AI use case

AI assistant for Shariah compliance screening and review

An AI assistant that screens Islamic financing contracts, deal structures and investments for Shariah compliance risks such as riba, gharar and exposure to prohibited activities, retrieves the relevant standards and fatwas, drafts the Shariah review documentation and flags issues for the Shariah board, which keeps sole authority over any ruling.

By Len Debets · Last verified 26 September 2026 · 1 public deployment

At least 400,000
Users served
Zoya (organization claim).
USD 63,000 to USD 378,000
Indicative value per year
An Islamic bank reviewing 1,500 financing contracts and structures a year. Worked example, see how it is calculated.

What problem does it solve?

Every Islamic financing product, contract and investment has to comply with Shariah principles: no interest (riba), no excessive uncertainty (gharar), no exposure to prohibited sectors, and the structure must follow the approved contract type. Shariah compliance teams review contracts clause by clause, check structures against the institution's approved standards and the fatwas of its Shariah board, screen investments against financial ratios, and document every review for the board and for Shariah audit.

Much of this work is manual and depends on specialists who understand both finance and fiqh. The Islamic Financial Services Board reports that the industry keeps growing and that new products and structures increasingly mimic conventional banking characteristics. AI can find clauses, compare them with standards and draft documentation, but an error in the tool can lead to a Shariah non compliance finding, so rulings must stay with qualified scholars.

How does it work?

  1. Load the reference base. The institution's approved standards (for example AAOIFI based policies), its Shariah board's fatwas and resolutions, and product templates are indexed.
  2. Read the contract or structure. Document AI splits the contract into clauses and identifies the contract type, pricing, penalties, ownership transfer and asset terms.
  3. Screen. Each clause is compared with the approved template and standards, and the model flags possible riba, gharar, prohibited activities or deviations, citing the standard.
  4. Screen investments. For equities and funds, business activity and financial ratio screens run on current data, with changes in status monitored.
  5. Draft the review. It drafts the Shariah review memo with findings and references; the Shariah compliance officer completes it and the Shariah board decides.
Audience
Employee facing
Autonomy
Copilot
Adoption
Emerging
Channels
Internal tools

What is it worth?

Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.

Value benchmarks for AI assistant for Shariah compliance screening and review
KPIMedianReported rangeData pointsClaimed by
Users servedNot pooled
at least 400,000
11 organization

Value drivers: Compliance quality, Employee productivity, Speed and cycle time.

Indicative value

An Islamic bank reviewing 1,500 financing contracts and structures a year

USD 63,000 to USD 378,000

Shariah compliance time released, valued at loaded cost per year

How this is calculated

Formula: reviews * hoursPerReview * timeSaved * hourlyCost. The low scenario uses every low input, the high scenario every high input.

InputLowHighBasis
Shariah reviews per year reviews, reviews per year1,5001,500The reference bank.
Compliance hours per review hoursPerReview, hours per review36Editorial assumption, replace with your own time study.
Share of review time saved timeSaved, fraction of time0.20.35Editorial assumption; no verified public benchmark was found.
Loaded cost of a Shariah compliance hour hourlyCost, USD per hour70120Editorial assumption, replace with your own loaded cost.

What it leaves out: Values compliance time only. It leaves out faster product approval, fewer Shariah non compliance events and income purification, and the cost of building and validating the reference base.

Who already uses it?

1 public deployment, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.

Zoya

United States · Wealth and asset management · 2026

ScaledGrade B

Zoya, a halal investing app, publishes Shariah compliance reports for more than 60,000 stocks and screens ETFs and mutual funds, then monitors holdings and alerts users when a stock's compliance status changes. It shows how screening against published Shariah criteria works at retail scale. The app does not describe its screening as generative AI, and it does not replace a Shariah board's review of a bank's own contracts.

  • Users served: at least 400,000, investors
    "Trusted by 400,000+ Investors"
    Claimed by: organization
Vendors: Zoya
Sources: Zoya: Zoya -
Quote checked, checked 26 September 2026

How do you implement it?

A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.

Data you need

  • The institution's approved Shariah standards, policies and product templates
  • Shariah board fatwas and resolutions, versioned
  • Past reviews with findings, for testing
  • Financial data for investment screening

Systems to integrate

  • Document management and contract repository
  • Product approval workflow
  • Market and financial data for investment screening

Complexity: Medium

The retrieval and drafting are standard; the hard parts are a curated, versioned reference base of the institution's own standards and fatwas, Arabic and local language sources, and scholar trust in the outputs.

  1. 1

    Curate the reference base with the Shariah board

    Agree which standards, fatwas and resolutions the assistant may use, who maintains them and how superseded rulings are marked, before any screening.

  2. 2

    Start with templates and deviations

    Begin by comparing contracts with approved templates and highlighting deviations, which is verifiable, before asking the model to judge substance.

