AI automation for financial services

The processes that actually pay

The automatable work in a bank, insurer, or asset manager is unglamorous and high volume. Four areas cover most of it:

• Reconciliation and exceptions. Matching transactions across ledgers, sub-ledgers, and custodians, and working the break queues. Rule-based matching clears the clean items; the value of AI is on the exceptions, the breaks a fixed rule cannot resolve, which is where analysts spend their days. The goal is higher straight-through processing and less exception ageing, not a bot that falls over on the first odd format.
• AML and KYC operations. Sanctions and PEP screening, customer due diligence document collection, and above all triaging transaction-monitoring alerts. Most alerts are false positives, and analysts burn hours clearing them. Automation can gather the evidence, draft the disposition, and prioritise the queue, while a person still makes and signs the call, because a suspicious-activity decision is not one you hand to a model.
• Document-heavy onboarding and lending. Loan files, mortgage packages, KYC documents, bank statements, pay stubs, and tax forms arrive as PDFs and scans. Intelligent document processing reads and extracts them so the data lands in the core system without a human retyping it. The extraction is automatable; the credit decision on top of it stays a governed, explainable human decision.
• Regulatory and management reporting. Recurring reports assembled by hand from several systems every month or quarter. Automation pulls and reconciles the inputs and produces the draft, with the numbers traceable back to source, which is exactly what an examiner will ask for.

Claims and servicing sit next door: first-notice intake, document classification, and status updates automate well, while the pay-or-deny decision does not. If insurance is your world, the AI in insurance page has the carrier-side detail.

Governance is the buying constraint

In most sectors governance is a later phase. In financial services it is the thing that decides whether the automation can ship at all. Models that inform lending or risk fall under model risk management expectations, with SR 11-7 the usual reference point. Adverse actions have to stay explainable under fair-lending rules. Customer data carries GLBA duties, and GDPR ones for EU customers. So access boundaries, full audit trails, and a documented human decision point go in from the first version. We treat that as part of the automation, not a compliance tax on it. The depth lives on the AI governance consulting page and in the AI compliance framework guide, which this page leans on rather than repeating.

How the engagement works

It starts with a fixed-scope assessment: 3–6 weeks, priced at $20,000 to $80,000, and where in that range depends on scope: how many systems and teams we assess, company size, and regulatory exposure. You get a written recommendation with the numbers behind it: which processes to automate, which to leave, what to buy versus build, and the controls each one needs. Builds that follow are scoped in phases, on your infrastructure, documented so your own team runs them. Much of this estate is RPA-plus-AI rather than a fresh build, which is the intelligent automation approach; if you want to find the first process yourself, the free automation opportunity assessment worksheet is the place to start.

Who does the work

The senior people at Tillerbridge are Nick Major, the engineer who builds these systems, and Isaac Major, the operator who has run the kinds of teams they land in. No bench, no offshore handoff, no vendor commissions, no software resale. We are a young firm and we will not claim financial-services clients we do not have; what we bring is how these processes and their obligations actually work, and our backgrounds are on the about page. This page is general information about automation and regulation, not legal or compliance advice.