AI for financial services & banking
Enterprise AI for banks and financial institutions — from use-case selection to production proofs of concept, built on AI Factory infrastructure with explainability and audit governance designed in from day one.
Book a scoping callSee the six use cases ↓ See the full AI Factory methodology →
The use-case portfolio
Each use case below follows the same shape: the problem it solves, the AI approach at a practical level, what data it needs, the governance considerations that matter in a regulated institution, and what a proof of concept looks like on AI Factory infrastructure.
Transaction-level fraud is a moving target: patterns shift faster than static rule sets can be updated, and every rule tightened to catch more fraud also blocks more legitimate customers.
Rule-based fraud systems generate high false-positive rates that erode customer trust, while sophisticated fraud patterns — account takeover, synthetic identity, mule-network activity — increasingly slip past static thresholds. Investigation teams spend most of their time on alerts that turn out to be legitimate transactions.
Anomaly-detection models trained on transaction behaviour, paired with graph-based features that capture relationships between accounts, devices, and counterparties — fraud rings show up as structure in the graph long before any single transaction looks unusual. Model outputs are scored, not binary, so investigation teams can triage by risk rather than react to every flag.
Historical transaction logs with confirmed fraud/non-fraud labels, account and device metadata, and enough volume to model normal behaviour per customer segment. Graph features need relationship data — shared devices, counterparties, IPs — which is often present but unassembled across existing systems.
Every flagged decision needs a feature-level explanation an investigator can act on and an auditor can review after the fact. Model risk management practices apply: documented validation, ongoing performance monitoring, and a defined process for retraining as fraud patterns drift.
A 4-8 week proof of concept scopes one transaction channel, trains and backtests a model against historical labelled data on AI Factory infrastructure, and benchmarks precision and false-positive rate against the existing rule set — with an explainability report reviewed alongside the model's performance.
Know-your-customer intake still runs on manual document review at most institutions — a bottleneck at onboarding and a recurring cost at every periodic refresh.
Identity documents, proof-of-address, and corporate registration paperwork arrive in inconsistent formats and quality, and manual review is slow, inconsistent across reviewers, and expensive to scale as onboarding volume grows or periodic KYC refresh cycles come due.
Document AI and OCR extract text and layout structure from scanned or photographed documents; an LLM extraction layer maps that content onto required KYC fields and cross-checks consistency across documents. Confidence scoring routes clean cases straight through and flags ambiguous ones for review.
A representative sample of the document types actually received — passports, driver's licences, utility bills, incorporation documents — spanning quality levels and formats, plus the target schema the KYC system expects the extracted fields to populate.
A human-in-the-loop review step is standard, not optional, for any low-confidence extraction or higher-risk customer profile. Full audit trails on what was extracted, by which model version, and what a human confirmed or corrected are required for regulatory examination.
4-8 weeks: build the extraction pipeline against a representative document sample, measure straight-through-processing rate and field-level accuracy against manually verified ground truth, and design the human-review workflow for the residual cases before any production rollout is discussed.
Traditional scorecards are stable and well understood, but they leave signal on the table — the question is whether a model-assisted layer can add lift without weakening the explainability regulators and credit committees require.
Legacy scorecards are conservative by design and can under-serve thin-file applicants, while any new model introduced into credit decisions has to clear a materially higher explainability and fairness bar than a typical machine-learning deployment.
Gradient-boosted models (for example, tree-based ensembles) trained on repayment history and application data, evaluated with model-agnostic explainability methods so every score decomposes into the features that drove it. The model is positioned to augment the existing scorecard and underwriting process, not replace the credit decision itself.
Historical loan performance data with outcome labels (default, delinquency, full repayment), application and bureau features, and enough history to validate performance across economic cycles and applicant segments without introducing proxy discrimination.
Formal model risk management: independent validation, disparate-impact and fairness testing across protected characteristics, adverse-action reason codes generated from the same explainability layer used for validation, and a documented model governance committee sign-off before any production use.
A 6-8 week proof of concept trains the model against historical labelled data, runs a fairness and explainability evaluation alongside a lift analysis versus the existing scorecard, and produces a validation report structured for a model risk committee to review.
Customers expect a conversational channel that actually resolves account and service questions — but a bank cannot deploy a chatbot that guesses at balances, policy, or regulated advice.
Call centre and web-chat volume for routine account and service inquiries is high and repetitive, but customers still escalate to a human for anything beyond the simplest question, and a poorly grounded conversational AI risks giving incorrect account, policy, or eligibility information.
Retrieval-augmented generation (RAG) grounds every response in verified account data and policy documentation rather than model memory, paired with guardrails that constrain the assistant to approved topics and a defined escalation boundary that hands off to a human agent at the edge of its scope — the same agent-architecture discipline behind Mihron AI's own production voice platform, Maya.
A curated knowledge base of policy, product, and FAQ content kept current, plus (where scoped) secure, permissioned access to account data through existing APIs — never raw database access — and a labelled set of historical inquiries to evaluate response quality against.
Guardrails against regulated-advice topics the assistant is not authorized to answer, logging of every conversation for audit and quality review, and a human escalation path that is tested, not assumed, before any customer-facing launch.
