AI governance for
financial
services.
Capital. Risk. Regulation. Accountability.
When AI influences credit, fraud, underwriting, claims, pricing or another consequential workflow, the institution needs more than model output: it needs evidence, counter-evidence, explicit authority, bounded action and a reconstructable decision record.
Start with one
consequential decision
Choose one AI-supported decision or action. We map the evidence it depends on, what could defeat it, who has authority to accept it, what the AI is permitted to do, and the record required to reconstruct what happened later.
The first scope can remain read-only or shadow-first. The institution keeps business authority and production control.
This is decision assurance around an existing workflow, not a claim of regulatory certification, guaranteed compliance, model approval or transfer of business authority to AI.
How AI Decision Assurance works →Agentic AI Governance →AI Audit Trail →
What is at stake
in this domain
In this domain the consequence of an AI decision reaches capital and the obligations that come with holding it. An answer that cannot be defended afterwards is not an answer.
A portfolio
[Decision: capital]
The questions are the same whether the decision concerns a patient, a portfolio or a pumping station. What changes is the consequence, not the test.
One financial decision,
step by step
The same path that every governed decision takes, read for a decision about capital, risk or an obligation.
Financial data — positions, transactions, market and reference data, and the records an institution already keeps.
A model or agent proposes — a risk it has identified, a pattern it has detected, a recommended action — and states what the proposal rests on.
The governed decision layer establishes the evidence behind the proposal, tests it against market structure rather than plausibility, and records what would defeat it.
An identified person with authority — or an explicit, pre-authorised boundary — decides whether the proposal may become action. Decision authority stays explicit.
Action is executed only within the boundary that was authorised.
The outcome is audited and evaluated against criteria agreed beforehand, so that the decision can be defended afterwards.
This is a reference path, not a description of a deployed system or of any client's process.
Four questions before
anything acts
Every governed decision passes the same test, in this domain as in the other two.
Verified premises
Are the premises verified or assumed?
In financial services: a price, a rating or a counterparty status taken as current when it is stale.
Adequate mechanism
Does it hold against the domain’s own mechanism?
In financial services: does the recommendation hold against how the market or the product actually behaves, not only against a fluent rationale?
Counter-evidence
What evidence would defeat it?
In financial services: which observation would overturn the recommendation, and is it being watched for?
Acceptance authority
Who signs before anything acts?
In financial services: the person accountable for the decision signs — and the record shows what was known, and what authority was exercised, at that moment.
Six disciplines,
one governance model
Each one exists so that a decision can be made, defended and measured — not merely produced.
Architecture & Infrastructure
The foundation beneath every governed decision
AI Platform
Connects models, agents, context, provenance and authority
Algorithms
Reasoning, prediction, optimisation, anomaly detection and decision-support engines
Governance
Human authority, provenance, bounded action, evaluation and safety
Research
The scientific and methodological foundation informing the architecture
Economics
Value measured against the organisation’s own baseline before scale
What we can design
and build here
We design and build the governed decision layer on top of the data and financial systems an institution already runs — provenance, acceptance authority and evaluation — without asking it to replace them.
Three things about a recommendation can be inspected: where its evidence came from, how it was evaluated against counter-evidence, and who accepted it.
The economic case is set before the work: baseline, target and evaluation in the institution's own units. Our CEO owns the ROI framework used to judge the engagement.
We do not manage capital or portfolios, execute trades, score credit, underwrite, or operate a bank's production systems, and we give no investment advice: the decision, and the accountability for it, stay with the institution.
Senior people
stay on the work.
Dr. Don Elan, our CEO, is a Chartered Financial Analyst who managed a $1B+ provident fund portfolio, and has served as a professor of finance in universities in the U.S. and Israel. He owns the ROI framework each engagement is judged against.
This is professional experience held before SINAIGROUP-AI was formed. It is shown as professional background, not as a corporate delivery by SINAIGROUP-AI.
Prof. Eli Shnaider, our CDO, holds a Ph.D. in Economics. Before SINAIGROUP-AI was formed he worked on quantitative and information-systems projects for public bodies in Florida.
Florida Department of Revenue, Office of Tax Research, 1984–1989. He computerised the department’s research operations, developed an econometric model to forecast state tax revenues, and developed software to analyse and estimate the fiscal and economic impact of legislative changes.
Florida Spaceport Authority, 1990–1991. Principal investigator on funded research into international competition in commercial space markets, SFA Contract 904.
Florida Department of General Services, through EconometricConsult Inc., 1990. A cost-effectiveness study comparing state agency office ownership with private full-service leasing.
That work is Prof. Shnaider’s own, in the public sector, before SINAIGROUP-AI was formed. It is not financial-services delivery and not a SINAIGROUP-AI engagement.
At SINAIGROUP-AI he owns quantitative forecasting and data governance.
Emanuel Shirbint, our CTO, brings 15+ years of data analytics, business intelligence and data-warehouse architecture across IT, finance, healthcare and utilities, and owns the technical architecture.
In roles held before SINAIGROUP-AI was formed, Dr. Elan created data-driven financial reporting systems and managed billion-dollar funds.
Before SINAIGROUP-AI, our CTO worked with investment firms on trading platforms and algorithms for financial markets.
