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

Saudi Arabia's Vision 2030 is turning AI from buzzword to backbone for finance: the FinTech Strategy sets clear targets - new fintech firms, jobs and GDP contribution - while the Saudi Data & AI Authority (SDAIA) and NSDAI unify data, cloud and governance to make projects like NEOM operational testbeds for bank-grade AI. Regulators, banks and fintechs are racing to embed real‑time analytics, automated onboarding and smarter credit decisions that support a diversified, non‑oil economy; Deloitte notes the Financial Sector Development Program is central to that shift.

For practitioners and leaders wanting practical skills, AI Essentials for Work bootcamp (15-week) at Nucamp - promptcraft & applied AI for business teaches promptcraft and applied AI across business functions to help teams deploy these use cases faster.

The result is a fast-moving Saudi market where infrastructure, policy and talent are aligning - think national data rails enabling local robo‑advisors and fraud detection at scale.

FinTech Strategy Target	Figure
Fintech companies by 2030	525
Jobs created by 2030	18,000
GDP contribution by 2030	13.3 billion SAR
Fintech investment market by 2030	2.12 billion SAR

“The digital transformation within the Ministry of Finance is not merely about developing systems or updating procedures. It represents a radical shift toward a more transparent, efficient, and accelerated financial future.”

Methodology: the Top 10 use cases were selected by cross‑checking macro impact, adoption signals and regulatory readiness across trusted Saudi sources - prioritising high economic upside, proven traction, and feasible compliance.

Economic weight came from Sidra Capital's estimate that AI could add Sidra Capital estimate of AI adding $135.2 billion to Saudi GDP by 2030, while adoption and near‑term priorities were validated against market surveys such as Cognizant's study of generative AI investment and use‑case intent in the kingdom (Cognizant generative AI adoption study for Saudi Arabia).

Practical feasibility and constraints were mapped to regulatory and data controls in Securiti's PDPL/NDMO guidance so each use case balances upside with requirements for consent, DPIAs and data localisation.

The result: cases that score high on impact, demonstrable deployment or sandbox readiness (SAMA), and a clear compliance path - so banks can move from pilot to production without getting tripped up by governance or talent gaps.

Metric	Source / Value
Projected AI economic impact (2030)	$135.2 billion - Sidra Capital
Recent AI adoption in banks	55% adoption in last 12 months - Finastra survey
Gen AI interest among decision‑makers	~88% - Cognizant study
Regulatory readiness	SAMA sandbox + PDPL/NDMO compliance checklist - Securiti

“AI is reshaping the financial sector by refining investment strategies and increasing operational efficiency.”

Saudi banks now face a fast‑moving fraud landscape - SAMA's rulebook makes clear that member organisations “should implement and maintain fraud detection systems” to spot anomalies across transactional and non‑transactional data (SAMA fraud detection systems rulebook), and local incident trends show digital banking fraud already accounts for a large share of cases.

That's why real‑time pipelines matter: AI frameworks like TensorFlow power low‑latency anomaly detection and behavior‑based scoring, while operational data warehouses and in‑memory platforms let institutions score 100% of transactions in milliseconds to stop attacks before money moves (AI-driven fraud detection using TensorFlow for real-time risk management, SAS Fraud Decisioning platform).

Combining graph/link analysis to unmask synthetic IDs with continuous model retraining reduces false positives and preserves customer trust - because in fraud prevention, even a few minutes of delay can turn a single breach into a mult‑million‑riyals headache.

“SAS helped us reduce case alert volume by 40%, improve our fraud detection rate by 35% and reduce false positives by 18% ...” - Pramote Lalitkitti, Senior Vice President of Fraud Management, Krungsri Consumer

Automated KYC is rapidly moving from neat demo to SAMA‑approved proof point in Saudi pilots: SAMA's Regulatory Sandbox expects applicants to show rigorous KYC/AML flows, detailed testing and a clear exit plan, making identity checks a gating item for acceptance (SAMA Regulatory Sandbox guide (LenderKit)).

Local pilots combine OCR, biometric liveness and ML risk‑scoring so banks and fintechs can onboard customers remotely while meeting SAMA's focus on investor and consumer safeguards - the same techniques that let vendors verify users in seconds and cut manual reviews dramatically (AI-powered KYC automation features (CFlow), Sumsub KYC verification outcomes).

