Finance & Insurance
AI that catches fraud, automates claims and files your regulatory reports on time
You get fraud detection agents, claims automation, risk modelling, and regulatory reporting workflows. All running on your cloud, connected to your core systems, with the audit trails your regulators expect.
Built on your AWS, Azure or GCP tenant. Regulator-ready. Fully auditable.
Proof of concept
To measurable ROI
AWS, Azure or GCP
Your compliance costs keep climbing. Your fraud losses keep growing. AI fixes both.
Financial services firms spend 6 to 10 percent of revenue on compliance. Insurance carriers lose $80 billion a year to fraudulent claims. Regulatory reporting consumes thousands of analyst hours every quarter. These are not technology problems. They are decision-speed problems.
We build AI agents that sit between your data and your decisions — detecting fraud in real time, triaging claims automatically, modelling risk continuously, and generating the regulatory filings your compliance team currently assembles by hand.
reduction in fraud losses with AI transaction monitoring (McKinsey, 2024)
fewer false positives in AML screening with machine learning (BCG X, 2024)
of claims processing steps automatable with document AI (Deloitte, 2024)
annual compliance cost reduction opportunity across global banking (McKinsey QuantumBlack, 2024)
From transaction data to regulatory filing in one governed workflow
We connect your core banking, claims management, market data feeds, and CRM systems to AI agents that detect fraud, automate claims, model risk, and generate the regulatory reports your compliance officers and auditors require
Core banking, claims, market feeds, CRM
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Fraud alerts
- Regulatory filings
- Risk reports
What we deliver for financial services and insurance
From fraud detection to regulatory reporting. Governed, auditable, regulator-ready.
Real-time fraud detection
Flag anomalous transactions and escalate fraud in real time.
ROI
Process
Ingest transaction streams
Card transactions, wire transfers, and digital payments streamed into the detection engine in real time
Score and classify
ML models score each transaction against behavioural baselines, network patterns, and known fraud typologies
Escalate with evidence
Confirmed alerts routed to investigators with transaction trails, risk scores, and recommended actions
Sources
McKinsey QuantumBlack, 'AI-powered fraud detection in banking,' 2024; BCG X, 'Reinventing Financial Crime Detection,' 2024 — reproduced for illustrative purposes
Claims automation
Read claims, validate coverage, and auto-settle routine cases.
ROI
Process
Extract claim data
Document AI reads submission forms, medical reports, police reports, and supporting evidence
Validate and triage
AI checks policy coverage, estimates loss reserves, and classifies claim complexity
Route or settle
Routine claims processed straight through. Complex claims routed to adjusters with pre-populated case files
Sources
Deloitte Insights, 'AI in Insurance Claims,' 2024; McKinsey, 'The Future of Insurance Claims,' 2024 — reproduced for illustrative purposes
Regulatory reporting automation
Extract, reconcile, and file regulatory submissions automatically.
ROI
Process
Map regulatory requirements
AI maintains a live map of reporting obligations across jurisdictions and regulatory frameworks
Extract and reconcile
Automated data extraction from core banking, GL, and risk systems with cross-source reconciliation
Generate submissions
Regulator-formatted reports produced with full audit trails and variance explanations
Sources
McKinsey QuantumBlack, 'AI in regulatory reporting,' 2024; Deloitte Insights, 'RegTech and the future of compliance,' 2024 — reproduced for illustrative purposes
Credit and underwriting risk models
Score credit and underwriting risk in seconds, not days.
ROI
Process
Aggregate risk data
Traditional bureau data combined with alternative signals: transaction history, behavioural patterns, market data
Score and explain
Ensemble models generate risk scores with feature-level explanations that satisfy regulatory model governance
Decision and monitor
Automated decisioning for standard risk; human review for edge cases. Continuous model performance monitoring
Sources
BCG X, 'AI-first underwriting,' 2024; McKinsey, 'Building the AI bank of the future,' 2024 — reproduced for illustrative purposes
AML and financial crime screening
Cut AML false positives by 60% while catching real threats.
ROI
Process
Monitor transactions
Every transaction, payment, and account event scored against customer risk profiles and network behaviour
Reduce false positives
ML models filter noise from genuine risk, cutting false alerts by 60 percent or more
Generate SARs
Suspicious activity reports auto-drafted with supporting evidence for compliance officer review
Sources
BCG X, 'Reinventing AML with AI,' 2024; McKinsey QuantumBlack, 'Next-gen transaction monitoring,' 2024 — reproduced for illustrative purposes
Customer lifetime value and retention
Predict churn and trigger personalised retention actions.
ROI
Process
Build customer profiles
Transaction behaviour, product usage, service interactions, and life-event indicators aggregated per customer
Predict churn and opportunity
ML models score attrition risk and cross-sell propensity for every customer, updated continuously
Trigger retention actions
Personalised retention offers and advisor alerts routed through existing CRM and communication channels
Sources
McKinsey, 'AI-powered customer engagement in banking,' 2024; Deloitte Insights, 'Personalisation at scale in insurance,' 2024 — reproduced for illustrative purposes
Document intelligence for KYC
Extract and validate KYC documents to accelerate onboarding.
ROI
Process
Ingest documents
Passports, utility bills, corporate registrations, and UBO declarations processed by document AI
Extract and validate
Key fields extracted, cross-referenced against sanctions lists and PEP databases, anomalies flagged
Onboard or escalate
Clean cases auto-approved with audit trail. Flagged cases routed to compliance with pre-populated review packs
Sources
Deloitte Insights, 'AI-powered KYC,' 2024; McKinsey, 'Digital onboarding in banking,' 2024 — reproduced for illustrative purposes
CVP, Global Industry Marketing, Microsoft
Kathleen Mitford
"Financial services firms are using Azure AI to detect fraud patterns across billions of transactions in real time. What previously took days of manual review now happens in seconds, with explainable outputs that satisfy regulator expectations for model transparency."
Originally published: Microsoft Customer Stories — HSBC — reproduced for illustrative purposes
How we work in regulated financial services environments
Financial services AI must satisfy regulators, not just business sponsors. We start by identifying the one use case that delivers the most measurable impact, then build it with the model governance, explainability, and audit trails your compliance and risk functions require from day one.
Everything runs on your cloud tenant. Your customer data, your transaction records, your risk models. Nothing leaves your perimeter. Your regulators and internal audit can inspect the system directly.
01
Regulatory and data mapping
We assess your data landscape, regulatory obligations, and model governance requirements. Then identify the highest-value use case that satisfies all three.
02
30-day proof of concept
A working AI agent on your cloud, connected to your core systems, producing auditable output in 30 days.
03
Model governance built in
Explainability, bias testing, audit trails, and model risk documentation structured to meet PRA, FCA, or equivalent regulatory expectations.
04
Scale in 12-18 weeks
From one validated workflow to measurable operational ROI across fraud, claims, risk, or compliance functions.
Can't find what you're looking for?
Send us a message via , or email humans@execxai.com
Frequently Asked Questions
Can't find what you're looking for? Send us a message via our AI assistant, or email humans@execxai.com