The Complete Guide to Using AI as a Finance Professional in Saudi Arabia in 2025

For finance professionals in Saudi Arabia, AI is no longer a curiosity but a strategic imperative: Vision 2030 and SDAIA's national push aim to add roughly $135bn to the economy by 2030 while projects like PIF's HUMAIN - backed by NVIDIA

AI factories

up to 500MW - are building sovereign compute and data residency that finance teams must respect; see the Vision 2030 AI agenda for details (Vision 2030 National AI Strategy for Saudi Arabia).

From Al Rajhi Bank and STC Pay using AI for fraud detection and credit scoring to broader benefits like real‑time forecasting and predictive analytics, Saudi firms are moving pilots into production under PDPL and NDMO guardrails; learn how AI is changing business operations in Saudi (How AI Is Changing Saudi Business Operations with Emerging Tech).

For finance pros who need practical, workplace-ready skills, the AI Essentials for Work bootcamp covers prompts, tools, and job-based AI applications - register to build usable AI capabilities (AI Essentials for Work bootcamp (Nucamp)).

Program	Length	Early Bird Cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for AI Essentials for Work (Nucamp)

Saudi Arabia has moved quickly from ambition to architecture: the Saudi Data & AI Authority's National Strategy for Data & AI lays out concrete targets - from building +20,000 data and AI specialists to attracting roughly 75B SAR of investment - so finance teams should see AI as a governance and capability shift, not just a toolset upgrade; read SDAIA's National Strategy for Data & AI to understand the roadmap (SDAIA National Strategy for Data and AI (Saudi Data & AI Authority)).

The authority is also operationalizing measurement with the newly launched National AI Index (July 23, 2025), a framework designed to evaluate institutional readiness across three pillars and multiple dimensions - a development that will make AI maturity a measurable input into vendor selection, risk assessments, and talent planning (Saudi National AI Index launch (July 23, 2025)).

Practically, this means finance leaders must prioritize data governance, ethical AI controls, and workforce reskilling so forecasting, fraud detection, and treasury automation comply with national standards and benefit from government incentives and test‑bed infrastructure.

Date	Attendees	Purpose	Structure
2025-07-23	Over 180 government representatives	Evaluate AI readiness & align national priorities	3 pillars · 7 core dimensions · 23 subcategories

"We are living in a time of scientific innovation, unprecedented technology, and unlimited growth prospects. These new technologies such as Artificial Intelligence and the Internet of Things, if used optimally, can spare the world from many disadvantages and can bring to the world enormous benefits." - His Royal Highness Prince Mohammed bin Salman bin Abdulaziz Al Saud

LEAP 2025 in Riyadh (Feb 9–12) has become the must‑attend AI conference for finance teams in Saudi Arabia, a four‑day hub where government, global cloud and chip players, and fintech innovators announced major deals and product launches that reshape payments, fraud prevention and treasury automation; the event drew well over 200,000 visitors and 1,000+ speakers and even featured fintech‑specific programming that highlighted AI‑driven compliance and investor platforms - read the LEAP 2025 official overview and highlights for context (LEAP 2025 official overview and highlights).

Highlights relevant to finance professionals included record‑breaking AI investments (a headline US$14.9bn tranche), large infrastructure commitments such as the Groq–Aramco $1.5bn inference partnership and Lenovo–ALAT's $2bn initiative, plus fintech announcements ranging from AI fraud tools to cap‑table automation and automated equity transfers reported at the event (LEAP 2025 fintech and investment announcements coverage).

For treasury, risk and compliance teams the conference was more than spectacle - think live demos like Golden Gloves VR and Project Primrose on the Tech Arena but, crucially, billions of dollars of cloud, data centre and AI commitments that create local compute, vendor options and regulatory considerations finance leaders must plan for.

Dates	Venue	Attendees / Exhibitors	AI Investments Announced
9–12 Feb 2025	Riyadh International Exhibition & Convention Centre (Malham)	200,000+ attendees · 1,800+ brands · 1,000+ speakers	US$14.9 billion (record‑breaking)

“LEAP 2025 is a defining moment because when the Kingdom works, the region works, and the whole world works.” - His Excellency Eng Abdullah Alswaha, Saudi Minister of Communications and Information Technology

Proven, production‑grade AI is already reshaping how Saudi finance teams stop fraud, speed disputes and protect customer trust: SAMA's national fraud analytics hub cut fraud losses by 36%, dropped average detection latency to under 250 milliseconds and drove a five‑fold increase in flagged transactions - an orchestration that caught more than $90M in suspicious flows in year one (see SAMA's real‑time fraud analytics hub SAMA real‑time fraud analytics hub case study).

Banks are pairing these national services with vendor and in‑house systems - Arab National Bank's deployment of IBM Safer Payments delivered cross‑channel, sub‑10ms decisioning and a unified fraud control panel that simplified compliance and investigations (Arab National Bank IBM Safer Payments case study).

At the model level, TensorFlow‑based pipelines (TFX, LSTM, autoencoders) are proving able to run scalable, low‑latency anomaly detection and adaptive risk scoring across millions of transactions - exactly the architecture data scientists recommend for real‑time risk management (TensorFlow real‑time fraud detection guide for banking).

