Your risk models are only as good as the data underneath them.
Banks, insurers, and asset managers run on data — but fragmented customer records, siloed core systems, and legacy actuarial workflows are forcing decisions on incomplete information. And regulators aren't waiting. We build the data foundations, predictive models, and AI systems financial institutions need to compete and comply — as one team, from strategy through go-live.
at least one function
pricing as high-risk AI — in force 2025
globally in 2023
is their #1 AI barrier
Customer 360 data platforms, credit risk modeling, fraud and AML detection, CECL/DORA compliance reporting, and next-best-action analytics — built on your core banking environment and integrated with CRM and digital channels.
Actuarial ML modernization (SAS/Excel to Python), claims analytics, underwriting automation, telematics and IoT data integration, loss ratio forecasting, and EU AI Act compliance for insurance pricing models.
Portfolio analytics platforms, client intelligence and segmentation, next-best-action systems, alternative data integration, and AI-powered reporting automation for RIAs, asset managers, hedge funds, and family offices.
What's reshaping financial services
data and AI right now.
Regulatory pressure, fraud sophistication, and competitive disruption from digital-native players are converging. Here's what's creating urgency — and opportunity — for financial services leaders.
The EU AI Act's high-risk AI system classification applies directly to credit scoring, creditworthiness assessment, and insurance pricing models. Organizations operating in or serving EU markets must now document AI systems, conduct conformity assessments, implement human oversight mechanisms, and monitor model accuracy and bias on an ongoing basis. US regulators are watching closely — CFPB guidance on algorithmic credit decisions is tightening in parallel.
Basel IV's refined standardized approach for credit risk and SR 11-7's model risk management requirements are intensifying scrutiny on how financial institutions build, validate, and monitor predictive models. Examiners are increasingly focused on model explainability, documentation quality, and the integrity of the data feeding models — not just the model outputs. Many institutions are discovering their MLOps infrastructure isn't built for this level of examination.
Financial services was one of the earliest adopters of GenAI for internal use — compliance document search, regulatory change management, and KYC/AML case summarization were natural first targets. The firms moving fastest are those with a clean, governed data layer underneath the models. Those without it are watching pilot programs stall because the AI's outputs can't be trusted or traced to source data.
Every challenge you're facing.
Exactly how we solve it.
Here's what we hear from CDOs, CROs, and analytics leaders in financial services every week — and the specific capability we bring to each one.
Customer records live in core banking platforms, CRM systems, loan origination systems, and mobile apps — each with its own schema, update cadence, and quality standard. A genuine customer 360 view doesn't exist, so personalization, risk scoring, and churn prediction all operate on incomplete pictures.
We build unified customer data platforms that reconcile identities across core banking, CRM, origination, and digital channels — creating a single governed source of truth on Snowflake, Databricks, or Azure that analytics, risk, and AI systems can all consume.
SR 11-7 requires independent validation, thorough documentation, and ongoing monitoring of every model used for material decisions. Most ML models built by internal teams or vendors weren't designed with this framework in mind — they produce predictions but can't explain them, and drift goes undetected until a regulator asks why the output changed.
Every predictive model we build for regulated institutions includes SHAP-based explainability layers, full model cards with documentation, independent validation frameworks, and MLOps pipelines with automated drift detection and retraining triggers — designed for the examiner's review from day one.
Basel IV, CECL, DORA, and SOX reporting cycles consume hundreds of analyst hours each quarter. Data is pulled from multiple systems, reconciled manually in Excel, and submitted with known quality gaps. When regulators ask follow-up questions, tracing back to source data takes weeks.
We automate regulatory reporting pipelines — from data extraction and reconciliation through submission-ready output — with full data lineage so every figure can be traced to its source in minutes, not weeks. Governance frameworks ensure data quality before it reaches the regulator.
Rule-based fraud systems catch the fraud patterns from three years ago. Modern financial crime — synthetic identity fraud, account takeover, first-party fraud, coordinated mule networks — requires behavioral ML to detect. The signals are in the transaction patterns, device fingerprints, and network graphs that rule engines were never designed to read.
We replace or augment legacy rule engines with behavioral ML models — trained on transaction sequences, device signals, network relationships, and velocity patterns — that detect the fraud typologies your current system misses, with explainability built in for SAR documentation.
