The Complete Guide to Using AI in the Financial Services Industry in Saudi Arabia in 2025

AI is fast becoming the spine of Saudi Arabia's financial future: Sidra Capital's analysis says AI could add $135.2 billion to the Kingdom's economy by 2030, and regulators like the Saudi Central Bank (SAMA) are even running sandboxes to let fintechs test new tools safely - so institutions that delay risk falling behind (see Sidra Capital's analysis).

On the ground, practical wins are already visible: Know‑Your‑Customer checks, once measured in days, can now clear in seconds and machine‑learning systems catch fraud patterns in real time, reshaping onboarding, risk and personalization across banks - read about real-world deployments in how AI is making banking faster, smarter & safer.

For leaders and practitioners in Saudi finance, the question is not whether to adopt AI but how to scale it responsibly, bridge infrastructure and skills gaps, and turn automation into measurable customer trust and new revenue streams.

“AI is reshaping the financial sector by refining investment strategies and increasing operational efficiency. At the same time it brings challenges such as biases in algorithms, cybersecurity vulnerabilities and also the need to keep up with evolving regulatory requirements. In this evolving environment, investors must carefully assess both the opportunities and the risks.” - Sidra Capital

Saudi Arabia's 2025 AI conference circuit brings together policy makers, bank leaders and infrastructure builders in a tightly focused program that turns Vision 2030 ambition into practical roadmaps: the Smart Data & AI Summit (second edition) on 27–28 Aug 2025 promises direct access to the Kingdom's booming data market at the JW Marriott Riyadh, the Intelligent Data, AI & Automation Summit (IDA) on 8–9 Oct 2025 centers on “Creating a Digital Legacy” and highlights SDAIA's push and NSDAI-driven priorities, and Connected World KSA (18–19 Nov 2025) concentrates on hyperscale infrastructure, sovereign cloud and AI deployments with 6,000+ international attendees, 150+ speakers and 200+ exhibitors; together they offer everything from executive roundtables and tech showcases to startup zones where founders demo real products - picture a hall buzzing with 200+ booths and a queue of CIOs lining up for demos.

For financial services leaders, these events are where regulatory strategy meets cloud and data architecture, and where live use-cases - fraud detection, sovereign-data plans, and high-performance compute - are benchmarked against the Kingdom's $100B Project Transcendence and broader digital transformation agenda.

"Instead of exporting oil, we will export data." - Mohammed Al-Jadaan, Minister of Finance, Kingdom of Saudi Arabia

Is Saudi Arabia investing in AI at LEAP 2025? Absolutely - the Riyadh summit turned proclamations into capital, with organizers announcing US$14.9bn in new AI funding that builds on more than $42.4bn in tech commitments since 2022; LEAP's momentum has even been credited with delivering over US$27.5bn in investment over recent years, signalling a sustained national push to fund compute, data centres and talent pipelines (see LEAP 2025 investment roundup).

Major deals disclosed at the event underscore where money is flowing: a Groq–Aramco Digital pact to expand AI inference infrastructure (including plans for a massive inferencing data centre), Lenovo and ALAR's US$2bn manufacturing-and‑robotics investment, SambaNova's $140m commitment, Google's new regional computing cluster, Qualcomm's ALLAM model availability and KKR's data‑centre backing to reach hundreds of megawatts of capacity - all concrete bets on a cloud-and‑infrastructure backbone tailored for real‑time AI in finance and beyond (read the LEAP 2025 full investment list).

The practical takeaway for financial services is vivid: capital is now wiring the Kingdom for high‑performance, low‑latency AI - imagine fraud models running inference across dedicated Saudi GPUs in under a second - which makes LEAP less a conference and more a funding pipeline for firms that want to embed AI at scale.

“LEAP 2025 is a defining moment because when the Kingdom works, the region works and the whole world works. LEAP has evolved from a movement to a multiplier effect – but now is our defining moment. Technology has catalysed Saudi Arabia as the biggest success story in youth and female empowerment in the 21st Century, and we are laser-focused on continuing that success story.” - His Excellency Eng Abdullah Alswaha, Saudi Minister of Communications and Information Technology

Project Transcendence is Saudi Arabia's bold, $100 billion‑backed bet to turn Vision 2030 into a tech economy: led by the Public Investment Fund and structured like the Alat model, it channels capital into data centres, cloud and GPU infrastructure, startups, R&D and workforce programs so the Kingdom can compete with regional and global AI hubs; coverage from CIO: Saudi Arabia launches $100 billion AI initiative explains the initiative's scope and the ambition to build Arabic‑language models and attract top talent, while reporting also points to a strategic PIF–Google partnership (with potential co‑investment in the $5–$10 billion range) to seed an AI hub in the Kingdom - details summarized by Unlock BC: PIF–Google collaboration on Saudi AI hub.

