AI Driven Digitalisation
Modernizes core-banking-adjacent platforms, payment systems, and policy admin workflows.
Legacy Modernization Digital TransformationAI for Financial Services That Passes Model-Risk Review.
We build AI for banks, lenders, and insurers that works in production and stands up to audit: fraud and AML, KYC onboarding, relationship-manager copilots, claims intake, and regulatory reporting. Every model ships with a model card, an evaluation history, and a full audit trail.
- 35-50%lower case-handling time
- 100%audit traceability on AI actions
- 6.4hrs/wksaved per relationship manager
Challenges
Financial Services Runs on Trust, Compliance, and Unit Economics
- 01
Fraud moves faster than rules.
Static rules flood investigators with false positives while new fraud patterns get through. Teams spend their day clearing alerts instead of stopping losses.
- 02
Onboarding and KYC are slow and manual.
Analysts re-key data from IDs, statements, and forms. Customers drop off, and every manual step is a compliance risk.
- 03
AI stalls at model-risk review.
Pilots work in a sandbox, then fail sign-off because nobody can explain the model, trace its data, or switch it off. Generic AI vendors rarely build for that bar.
Value chain
Where AI Moves the Needle, From Onboarding to Compliance
AI in banking and financial services means using machine learning and generative AI to detect fraud, automate KYC and document checks, assist relationship managers, speed up claims, and modernize regulatory reporting, with explainability and model-risk controls built in.
Stage 01
Onboard
- Automated KYC document processing with risk scoring and human review
- ID and address verification
- Account-opening workflows
KPI it movesTime to onboardDrop-off rateManual touches per file
Stage 02
Underwrite
- Credit and policy underwriting assistants that summarize applications and financials
- Explainable scoring
KPI it movesDecision timeStraight-through rate
Stage 03
Service
- Relationship-manager copilots
- Agent assist for contact centers
- Policy and product Q&A with citations
KPI it movesRM hours savedFirst-contact resolution
Stage 04
Claims and Collections
- Claims intake and triage
- Document extraction
- Collections prioritization
KPI it movesClaims cycle timeCost per claimRecovery rate
Stage 05
Comply
- Fraud and AML transaction monitoring
- Case triage agents and investigator copilots
- Regulatory reporting modernization
KPI it movesFalse-positive rateCase-handling timeReporting cycle time
For insurers, the same stages map to quote, bind, service, claims, and compliance.
Use-case spotlights
Three Places Banks and Insurers Start
Fraud Detection and AML
- Problem
- Rule-based alerts bury investigators in false positives.
- We build
- AI-powered fraud detection and transaction monitoring that combines rules with machine learning, explainable risk scores, and case-triage agents that summarize context with citations to source records.
- KPI
- Case-handling time down 35-50%
KYC and Customer Onboarding
- Problem
- Analysts re-key data from IDs, statements, and forms.
- We build
- Automated KYC document processing that extracts and checks data, scores risk, and routes edge cases to a human reviewer.
- KPI
- Faster onboarding, fewer manual touches
Claims Intake and Triage (Insurance)
- Problem
- Claims arrive as emails, PDFs, and photos, and adjusters sort them by hand.
- We build
- A claims intake layer that classifies, extracts, and prioritizes each claim and flags likely fraud for review.
- KPI
- Shorter claims cycle, lower cost per claim
Every model ships with a model card, an evaluation history, and a full audit trail.
Fits your stack
Built Around Your Core Systems, Not Instead of Them
Your systems
- Core banking: Temenos, Finacle
- Treasury and risk: Murex, Calypso, SAS
- Insurance policy admin and claims: Guidewire, Duck Creek
- CRM: Salesforce Financial Services Cloud
SMI governed layer
- Customer and transaction data with audit-ready lineage
- Models with explainable scoring
- Copilots and agents with citations
- Deployed in your tenant and region
Audited outcomes
- Faster onboarding and KYC
- Fewer false positives
- RM and adjuster hours saved
- Faster regulatory reporting
- Model-risk governance: model cards, approval workflow, ongoing evaluation, and a kill-switch policy, designed to align with SR 11-7
- Audit trail on every AI action: prompt, response, citation, model, and version
- Zero-retention configuration on frontier model calls
- SOC 2-aligned controls; data handling aligns with SOX, GLBA, CCPA, and GDPR
Enterprise BFSI AI integration with core banking systems and financial applications, deployed in your own cloud tenant.
Services that apply
How Our Three Service Hubs Apply to Financial Services
Data Engineering & BI
Risk and exposure analytics, AML alert data, regulatory reporting, and customer 360 with audit-ready lineage.
Data Modernisation Data Migration Power BIGen AI Services
KYC summarization, investigator and RM copilots, policy and compliance Q&A, every action audited.
RAG Knowledge Base CopilotFAQ
Frequently Asked Questions
How is AI used in banking?
AI in banking detects fraud and money laundering, automates KYC and onboarding, assists relationship managers, speeds up credit decisions, and modernizes regulatory reporting. The value comes when it runs in production under model-risk controls, not in a sandbox.
What is generative AI in banking used for?
Generative AI in banking summarizes KYC files and case histories, drafts customer and regulatory responses, and answers policy questions with citations to source documents. A human reviews anything that affects a customer or a filing.
How does AI-powered fraud detection work?
It combines rules with machine learning models that score transactions in real time, explains why each alert fired, and hands investigators a case summary with links to the underlying records. That cuts false positives without hiding the logic from auditors.
Will your AI pass our internal model-risk review?
Every model ships with a model card, evaluation history, approval record, and kill-switch policy, and the design aligns with SR 11-7 model risk guidance. We share the model-risk pack early so your second line can review it before go-live.
How do you handle data residency and regulatory requirements?
We deploy in your cloud tenant and region, use zero-retention settings on frontier model calls, and log every AI action. Data handling aligns with SOX, GLBA, CCPA, and GDPR requirements; we share a data-handling appendix on the first call.
Can you work with our core banking and insurance systems?
Yes. We are platform-agnostic and integrate with core banking, treasury, and risk systems such as Temenos, Finacle, Murex, Calypso, and SAS, and with insurance policy and claims platforms.
Do you work with insurers as well as banks?
Yes. The same building blocks apply to underwriting, policy servicing, and claims intake and triage, under the same governance controls.
How quickly can we see results?
Start with a 60-minute briefing. We then scope a 90-day pilot on one use case, such as fraud triage, KYC automation, or an RM copilot, with KPIs agreed up front.
Financial Services & Insurance offer
Book a 60-Minute BFSI Briefing.
Bring one problem, such as fraud alert volume, KYC backlog, or RM admin time. You leave with relevant case examples and an outline for a 90-day pilot your risk team can review.
Book a Briefing- Journey dating back to 2006
- SOC 2-aligned controls
- Deployed in your tenant and region
- Audit trail on every AI action