  3. 3

    Test on past reviews

    Run the assistant on past contracts with known findings, including hard cases such as hybrid and cross border structures, and share the results with the board.

  4. 4

    Draft documentation, not rulings

    Use the assistant to draft review memos and audit working papers with references, leaving conclusions to the compliance officer and rulings to the board.

Guardrails

  • The assistant never issues or implies a Shariah ruling; it flags and cites
  • Only board approved standards and fatwas are in the reference base, with version control
  • Every finding cites the clause and the standard or fatwa it relies on
  • Uncertain and complex structures are routed to scholars without a suggested conclusion

KPIs to instrument

  • Review time per contract type, before and after
  • Findings confirmed or rejected by compliance officers
  • Issues found later in Shariah audit that the assistant missed
  • Share of findings with a correct citation

Human in the loop

Shariah compliance officers review every finding and complete every memo. The Shariah board retains sole authority over rulings and approves the reference base. Shariah audit samples the assistant's work, and the board is told how the tool works and where it is weak.

Common failure modes

Confident errors on complex structures
A model may catch explicit interest clauses yet misjudge hybrid or novel structures. Route these to scholars and measure accuracy by structure type.
Outdated or foreign rulings
The assistant cites a superseded fatwa or another institution's standard. Keep only approved, versioned sources in the reference base.
Loss of scholar trust
Opaque outputs make the board reject the tool. Show sources for every finding and involve scholars in testing.

What are the risks and rules?

EU AI Act

Minimal risk

An internal assistant that screens contracts for compliance with Shariah standards is not listed in Annex III: it assesses contracts, structures and securities, not the creditworthiness of natural persons (Annex III point 5(b)). If a customer facing version answers product questions, it must disclose that people are interacting with an AI system under Article 50(1). National Islamic finance regulators set their own Shariah governance expectations.

Rules that apply

EU AI ActGDPRSDAIA AI ethics principlesCBUAE guidance on AI and MLISO/IEC 42001BNM Shariah governance policyAAOIFI Shariah standards

Guidance

  • NIST AI Risk Management Framework (NIST, North America). A general structure to document how the assistant works, test it and manage its limits, which supports the transparency a Shariah board needs.

Controls to put in place

  • Board approved reference base with version control and an owner
  • Model documentation shared with the Shariah board, including known limitations
  • Audit trail of findings, sources and human decisions per review
  • Periodic Shariah audit sampling of assistant supported reviews

Frequently asked questions

Can AI decide whether a product is Shariah compliant?
No. It can find clauses, compare them with approved standards and draft documentation, but rulings belong to the Shariah board. The assistant should flag and cite, never conclude.
Is automated Shariah screening already in use?
For listed investments, yes, although not as generative AI: Zoya says it publishes Shariah compliance reports for over 60,000 stocks, applies the AAOIFI screening methodology under the guidance of its Shariah advisors and is trusted by more than 400,000 investors. For bank contracts we did not find a verified public deployment with results.
Where does AI struggle in Shariah review?
With hybrid instruments, cross border structures and new products, where judgement depends on context. These cases should go to scholars without a suggested conclusion.

How to cite this page

Blits.ai AI Use Case Library, "AI assistant for Shariah compliance screening and review", last verified 26 September 2026, https://www.blits.ai/ai-use-cases/shariah-compliance-screening. Licensed under CC BY 4.0. Method: how we verify use cases.

Changelog
  • 27 September 2026: First published

Related use cases

Banking

AI agent for corporate credit analysis and credit memo drafting

An AI agent that gathers a corporate borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and drafts a committee ready credit memo in which every figure links to its source, for the relationship and credit teams to challenge, complete and sign.

Deployments
2 public, best grade B
Autonomy
Copilot
Cross industryBanking

AI for policy drafting and policy gap analysis

An assistant that takes a new or changed obligation, finds every internal policy, standard and procedure it touches, flags clauses that now conflict or are silent, and drafts the updated wording in house style as a redline for the policy owner to approve.

Deployments
3 public, best grade B
Autonomy
Copilot
Cross industryBanking

AI regulatory horizon scanning and obligation mapping

An AI system that continuously reads publications from the regulators and standard setters an organization answers to, classifies each item by relevance and urgency, breaks new rules into individual obligations and maps them to the internal policies and controls that meet them, so compliance owners see what changed and where the gaps are.

Deployments
2 public, best grade B
Autonomy
Assist
Banking

AI examination of trade documents under letters of credit and collections

AI that reads the full document presentation under a letter of credit or collection (bill of lading, commercial invoice, packing list, certificates), extracts and cross checks the data, tests it against the instructions and the ICC rules (for letters of credit, the credit terms, UCP 600 and ISBP), and lists discrepancies by severity with the rule cited, so qualified examiners focus on the genuine exceptions.

Deployments
3 public, best grade B
Autonomy
Supervised agent