The takeaway for treasury, fraud and AML teams: expect measurable drops in losses and dispute times, but plan now for explainability, cross‑institutional intelligence sharing and UX‑safe step‑up controls - after all, today's models must decide faster than a human blink to stop a stolen card in its tracks.

Case	Key Outcome(s)
SAMA national fraud analytics hub	Fraud losses −36% · Detection <250 ms · Flagged transactions 5× · >$90M preemptive halts
Arab National Bank (IBM Safer Payments)	Cross‑channel protection · Decisions in <10 ms · Deployed with unified control panel
TensorFlow implementations (Datahub Analytics)	Scalable real‑time pipelines (TFX), LSTM/autoencoders for anomaly detection and lower false positives

“IBM Safer Payments offers exceptional speed, making decisions in less than 10 milliseconds. This gives us the breathing space to resolve issues effectively and provide the best support to our customers.” - Khurram Inayat, Portfolio Manager, Arab National Bank

Practical AI for Saudi finance teams starts with real, measurable workflows: deploy real‑time transaction scoring and case‑management interfaces to stop fraud at the point of sale, use network analysis to detect mule accounts during onboarding, and run TensorFlow‑based anomaly detectors in production for continuous risk scoring - approaches proven in the Kingdom to cut false positives and speed decisions.

Bank Albilad's SAS implementation, for example, assesses 100% of transactions and new digital accounts in real time, slashes false positives, and integrates ML outputs into alert‑and‑investigation dashboards so compliance teams can focus on true threats (SAS Bank Albilad real-time fraud management case study).

To build and operate these systems, teams need both engineering pipelines (TFX, streaming with Kafka and TensorFlow Serving) and human skills - retraining cycles, XAI tools like SHAP/LIME for explainability, and vendor‑plus‑build governance - and hands‑on training tailored to Saudi datasets and regulations accelerates that readiness (TensorFlow real‑time fraud detection guide, NobleProg AI for Finance training in Saudi Arabia).

One memorable metric to plan around: modern AI stacks can push approval rates to near‑instant, auditable decisions - so design pipelines, UX step‑ups, and retraining loops now rather than after a costly breach.

98% of transactions approved within seconds

Metric	Result (Bank Albilad)
Transactions assessed in real time	100%
New digital accounts scored in real time	100%
Efficiency increase for investigators	30%
Reduction in false positives	50%
Increase in fraud loss prevention	70%
Transactions approved within seconds	98%

Implementation essentials for Saudi finance teams start with treating data protection, latency and governance as inseparable design criteria: follow PDPL and SAMA rules by encrypting data at rest and in transit (AES/RSA), keeping exclusive control of keys, and embedding data residency into architecture so models and inference run close to Saudi datasets to meet real‑time decisioning needs; see the practical obligations and controls in Securiti PDPL and SAMA compliance checklist for Saudi Arabia financial sector (Securiti PDPL and SAMA compliance checklist for Saudi financial sector).

Operational governance means maintained ROPA and audit trails (processing records retained for the processing duration plus five years), routine DPIAs for automated decisioning, strict vendor contracts with deletion and BCP clauses, and IAM controls - MFA, RBAC and PAM - so access is airtight.

Incident playbooks must reflect regulation‑grade timing: notify SDAIA via the National Data Governance Platform within 72 hours and have containment, remediation and customer‑notification steps rehearsed.

For secure collaboration and data sovereignty during M&A, audits or cross‑border work, deploy encrypted virtual data rooms and DRM that support on‑prem or Saudi‑hosted options to avoid regulatory friction (Kiteworks encrypted virtual data rooms for Saudi Arabia collaboration and M&A Kiteworks encrypted virtual data rooms for Saudi collaboration).

The bottom line: bake encryption, traceable governance and low‑latency local inference into project budgets and contracts now - those measures turn regulatory checklists into operational resilience.

Focus	Required Action
Encryption & Key Control	Encrypt in transit/at rest (AES/RSA); retain exclusive key control
Incident Response	Contain, remediate, notify SDAIA within 72 hours
ROPA & Retention	Maintain processing records; retain for processing + 5 years
DPIA & Audits	Run DPIAs for large‑scale/automated processing; schedule regular audits
Vendors & VDRs	Contractual security, deletion clauses, data localization; use encrypted VDRs
Access Controls	MFA, RBAC, PAM and periodic access reviews

Yes - LEAP 2025 marked a turning point: Saudi Arabia alone inked more than $20 billion in AI infrastructure agreements as part of Vision 2030's compute push, with incentives tied to data sovereignty and energy availability that make the Kingdom a magnet for on‑shore data centres (see the Global Data Center Hub H1 2025 data center recap).

That local momentum sits inside a broader capital rush - hyperscalers pledged roughly $390 billion in AI infrastructure in H1 2025 - and even private AI players are chasing massive raises (OpenAI $40 billion funding drive report).

For finance teams this means cloud choices, pricing and data‑residency clauses will matter as much as vendor features: billions are turning compute into the new strategic asset, and procurement, treasury and compliance will need to budget for green‑power commitments, on‑premise GPU capacity and tighter SLA and residency terms as inference increasingly runs inside Saudi borders.

