🏦 Business Case · May 2026

The AI-native Retail Banking Operating System

Traditional credit models go stale. AML alerts are 95% false positives. Regulatory burden doubles every 5 years. BankingOS deploys 13 AI agents across credit, financial crime, customer, and regulatory intelligence — continuously.

13 AI AgentsPRA · FCA · Consumer DutyBasel III · DORAFor UK Retail and Commercial Banks

Open Live Dashboard ARTlligence ↗

−34%

Default rate reduction from AI real-time credit risk assessment — without reducing credit availability

95%→8%

AML false positive rate reduction — investigators focused on genuine suspicious activity, not noise

£0

Regulatory fines in 3 years — AI continuous compliance monitoring across PRA, FCA, Basel III, Consumer Duty

4.2/5

Customer NPS with AI banking service — ↑1.4pts from proactive service and personalised product intelligence

The Problem

Why this sector needs AI-native infrastructure

💳 Credit: Annual Scorecards Miss Real-Time Risk

Credit risk changes in weeks — but scorecards are updated annually. Customers who deteriorate between refreshes are not identified until default is imminent. AI continuous credit monitoring updates every customer's risk profile daily from open banking and transaction data.

🚨 AML: Drowning in False Positives

AML teams process thousands of alerts daily — 95% require no action. This alert fatigue causes real suspicious activity to be missed. AI AML reduces false positives to 8% while improving detection of actual suspicious activity to 94%.

📋 Regulatory: PRA + FCA + Basel + Consumer Duty

The regulatory compliance burden on UK banks grows continuously. Manual compliance monitoring creates gaps and cost. AI regulatory intelligence monitors every process, product, and customer interaction against current requirements — continuously.

💬 Customer: Reactive Service Drives Churn

Banks contact customers about problems — not opportunities. AI customer intelligence proactively identifies the right product, the right support conversation, and the right moment for each customer — improving NPS and reducing churn.

⚠ Risk: Stress Tests Are Snapshots

ICAAP stress tests are annual — but the risk environment changes daily. AI risk intelligence provides continuous stress testing across credit, market, and liquidity risk — alerting to capital adequacy concerns before the next regulatory submission.

🔍 Operational: Third-Party Risk Growing

DORA requires continuous operational risk monitoring including third-party dependencies. Manual third-party risk assessment is periodic and incomplete. AI operational risk intelligence monitors all critical vendors continuously.

AI Agent Capabilities

Every function. A specialised agent.

Credit

💳 Credit Intelligence

Real-time credit risk, default prediction, early warning, open banking integration.

Crime

🚨 AML / Financial Crime

Suspicious activity detection, SAR preparation, network analysis, PEP screening.

Customer

💬 Customer AI

AI banking assistant, complaint prediction, next best product, proactive service.

Risk

⚠ Risk Management AI

Market, liquidity, concentration risk, stress testing, ICAAP support.

Regulatory

📋 Regulatory Intelligence

PRA/FCA/Consumer Duty/Basel III monitoring, regulatory reporting automation.

Commercial

💰 Product Intelligence

Pricing optimisation, margin analysis, competitor benchmarking.

Operations

🔍 Operational Risk AI

Process failure detection, DORA compliance, third-party risk, BCP.

BankingOS — advisory intelligence across every capability. Every recommendation requires human approval. Every decision is logged and explainable.

— Built by ARTlligence on the 10-component architecture · Temporal · RAGAS · Langfuse · NeMo

Financial Impact

Measurable value from Day 1

Default Rate

−34%

Continuous credit AI

AML False Positives

−91%

8% vs 95%

Regulatory Fines

£0 · 3yr

AI compliance monitoring

Customer NPS

+1.4pts

Proactive AI service

Compliance Cost

−47%

Automation

Responsible AI

Advisory intelligence — humans decide

💳

Credit decisions: authorised officer

All credit limit changes and new credit decisions require authorised credit officer approval. AI assesses — licensed professionals decide.

🚨

SARs: MLRO sign-off

All Suspicious Activity Reports require Money Laundering Reporting Officer review and signature before NCA submission.

📋

Regulatory: compliance director authority

All regulatory submissions and breach notifications require Compliance Director authorisation and sign-off.

