AI Solutions for Insurers, Wealth Managers and Fintechs
Claims automation, underwriting models, portfolio analytics and payments fraud detection, trained on your own book of business. Every system ships with the explainability, audit trail and monitoring your risk and compliance teams will ask for.
- Claims triage and straight-through processing
- Insurance and lending underwriting models
- Portfolio analytics and advisor copilots
- Real-time payments fraud scoring
- Policy, statement and contract extraction
Proof of concept in 2–4 weeks. Production release in 6–14 weeks.
What is AI in financial services?
AI in financial services is the use of machine learning and generative AI to automate and improve decisions in insurance, wealth and asset management, payments and lending. The highest-return applications are claims processing, underwriting, fraud detection and document-heavy operations, where volumes are high and every decision must be explainable.
Insurers, wealth managers and fintechs share a problem: the data that should drive decisions sits in policy documents, statements and claims notes that nobody has time to read. Models trained on your own history turn that backlog into faster decisions, without handing judgement to a black box your regulator cannot inspect.
Last reviewed by the Perimattic AI engineering team.
How is AI used in financial services?
Most of the value sits in six use cases. Each one replaces a manual review step that is slow, inconsistent or expensive, while keeping a human in control of the decisions that matter.
Claims automation
Models read first notice of loss, classify claim complexity, extract policy and damage details, and route simple claims to straight-through processing while flagging likely fraud for investigators.
Data it needs: Historical claims with outcomes, adjuster notes and documents
Underwriting and risk scoring
Risk models score applications using structured data, third-party signals and submitted documents, giving underwriters a recommendation with reasons rather than a raw file.
Data it needs: Past applications with loss or default outcomes
Portfolio analytics
Factor, risk and scenario models for asset and wealth managers, plus generative copilots that draft client reviews from portfolio data and house views.
Data it needs: Holdings, transactions, market data and research notes
Payments fraud detection
Real-time scoring for card, ACH, Faster Payments and wallet transactions, with device and behavioural signals and reason codes for chargebacks and disputes.
Data it needs: Labelled payment events and confirmed fraud cases
Client intelligence
Churn, next-best-action and propensity models that tell advisors and relationship managers which clients to call and why.
Data it needs: CRM history, product holdings and engagement events
Document intelligence
Extraction from policies, statements, prospectuses and contracts, with citations back to the source page and confidence scores for human review.
Data it needs: Representative documents for each type
Which financial services AI use case should you start with?
Document extraction and claims triage usually pay back first because they need the least historical labelling and the saving is visible in weeks. Underwriting and pricing models follow once fairness testing and regulatory review are planned.
| Use case | Data it needs | Time to first result | Watch out for |
|---|---|---|---|
| Document extraction | Sample documents per type | 2–4 weeks | Scan quality; handwritten fields |
| Claims triage | Claims + outcomes + notes | 4–8 weeks | Fair claims handling rules; customer communication |
| Payments fraud | Payment events + fraud labels | 4–8 weeks | Latency in the payment path; class imbalance |
| Advisor copilot | Portfolios + research + policies | 4–8 weeks | Suitability rules; hallucination guardrails |
| Underwriting model | Applications + loss outcomes | 8–14 weeks | Proxy discrimination; regulator filing requirements |
Built for insurance, investment and operational resilience rules
Financial services AI has to satisfy regulators, auditors and clients' own due diligence. These are the frameworks we design around from scoping onward.
DORA
ICT risk, incident reporting and third-party evidence for AI systems and external model providers in the EU.
NAIC AI Model Bulletin
Governance, testing and documentation for AI used in insurance decisions, as adopted by US states.
EU AI Act
Risk assessment and pricing in life and health insurance is high-risk under Annex III; documentation and logging included.
FCA Consumer Duty
Outcomes testing so automated decisions do not produce worse results for groups of customers.
SOC 2 and ISO 27001
Access control, change management and audit evidence your clients' due diligence will request.