Exact published salary figures for “AI experts” in Saudi Arabia aren't provided in these sources, but the market context makes the answer clear for finance leaders: fierce demand, growing training pipelines, and major national investment are pushing compensation and hiring competition upward - so expect to budget premium pay or invest in rapid upskilling.

SDAIA's National Strategy aims to build +20,000 data and AI specialists by 2030 and backs academy, scholarship and internship tracks to feed that pipeline (SDAIA National Strategy for Data & AI), while independent analysis shows job postings grew ~54% annually and LEAP 2025 attracted roughly $1.79 billion in AI funding to support academic partnerships and research (How Saudi Arabia is Growing Its Tech Talent to Lead in AI).

For finance teams that need to hire or retain model builders and MLOps engineers, the practical playbook is clear: factor higher salaries into TCO, pair recruitment with SDAIA Academy pathways, and use partnerships with universities and training programs to bridge gaps before market rates fully normalize (SDAIA recruitment & academy programs).

A memorable benchmark: national ambitions and billions in event‑level funding mean talent is being trained at scale - but not yet enough to erase competition, so compensation and fast on‑ramps matter now more than next year.

Metric	Value / Source
Target AI specialists by 2030	+20,000 (SDAIA National Strategy)
LEAP 2025 AI funding	~$1.79 billion (Arthur Lawrence)
AI job posting growth (2018–2022)	~54% annual growth (Arthur Lawrence)
AI‑aligned graduates since 2019	~38,000 (Arthur Lawrence)

"We are living in a time of scientific innovation, unprecedented technology, and unlimited growth prospects. These new technologies such as Artificial Intelligence and the Internet of Things, if used optimally, can spare the world from many disadvantages and can bring to the world enormous benefits." - His Royal Highness Prince Mohammed bin Salman bin Abdulaziz Al Saud

When moving from pilot to production, Saudi finance teams should treat experiments as business decisions: design pilots with clear ROI metrics (fraud losses prevented, false-positive rates, operational cost) and built-in control groups so outcomes can be trusted when scaled - see practical ROI frameworks in

How to calculate ROI on fraud prevention

(How to calculate ROI on fraud prevention - Fortify guide on ROI for fraud prevention).

The vendor-versus-build choice hinges less on hype and more on total cost of ownership: licence fees are only the start - expect integration, tuning, analyst time and customer-ops to drive long-term costs, and push vendors for real, de-duplicated savings and scalability guarantees.

In Saudi Arabia specifically, any pilot must align with national regulators and industry practice - use the SAMA Fraud Detection Rulebook (Saudi Central Bank guidance) and local compliance primers to set detection indicators and escalation rules, for example the Fraud and Compliance Guide for Saudi Fintechs - TrustDecision.

Scale only after proving that tuning doesn't create

mountains of alerts

that require more headcount than the system saves; mandate measurable handover criteria, vendor SLAs for false-positive reduction, and a roadmap for continuous retraining so pilots become resilient, auditable programs across the enterprise.

The road ahead for Saudi finance teams is clear: move quickly from pilot to governed production, measure results, and invest in people and on‑shore compute so AI becomes an operational advantage rather than a compliance headache.

Start by locking down data residency and PDPL/NDMO controls while designing pilots with hard KPIs - fraud and credit scoring have already delivered measurable wins in the Kingdom (SAMA's real‑time fraud hub cut losses 36% and detects suspicious flows in under 250 milliseconds, preemptively halting >$90M in year one) so use those metrics as your benchmark (SAMA real-time fraud hub case study (DigitalDefynd)).

Pair vendor partnerships and HUMAIN‑aligned cloud choices with local SLAs announced at LEAP and Vision 2030 planning to keep latency low and inference inside Saudi borders (Saudi Vision 2030 AI adoption & HUMAIN overview (7startup)).

Finally, close the skills gap fast: practical, job‑focused training accelerates adoption - consider targeted programs like Nucamp's AI Essentials for Work (15 weeks) to get finance staff prompt‑engineering, tool literacy, and real‑world AI workflows on the job (Nucamp AI Essentials for Work bootcamp registration (15-week)).

Treat each pilot as a contract: defined ROI, explainability requirements, retraining cadence, and an on‑ramp for scale - do that and AI shifts from an experiment into the backbone of faster, safer finance in Saudi Arabia.

Next Step	Why It Matters	Source / Metric
Prove & measure pilots	Set KPI thresholds before scaling	SAMA fraud hub: −36% fraud losses · detection <250 ms · >$90M halted (SAMA real-time fraud hub case study (DigitalDefynd))
Budget for local compute & contracts	On‑shore inference reduces latency & regulatory friction	LEAP / Vision 2030 infrastructure commitments; HUMAIN & event announcements (Saudi Vision 2030 AI adoption & HUMAIN overview (7startup))
Upskill finance teams	Practical training turns tools into outcomes	AI Essentials for Work: 15 weeks · early bird $3,582 · registration (Nucamp AI Essentials for Work bootcamp registration)