck into an operational control: ingest remote ID evidence (NBU BankID, QES, chip reads and video verification), tag PEPs and beneficial‑ownership chains, and prefill the NBU's required XML/JSON submission templates so statistical reporting and suspicious‑activity narratives arrive complete, timestamped and auditable.

By mapping Diia authentication artifacts to the NBU's Financial Monitoring rules (remote identification models, updated risk thresholds and case‑based SARs) and to the NBU reporting calendar and quality controls, the assistant enforces primary/secondary/expert checks, automates routine escalations when a UAH 400,000 threshold or a high‑risk PEP flag trips, and delivers investigator‑ready one‑paragraph ledes plus evidentiary tables - cutting manual toil and helping banks meet tight filing windows.

See the NBU's Financial Monitoring regulation and the NBU Statistical Reporting Framework for the filing formats and control points that any production system must follow.

Compliance Hook	What the Assistant must deliver
Remote identification models	NBU BankID, qualified electronic signature, chip reads, video verification
Reporting formats & controls	XML/JSON submissions; primary/secondary/expert validation; filing calendar
Thresholds & SARs	UAH 400,000 reporting threshold; case‑based SARs and rapid escalation rules

“Failure to follow these FATF-aligned KYC requirements can lead to regulatory fines, loss of banking licenses or correspondent relationships ...”

Back‑office onboarding becomes a competitive advantage when CRM workflows are wired to Diia's document APIs: one click for document multisharing pulls passport fields and certificates straight into application forms, Diia.Check or QR/barcode validation returns a document's validity plus full name and age in seconds, and Diia.Signature lets customers sign and authorize remotely so contracts arrive audit‑ready without a courier run.

That combination - automatic field fill, instant validation (scan the Diia barcode or enter the 13‑digit code), and phone‑based signing - turns a drawer‑full of paperwork into a single digital dossier and slashes manual entry and error rates for onboarding teams; Diia already reaches more than 20 million users and is integrated by dozens of banks, making it a realistic automation backbone.

For fintechs inside Diia.City, the legal‑and‑tax regime lowers pilot costs and speeds product experiments, so teams can build CRM‑triggered flows that escalate only exceptions to human review.

See the official Diia integration guide for developers for scenarios and technical steps, the Diia validation API documentation for barcode and QR flows, and the Diia.City residency benefits and incentives overview.

Correlating SIEM incident triage with financial signals turns a noisy alert feed into an operational lifeline for Ukrainian banks: automated SOAR playbooks can enrich and score SIEM alerts with customer‑facing telemetry (transaction spikes, unusual cash withdrawals) so analysts see a single, prioritised case that ties cyber indicators to potential fraud or liquidity events - cutting hunt time and preventing costly delays.

Vendor case studies show the scale: SIEMs can produce as many as 150,000 alerts a day, so automation that closes routine false positives and centralises context not only reduces analyst burnout but speeds response and improves auditability (Swimlane SIEM alert triage case study, Devo SOAR alert triage use case).

Elastic and Datadog examples prove the payoff: automated investigations have closed thousands of alerts in days, avoided the need for dozens of extra hires, and let SOCs focus on high‑confidence incidents instead of noise (Elastic automated SIEM investigations case study, Datadog automate security workflows and cloud SIEM).

In practice a compact SOAR+SIEM pipeline that attaches a one‑paragraph financial lede and a short evidentiary table to each escalated case converts an overflowing inbox into a handful of investigator‑ready incidents - a small, tangible win that preserves customer trust when it matters most.

Metric	Example value / impact
SIEM alerts per day	Up to 150,000 (Swimlane)
Time saved per triage alert	~14 minutes (Swimlane)
Automated closures	Thousands of alerts closed in days; 50,000+ in 30 days (Elastic)
Efficiency gain	60% SOC efficiency increase reported in vendor case studies

“I'm 100% convinced that every customer that is operating a SIEM system, that's operating a log management solution, a SOC whatsoever – if they want to survive, they need some kind of automation.”

Practical next steps in Ukraine start small, pragmatic and measurable: pilot a sandboxed LLM on a single workflow (for example, use ChatGPT to compress a 200‑page report into a two‑paragraph executive lede in minutes and measure accuracy and reviewer time saved) while training a core team in prompt engineering so outputs are repeatable and auditable; guidance on prompt categories and sandboxes is well covered in Deloitte's primer on prompt engineering for finance (Deloitte - Prompt Engineering for Finance), and operational primers show quick wins - report drafting, market scans and compliance summaries - that free hours for analysts.

Redcliffe Training - ChatGPT for Banks

Pair these pilots with a living prompt library and simple acceptance tests (accuracy thresholds, false‑positive caps, audit trail requirements), then scale to adjacent use cases - customer support, AML summaries, or underwriting extraction - only after human‑in‑the‑loop checks prove effective.

For teams seeking structured upskilling, consider a practical course that teaches prompt design and safe workflows: Nucamp AI Essentials for Work - 15‑Week bootcamp (register).

Bootcamp	Length	Early bird cost	Registration
AI Essentials for Work	15 Weeks	$3,582 (early bird)	Register for Nucamp AI Essentials for Work - 15 Weeks