Most insurance actuarial teams built their pricing and reserving models in Excel or legacy SAS environments. These models can't scale to new data sources like telematics, IoT, or alternative credit data. They take weeks to update, can't be version-controlled properly, and their outputs are increasingly difficult to defend to regulators asking about bias and fairness.
We migrate actuarial workflows from Excel and SAS into Python-based ML environments on cloud platforms — rebuilding pricing and reserving models with modern ML techniques, automated retraining, version control, bias monitoring, and the documentation quality EU AI Act and SR 11-7 require.
Neobanks and digitally-native insurers are deploying next-best-action engines, personalized product recommendations, and AI-driven onboarding flows. Traditional institutions have the data advantage — decades of transaction history and relationship depth — but can't activate it because the data isn't unified and the AI layer doesn't exist yet.
We build next-best-action systems, AI-powered relationship intelligence, and compliance-aware GenAI assistants for relationship managers and advisors — on IBM watsonx or Azure OpenAI, with your data, your governance controls, and auditability built in from day one.
Finding engineers with both financial domain depth and modern ML skills — Python, MLOps, Snowflake, behavioral modeling — takes months. By the time you hire, the regulatory deadline has moved, the pilot window has closed, or the project has been descoped. You need credentialed talent that can integrate into your team in weeks.
We source vetted LATAM ML engineers and data scientists with financial services domain experience — risk modeling, fraud detection, regulatory data — at 40–60% below US market rates, in US time zones, ready to embed in your team within 2–4 weeks.
What you get that the big firms
don't actually deliver.
Big Four firms know the regulatory landscape but delegate the technical work. Fintech vendors go deep in one product and can't cross into adjacent problems. We're built differently — and the differences compound on compliance-sensitive, technically complex financial services engagements.
The people who scope your engagement are the ones who build it. No senior partner pitch followed by junior analysts building your credit models. Risk-sensitive financial services work — where model errors have regulatory and financial consequences — deserves senior technical judgment throughout.
Most specialist firms go deep in one layer and stop. A risk model vendor can't fix your data pipeline. A data engineering shop can't build explainable ML. We cover data infrastructure, predictive modeling, and GenAI application development as one integrated team — no context lost between phases.
SR 11-7 documentation, EU AI Act conformity requirements, DORA data resilience standards — we design for these from the first architecture session. Compliance isn't a sign-off step; it shapes every model design, data lineage decision, and deployment pattern we recommend.
IBM watsonx is purpose-built for regulated industries — with governance, auditability, and on-premise deployment options that matter for financial services. As an IBM Silver Partner with hands-on watsonx deployment experience, we provide access and depth that most boutique firms can only advertise.
Not ready for a full engagement?
Start with a defined pilot.
Every financial services data transformation starts with understanding where the gaps are. Our fixed-scope starter programs give you a clear picture — and a clear path forward — with defined outputs and timelines before any long-term commitment.
View All Starter ServicesWe audit your current data infrastructure, map your regulatory environment (SR 11-7, EU AI Act, DORA), inventory your AI/ML model portfolio, and deliver a prioritized roadmap with compliance gaps, quick wins, and realistic cost estimates.
See what's includedA scoped proof-of-concept replacing or augmenting one rule-based fraud detection process with a behavioral ML model. You leave with a working model, performance benchmarks vs. the existing system, and a deployment plan with SR 11-7 documentation.
See what's includedWe automate one regulatory reporting workflow — CECL, Basel IV, or DORA — from data extraction through submission-ready output, with full data lineage. Delivered on your existing platform stack, ready to extend to other reports.
See what's includedA working GenAI assistant for one high-value compliance use case — regulatory change management, KYC case summarization, or policy document search — on IBM watsonx or Azure OpenAI, with auditability, access controls, and a deployment plan included.
See what's includedLet's talk about what your
financial data should be doing.
Whether you're modernizing risk models, automating compliance reporting, deploying fraud ML, or building the customer 360 your AI initiatives need — we've done it. Let's find out where to start.
Financial services data & AI —
the questions we hear most.
From CDOs at regional banks to CROs at national insurers — here are the questions that come up in every first conversation.
