/ Financial Services
Financial services technology that survives the audit
From global asset managers to regional banks, we modernize the infrastructure, put AI into production, and keep pace with the regulators, without breaking the security and reliability your operations run on. Every platform we build assumes an examiner will read it.
- 40+ finance systems consolidated
- Sub-100ms fraud scoring
- SOX and SOC 2 delivery
- ISO 27001
Financial services since 2006
Data, AI, and operations for banks and insurers.
Why regulated data stalls
Every number needs
a paper trail.
Mergers leave dozens of finance systems behind, each with its own version of the truth. Fraud models wait on overnight batches, and audit season turns into archaeology. The fix is one governed platform where lineage is a query, controls are code, and the numbers reconcile before anyone asks.
90dFrom prototype to production
Azure Data Lake, Databricks, AKS, and Synapse connected into one governed foundation for a global financial services firm.
Read the case study
/ What we build for finance
Six builds. One standard of evidence.
01
Data platform modernization
Legacy finance systems consolidated onto governed cloud platforms built for real-time analytics and AI. One engagement folded 40+ systems into a single core with zero reporting disruptions and $180K+ in annual savings.
SAP S/4HANADatabricksAzureSnowflake
Databricks
Snowflake02
Fraud detection in real time
Machine learning that scores transactions in under 100 milliseconds and explains itself to compliance. One deployment cut fraud losses by $230K a year and false positives by 52%, which is the difference between a review queue and a review department.
PythonTensorFlowKafkaDatabricks
Python
TensorFlow
Kafka03
Regulatory compliance engineering
Data lineage, audit trails, and access controls designed in from day one and mapped to SOX and SOC 2. Audit readiness stops being a season and becomes the default state of the platform.
Unity CatalogCollibraAudit logging
04
Customer 360 and personalization
Customer data unified across every touchpoint into one governed profile. Where we have run it, engagement lifted 35% and cross-sell doubled, measured against baseline rather than asserted.
SalesforceBrazeSnowflake
Salesforce
Braze05
Risk analytics
Real-time risk scoring, scenario modeling, and regulatory capital optimization on live data instead of last night’s batch. Dashboards the risk committee can read without a translator.
DatabricksMLflowPower BI
MLflow
Power BI06
Core modernization
Core banking systems modernized and manual processes automated for digital-first operations. Engagements we have delivered hold 99.7% uptime at 45% lower run cost.
AzureAWSServiceNow
AWS
ServiceNow/ Compliance
Built to be examined.
The frameworks and regulations our financial services work is delivered against. Controls and evidence are part of the build, not a project that starts when the audit letter arrives.
SOC 2 Type II
Security controls certified
SOX Compliance
Financial reporting controls
GDPR/CCPA
Privacy regulations
PCI-DSS
Payment card security
Basel III/IV
Banking regulations
Dodd-Frank
Financial reform compliance
/ Results
Numbers that hold up under review.
Fortune 500 Financial Services Client
67%Reduction in Allocation Processing Time
Modernizing Finance & Reporting with SAP Transformation
Read the case study
Fortune 500 Financial Services Client
87%Reduction in Data Retrieval Time
Modernizing Wealth Advisory CRM with ACI’s Salesforce Expertise
Read the case study
Fortune 500 Financial Services Client
83%Reduction in Model Deployment Time
Driving Enterprise Data Transformation with ACI’s Azure Lakehouse
Read the case study/ How an engagement runs
Five phases. No mystery.
01
Assess
Inventory the estate, map the regulatory obligations, and pick the first use case worth real money. If a workload should not move yet, this is where we say so.
02
Design
Platform architecture, lineage model, and access controls mapped to the frameworks your examiners already use.
03
Build
Pipelines, controls, and quality gates shipped in increments, with audit evidence generated as a byproduct of normal delivery.
04
Reconcile
Parallel run against the source until the numbers match and the business signs off. No regulated workload cuts over on faith.
05
Run
Around-the-clock operations under SLA with our managed team, or a clean handover to yours with runbooks that survive an examiner.
The audit trail starts in the pipeline.
Fraud models, risk dashboards, and regulatory reports are only as trustworthy as the data underneath them. Our data engineering practice builds that layer: governed, lineage-complete, and reconciled against source before anything depends on it.
Data Engineering/ Why ACI
Why financial institutions build with us
/ Controls
Delivery mapped to SOX, SOC 2, PCI-DSS, and Basel reporting requirements.
Lineage and audit trails are designed in, not bolted on.
/ Delivery
The architects who scope the platform are the ones who build it.
Parallel runs and reconciliation before any regulated workload cuts over.
/ Scale
Founded 2006.
1,200+ engineers across 11 global delivery hubs. 500+ enterprise projects.
/ Operations
ISO 27001 certified and CMMI Level 3 appraised, with 24/7 managed operations under published SLAs.
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Read/ Financial services FAQ
Regulated questions,
answered straight.
How do you handle compliance and audit in financial services?
Lineage, access control, and audit trails designed in from day one, mapped to the controls your regulators and internal audit expect. When examiners ask where a number came from, it should be a query, not a fire drill.
What projects do you most often run for banks and insurers?
Post-merger system consolidation, AI-ready data platforms, CRM and advisor productivity, and compliance-ready cloud migration. The thread through all of them is trustworthy data that survives an audit.
Can you modernize without disrupting regulated workloads?
Yes. Parallel run, phased cutover, and reconciliation against the source until the numbers match. We do not cut over a reporting system on faith.
How do you approach AI in a regulated firm?
Governed data, a model registry, evaluation, and human review where the stakes need it. Explainability is not optional here, so we build it in rather than bolt it on.
What about data residency and security?
Encryption, least-privilege access, and residency controls per jurisdiction baked into the landing zone. The security review should be a checkbox you already passed, not a surprise at the end.