AI Process Owner — Transaction Monitoring & Customer Lifecycle

About Finom

Finom is a European tech startup headquartered in Amsterdam, and we’re on a journey towards revolutionizing the financial landscape for entrepreneurs worldwide. Our mission is to develop an all-in-one financial B2B solution that integrates banking functions, accounting, financial management, and invoicing into a seamless, mobile-first platform.

We recently closed a €115 million Series C equity round (around $133 million), bringing our total funding to approximately $346 million. This significant investment follows a $105 million growth funding round from General Catalyst, a long-term backer since 2021 known for supporting companies like Airbnb, HubSpot, KAYAK, and Stripe.

Finom's platform goes beyond traditional banking, offering invoicing and a growing suite of features, including AI-enabled accounting, aiming to simplify financial management for entrepreneurs. We're actively expanding our reach across key EU markets like Germany, France, the Netherlands, Italy, and Spain.

At Finom, we’re not just redefining the entrepreneurial experience — we’re empowering our employees to make a real difference. Your work matters, and your impact extends far beyond product metrics. We nurture innovation and an inspiring work environment where bold ideas thrive, prioritizing thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and our business as a whole.

Maintaining our start-up spirit, we prioritize thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and, of course, our business.

AI Process Owner — Transaction Monitoring & Customer Lifecycle

Team: AFC Product Operations

Focus: Transaction Monitoring — AI alert resolution

Scope: Dedicated to AI automation of the transaction monitoring programme within 1LoD, with scope to extend across the customer lifecycle over time

At Finom, AI is not just a buzzword. It runs through every element of how we work, and we are implementing it in production, in real processes, with real decisions behind it, with operational teams in the leading role to build AI operational processes.

About the role

This position sits in our Customer Lifecycle team — the team that monitors the customer from the moment their account is opened at Finom and throughout their entire life with us. That remit covers transaction monitoring, regular (periodic) reviews and general ongoing customer monitoring.

This role is specifically focused on transaction monitoring alert resolution , with the possibility to extend scope across the wider customer lifecycle in future, depending on your experience and your success in the position.

The role is a full dedication to projects that automate elements of our transaction monitoring programme using AI . Concretely, that means three things:

Designing and implementing AI-driven alert resolution — moving work that analysts do manually today into AI-supported and AI-executed flows.

Supporting analysts through the transition from manual investigation to AI output validation and model validation — a genuine change in what the job of an analyst looks like.

Building AI agents that replace manual work end to end, and defining where an agent can act and where a human must.

You will be the person who understands both sides of the equation: what a solid AML framework requires, and what AI can realistically and defensibly do inside that framework. Knowing where the limits are matters as much as knowing what is possible.

Key responsibilities

Own AI implementation for transaction monitoring alert resolution — from identifying the highest-value manual steps, to specifying the solution, to running it in production.

Understand the alert resolution process from pick up to false positive, true positive, or offboarding.

Build AI-driven investigation capability — AI-supported investigation of customers, of suspicious transactions, and of behavioural and network patterns across our customer base.

Build and iterate on AI agents that take over manual analyst work, with clear boundaries, escalation logic and audit trails.

Redesign the alert resolution process around AI: what is automated, what is validated, what stays fully human, and how quality is evidenced.

Define the AI/AML boundary — where AI can be used within a transaction monitoring framework, where it cannot, and how we document and defend that position to 2nd Line risk management, audit and regulators.

Support and upskill analysts through the transition to model and output validation, including guidance, training and new working procedures.

Work hands-on with data — analyse alert and customer data to find inefficiency, false-positive drivers and automation opportunities, and to measure whether changes actually worked.

Partner with technical teams and stakeholders — AI team, Data, Engineering, Product, risk management — translating operational and AML needs into technical requirements, and technical constraints back into operational reality.

Keep the customer in view — every alert, freeze, request for information and delay lands on a real customer. Efficiency gains should improve, not degrade, the customer experience.

Own documentation and process ownership — procedures, decision logic, model rationale and controls kept to audit standard.

Key metrics

Your success will be measured on the efficiency and optimisation of the process , including:

Reduction in manual handling time per alert (cycle time) and in overall alert backlog.

Share of alerts resolved or pre-processed by AI, at maintained or improved quality.

Quality and defensibility of AI-assisted decisions — measured through QA, validation sampling and audit outcomes.

Analyst capacity released

Customer-facing impact: fewer unnecessary touchpoints, faster resolution

About you

3+ years of experience in a fintech, bank, EMI or PSP environment , in transaction monitoring and AML.

Hands-on experience in first line of defence alert resolution — you have worked alerts yourself and know what makes them slow, noisy or hard to close.

Solid understanding of AML frameworks and, critically, how to apply them to resolve alerts in practice rather than in theory.

Genuinely AI-oriented — you already use AI across your own work and have experience implementing AI in projects, not just experimenting with it.

Clear view of the limits of AI within a transaction monitoring framework — what can be automated, what needs human judgement, and what regulators and auditors will accept.

Technically minded with strong data analysis skills. Comfortable working with data tools such as Databricks, Metabase and Power BI ; SQL is a real advantage.

Customer-minded — you treat customer impact as a first-order consideration, not a side effect.

Structured, pragmatic and comfortable owning a process end to end in a fast-moving environment.

Professional fluency in English.

Nice to have

Experience building or deploying AI agents or LLM-based workflows in a regulated or operational setting.

Exposure to model validation, model risk management or AI governance

Experience with transaction monitoring vendor systems and rule tuning / threshold optimisation.

Background in periodic reviews, KYC/CDD or ongoing customer due diligence — relevant if the scope extends across the customer lifecycle.

Experience with SAR/STR reporting

Python or similar for data work and prototyping.

What's in it for you

A rare combination: a real AML mandate and a real AI mandate in the same role. You are not advising on automation from the sidelines — you are building it and owning the outcome.

Genuine ownership. This is a process owner role: the decisions on how transaction monitoring works sit with you.

Visible impact from day one, on a programme that directly affects both our regulatory standi