For financial services, the takeaway is concrete: expect domestic compute capacity, Arabic‑tuned models and startup pipelines to lower latency for realtime fraud and compliance engines, expand Arabic NLP for customer journeys, and create an ecosystem where banks and fintechs can co‑invest in bespoke AI solutions rather than importing them wholesale.

Cutting across banks, fintechs and regulators, a compact set of AI and adjacent technologies is rewriting how finance works in the Kingdom: generative AI and AI agents are streamlining customer service and automating analyst workflows, while NLP and Arabic‑tuned LLMs enable better regulatory drafting and localized chatbots; machine‑learning engines power real‑time fraud detection, AML/KYC and credit scoring, and embedded finance / Banking‑as‑a‑Service (BaaS) models are unlocking SME lending and new distribution channels.

These shifts rest on expanding GPU and data‑centre capacity, a sharper focus on ESG data and Open Finance, and supportive sandboxes from SAMA that let innovators test in a controlled environment - evidence that Saudi institutions lead regional AI adoption and are quick to prioritise returns.

For practitioners the result is concrete: faster onboarding, 24/7 conversational support, and smarter risk signals that surface anomalies before millions in losses accrue; for leaders it means pairing tech investment with talent and governance so efficiency gains translate into trusted customer outcomes (see the Finastra adoption survey and Sidra Capital's economic outlook, and explore Arabic LLM use cases at Nucamp AI Essentials for Work syllabus).

“AI is reshaping the financial sector by refining investment strategies and increasing operational efficiency. At the same time it brings challenges such as biases in algorithms, cybersecurity vulnerabilities and also the need to keep up with evolving regulatory requirements. In this evolving environment, investors must carefully assess both the opportunities and the risks.” - Sidra Capital

Regulatory clarity is becoming a competitive advantage for Saudi financial services: the Saudi Data & AI Authority (SDAIA) anchors the National Strategy for Data & AI with practical tools - open‑data targets, test‑bed environments and an AI Adoption Framework - while sector rules like the Personal Data Protection Law (PDPL) and SDAIA's Generative AI Guidelines require firms to treat automated profiling, consent and data transfers as first‑class compliance issues; see the SDAIA National Strategy for Data & AI and the detailed laws and policies.

Expect mandatory AI risk assessments, aggressive data‑classification rules and an emphasis on ISO 42001‑style AI management as the Kingdom moves from voluntary guidance to a binding regime (the Draft Global AI Hub Law and related standards are being advanced so organisations can prepare now).

Practically, that means piloting models in regulatory sandboxes, logging auditable decision trails for credit and fraud systems, and localising governance for Arabic‑tuned LLMs so chatbots and compliance tools meet both PDPL and SDAIA ethics principles; for a roadmap to align governance with Saudi policy, start with SDAIA's published strategy and governance materials and the Modulos primer on Saudi AI governance.

“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

Practical AI in Saudi financial services is already solving mission‑critical problems: real‑time fraud detection and AML that once took hours now run in milliseconds, letting banks interrupt suspicious payments before losses mount.

Arab National Bank's rollout of IBM Safer Payments gave investigators a single control panel and decisioning under 10 milliseconds, deployed across channels in about 90 days, so suspicious transactions can be paused while teams contact customers - read the Arab National Bank IBM Safer Payments case study on IBM's site: Arab National Bank IBM Safer Payments case study.

Bank Albilad's SAS implementation shows how embedded ML, network analysis and unified data reduce false positives (‑50%), boost investigation efficiency (+30%) and increase fraud loss prevention (+70%), while assessing 100% of transactions in real time - helpful for spotting mule‑account rings and strengthening KYC. Device fingerprinting adds another layer: behavioral device signals reduce onboarding friction and flag reused or spoofed devices for AML teams, with vendors reporting dramatic onboarding speedups in Saudi pilots (see the FOCAL device fingerprinting pilot in Saudi Arabia writeup: FOCAL device fingerprinting pilot in Saudi Arabia).

Underpinning these use cases are scalable ML platforms and streaming pipelines (TensorFlow/TFX patterns) that make low‑latency scoring, continuous retraining and explainable AI practical for SAMA‑aligned deployments - so the “so what?” is simple: faster decisions, fewer false alarms, and fraud stopped before customers even notice.

