Financial Process Automation in Financial Services

Financial services are among the most heavily regulated industries in Slovakia and across the European Union. Automating financial processes here is therefore not just a matter of efficiency, but also of auditability, data security and regulatory compliance. A company that processes payments, loans, insurance policies or investment instructions cannot afford to roll out automation as casually as, say, an e-shop with an order process. This distinction – between "ordinary" business automation and automation in a regulated environment – is the subject of this article.

Ordinary business process automation focuses mainly on speed and eliminating manual work. In the financial sector, an additional layer of requirements comes into play: every step of the process must be provable, traceable after the fact, and compliant with regulations such as AML (anti-money laundering measures), KYC (know your customer) or GDPR when processing clients' personal and financial data.

This means that automating financial processes must account, from the outset, for:

• An audit trail – who triggered or approved the process, when and why, including logs of individual steps.
• Access control – clearly defined roles and permissions for sensitive operations.
• Data retention – compliance with statutory retention periods and archiving methods.
• Explainability of decisions – particularly where AI is involved in the process, it must be clear what basis the system used to make a recommendation or decision.

While ordinary business automation can be built on off-the-shelf tools such as Make or Zapier (their capabilities and limits are covered in the article Workflow automation: Make, Zapier, or a custom-built solution?), in the financial sector companies more often lean towards custom-built solutions or, at minimum, thoroughly vetted, certified platforms. Here, the case for a custom solution is reinforced by audit and security requirements that off-the-shelf no-code tools do not always meet.

Not every process in a financial company has the same potential. When prioritising, it is worth looking at processes that are:

1. Highly repetitive – processed dozens or hundreds of times a day or month.
2. Rule-based – governed by clearly defined logic the system can verify (limit checks, formal correctness of documents, data matching).
3. Prone to human error – re-keying data between systems, manually reconciling statements, repeatedly entering the same information.

Typical candidates include invoice and accounting document processing, payment matching, client onboarding (KYC checks), reporting for management and the compliance department, or approval processes for loans and insurance claims. The same prioritisation logic – frequency, clear rules, error-proneness – applies across industries, not just in finance.

Specifically for invoicing and accounting, there are several proven approaches to automation that also work outside the financial sector. In financial institutions, these are typically supplemented with an additional check against internal limits and approval matrices.

Robotic process automation (RPA) has been used in financial services for a long time – particularly where legacy systems without APIs need to be connected, or where repetitive tasks must be carried out exactly according to a given scenario. When RPA makes sense and when a different approach is preferable is explained in the article RPA (Robotic Process Automation): what it is and when it pays off.

Alongside RPA, AI agents are gaining ground – for example, in pre-processing documents, categorising transactions, or preparing materials for approval. The difference between the two is fundamental: RPA carries out precisely defined steps, whereas an AI agent can work with unstructured inputs and make recommendations. This means an AI agent should generally operate in a "human-in-the-loop" mode – it proposes, but a human makes the final decision or approval. A specific deployment in accounting, including questions of control and approval, is described in the article AI agents in accounting: how deployment works in a company.

Personal data protection also deserves particular attention when deploying AI. Since financial institutions handle clients' sensitive data, questions of data processing and retention need to be addressed at the architecture design stage, not as an afterthought. A practical look at this topic is offered in the article AI agents and GDPR: what to watch out for when processing personal data.

Financial companies typically work with several systems at once – a core banking or insurance system, a CRM, accounting software, and possibly an internal client portal. Without connecting these systems, automation remains only surface-level – data may be processed faster in one place, but it still has to be re-keyed manually between systems.

The solution is API-based system integration, which enables data exchange between systems without manual intervention. How such integration works in practice, and what to watch out for when connecting different systems, is covered in the article Connecting company systems via API: how to automate data exchange. The principles of such integration carry over into the financial environment as well, where additional emphasis is placed on encrypting data in transit and managing access rights between systems.

The table below summarises the additional layers that need to be addressed when automating in the financial sector, compared with ordinary business automation:

When evaluating automation in the financial sector, it is worth tracking concrete operational metrics rather than general claims about savings. Meaningful indicators include:

• Number of manual interventions needed to complete a single process, before and after automation.
• Error rate – how many cases required additional correction or a complaint.
• Time to approval – how long a single cycle takes, from submission to final decision, measured internally rather than as a vendor's promised turnaround.
• Number of escalations to the compliance or control department.

The key is to establish a baseline before automation is deployed, so that the same period and the same transaction volume can be compared. Without this step, any evaluation of the effect becomes little more than a guess.

The chart below is neither a measurement nor a promise of a specific saving – it is an illustrative diagram showing only the general direction in which the relative processing-load index can move as automation progresses. Manual processing is set in the chart as the baseline = 100; the other values are illustrative, not measured.

The real ratio always varies depending on the type of process, transaction volume and the degree of regulatory checks the process must include – the chart therefore serves only as orientation for discussing where it makes sense to deploy automation.

Automating financial processes in financial services is not something worth doing all at once, across the board. A proven approach is to select one or two processes with clear logic, set measurable metrics for them, and only expand automation further once functionality and regulatory compliance have been verified. Software for financial services that has this kind of architecture built in from the start can later be extended without needing to rebuild the foundation.

Automation in a regulated environment is not about handing over as much as possible to the system, but about knowing exactly where the line between automation and human decision-making should be.

If your company is weighing which processes to automate while staying compliant with regulation, take a look at our financial services solution or arrange a no-obligation consultation via the contact form, where we can go through the specific processes in your company together.