GDPR, UK GDPR and GLBA
Lawful basis, minimisation and Article 22 safeguards for automated decisions about individuals.
Typical systems in scope
We design and document systems to meet these requirements. Certification and regulatory sign-off stay with your organisation; we supply the evidence your reviewers ask for.
From scoping call to production in five stages
Every engagement follows the same staged process, priced before the work starts and measured against a baseline agreed up front.
- 11–2 weeks
Scoping and data assessment
Use case defined, data readiness scored, compliance constraints listed, and a fixed quote agreed.
- 21 week
Baseline and evaluation design
Today's accuracy, cycle time or loss rate measured, and success thresholds agreed in writing.
- 32–8 weeks
Model development
Data preparation, training and iteration against a held-out test set, with weekly demos on real data.
- 42–4 weeks
Integration
Inference wired into the systems your teams already use, with auth, audit logging and fallback paths.
- 5Ongoing
Production and monitoring
Deployment, drift and accuracy monitoring, alerting, retraining and runbooks for your operations team.
For insurers and investment firms, scoping also maps each model to the regulation it falls under, so the documentation pack is written alongside the model rather than reconstructed before an audit.
What makes Perimattic a fit for financial services AI
We build AI that runs in production inside regulated operations. Here is work we have shipped, and the principles we apply to every financial services engagement.
Explainability by default
Every model returns the reasons behind its output, so underwriters, adjusters and advisors can defend a decision to a customer or regulator.
Compliance as an architecture constraint
Data residency, audit logging and access control are decided at the design stage, with documentation written as the model is built.
Integration into your platforms
Models connect to policy admin, portfolio and payments systems through secured APIs, with fallbacks if a model is unavailable.
Ownership and portability
You own the models, code and pipelines, deployed in your cloud tenancy, with no lock-in to a proprietary platform.
Evidence for DORA, SOC 2 and model risk reviews
Financial firms using AI face DORA in the EU, SOC 2 requests from clients and model risk rules such as OSFI E-23. Perimattic AI Suite organises AI telemetry into the records those reviews ask for.
Services behind these financial services systems
AI in financial services: frequently asked questions
How is AI used in financial services?
Financial firms use AI for claims automation, underwriting and risk scoring, portfolio analytics, payments fraud detection, client intelligence and document processing. Insurers see the fastest return from claims triage and document extraction; wealth managers from advisor copilots and client analytics.
Can AI automate insurance claims processing?
Yes, for a large share of claims. Models classify claims by complexity, extract details from first notice of loss and supporting documents, and route simple claims to straight-through processing. Complex or suspicious claims go to adjusters with a summary and fraud indicators, so people spend time where judgement is needed.
How does AI support underwriting?
An underwriting model scores each application using structured data, third-party sources and extracted document fields, and returns a recommendation with the factors behind it. Underwriters keep the final decision on referred cases, and the model is tested for proxy discrimination before release and monitored afterwards.
How does AI detect payments fraud in real time?
A fraud model scores each payment as it is processed, using amount, merchant, device, location and the customer's own behaviour. High-risk payments are blocked or challenged with step-up authentication, and every score carries reason codes for disputes and chargebacks.
Is generative AI safe to use with client financial data?
It can be, with the right architecture. We run models in your own cloud tenancy or through enterprise endpoints that do not train on your data, retrieve only the records a user is entitled to see, cite sources for every answer, and log each request for audit.
Which regulations apply to AI in insurance and investment firms?
It depends on jurisdiction and use. Common ones are DORA for EU operational resilience, the NAIC AI Model Bulletin for US insurers, the EU AI Act for life and health pricing, FCA Consumer Duty in the UK, and GDPR for automated decisions about individuals.
How long does a financial services AI project take and what does it cost?
A proof of concept for one use case typically takes 2–4 weeks and starts from $3,500. A production system with integration and monitoring takes 6–14 weeks and typically costs $10,000–$30,000. Large document corpora or multi-system integrations are quoted after scoping.