The practical payoff is immediate: faster conversion, fewer manual cases and a compliance path that regulators can supervise during the Sandbox window - picture a new customer cleared in ~20 seconds instead of days, so teams can scale without trading off control.

Metric	Value
Sandbox lifecycle	Application 30 days · Evaluation 60 days · Testing 6 months
Average onboarding time (vendor)	~20 seconds - Sumsub
Onboarding pass rate uplift	~75% higher pass rates - Sumsub
Reported ROI	240% average ROI - Sumsub / Forrester

“Since integrating Sumsub, we've received positive feedback from our users about the quick signup process and verification time. It takes only a couple of minutes from downloading the app to booking a rental car.”

AML case triage and SAR drafting in Saudi banking require a tight, auditable workflow that moves alerts from detection to decisive filing without losing context: start with automated alerting and risk‑based triage, create a consolidated case record, enrich with external data, run transaction and source‑of‑fund analysis, then draft a concise, evidence‑led SAR narrative for filing - processes detailed in global best practices and useful for SAMA‑aligned programs.

Intelligence‑led platforms can reduce false positives and surface networked risk quickly (see Quantexa's guide to AML investigations), while SAR narrative checklists and examples help ensure reports are actionable for investigators (Abrigo's SAR narrative best practices).

AI‑assisted tools and case managers speed investigations, standardize quality and preserve the audit trail so a single clear narrative doesn't become a dead end for law enforcement.

Remember the hard deadlines and retention rules that shape workflows: timely decisions trigger filing windows and long‑term recordkeeping that regulators expect - build your SAR pipeline to meet both operational speed and regulatory rigor.

For practical reference, see the BSA/AML manual on SAR timing, quality and record retention for the steps that should be baked into any compliant workflow.

Key SAR Workflow Item	Reference / Rule
Decision & filing window	File within 30 days (60 days if no suspect identified) - BSA/AML manual
Record retention	Retain SARs & supporting docs for 5 years - BSA/AML manual
Ongoing reporting cadence	Report continuing activity typically every 90 days (120 days in some guidance) - BSA/AML manual
Investigation workflow	Alert → Triage → Case creation → Enrichment → Transaction analysis → SAR filing - Quantexa / Lucinity guidance

Credit scoring in Saudi Arabia is moving beyond bureau-only decisions toward NSDAI‑ready, hybrid underwriting that blends traditional histories with alternative signals to bring thin‑file and “no‑hit” customers into the scorable pool: Equifax notes alternative inputs (telco, utility, bank transaction and employment data) can reduce unscorable consumers by up to 60% and approve over 20% more applicants while uncovering millions of previously invisible profiles (Equifax: alternative data for credit risk).

Practical models follow Teradata's playbook for fusing device, payment and public records into features that boost predictive power and help lenders assess capacity and character when bureau data is thin (Teradata: alternative data in credit underwriting).

For Saudi banks and fintechs aligning with NSDAI and national programs, the opportunity is clear: build disciplined pipelines, consented data flows and explainable ML so a previously invisible applicant becomes a reliable borrower - fast, auditable and ready for scale.

Alternative data type	Example / source
Telco & utility payments	Equifax - Payment Insights
Bank transactions & cashflow	Equifax - Cashflow Insights
Device & behavioural metadata	Teradata / Credolab - app & device signals
Public records & rent	Teradata - property and payment histories

Personalized wealth management in Saudi Arabia is increasingly driven by robo‑advisors that blend algorithmic portfolio construction with strict Shariah filters, a combination that opens low‑cost, goal‑based investing to millennials and the mass‑affluent; platforms commonly use ETFs, sukuk and gold to build diversified halal portfolios and often appoint a Shariah committee or advisor to ensure compliance (Guide to Shariah‑Compliant Robo‑Advisors - Ethis explainer).

Local offerings are evolving to include Saudi‑focused ETFs alongside global exposures - bringing choices that range from retirement planning to targeted goals such as Hajj or Umrah - while providers emphasize accessibility through low minimums and automated rebalancing (Robo‑Advisory Market in Saudi Arabia - Analysis by Sharikat Mubasher).

