Banks · lenders · insurers · payments · fintech · wealth operations

AI consultancy for financial services.

Reduce document handling and case preparation while keeping regulated decisions accountable.

Binarify helps financial-services teams improve onboarding, financial-crime review, customer operations, case administration and reporting around their existing systems. We begin with one bounded workflow, its controls and a measurable baseline.

Discuss your financial workflow Explore the workflows ↓

30-minute discovery call · fixed scope before implementation

Where AI fits

Improve the work around the decision.

The UK Financial Conduct Authority’s published AI approach applies existing outcome, accountability and governance frameworks to AI. That is a useful design principle beyond the UK: the model does not remove the firm’s responsibility for the process it changes.

The best first scopes usually reduce evidence collection, reconciliation, drafting or routing. They leave a named person responsible for customer acceptance, credit, claims, advice, suspicious-activity reporting and other consequential decisions.

Some improvements need no generative AI. Existing platform configuration, deterministic validation, better case fields or a supported integration may solve the bottleneck with less risk and maintenance.

Financial-services AI use cases

Five workflows with evidence, ownership and controls.

These are implementation patterns to assess against a firm’s permissions and obligations. They are not legal advice, completed client results or a claim that every core platform supports the same integration.

Banks · lenders · payments · wealth firms

Customer onboarding and KYC document review

The friction: Applications, identity evidence and business documents arrive through several channels. Teams repeatedly classify files, re-key fields and chase missing information before a case can be assessed.

The approach: Classify approved document types, extract agreed fields with source references, run deterministic checks and prepare an exception-led case for an authorised reviewer. Identity verification, risk classification and acceptance remain governed decisions.

MeasureTime to review-ready case · straight-through completeness · correction rate · avoidable customer contacts

Read the onboarding workflow guide →

Financial crime · compliance · operations

AML alert investigation support

The friction: Investigators move between transaction, customer, screening and case systems to assemble context. Backlogs grow while skilled reviewers spend time collecting facts instead of assessing them.

The approach: Retrieve approved evidence, build a dated activity timeline, summarise why an alert fired and surface missing checks. An accountable investigator validates the evidence, records the disposition and controls any escalation or report.

MeasureAge of open alerts · investigation handling time · rework after quality assurance · material evidence omissions

Read the AML alert triage guide →

Retail service · complaints · vulnerability teams

Customer service and complaint intake

The friction: Requests arrive by message, email and call with incomplete context. Agents search product, account and policy systems while customers repeat information and wait for the right team.

The approach: Identify the request, retrieve approved information, draft a grounded response and route complaints, vulnerability signals, fraud concerns and regulated-advice questions to trained staff. Keep the original communication and review trail.

MeasureTime to useful response · transfers · repeat contacts · reopened cases · complaint recognition accuracy

Lending · insurance · servicing teams

Loan, claim and servicing document operations

The friction: Statements, evidence, forms and correspondence must be compared with a live case. Missing fields and inconsistent identifiers create queues and late rework.

The approach: Extract evidence into a controlled schema, compare it with the case record and policy rules, and prepare an exception list with links to each source. A qualified person decides eligibility, affordability, liability, settlement or forbearance.

MeasureHandling time per case · first-time completeness · late exceptions · correction and appeal rate

Operations · risk · finance · compliance

Operational and regulatory reporting support

The friction: Teams reconcile definitions and identifiers across core systems, spreadsheets and case tools before they can explain a movement or assemble evidence.

The approach: Use deterministic code for calculations, controlled data mappings for reconciliation and AI for evidence-linked commentary or exception summaries. Every reported figure retains its lineage and owner.

MeasurePreparation time · unresolved variances · data freshness · corrections after sign-off · actions closed

Controls before scale

Design the review, fallback and evidence trail.

A reviewer needs the original source, extracted fact, applicable rule, model output and changes in one place. A confidence score alone cannot explain whether a document is acceptable or a transaction is suspicious.

Configure

Use existing workflow, rules or AI functions when the regulated system already supplies them with suitable controls.

Connect

Integrate approved systems when staff repeatedly copy the same customer, document or case facts.

Build

Create a focused layer when a valuable gap remains and the firm can test, govern and maintain it.

How we build custom AI solutions →

Customer outcomes and accountability

A faster case must still be a fair and supportable case.

The FCA’s Consumer Duty requires relevant UK firms to act to deliver good outcomes, including consumer understanding and support. A workflow should therefore measure incorrect replies, missed vulnerability or complaint signals, abandoned applications and correction routes alongside speed.

The Financial Action Task Force’s digital identity guidance explains how reliable digital identity can support customer due diligence under a risk-based approach. It does not make every digital check sufficient for every customer or jurisdiction.

Rules differ across markets and products. The client’s authorised compliance, legal, risk and security owners approve the operational requirements and remain accountable for the resulting service.

First engagement

Assess one queue before funding an integration.

The AI Impact Diagnostic follows one workflow from intake to decision or hand-off. It checks the baseline, source systems, data quality, existing product capability, control requirements, expected benefit and continuing cost.

You receive a workflow map, findings and a contained recommendation—or a clear reason to configure the current platform, repair the data or stop.

What the diagnostic includes →

Practical questions

Before commissioning AI for financial services.

Which financial-services firms do you work with?

The workflow patterns are relevant to banks, lenders, payment providers, insurers, brokers, fintechs and wealth or asset-management operations. We assess the exact regulated activity, customer type, jurisdiction, systems and accountable functions before defining a scope.

Can AI make lending, insurance or investment decisions?

We do not begin with an autonomous consequential decision. AI can retrieve information, identify missing evidence and prepare a recommendation, but regulated eligibility, affordability, pricing, claims, advice and investment decisions need the firm’s approved criteria, governance and meaningful human control.

Can you connect to our core platform or case-management system?

We first check the product, edition, permissions, API, data residency and existing automation. Where supported, a focused integration can read or write only the fields needed for the workflow. If safe integration is not available, a review workspace or controlled export may be the better pilot.

How do you protect customer and transaction data?

The design starts with purpose, minimum necessary data, role-based access, retention, environment separation, logging and supplier review. Sensitive production records are not needed for the first conversation, and a pilot can begin with synthetic or suitably de-identified cases where appropriate.

Does the system replace compliance officers or investigators?

No. It should remove avoidable evidence gathering and drafting while preserving the investigator’s judgement, independence and accountability. The person must be able to inspect sources, correct the case and record why they accepted or rejected the prepared work.

How do you validate a financial-services AI workflow?

We build a representative test set that includes incomplete, conflicting, unusual and high-risk cases. We measure evidence accuracy, missed exceptions, unsupported statements, human overrides, latency and downstream rework before deciding whether the workflow can move beyond recommendation mode.

How much does a financial-services AI project cost?

The AI Impact Diagnostic starts at $2,500 USD and normally takes one to two weeks. Implementation sprints start at $15,000 USD. The diagnostic fee is credited against the sprint if you proceed, and scope, price, milestones and expected running costs are agreed in writing.

Can you support firms in several countries?

Yes. Delivery is remote for clients in the US, UK, Canada, South Africa, Australia, UAE and Singapore. The client remains responsible for regulatory interpretation and approval; we work with its compliance, legal, security and risk owners to translate those requirements into the implemented workflow.

Bring one queue and its review standard.

Tell us its volume, current handling time, source systems and exceptions. No customer, account or transaction records are needed for the first call.

Book a 30-minute call AI automation consulting →