AI for Fintech
Fraud and reconciliation reviews eat analyst hours, and an LLM's output cannot be trusted in a regulated workflow without an audit trail. We build the audit trail first.
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- What we build: auditable AI agents for transaction monitoring, reconciliation and KYC review.
- Who for: heads of product and CTOs at payment, lending and neobank platforms.
- In what timeframe: a scoped PoC on your real data in a few weeks, shadow mode before it acts.
Where Fintech operations break
Transaction monitoring triage
Rules engines over-flag; analysts spend the day clearing false positives that are obvious in hindsight but still need a human to close with a reason.
Reconciliation breaks
Matching internal records against provider and bank statements is manual and repetitive, and the exceptions that need judgement are buried under the ones that do not.
KYC / onboarding review
Document checks and adverse-media screening are high-volume and rules-driven, but a naive automation with no audit trail is unusable the moment a regulator asks how a decision was made.
AI agents we build for fintech
Reconciliation agent
Matches transactions across your ledger and provider statements, clears the clean matches and packages each break with the evidence a human needs to resolve it.
Monitoring triage agent
Reviews flagged transactions against your rules and history, closes the clear false positives with a logged rationale and escalates genuine risk.
KYC review agent
Extracts and validates onboarding documents, runs watchlist and adverse-media checks, and assembles a review packet with pass / fail / manual-review paths.
Support triage agent
Answers first-line account and payment questions grounded in your own help content and account data, and hands off with full context when it cannot resolve.
What it plugs into
The agent writes reconciled, structured records back into your systems of record - it does not just read and leave a human to re-key its output across three tools.
Where the agent stops
Handled end-to-end
- Clearing clean reconciliation matches
- Closing obvious false-positive alerts with a logged reason
- Extracting and validating KYC documents
- First-line, read-only support answers
Always goes to a human
- Any movement of funds
- Sanctions or high-risk typology hits
- Reconciliation breaks above a set value
- Anything the confidence threshold flags as ambiguous
Built on real fintech infrastructure
Northell has shipped production fintech systems where correctness and an auditable trail are the whole job, not a feature - work such as the Finixflo platform. The AI agents here apply that same ledger-and-audit discipline: every action recorded before it touches a system of record. We do not yet publish a fintech-specific AI case metric, and we will not invent one - the honest proof today is the fintech-infrastructure track record these agents are built on.
See the Finixflo case study →How it is assembled
Every agent action is written to an audit log before it touches a system of record — the same discipline we run on our own production agents.
What happens to our transaction data, and can this run inside our own cloud?
For sensitive fintech workloads the agent runs inside your own cloud boundary, with inference against models under data-handling terms that keep your data out of training. Data residency, retention and the audit-log store are scoped at kickoff, before any integration is built - so the compliance answer exists before the first line of code, not after a review flags it.
From PoC to production
Scoping & data review
→ Scoping doc + a workflow chosen on volume and audit riskPoC on real data
→ A working agent evaluated against your current baselineShadow mode
→ The agent runs against live traffic, taking no action, while accuracy is confirmedProduction + monitoring
→ Widened autonomy, audit log, drift monitoring and runbooksWho builds it
Named engineer bios for this vertical are being finalised — we do not publish stock avatars or invented names.
FAQ
Can an AI agent make decisions a compliance team will accept?
Only if every decision is reconstructable. We log each agent action — the inputs it saw, the model version, the confidence score and the output — to an append-only audit trail before it touches a ledger or a case record. A reviewer can replay any decision months later and see exactly why the agent did what it did. Without that trail, no amount of accuracy makes an agent defensible in a regulated fintech workflow.
How do you stop an LLM from hallucinating in a fraud or lending decision?
By not letting the model make the decision alone. The LLM extracts and summarises; deterministic rules and confidence thresholds gate the actual action, and anything ambiguous routes to a human analyst with the evidence attached. The agent is a triage and reconciliation layer that removes the volume of obvious cases, not an unsupervised approver of edge cases.
Does this integrate with our core banking or ledger system?
Yes — write-back into the ledger is the point, not an afterthought. We integrate with core banking APIs, double-entry ledgers, payment service providers and KYC/AML providers, and the agent writes structured, reconciled records back rather than leaving a human to re-key its output across three systems.
What fintech workflows are the best fit for an AI agent?
High-volume, rules-heavy review work: transaction monitoring triage, reconciliation breaks, KYC document review, and first-line support on account and payment queries. These are workflows where analysts spend hours on cases that are obvious in hindsight, and where an auditable agent can clear the obvious ones and escalate the rest.
Is our transaction data used to train a model?
No. We build on models via API or self-hosted deployment with data-handling terms that keep your data out of training, and for sensitive workloads we can run inference inside your own cloud boundary. Data residency and retention are scoped at kickoff, before any integration work starts.
How long until we can put this in front of real transactions?
A scoped PoC on a slice of your real data typically runs a few weeks and ends with an evaluation against your current baseline — for example, how many reconciliation breaks the agent clears correctly versus your analysts. We put it into shadow mode against live traffic before it takes any action, then widen its autonomy as the evaluation holds.
Prove it on your data first.
A scoped PoC, engineered to become the real product — timeline and scope on the call, no price on the page.
Scope an AI PoC →