The real payoff: a previously underserved investor can open a halal portfolio in minutes and watch an automated engine keep it aligned with both faith and financial goals - turning ethical intent into measurable wealth outcomes without the traditional advisory price tag.

Regulatory compliance is fast becoming a RegTech race in Saudi banking: SAMA's move to 27 new reporting rule updates raises the bar for data granularity and means regulators will expect more frequent, field‑level accuracy rather than high‑level monthly rollups - so “spreadsheets won't cut it” and institutions must invest in automated pipelines, lineage and sign‑off workflows to stay airtight (SAMA's 27 reporting updates).

At the same time, PDPL, NDMO and SAMA principles put consent, DPIAs, strict vendor controls and robust retention rules at the centre of any production reporting stack, so RegTech that combines data quality, universal interfaces for SAMA/CMA submissions and audit‑ready governance is the practical path from sandbox pilots to live compliance (PDPL, NDMO & vendor controls).

The near‑term “so what?”: automating lineage, validation and breach workflows (including SDAIA notifications) converts regulatory pain into operational insight and makes timely, defensible filings routine rather than heroic.

Item	Requirement / Note
SAMA reporting updates	27 new rule updates increasing data granularity - Nasdaq
Breach notification	Notify SDAIA within 72 hours - Securiti
Record retention	Maintain records for processing duration + 5 years - Securiti

“In addition to these reporting updates, SAMA has launched initiatives like Regulatory Sandbox, designed to attract a mix of local and international fintechs.”

STC's customer‑support playbook shows how conversational agents scale when data, language and analytics are aligned: harmonized platforms ingesting roughly 20 PB of daily data feed speech‑to‑text, speaker‑diarization and topic‑extraction pipelines that let LLM‑based assistants triage calls, surface root causes and suggest upsell offers in Arabic and English - turning call audio into measurable savings and faster, culturally fluent service.

Teradata's case study explains how Vantage and ClearScape unify disparate signals for personalized engagement and churn protection (Teradata case study: STC AI customer-support playbook), while the STC multilingual website built by Teqnovos demonstrates 100% bilingual support and tight integration with support systems to close the loop from web to bot to agent (Teqnovos case study: STC bilingual website and chatbot integration).

With Saudi Arabia's AI chatbot market already measured in the hundreds of millions of dollars, these stack‑level moves show the practical “so what?”: faster resolutions, fewer live transfers and support that understands local language and context (IMARC report on Saudi Arabia AI-powered chatbots market).

Metric	Value / Source
Daily data ingested	~20 PB - Teradata
Daily minutes of data	156M minutes - Teradata
Daily SMS	1.4M - Teradata
Multilingual support	100% - Teqnovos
Web traffic uplift	30% increase - Teqnovos

“We just developed an AI chatbot for stcTV based on ChatGPT. A customer can interact with the chatbot and make requests like, ‘I like Breaking Bad. Recommend something similar.' …” - Ahmad Hussain, stc

Detecting money‑laundering rings that stitch transactions across banks requires more than better rules - it needs shared intelligence without sharing raw customer data, and federated learning is the practical bridge.

By training models locally and aggregating only updates, Saudi banks and regulators can unmask networked schemes like “smurfing” or cross‑institution layering while keeping personally identifiable information on‑premise; Lucinity's approach and Project Aurora with the BIS show how patented privacy measures and PETs (homomorphic encryption, differential privacy) make that collaboration feasible (Lucinity: Federated Learning for Financial Crime Prevention).

FinRegLab's FAQs and Consilient's analysis underline the upside: richer training data across nodes improves true‑positive detection, trims false positives dramatically, and lets a central hub safely redistribute learnings so a pattern visible across five banks becomes a usable alarm for all (FinRegLab FAQs on Federated Machine Learning in Anti‑Financial‑Crime Processes, Consilient: Federated Machine Learning for Financial Crime Prevention).

The “so what?” is tangible: federated hubs turn isolated alerts into networked intelligence, refocusing scarce AML teams onto real threats instead of drowning them in false positives.

