AI Agent Development Services for Fintech
AI in fintech that acts, not just answers.
A chatbot tells a compliance analyst what documents are needed. An AI agent retrieves the applicant record, runs the sanctions check, scores the identity verification result, and routes the case to human review with a structured summary, all before the analyst opens their queue. We build fintech AI agents with defined scope, explicit escalation logic, and audit trails built for regulatory review. Each agent handles one workflow well rather than many workflows poorly.
KYC/AML agents that screen applicants, flag exceptions, and route cases for human review
Underwriting data extraction agents that pull from bureaus, statements, and filings into a structured decision package
Fraud triage agents that rank alerts by risk signal and surface only the cases that need a compliance analyst
Customer support agents that resolve routine account queries end-to-end without queue dependency
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Your compliance team drowning in KYC/AML manual review queues while onboarding slows to a crawl?
Loan underwriting stalled because data lives across bureau reports, bank statements, and tax filings that someone has to pull and reconcile by hand?
Plain answer
AI in fintech, built by RaftLabs as agents rather than chatbots, covers KYC/AML screening, underwriting data extraction, fraud triage, reconciliation, and regulatory reporting. Unlike chatbots that only answer, these agents act end-to-end inside guardrails, with human sign-off on money movement and audit trails built for regulatory review. A focused v1 is scoped and priced before development starts.
What to remember
- Agents are built on LangGraph as stateful, multi-step workflows with mandatory human-in-the-loop checkpoints before any auto-decline or credit decision.
- Open banking data retrieval uses authorised AISP APIs (TrueLayer, Plaid) rather than screen scraping, with customer consent handled by the provider's own flow.
- Every screening step, data source queried, and routing decision is logged for audit.
- A focused single-workflow agent is scoped and priced before development starts; a multi-agent system across KYC, fraud, and underwriting grows from the proven v1.
The compliance queue that clears itself before the analyst logs in.
A chatbot tells a compliance analyst what documents are needed, then waits. That is where the help ends.
An AI agent goes the other way. It retrieves the applicant record, runs the sanctions check, scores the identity verification result, and routes the case to human review with a structured summary, all before the analyst opens their queue.
By the time a person picks up the case, the screening is done and the evidence is attached. What reaches them is the judgment call, not the busywork.
That is not a chatbot. It is an agent that acts.
We build fintech AI agents with defined scope, explicit escalation logic, and audit trails built for regulatory review. Each agent handles one workflow well rather than many workflows poorly. We confirm compliance and data-security scope during discovery, because that is where most projects hit unexpected complexity. The stakes explain the caution. Nasdaq's 2024 Global Financial Crime Report estimated that more than $3 trillion in illicit funds moved through the financial system in 2023. That is why any agent touching screening or fraud carries escalation logic and a full audit trail from day one.
Proof
- shipping production software across fintech, healthcare, hospitality, and logistics
- Since 2015
- RaftLabs delivery record
- average client rating across delivered projects
- 4.9/5
- Clutch, verified reviews
- credit decisions and regulatory submissions stay with a qualified reviewer; the agent prepares, a person signs off
- Human-in-the-loop
- Every fintech agent build
RaftLabs has shipped production software since 2015 for clients across the US, UK, Europe, Canada, and the UAE. The team that scopes your workflow is the team that ships it: no offshore handoff after the contract is signed. Every screening step and routing decision is logged for audit, and credit decisions and regulatory submissions stay with a qualified human reviewer. A fintech agent rarely stands alone: it builds on AI for fintech for the underlying scoring models, intelligent document processing for KYC and statement extraction, and fintech automation for the rule-based workflows around it. See our track record across fintech and financial services.
An agent pays off when the workflow is high-volume and the rules are already yours.
Everything on the left should already be true for your operation. Even one thing on the right, and a plain chatbot or a manual process is the smarter first step.
A high-volume KYC/AML, underwriting, fraud, or reconciliation workflow your compliance team runs by hand, the same way, every day.
Core systems to integrate against: core banking, payment processors, open banking providers, or KYC/AML screening tools.
A regulatory context with defined risk thresholds and audit-trail requirements the agent must respect.
You need a simple FAQ responder for customers, not a system that takes action inside regulated workflows.
The decision depends on human judgment the agent was never given the context to make.
No defined risk thresholds or escalation rules exist yet for the workflow.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
Buy
Run the build-vs-buy numbersChoose a maintained product when it handles the core compliance and underwriting operations workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
Build
Own the system when compliance and underwriting operations shape margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
What we build
Fintech agents, one workflow each
KYC/AML screening agent
Built on LangGraph as a stateful workflow, identity verification and sanctions screening run in parallel, with results consolidated into a structured risk summary and cases below the auto-pass threshold routed to compliance with full evidence attached.
Underwriting data extraction agent
Open banking transaction history (retrieved through authorised AISP APIs like TrueLayer and Plaid), filed accounts data, and bureau reports assemble into a single decision package matched to your credit model's input template, with a mandatory human checkpoint before any decision.
Fraud triage agent
Alerts enrich with device fingerprint, counterparty reputation, and historical pattern data, scored against a configurable risk framework so analysts work highest-risk cases first.
Customer support agent
Routine account queries resolve end-to-end with authentication enforced before any account data returns, and explicit escalation for disputes, closures, or fraud concerns.
Reconciliation agent
Settlement files, ledger entries, and bank statements match automatically, with a structured breaks report delivered before the finance team starts their morning review.
Regulatory reporting agent
Data extraction, transformation, and schema validation assemble draft reports (MiFID II, SARs, AML returns) for compliance officer review, never autonomous submission.