Professional experience held before SINAIGROUP-AI was formed, shown as background. It is not a SINAIGROUP-AI client engagement, deployment, trading result or investment advice.
SINAIGROUP-AI has no signed bank or financial-services client contracts and no financial-services production deployments. What this page describes is a method and a reference architecture, applied with an institution on its own data and under its own authority.
The research
behind the method
This is the research the method rests on: work published before SINAIGROUP-AI was formed and attributed to Prof. Eli Shnaider personally. It is his prior scholarly work, not research carried out by SINAIGROUP-AI.
None of these records establishes a SINAIGROUP-AI client engagement, a deployment or an investment-performance result. Where the subject of a study is financial markets, that is the subject of the research, not evidence of work carried out for a financial institution. Peer-review status was not established for this record set, so no peer-review claim is made. The three groups below are disjoint: 15 unique records in total, each listed once. Full bibliographic details and DOI links are in R&D.
Financial markets and corporate financial performance · 2 records
Research on the financial performance of publicly listed companies, including a decision-support method reported as evaluated in a stock-market application.
2021 · Decision Support System Based on Fuzzy Logic for Assessment of Expected Corporate Income Performance. International Journal of Computational Intelligence and Applications.
2021 · Identifying financial ratios associated with companies' performance using fuzzy logic tools. Journal of Intelligent & Fuzzy Systems.
Economic forecasting and public revenue · 6 records
Economic forecasting, long-term growth, and corporate tax-revenue modelling.
2018 · Factors affecting long-term economic growth-consistency and stability by soft regression estimation. International Journal of Society Systems Science.
2008 · BACKGROUND FACTORS FACILITATING ECONOMIC GROWTH USING LINEAR REGRESSION AND SOFT REGRESSION. Fuzzy Economic Review.
2000 · HEURISTIC SIGNIFICANCE TEST FOR ECONOMIC MODELING. Fuzzy Economic Review.
1993 · Expert systems for economic/business forecasting. Proceedings of the 1993 ACM/SIGAPP Symposium on Applied Computing (SAC '93).
1992 · A system for forecasting corporate-tax revenue based on fuzzy logic and fuzzy set theory. Information Sciences.
1989 · The use of fuzzy set theory for forecasting corporate tax revenues. Fuzzy Sets and Systems.
Method, not tied to a domain · 7 records
Soft regression and interval-based modelling, multicriteria selection under uncertainty, interval arithmetic in expert systems, and detection of biased or over-optimistic projections.
2021 · Data Mining Method for Identifying Biased or Misleading Future Outlook. International Journal of Information Technology & Decision Making.
2019 · Modeling Technique Based on the Ranges of Values: Implementation Using Conventional Regression Method. Computational Economics.
2018 · UTILIZING INTERVALS OF VALUES IN MODELING DUE TO DIVERSITY OF MEASUREMENTS. Fuzzy Economic Review.
2014 · THE SOFT REGRESSION METHOD - SUGGESTED IMPROVEMENTS. Fuzzy Economic Review.
2010 · Fuzzy multicriteria selection of alternatives: The worst-case method. International Journal of Intelligent Systems.
1994 · On the use of interval mathematics in fuzzy expert systems. International Journal of Intelligent Systems.
1992 · Parallel pattern recognition using fuzzy cooperative expert systems. Proceedings of the 1992 ACM/SIGAPP Symposium on Applied Computing.
Published research is not a product, a deployment or a validated result. SINAIGROUP-AI has no signed bank or financial-services client contracts and no financial-services production deployments.
Vendor-neutral
by design
We architect around an organisation’s existing stack, integrate third-party technologies, and design and build the governed decision layer — provenance, acceptance authority and evaluation — on top of it. Technology is selected for the problem, not for a vendor relationship.
Governed AI must produce
measurable value
Baseline
What happens today? What the current process costs or produces, established before anything is built.
Target
What measurable outcome should change? Agreed in advance, in the organisation’s own units, not in generic percentages.
Evaluation
Did it change, and what was it worth? Measured against the baseline after the pilot, including the cases where it did not.
What a first conversation covers
A first conversation covers one decision: which decision about capital, risk or an obligation matters, what evidence it depends on, who is accountable for it today, and what happens if it is wrong.
We set out what we would design, what we would measure and what we would need from you.
Go deeper on
authority and auditability
AI Decision Assurance
Evidence, counter-evidence and authority around one consequential decision.
Open →Agentic AIAgentic AI Governance
Govern delegated action when AI agents can trigger workflows.
Open →AuditabilityAI Audit Trail
Reconstruct the evidence, system state, authority, action and outcome.
Open →AuthorityHuman Authority in AI
Define who may accept, reject, escalate or stop consequential AI-supported actions.
Open →Start with a workflow
that already carries consequence
Three practical entry points for financial-services teams deploying or scaling AI.
AI Governance for Banking Agents
Evidence, permissions, escalation, human authority and auditability around agent actions.
Open → InsuranceUnderwriting & Claims Decision Assurance
Make evidence, counter-evidence, authority and the decision record explicit.
Open → FraudFraud Decision Governance
Separate detection from intervention authority and preserve the decision trail.
Open →The domain changes.
The governance problem does not.
Discuss a high-stakes
financial services decision
Tell us what decision matters, what evidence it depends on, and what happens if it is wrong.