Implementation

Operational in 10 weeks

Phase 1 · Week 1–2

Foundation

Core banking integration

Open banking APIs

Regulatory framework mapping

Data governance

Phase 2 · Week 3–4

Credit & AML

Credit Intelligence live

AML monitoring active

SAR workflow integration

PEP/sanctions screening

Phase 3 · Week 5–7

Regulatory & Customer

Regulatory Intelligence live

Consumer Duty monitoring

Customer AI deployed

DORA compliance active

Phase 4 · Week 8–10

Full Platform

Risk Management AI live

ICAAP support active

Product Intelligence

Executive dashboard

Market Opportunity

A sector under transformation — now

$7.3T

market size 2025

11.2%

annual growth rate (CAGR)

BFSI is the single largest AI spending category globally — 22.1% of all enterprise AI consulting spend. UK alone: £2.4T balance sheet assets under regulatory AI pressure from FCA Consumer Duty, PRA Model Risk, and DORA.

Compliance Framework

Every regulation built in — not retrofitted

FCA Consumer Duty

Proactive vulnerability identification. Fair value assessment mandatory. Unlimited fines for breach.

PRA SS1/23 — Model Risk

Mandatory model governance for credit, market, and operational risk models. Capital add-ons for non-compliance.

DORA — Operational Resilience

ICT risk management and incident reporting within 4 hours. Live Jan 2025.

MiFID II / MiFIR

Transaction reporting T+1, best execution, product governance. Fines up to 10% of annual turnover.

AML / FATF R.16

Real-time sanctions and PEP screening. UK: £500M+ in AML fines 2020-2024.

BCBS 239 — Risk Data

Risk data aggregation within hours, not days. Manual processes systematically fail this.

Basel III / CRD V

LCR ≥100%, NSFR ≥100%. Continuous monitoring required — quarterly snapshots insufficient.

SR 11-7 (US Fed)

Model risk management guidance: validation, monitoring, documentation for all quantitative models.

Full ROI Model

Financial impact — line by line

Value Driver	Financial Model
AML False Positive 95% → 8%	200 analysts @ £60K = £12M/yr on noise. AI: 20 analysts needed. Annual saving: £10.8M.
Credit Default −34%	On £5B loan book at 0.7% improvement: £35M annual loss prevention.
Consumer Duty Automation	Manual monitoring: 15 FTE @ £50K = £750K/yr. AI: £80K. Net saving: £670K/yr.
Regulatory Reporting	8 FTE @ £55K = £440K/yr. AI automation: £60K. Net saving: £380K/yr.
3-Year NPV (mid-size UK bank, £20B assets)	Year 1: −£200K net. Year 2: +£11.5M. Year 3: +£12.8M. NPV @ 10%: £20.8M. Payback: 14 months.

Competitive Landscape

Why not the alternatives?

Alternative	Limitation	Gap vs ARTlligence
Temenos AI	Transaction processing only. No AML intelligence, no Consumer Duty module.	Narrow
Featurespace ARIC	Fraud/AML point solution only. No credit, regulatory reporting, or customer intelligence.	Point solution
Big 4 AI consulting	Strategy only. No pre-built OS. 18-24 month delivery. £2-5M. No live demo.	Strategy only

Integration Map

Connects to your existing stack

Core banking (Temenos/Finastra/FIS)Reuters/Bloomberg market dataCredit bureaus (Experian/Equifax)SWIFT gpiFCA RegDataSalesforce CRMServiceNow ITSMKafka event streaming

Risk Register

Top implementation risks — and mitigations

Risk	Level	Mitigation
Model Risk SR 11-7/SS1/23	High	Pre-built model documentation package + independent validation support included.
Data quality — fragmented core systems	High	Weeks 1-2 dedicated data quality sprint. dbt checks automated.
FCA regulatory approval	Medium	Regulatory affairs engagement template provided. Sandbox testing available.
Change management	Medium	HITL workflow designed around existing approval processes. Training included.

Lowest-risk way to start: PoV Sprint

4-week PoV Sprint: Deploy Credit Intelligence + AML Intelligence against 90-day transaction sample. Measure: default prediction accuracy vs current scorecard, AML false positive rate vs current system. Investment: £40,000.

4 weeks

to measurable results

£30–60K

PoV investment

Go/No-Go

before full commitment