Guardrails, model risk, and auditability
An agent that touches money or a credit decision cannot be a black box. Four controls sit underneath every fintech agent we build, and they are the first thing a regulator or an internal risk team asks to see.
- Tool-calling with allow-lists
- The agent calls only the APIs and actions you approve. It cannot move funds, close an account, or file a report unless that tool is explicitly in scope. No open-ended code execution against production systems.
- Model risk under SR 11-7
- SR 11-7 is the US supervisory guidance on model risk management. We document the model, its inputs, its limits, and its validation so your risk team can defend the outcome. The agent scores and prepares; it never issues the final credit decision alone.
- Human sign-off on money movement
- Any payment, auto-decline, account closure, or regulatory submission stops at a human checkpoint. The agent assembles the evidence and a recommendation. A qualified person approves it.
- Audit trail and PII handling
- Every data source queried, screening step, and routing decision is logged with a timestamp and the reasoning behind it. PII is accessed on a minimum-necessary basis and encrypted in transit and at rest.
Where this is heading: the first wave of fintech AI was a copilot that suggested while a human did the work. The next wave is constrained autonomy. The agent completes the routine cases inside hard guardrails and escalates the ones that need judgment. The firms that pull ahead will not be the ones that hand the most decisions to a model. They will be the ones whose agents are the most auditable, because that is what a regulator asks to see. We build for that review, not around it.
Have a fintech AI agent project?
Tell us the workflow you want to automate, your core platform, and your regulatory context. We will scope what an agent can handle and give you a fixed cost.
How it works
From scope to live agent
- Step 0101
Workflow and compliance scoping
We map your target workflow, regulatory context, and system integrations. You leave with a written scope document and a fixed-price quote.
- Step 0202
State machine and escalation design
Workflow state machine, human-in-the-loop checkpoints, and audit trail designed against your compliance requirements. Exception paths and escalation rules are agreed with your compliance team before any agent logic is built.
- Step 0303
Build and integrate
Agent logic, API integrations, and escalation UI built in parallel, tested against real case scenarios. Each checkpoint is verified with your team before the next workflow is connected.
- Step 0404
Launch and compliance review
Compliance team reviews the audit trail and escalation logic before full production rollout. The agent goes live on a controlled caseload first, then expands once the audit trail holds up in review.
Proof
Fintech platforms we've shipped
- 01
Mobile point-of-sale platform for a UAE fintech
- installs on Google Play
- 10K+
- 02
InvestIQ IPO and market events tracker
- live IPO, buyback, and OFS data pulled into one view
- NSE + BSE
- 03
AI phone agents automate global feedback loops through voice interviews
Text surveys only reached people comfortable filling out forms. We built a voice platform that calls anyone with a phone, runs the interview, and has the findings ready when the last call ends.- from concept to launch
- 12 weeks
- delay as insights available as soon as interviews end
- 0
What it costs
Every project starts at $10000.
A focused v1 covers one agent workflow, such as KYC/AML screening or fraud triage, with audit trails built for regulatory review.
Starting investment
Starts at $10000
A focused v1 is scoped and priced before development starts. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
Work with us
Tell us where the work is stuck.
Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
A chatbot answers questions. An AI agent retrieves data from the core banking API, runs the required checks, and delivers a structured response without a human touching the query. Agents operate as stateful, multi-step processes with explicit scope boundaries, a list of query types handled, a list escalated, and defined decision points requiring human approval.
Compliance requires data residency, encrypted handling, minimum-necessary access controls, and audit logs of every agent action. For open banking retrieval, the agent uses an authorised AISP API rather than screen scraping. For AML workflows, the agent applies your firm's defined risk thresholds consistently with a full audit trail of every decision.
Common integrations include core banking platforms (Mambu, Thought Machine, Temenos), payment processors (Stripe, Adyen, Worldpay), open banking providers (TrueLayer, Plaid), KYC/AML providers (ComplyAdvantage, Refinitiv), credit reference agencies, and loan origination and transaction monitoring systems. We confirm integration scope during discovery.
The regulatory-acceptance question. In banking, data sourcing and the compliance engine are the opening probes, not the closing ones. Can the agent reproduce a decision: inputs, steps, output, confidence, six months later? Show a real sample audit report with PII redacted. Compliance requirements like PSD2, FCA rules, and AML obligations are built into the agent's guardrails from the start, not added later. Who handles exceptions, and does it plug into your existing KYB providers? An agent without an exception path is a demo.
The biggest hidden driver is humans reviewing false positives. Buyer care is noise reduction, not detection maximalism. Ask for onboarding pass-through rates, not only detection rates. Ask for line-itemed inference, engineering, and compliance costs, not a headline license.
The build-versus-buy objection: the buyer's first instinct is that banks already own the data. The honest answer is that data is not the moat; the exception paths, the audit trail, and the false-positive tuning are. What can the platform not do that customers regularly ask for? Walk through what migrating off looks like. Test the bill and behavior at 2x, 3x, 10x volume: API rate limits, retraining cadence, monitoring under peak load, before signing.
Four rituals. One: the six-months-later test. Reproduce one decision: inputs, steps, output, confidence. Two: the court test. Would this hold up as actually knowing your customer? Three: the false-positive question. Onboarding pass-through rates, not detection rates. Four: the exception question. Who handles what the agent cannot, and where does it escalate? The compliance queue that clears itself before the analyst logs in: that is the outcome to verify, not the detection score.
A focused agent covering one workflow, one or two integrations, and defined escalation logic is scoped and priced in writing after discovery. A multi-agent system covering KYC screening, fraud triage, and underwriting data extraction grows from the proven v1.