“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.”

To start an AI pilot in Saudi Arabia's financial services sector, begin by tying the use case to Vision 2030 and SDAIA priorities so the project reads as a national‑priority win - the SDAIA National Strategy for Data and AI (Saudi Arabia official strategy) is the playbook for alignment and access to test‑beds; next, choose compliant, KSA‑resident infrastructure (HUMAIN and PIF‑backed compute initiatives are explicitly built to host high‑performance models) and document data‑residency and PDPL controls up front (the market now expects tender‑ready compliance packs).

Design the pilot as a short, business‑centric PoC with clear KPIs tied to cost, throughput and regulatory artefacts (NDMO/PDPL alignment), partner with a local bank or fintech for data access and operational validation, and include an MLOps plan that proves reproducible deployment and audit trails so sandboxes can transition to production.

Leverage government funding and SDAIA test environments where possible, commit to local talent or training pathways, and make the first deliverable a procurement‑grade demo that shows measurable value and compliance - that “passport” dramatically accelerates scaling and contracting in Saudi markets; see practical entry paths and infrastructure notes in the Vision 2030 AI adoption briefing from 7startup.

Vision 2030 AI adoption briefing by 7startup and the Saudi National Strategy for Data and AI (NSDAI) primer by Access Partnership are useful references when preparing a pilot package.

“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

Building the human and institutional muscle to turn Saudi AI ambition into bankable products means stitching together large-scale training, deep industry partnerships and live innovation environments: public and private players are committing huge resources - AWS and HUMAIN's proposed “AI Zone” promises cloud, tooling and a multi‑billion dollar ecosystem to scale genAI skills and startups, while HUMAIN's deals with NVIDIA and Cisco aim to seed hyperscale GPU capacity and hands‑on labs that will train thousands of engineers (including an 18,000‑GPU supercomputer in the first phase) and create industrial‑grade testbeds for finance use cases; see the coverage of the AWS–HUMAIN AI Zone and the HUMAIN–NVIDIA partnership.

Government and academic levers are aligned too: SDAIA's national roadmap targets tens of thousands of specialists by 2030, universities have expanded AI degrees and bootcamps, and industry MoUs - like Fintech Saudi's collaboration with Kyndryl - connect fintech founders to incubation platforms and managed cloud stacks so banks and startups can prototype faster.

The practical result for financial services is clear: lower barriers to experimentation, quicker access to Arabic‑tuned models and MLOps-ready infrastructure, and talent pipelines that feed production teams instead of just CVs - a combination that turns pilot projects into repeatable, compliant product lines for KSA's banks and fintechs.

“AI, like electricity and internet, is essential infrastructure for every nation.” - Jensen Huang, NVIDIA

The path from pilots to production in Saudi Arabia is now a practical roadmap: align projects with SDAIA's AI Adoption Framework and Vision 2030 priorities, design short, measurable pilots that satisfy PDPL and sandbox requirements, and pair those pilots with workforce development so models don't stall for lack of talent; SDAIA's materials are a useful first stop for governance and ethics guidance (SDAIA AI Adoption Framework and Vision 2030 guidance).

Backed by national capital and rising compute - Cognizant notes a 29.7% jump in live IT capacity to 109MW in 2023 and multi‑billion dollar funds targeting AI - this is the moment to secure KSA‑resident infrastructure, lock in data‑residency controls, and negotiate partnerships that give low‑latency access to Arabic‑tuned models rather than brittle, off‑shore workarounds.

Decision‑makers should prioritise three concrete next steps: choose one business KPI (fraud, SME lending or customer automation) and prove ROI in a 3–6 month pilot; build a compliant MLOps pipeline and audit trail that satisfies SAMA and PDPL; and invest in people via targeted training - for example, practical courses like Nucamp's AI Essentials for Work help non‑technical staff learn prompting, tooling and business use cases to accelerate adoption (Nucamp AI Essentials for Work syllabus - practical AI training for business).

With Saudi institutions already among global leaders in AI adoption, the combination of governance, local compute and skilling turns ambition into repeatable, revenue‑generating products rather than one‑off experiments (Finastra survey: Saudi Arabia leads AI adoption in financial services).

“Despite the challenging economic climate, it's clear from our research that investment in AI, BaaS, and embedded finance remain key priorities for financial services organizations over the next 12 months.” - Simon Paris, CEO, Finastra