Benefit	Evidence / Source
Keeps sensitive data local	Lucinity - federated learning, Secure Lockbox
Improves cross‑institution detection	FinRegLab - aggregated training improves true positives
Reduces false positives	Consilient / FinRegLab - provider claims drop from ~95% to ~12%

“The biggest thing holding back the adoption of advanced AI systems is the global concern over data security. With our patented federated learning technology, algorithms from one market can share essential learnings with another market, and the data remains safe, private, and secure.” - Guðmundur Kristjánsson, CEO, Lucinity

Saudi SOCs aiming to harden financial services should treat AI‑powered log analysis as the linchpin of modern incident response: with log volumes

growing 50 times faster than traditional business data,

manual triage is no longer tenable, and AI can cut through noise to surface the few signals that matter in real time (AI log analysis best practices (LogicMonitor)).

Core best practices map directly to the Saudi context - centralise high‑quality telemetry, encrypt logs end‑to‑end, and run continuous model retraining so baselines stay current - then fold those insights into the SOC stack (EDR, SIEM, SOAR) for automated containment and faster MTTR (AI intrusion detection components and best practices - Faddom).

Complementary tactics - federated learning to share threat intelligence without exposing raw logs, deception/honeypots to enrich training data, and graph‑based models for lateral‑movement detection - sharpen detection while reducing false positives so analysts focus on real incidents (AI threat detection real-world applications - Oligo Security).

The payoff for Saudi banks and fintechs is tangible: faster, evidence‑rich incident response that turns an avalanche of opaque logs into clear, actionable alarms before a small anomaly becomes a costly breach.

Arabic LLMs are finally moving from novelty to practical toolkits for Saudi banks, but the language's quirks mean caution: a Riyadh CDMA study found GPT‑4 often produces correct Arabic code summaries yet leans verbose and can miss the author's intent, with readability scores ranging from 30.29 to 100 - IEEE 2025 study: Evaluating LLMs for Arabic code summarization, while an independent review that compared eight Arabic models shows mixed strengths - Claude and GPT‑4 handle formal Modern Standard Arabic well, Jais and AraBERT excel at domain tasks, CAMeL helps with dialects, and Tashkeela aids diacritization - Localazy analysis of eight Arabic LLMs' translation accuracy.

so what

for KACST/ALLaM‑era deployments in Saudi financial services: LLMs can accelerate document summarization, extract clauses and draft first‑cut regulatory language, but legal and SAR‑level filings remain in the

not ready for autonomous use

Item	Note / Source
GPT‑4 Arabic summaries	Generally correct but verbose; readability 30.29–100 - IEEE CDMA 2025
Key LLM challenges	Dialects, morphology, script/diacritics; model performance varies by task - Localazy analysis

box - human legal review, dialect checks and diacritization fixes are essential to keep filings defensible and culturally accurate, or a playful dialectal greeting could accidentally turn into a compliance headache.

Conclusion - banks and fintechs in Saudi Arabia should treat today's sandboxes and rulebooks as a launchpad, not an obstacle: embrace SDAIA's Data & Privacy Regulatory Sandbox to trial Privacy‑Enhancing Technologies and Privacy‑by‑Design patterns under a regulator's watch (SDAIA Data & Privacy Regulatory Sandbox 2025), pair that with SAMA's live fintech sandbox to validate customer‑facing flows and compliance guardrails (SAMA live fintech Regulatory Sandbox), and invest in people and processes so pilots don't stall at production handover.

Practical next steps: design consented, auditable data pipelines; pilot PETs and federated approaches for cross‑institution intelligence; and harden reporting automation to meet SAMA's granular requirements.

Upskilling matters just as much - short, applied programs that teach promptcraft, prompt‑based workflows and workplace AI literacy will help teams operationalize models safely; practitioners can find a pathway in courses like Nucamp's Nucamp AI Essentials for Work bootcamp.

The payoff is concrete: a supervised test lane where novel models face real regulatory checks, turning risk into repeatable, scalable services that protect customers and unlock measurable efficiency gains.

Sandbox	Purpose / Note
SDAIA Regulatory Sandbox 2025	Start date: 21 May 2025 · Tests PETs, Privacy by Design, PDPL compliance
SAMA Regulatory Sandbox	Live testing environment for financial institutions and fintechs to trial new business models