4-6 weeks: scope one inquiry category, build the retrieval-grounded assistant against a curated knowledge base on AI Factory infrastructure, and evaluate response accuracy, escalation behaviour, and guardrail effectiveness against a held-out set of real inquiry examples.
Recurring risk and regulatory reports consume analyst time on assembly and formatting that adds little judgment — work that is a strong fit for automation with a human sign-off retained.
Risk teams spend a disproportionate share of reporting-cycle time pulling data from multiple systems, reconciling it, and drafting narrative sections of recurring reports by hand — work that is repetitive, deadline-driven, and error-prone under time pressure.
LLM summarization pipelines pull structured data from source systems, generate first-draft narrative sections against a fixed report template, and flag numerical anomalies or period-over-period variances for analyst attention — producing a reviewable draft rather than a final report.
Structured access to the source systems that feed the report (risk data marts, GL, exposure systems), historical report templates and prior editions to learn the expected structure and tone, and defined validation rules for the numerical checks the pipeline should run.
A human sign-off step on every generated report before submission is non-negotiable; the pipeline drafts, it does not publish. Version control and change logs on report templates, and a clear record of which sections were AI-drafted versus analyst-written.
4-6 weeks: automate the draft-generation pipeline for one recurring report type, run it in parallel with the manual process for a full reporting cycle, and measure time saved and accuracy of the automated draft against the analyst-produced version.
Policy and procedure documentation at a bank is extensive, frequently updated, and scattered across systems — staff spend real time just finding the current version of an answer.
Staff across compliance, operations, and customer-facing roles need fast, accurate answers to policy and procedure questions, but documentation lives across multiple systems, gets superseded frequently, and search tools return documents rather than answers.
An enterprise RAG knowledge assistant indexes internal policy, procedure, and product documentation, retrieves the relevant passages for a given question, and generates an answer with a citation back to the source document and its version — so staff can verify, not just trust.
A defined document corpus with clear ownership and an update process, access controls that map to the assistant's retrieval scope so staff only see what they're permissioned for, and a way to flag when a source document has been superseded.
Citations to source on every answer, access controls enforced at the retrieval layer (not just the chat interface), and a documented process for keeping the indexed corpus current so the assistant never serves a superseded policy as if it were live.
4-6 weeks: index one department's documentation corpus, build the retrieval-grounded assistant on AI Factory infrastructure, and evaluate answer accuracy and citation correctness against a set of real staff questions with known correct answers.
How we deliver
Every use case above is scoped and built through the same AI Factory methodology: strategy first, data readiness second, platform selection third, then the use-case build and a path to operating it at scale.
Vision, roadmap, governance model, build-vs-buy decisions, and the KPIs that define success before a line of code ships.
Data readiness assessment, feature store design, and the compliance, privacy, and regulatory review that has to happen before training starts.
Platform selection and integration: NVIDIA AI Enterprise, Run:AI, orchestration, and the MLOps pipeline that will run your models in production.
The use-case portfolio above, scoped and prioritized, built out as proofs of concept against real data and real workflows.
Operational scaling, drift monitoring, GPU cluster scaling, and the security posture that keeps a production system production-grade.
Why Mihron AI
Banking-sector AI delivery needs a team that already operates under regulatory scrutiny — not one learning governance for the first time on your engagement.
Mihron AI's team brings enterprise data and AI delivery experience from the global pharmaceutical sector — a regulatory environment with its own strict standards for data integrity, validation, and audit trail.
Project work recognized within the Microsoft and Azure AI ecosystem, reflecting delivery on modern enterprise AI platform tooling rather than research-only prototypes.
Mihron AI operates Maya, a production AI voice platform, under Canadian healthcare privacy law (PHIPA) and federal privacy law (PIPEDA) today — the same compliance discipline this page applies to banking engagements is how the team already runs.
FAQ
The six use cases banks most commonly prioritize are fraud detection, KYC document processing, credit scoring and underwriting, GenAI customer service, risk reporting automation, and internal knowledge assistants. Most engagements select three as initial proofs of concept, chosen by data readiness and regulatory complexity rather than by assumed size of impact.
Document AI and OCR extract text and structure from identity and compliance documents, an LLM extraction layer maps that content to required KYC fields, and a human reviewer confirms low-confidence or high-risk cases before onboarding proceeds. The result is structured, reviewable data instead of a manual document read.
AI agent architecture is the orchestration layer that lets a model plan, call tools, retrieve data, and complete multi-step work within defined guardrails and human-in-the-loop checkpoints — the difference between a single-turn chatbot and a system that can carry out a banking workflow safely.
A proof of concept for one use case typically runs 4 to 8 weeks on AI Factory infrastructure: use-case scoping and data access, a working model or agent against real (or representative) data, an evaluation pass, and a governance review before any path to production is discussed.
Models are chosen and evaluated for explainability from the outset — gradient-boosted models with feature-attribution methods for credit scoring, and anomaly-detection models with traceable feature contributions for fraud — so every flagged or scored decision can be reviewed and defended in an audit.
One scoping call. We'll walk through your priority use case, your data readiness, and what a 4-8 week proof of concept looks like on AI Factory infrastructure.
Custom scope, priced after discovery — no published enterprise pricing on this page.