Do you work with organisations that already have a core banking platform?

Yes. Most financial services engagements build on top of an existing core banking, CRM, or claims platform. We integrate via APIs and standard connectors, and design systems to work within your existing technology architecture rather than replacing it.

How do you handle model risk requirements for AI systems?

We build documentation, logging, and explainability into AI systems as a core part of the design. The specific model risk framework, approval process, and documentation requirements are defined with your risk and compliance teams. We deliver systems that meet those requirements, not systems that require retrofit.

Can you build a complaints management workflow for our contact centre?

Yes. Complaints routing, case lifecycle management, SLA enforcement, escalation tracking, and resolution governance are areas we have engineering depth in. We build structured workflows that connect to your existing contact centre and CRM systems.

What does a typical fraud detection project involve?

A fraud detection project typically involves transaction data pipelines, feature engineering, model development and evaluation, a review and alerting workflow for analysts, and ongoing monitoring infrastructure. Scope and timeline depend on transaction volume, data availability, and integration complexity.

Do you provide AI systems for regulated insurance operations?

Yes. We work with insurance organisations on claims triage, underwriting support, customer operations, and data platform builds. Regulatory scope and compliance requirements are reviewed with your team during discovery.

Financial Services and Insurance AI Solutions

Where AI is reshaping financial services frontlines

3–15%

higher revenue per relationship manager

McKinsey—Bank Frontline AI

20–40%

lower cost to serve

McKinsey—Bank Frontline AI

Key Challenges

Engineering challenges in financial services

The problems organisations in this sector bring to us most consistently.

Data

Fragmented customer data across systems

Customer records split across core banking, CRM, and servicing platforms
Staff manually assemble context before every customer interaction
No unified view means relationship managers work from incomplete data
Manual data assembly slows service and introduces context errors
A connected data layer enables AI decisioning across the customer lifecycle

Process

Manual coordination in complaints and disputes

Complaints and disputes route manually across multiple teams and approvals
Without structured routing, cases stall and SLA deadlines are missed
Ownership is ambiguous — no single team is accountable for resolution
Incomplete audit trails create regulatory exposure and evidence gaps
Structured case automation restores SLA compliance and accountability

Governance

Compliance controls across AI and automation

AI deployments require model risk documentation and approval processes
Explainability records must be produced for every decision that affects customers
Human oversight requirements add steps that standard dev cycles do not support
Retrofitting compliance controls after deployment is costly and incomplete
Governance designed in from architecture reduces compliance friction at launch

Technology

Anomaly detection across transaction volumes

Fraud and rule violations arrive at high volume across transaction streams
Too many alerts overwhelm operations teams and reduce signal quality
Pattern detection at scale requires purpose-built model architecture
Alert quality matters as much as detection rate — false positives erode trust
Detection infrastructure must route actionable alerts into operational workflows

Connecting customer data and frontline workflows with agentic AI

Improved frontline access to customer contextReduced manual coordinationMore consistent workflow executionTraceable approvals and actions

The challenge

Banking frontline workflows depend on fragmented customer data, manual coordination, and disconnected service systems. Relationship managers assemble context manually before each interaction, limiting the time available for value-adding customer work.

McKinsey reports that banks rewiring selected frontline domains with agentic AI have seen 3% to 15% higher revenue per relationship manager and 20% to 40% lower cost to serve.

McKinsey and Company, 2025

What we engineer

Customer and interaction data pipelines
AI-assisted task prioritisation and knowledge retrieval
Workflow and approval orchestration
CRM and core banking system integration
Human review and escalation routing

Also in this industry

Real-time anomaly detection and fraud signal prioritisation

Service Pathways

How we work with financial services organisations

Financial services engagements often start with a specific friction point: a complaints workflow that lacks structure, a customer data layer that cannot support AI decisioning, or a detection capability that generates too much noise to action. We map the problem, identify the service combination that addresses the root cause, and build in phases.

Agentic AI and Automation

Automate customer operations, complaints workflows, and approval processes. Agentic systems handle structured coordination across CRM, core banking, and servicing platforms with human review and audit logging built in.

Common applications

Complaints and disputes routing and lifecycle management
Customer onboarding document verification workflows
Approval routing for credit and underwriting decisions
Regulatory reporting data aggregation

AI Product Development

Build AI-enabled capabilities inside banking and insurance products. Includes fraud detection models, credit risk scoring, customer service AI, and intelligent workflow features embedded in existing financial platforms.

Common applications

Transaction anomaly and fraud detection
Credit risk and underwriting model development
Customer churn and propensity scoring
Document classification and extraction for KYC

Data Engineering and MLOps

Build the governed data infrastructure that financial services AI requires. Includes customer data products, secure feature pipelines, model risk documentation, and deployment controls aligned to your model governance framework.

Common applications

Customer and transaction data product development
Secure feature engineering pipelines
Model risk documentation and validation infrastructure
Model monitoring with performance and drift tracking

Technology

Technology we use in financial services projects

A curated set of platforms and tools relevant to this industry's data and AI requirements.

PythonBackend

JavaBackend

.NETBackend

PostgreSQLDatabase

AWSCloud

AzureCloud

DockerContainers

KubernetesOrchestration

TerraformInfrastructure

DatadogMonitoring

PrometheusMonitoring

GrafanaMonitoring

GitHubSource Control

SonarQubeCode Quality

Technology selection is guided by project requirements, existing environments, and client preferences. This list is not exhaustive.

Governance

Governance and control considerations

Financial services AI and automation operates inside model risk, audit, and operational risk frameworks. We design systems to support these requirements from the architecture stage rather than retrofitting controls after delivery.

Model risk and explainability

AI systems are designed with explainability records, decision logging, and documentation suitable for model risk review. We work within your model risk framework rather than around it.

Human oversight and escalation

Automated workflows include defined escalation paths, approval steps, and override capabilities for compliance-sensitive decisions. Human review is built into the process, not bolted on.

Audit trail and permissions

All workflow, model, and data access events are logged with user and system attribution. Role-based access controls are implemented at the data and application layer.

Regulatory alignment scope

We do not assert regulatory compliance on your behalf. Regulatory requirements for specific jurisdictions and product types are reviewed with your compliance and legal teams as part of design.

Engagement Models

Engagement options for financial services organisations

We work with banks, insurers, and financial institutions at different stages of their AI and engineering journey. Some engagements deliver a specific product or platform. Others provide embedded engineering capacity inside an existing financial services technology team.

Defined project delivery

We scope, architect, and deliver a specific AI product, data platform, or automation system. You own the output completely. Suitable for organisations with a clear problem and defined success criteria.

Discuss a project

Engineering Teams

Embed engineers directly inside your team. We provide backend, data, AI, or platform engineers who work in your processes, tools, and codebase. Suitable for organisations that need capacity, not just delivery.

Build your team

Exploratory discovery

Not sure where to start? We work with you to assess your data environment, map the AI opportunities, and identify where engineering investment will have the highest impact.

Start a conversation

Trusted by teams running on the world's leading clouds

Our engineers hold accreditations and certifications across all three platforms

Insights for Financial Services

All insights

Get in Touch

Talk to our financial services team

Tell us about your operational challenge. We respond within one business day.

Dedicated project manager from day one
Fixed-scope or continuous engagement options
Full IP ownership — all deliverables are yours
Response within one business day

FAQs

Frequently asked questions

Ready to build more governed financial services AI?

Financial services organisations work with us to connect customer data systems, build AI-enabled operations workflows, and deliver automation that fits inside their compliance and governance frameworks.

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